Monitoring method, device and equipment, computer readable storage medium and program product

Abstract

The invention provides a monitoring method, device and equipment, a computer readable storage medium and a program product, and can be applied to the technical field of computers. The method comprises the following steps: acquiring processing time data and a dependency relationship of each batch processing node in a to-be-monitored system in a preset historical time period, and determining a key path of the to-be-monitored system according to the processing time data and the dependency relationship; determining the operation state of each batch processing node every preset duration, and determining the current real-time key path of the to-be-monitored system based on the operation state of each batch processing node; determining prediction processing time of each batch processing node according to the real-time critical path and the critical path; and monitoring whether each batch processing node is abnormal or not according to the prediction processing time. The operation stability of the bank core system can be effectively improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a monitoring method, device, equipment, computer-readable storage medium, and program product. Background Art

[0002] At present, the bank system's monitoring of batch node usage is mainly based on modeling and analysis of the bank system's historical operating data, and real-time analysis of the operation of batch nodes based on certain algorithms. When the operating time of batch nodes deviates from the predicted value, an early warning reminder is generated.

[0003] Since the business carried out by banks is wide-ranging and diverse, banking systems can be divided into core systems and non-core systems based on the impact of different abnormalities on the business and the timeliness of restoration to normal.

[0004] However, uniformly monitoring all banking systems in the above manner may result in untimely discovery of abnormalities in batch processing of core businesses, slow handling efficiency, and affect the operational stability of core systems. Summary of the invention

[0005] The present application provides a monitoring method, apparatus, device, computer-readable storage medium and program product, which can effectively improve the operational stability of a bank's core system.

[0006] In a first aspect, the present application provides a monitoring method, the method comprising:

[0007] Obtain processing time data and dependency relationships of each batch processing node in the system to be monitored within a preset historical time period, and determine the critical path of the system to be monitored based on the processing time data and dependency relationships;

[0008] Determine the operating status of each batch processing node at each preset time interval, and determine the current real-time critical path of the system to be monitored based on the operating status of each batch processing node;

[0009] Determining the predicted processing time of each batch processing node based on the real-time critical path and the critical path;

[0010] Based on the predicted processing time, monitor each batch processing node for abnormalities.

[0011] In a possible implementation, determining a critical path of the system to be monitored based on the processing time data and the dependency relationship includes:

[0012] Constructing a first directed graph based on the processing time data of each batch processing node and the dependency relationship between each batch processing node;

[0013] Based on the first directed graph, a topological sorting algorithm is used to determine the critical path.

[0014] In a possible implementation manner, determining the current real-time critical path of the system to be monitored based on the operating status of each batch processing node includes:

[0015] Determine a target batch processing node whose operation status is a completed status;

[0016] The processing time of the target batch processing node is updated to the processing time of the target batch processing node in a preset historical time period;

[0017] Constructing a second directed graph based on the updated processing time of the target batch processing node;

[0018] Based on the second directed graph, a topological sorting algorithm is used to determine the real-time critical path.

[0019] In a possible implementation, the above method further includes:

[0020] Obtain the historical processing time data of each batch processing node;

[0021] Based on the historical processing time data, a processing time prediction value corresponding to each batch processing node is determined.

[0022] In a possible implementation manner, determining the predicted processing time of each batch processing node according to the real-time critical path and the critical path includes:

[0023] Determine whether the above real-time critical path is consistent with the above critical path;

[0024] When the real-time critical path is inconsistent with the above critical path, the predicted processing time of each batch processing node is determined based on the above processing time prediction value and a preset tightening coefficient; the above tightening coefficient is greater than 0 and less than 1.

[0025] In a possible implementation, the above method further includes:

[0026] Get the actual processing time of each batch processing node;

[0027] According to the actual processing time and the predicted processing time of each batch processing node, the remaining processing time of each batch processing node is output.

[0028] In a second aspect, the present application provides a monitoring device, the device comprising:

[0029] The preprocessing module is used to obtain the processing time data and dependency relationship of each batch processing node in the monitored system within a preset historical time period, and determine the critical path of the monitored system based on the processing time data and the dependency relationship.

[0030] The processing module is used to determine the operating status of each batch processing node at each preset time interval, and determine the current real-time critical path of the system to be monitored based on the operating status of each batch processing node.

[0031] The prediction module is used to determine the predicted processing time of each batch processing node according to the real-time critical path and the critical path.

