Intelligent task scheduling methods, devices, computer equipment, and storage media

Abstract

This application belongs to the fields of artificial intelligence and operation and maintenance optimization, and is applied to the field of task scheduling optimization. It relates to a task intelligent scheduling method, device, computer equipment, and storage