[0032] The monitoring module is used to monitor whether there are any abnormalities in each batch processing node based on the predicted processing time.

[0033] In a third aspect, the present application provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor;

[0034] The memory stores computer-executable instructions;

[0035] The processor executes the computer-executable instructions stored in the memory to implement the monitoring method provided in the first aspect.

[0036] In a fourth aspect, the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the monitoring method provided in the first aspect.

[0037] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the monitoring method provided in the first aspect.

[0038] The monitoring method, device, equipment, computer-readable storage medium and program product provided by the present application dynamically determine the critical path of the system to be monitored by regularly collecting and analyzing the operation data of batch processing nodes, and dynamically adjust the predicted processing time of batch processing nodes. Compared with the fixed predicted processing time adopted in traditional technology, the completion time of the core system batch processing can be accurately grasped, and a reasonable planning time window can be provided for production activities such as stress testing and switching drills, which effectively improves the operating stability of the core system, the timeliness of abnormal handling, and ensures production safety and business continuity. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0040] Figure 1 A flow chart of a monitoring method provided in an embodiment of the present application;

[0041] Figure 2A sub-process diagram of a monitoring method provided in an embodiment of the present application;

[0042] Figure 3 A schematic diagram of a program module of a monitoring device provided in an embodiment of the present application;

[0043] Figure 4 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application.

[0044] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0045] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0046] The following is an explanation of some of the terms involved in the embodiments of the present application:

[0047] 1. Directed Graph

[0048] A directed graph is a special graph structure that contains a set of vertices and a set of directed edges. Each edge connects a pair of ordered vertices, indicating a directed connection from one vertex to another.

[0049] Specifically, the above-mentioned "vertex" is a basic element in a directed graph and can represent anything. The above-mentioned "edge" is a directed line segment connecting two vertices, indicating a directional connection from one vertex to another vertex. Each "edge" has a clear direction, that is, a unidirectional connection from one vertex to another vertex.

[0050] 2. Topological sorting

[0051] Topological sorting refers to a linear sequence of all vertices in a directed graph. This sequence satisfies two conditions: first, each vertex appears only once in the sequence, and second, if there is a path from vertex A to vertex B, then vertex A appears before vertex B in the sequence.

[0052] 3. Critical Path

[0053] The critical path is the logical path with the longest delay from input to output in a project or system. It represents the shortest completion time of the project or system.

[0054] Among them, the total time of all activities or tasks on the critical path is the longest, thus determining the shortest completion time of the entire project or system. Any delay or delay on the critical path will directly affect the completion time of the entire project or system.

[0055] At present, the banking system's monitoring of batch node usage mainly involves modeling and analyzing historical operating data, conducting real-time analysis of batch operation conditions based on certain algorithms, and generating early warning alerts when the batch node operating time deviates from the predicted value.

[0056] Since the business carried out by banks involves a wide range of types, they can be divided into core systems and non-core systems according to the degree of impact of different system anomalies on the business and the timeliness of restoration to normal. However, the unified monitoring of all banking systems in the above way may lead to delayed discovery of batch processing anomalies of core businesses and slow handling efficiency because the universal rules cannot be well adapted to the core system, affecting the normal development of related businesses, which is inconsistent with the original intention of early warning work.

[0057] In view of the above technical problems, a monitoring method is provided in an embodiment of the present application. By performing critical path calculation on the core batch system, the predicted processing time of the batch processing nodes on the critical path is dynamically adjusted. Compared with the traditional technology that uses a fixed predicted processing time, the core system batch processing completion time can be accurately grasped, and a reasonable planning time window can be provided for production activities such as stress testing and switching drills, thereby effectively improving the operating stability of the core system, the timeliness of abnormal handling, and ensuring production safety and business continuity.

[0058] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0059] Reference Figure 1 , Figure 1 This is a flow chart of a monitoring method provided in an embodiment of the present application. In some embodiments of the present application, the monitoring method includes:

[0060] S101. Obtain processing time data and dependency relationships of each batch processing node in the system to be monitored within a preset historical time period, and determine a critical path of the system to be monitored based on the processing time data and dependency relationships.

[0061] In some implementations, a reasonable historical time period may be pre-set to collect sufficient data for analysis, and this time period needs to cover the typical operating mode and possible abnormal conditions of the system to be monitored.

[0062] Optionally, the day before the day when the system to be monitored is running can be used as the above historical time period.

[0063] In some embodiments, the processing time data of each batch processing node within a preset historical time period can be collected from the system to be monitored. Optionally, the processing time data may include the time when the batch processing node starts processing, the time when the processing is completed, and the difference between the two time points (i.e., the processing time).

[0064] At the same time, the dependency relationship between the above batch processing nodes is obtained, that is, which batch processing nodes are the preceding nodes and which batch processing nodes are the following nodes are obtained.

[0065] Optionally, the above dependency relationships may be obtained through system documents, flow charts, or relationship tables in a database.

[0066] In some implementations, the critical path of the system to be monitored can be determined based on the above processing time data and dependencies. The critical path can be the longest path among all tasks in the system to be monitored, which determines the completion time of the entire system to be monitored.

[0067] Optionally, the above-mentioned system to be monitored can be a core system of the bank or a non-core system, which is not limited in the embodiments of the present application.

[0068] S102 . Determine the operating status of each batch processing node at a preset time interval, and determine the current real-time critical path of the system to be monitored based on the operating status of each batch processing node.

[0069] In some implementations, a preset duration (e.g., every 15 minutes, every hour, etc.) may be set to regularly determine the operating status of each batch processing node (e.g., running, completed, waiting to run, etc.). Specifically, the selection of the above preset duration may be determined based on the actual situation and requirements of the system to be monitored.

[0070] In some implementations, the current real-time critical path of the system to be monitored may be determined based on the operating status of each batch processing node. The real-time critical path may vary due to changes in the status of the batch processing nodes (such as a node failure, a change in processing speed, etc.).

[0071] S103: Determine the predicted processing time of each batch processing node according to the real-time critical path and the critical path.

[0072] In some implementations, the real-time critical path may be compared with the critical path determined in step S101 to analyze the difference between the two. Based on the analysis results, it is determined whether to dynamically adjust the future predicted processing time of each batch processing node.

[0073] For example, when the above-mentioned real-time critical path is consistent with the critical path determined in step S101, the future predicted processing time of each batch processing node can be kept unchanged; when the above-mentioned real-time critical path is inconsistent with the critical path determined in step S101, the future predicted processing time of each batch processing node can be tightened, that is, the future predicted processing time of each batch processing node can be shortened.

[0074] S104. Based on the above-mentioned predicted processing time, monitor whether each batch processing node has an abnormality.

[0075] In some implementations, in each monitoring cycle, the actual processing time of each batch processing node may be compared to see whether it is greater than the predicted processing time. If the actual processing time is greater than the predicted processing time, it is determined that the batch processing node is abnormal.

[0076] In some implementations, if any batch processing node is determined to be abnormal, the corresponding alarm mechanism can be triggered immediately to notify relevant personnel to handle it in time to ensure the stability of system operation and business continuity. In addition, the abnormal information can also be recorded for subsequent analysis and improvement.

[0077] The monitoring method provided in the embodiment of the present application dynamically determines the critical path of the system to be monitored by regularly collecting and analyzing the operation data of the batch processing nodes, and dynamically adjusts the predicted processing time of the batch processing nodes. Compared with the fixed predicted processing time adopted in the traditional technology, the completion time of the core system batch processing can be accurately grasped, and a reasonable planning time window can be provided for production activities such as stress testing and switching drills, which effectively improves the operation stability of the core system, the timeliness of abnormal handling, and ensures production safety and business continuity.

[0078] Based on the contents described in the above embodiments, Figure 2 , Figure 2 This is a sub-flow diagram of a monitoring method provided in an embodiment of the present application. In some embodiments of the present application, the monitoring method further includes:

[0079] S201. Obtain historical processing time data of each batch processing node.

[0080] S202: Based on the above historical processing time data, determine the processing time prediction value corresponding to each batch processing node.

[0081] In some implementations, historical time data of all batch processing nodes in the system to be monitored may be obtained, pre-processed, modeled and analyzed, and a predicted processing time value corresponding to each batch processing node may be output.

[0082] Optionally, the preprocessing includes:

[0083] 1. Convert data types.

[0084] In some implementations, this includes checking and converting data types, such as converting string-type dates and times into date and time objects.

[0085] In some implementations, relevant data of date data and node time information in the original data may also be extracted and sorted in date order.

[0086] 2. Handle missing values ​​and outliers

[0087] Missing value handling includes: Deleting records containing missing values ​​(if there are not many missing values). Filling missing values ​​using statistical values ​​such as mean, median, or mode. Using interpolation methods (such as linear interpolation) to fill missing values ​​in time series data.

[0088] Outlier processing includes: identifying outliers using methods such as box plots and Z scores. Decide whether to delete outliers, replace them with mean / median / mode, or retain and mark them as special values ​​based on business needs.

[0089] In some implementations, the pre-processed historical time data may be used to train the prediction model. After the trained prediction model is obtained, the trained prediction model may be used to predict the future processing time of each batch processing node.

[0090] Based on the contents described in the above embodiments, in some embodiments of the present application, a first directed graph can be constructed based on the processing time data of each batch processing node and the dependency relationship between each batch processing node; based on the first directed graph, a topological sorting algorithm is used to determine the above critical path.

[0091] In some implementations, the processing time data of each batch processing node can be collected from the system to be monitored. This includes the time required for each batch processing node to complete the processing from the start of processing. At the same time, the dependencies between the batch processing nodes are collected.

[0092] In some embodiments, each batch processing node can be used as a vertex, and the processing time of the batch processing node can be used as an edge pointing to the batch processing nodes with a sequential dependency relationship to construct a first directed graph. That is, in the first directed graph, each batch processing node is represented as a vertex. Exemplarily, if node A must be processed after node B, a directed edge is drawn from node B to node A, and the direction of the directed edge represents the order in which nodes A and B are processed.

[0093] In some implementations, a topological sorting algorithm may be used to implement the calculation of the critical path.

[0094] In the process of topological sorting, the predecessor node of each batch processing node (i.e., the node connected by the edge pointing to the node) can be tracked. By calculating the longest path from the starting node to each batch processing node, it can be determined which batch processing nodes are on the critical path.

[0095] In some implementations, the nodes on the critical path may be used as batch operation paths to be estimated, and the predicted processing time of the nodes on the critical path may be tightened. For example, the predicted processing time of the nodes on the critical path may be shortened using a preset tightening coefficient; the tightening coefficient may be greater than 0 and less than 1.

[0096] In the above way, the first directed graph can be constructed based on the processing time data and dependency relationship of the batch processing nodes, and the critical path of the system to be monitored can be determined using the topological sorting algorithm. This helps to identify bottlenecks and potential problems in the system to be monitored, and provides strong support for performance monitoring and optimization.

[0097] Based on the contents described in the above embodiments, in some embodiments of the present application, the current real-time critical path of the system to be monitored is determined based on the operating status of each batch processing node in the above step S102, including:

[0098] Determine a target batch processing node whose running status is a completed status; update the processing time of the target batch processing node to the processing time of the target batch processing node within a preset historical time period; construct a second directed graph based on the updated processing time of the target batch processing node; based on the second directed graph, use a topological sorting algorithm to determine the real-time critical path.

[0099] In some embodiments, during the actual operation of the system to be monitored, the operating status of each batch processing node can be determined at every preset time interval, the processing time of the batch processing node whose operating status is completed can be updated to the processing time of the batch processing node in the above-mentioned preset historical time period, and a new directed graph (the above-mentioned second directed graph) can be reconstructed to calculate the real-time critical path of the system to be monitored.

[0100] In some embodiments, it is possible to determine whether the above-mentioned real-time critical path is consistent with the above-mentioned critical path; when they are inconsistent, based on the above-mentioned predicted processing time value and a preset tightening coefficient, re-determine the predicted processing time of each batch processing node; the above-mentioned tightening coefficient is greater than 0 and less than 1.

[0101] Among them, when the above-mentioned real-time critical path is inconsistent with the above-mentioned critical path, it indicates that the critical path has changed. At this time, the predicted processing time of the batch processing node can be tightened.

[0102] For example, if the predicted processing time value is T minutes and the tightening coefficient is k (0 < k < 1), then the adjusted predicted processing time is k * T minutes.

[0103] In the embodiments of the present application, when the above-mentioned real-time critical path is inconsistent with the original critical path, by applying the tightening coefficient to adjust the predicted processing time, a more conservative and reliable predicted processing time can be provided, which helps to better plan and manage batch processing tasks. When dealing with a system with a high degree of uncertainty, it can help to identify potential risks in advance.

[0104] Based on the content described in the above embodiments, in some embodiments of the present application, the above-mentioned monitoring method further includes:

[0105] Obtain the actual processing time of each batch processing node; according to the actual processing time and the predicted processing time of each batch processing node, output the remaining processing time of each batch processing node.

[0106] In some embodiments, it is possible to monitor the processing progress of each batch processing node in real time and record the time point when each batch processing node starts to process the task.

[0107] In some embodiments, for each batch processing node being processed, compare its current actual processing time with the previous predicted processing time. If the node is still being processed and has not reached the predicted total processing time, then the remaining processing time = predicted processing time - actual elapsed processing time.

[0108] If the node has completed processing, the remaining processing time should be 0 (or output a completed status according to business requirements).

[0109] In some embodiments, the information of the remaining processing time can be updated in real time so that the system administrator or user can view it at any time. In addition, based on the remaining processing time, the completion time of the batch system can be predicted more accurately, providing a reasonable time window for other business operations.

[0110] In the embodiment of the present application, by obtaining the actual processing time of each batch processing node and calculating and outputting the remaining processing time based on the predicted processing time, it helps the system administrator or user to better understand the system's processing capacity and task progress.

[0111] Based on the contents described in the above embodiments, a monitoring device is also provided in the embodiments of the present application. Figure 3 , Figure 3 This is a schematic diagram of a program module of a monitoring device provided in an embodiment of the present application. In some embodiments, the monitoring device 30 includes:

[0112] The preprocessing module 301 is used to obtain the processing time data and dependency relationship of each batch processing node in the system to be monitored within a preset historical time period, and determine the critical path of the system to be monitored based on the processing time data and dependency relationship.

[0113] The processing module 302 is used to determine the operating status of each batch processing node at each preset time interval, and determine the current real-time critical path of the system to be monitored based on the operating status of each batch processing node.

[0114] The prediction module 303 is used to determine the predicted processing time of each batch processing node according to the above-mentioned real-time critical path and the critical path.

[0115] The monitoring module 304 is used to monitor whether each batch processing node has an abnormality according to the predicted processing time.

[0116] In some embodiments, the preprocessing module 301 is specifically used for:

[0117] Constructing a first directed graph based on the processing time data of each of the batch processing nodes and the dependency relationship between each of the batch processing nodes;

[0118] Based on the first directed graph, the critical path is determined using a topological sorting algorithm.

[0119] In some embodiments, the processing module 302 is specifically configured to:

[0120] Determine a target batch processing node whose operation status is a completed status;

[0121] updating the processing time of the target batch processing node to the processing time of the target batch processing node within the preset historical time period;

[0122] constructing a second directed graph based on the updated processing time of the target batch processing node;

[0123] Based on the second directed graph, the real-time critical path is determined using a topological sorting algorithm.

[0124] In some embodiments, the pre-processing module 301 is further used to:

[0125] Obtaining historical processing time data of each of the batch processing nodes;

[0126] Based on the historical processing time data, a processing time prediction value corresponding to each of the batch processing nodes is determined.

[0127] In some embodiments, the prediction module 303 is specifically used to:

[0128] determining whether the real-time critical path is consistent with the critical path;

[0129] When the real-time critical path is inconsistent with the critical path, the predicted processing time of each batch processing node is determined based on the processing time prediction value and a preset tightening coefficient; the tightening coefficient is greater than 0 and less than 1.

[0130] In some embodiments, the above also includes a reminder module for:

[0131] Obtaining the actual processing time of each of the batch processing nodes;

[0132] According to the actual processing time and the predicted processing time of each of the batch processing nodes, the remaining processing time of each of the batch processing nodes is output.

[0133] It should be noted that the specific implementation content and beneficial effects of the above-mentioned monitoring device provided in the embodiments of the present application can refer to the various steps and beneficial effects of the monitoring method described in the above embodiments, and will not be repeated here.

[0134] Furthermore, based on the contents described in the above embodiments, an electronic device is also provided in an embodiment of the present application, which includes at least one processor and a memory; wherein the memory stores computer-executable instructions; the at least one processor executes the computer-executable instructions stored in the memory to implement the various steps in the monitoring method described in the above embodiments.

[0135] In order to better understand the embodiments of the present application, refer to Figure 4 , Figure 4 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application.

[0136] like Figure 4 As shown, the electronic device 40 of this embodiment includes: a processor 401 and a memory 402; wherein:

[0137] Memory 402, used to store computer-executable instructions;

[0138] The processor 401 is used to execute the computer-executable instructions stored in the memory to implement the various steps in the monitoring method described in the above embodiment.

[0139] Optionally, the memory 402 may be independent or integrated with the processor 401 .

[0140] When the memory 402 is independently provided, the device further includes a bus 403 for connecting the memory 402 and the processor 401 .

[0141] An embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, each step in the monitoring method described in the above embodiment is implemented.

[0142] An embodiment of the present application provides a computer program product, including a computer program. When the computer program is executed by a processor, the various steps in the monitoring method described in the above embodiment are implemented.

[0143] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0144] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0145] In addition, each functional module in each embodiment of the present application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The above-mentioned module-composed unit can be implemented in the form of hardware or in the form of hardware plus software functional units.

[0146] The above-mentioned integrated module implemented in the form of a software function module can be stored in a computer-readable storage medium. The above-mentioned software function module is stored in a storage medium, including a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to perform some steps of the method described in each embodiment of the present application.

[0147] It should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the method disclosed in the application may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0148] The memory may include high-speed memory, and may also include non-volatile storage, such as at least one disk memory, and may also be a USB flash drive, a mobile hard disk, a read-only memory, a magnetic disk or an optical disk, etc.

[0149] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

[0150] The above storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory, electrically erasable programmable read-only memory, erasable programmable read-only memory, programmable read-only memory, read-only memory, magnetic memory, flash memory, magnetic disk or optical disk. The storage medium can be any available medium that can be accessed by a general or special purpose computer.

[0151] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A monitoring method, characterized in that: The method comprises: Obtaining processing time data and dependency relationships of each batch processing node in the system to be monitored within a preset historical time period, and determining a critical path of the system to be monitored based on the processing time data and the dependency relationships; Determine the operating status of each of the batch processing nodes at intervals of a preset duration, and determine the current real-time critical path of the system to be monitored based on the operating status of each of the batch processing nodes; Determining the predicted processing time of each of the batch processing nodes according to the real-time critical path and the critical path; Based on the predicted processing time, each of the batch processing nodes is monitored to determine whether an abnormality exists.

2. The method according to claim 1, characterized in that Determining the critical path of the system to be monitored according to the processing time data and the dependency relationship includes: Constructing a first directed graph based on the processing time data of each of the batch processing nodes and the dependency relationship between each of the batch processing nodes; Based on the first directed graph, the critical path is determined using a topological sorting algorithm.

3. The method according to claim 1, characterized in that Determining the current real-time critical path of the system to be monitored based on the operating status of each of the batch processing nodes includes: Determine a target batch processing node whose operation status is a completed status; updating the processing time of the target batch processing node to the processing time of the target batch processing node within the preset historical time period; constructing a second directed graph based on the updated processing time of the target batch processing node; Based on the second directed graph, the real-time critical path is determined using a topological sorting algorithm.

4. The method according to claim 1, characterized in that: The method further comprises: Obtaining historical processing time data of each of the batch processing nodes; Based on the historical processing time data, a processing time prediction value corresponding to each of the batch processing nodes is determined.

5. The method according to claim 4, characterized in that The step of determining the predicted processing time of each of the batch processing nodes according to the real-time critical path and the critical path includes: determining whether the real-time critical path is consistent with the critical path; When the real-time critical path is inconsistent with the critical path, the predicted processing time of each batch processing node is determined based on the processing time prediction value and a preset tightening coefficient; the tightening coefficient is greater than 0 and less than 1.

6. The method according to any one of claims 1 to 5, characterized in that: The method further comprises: Obtaining the actual processing time of each of the batch processing nodes; According to the actual processing time and the predicted processing time of each of the batch processing nodes, the remaining processing time of each of the batch processing nodes is output.

7. A monitoring device, characterized in that: The device comprises: A preprocessing module, used to obtain the processing time data and dependency relationship of each batch processing node in the system to be monitored within a preset historical time period, and determine the critical path of the system to be monitored according to the processing time data and the dependency relationship; A processing module, used for determining the operation status of each batch processing node at each preset time interval, and determining the current real-time critical path of the system to be monitored based on the operation status of each batch processing node; A prediction module, used to determine the predicted processing time of each of the batch processing nodes according to the real-time critical path and the critical path; A monitoring module is used to monitor whether each of the batch processing nodes has an abnormality according to the predicted processing time.

8. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the monitoring method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the monitoring method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The invention comprises a computer program, which implements the monitoring method according to any one of claims 1 to 6 when executed by a processor.

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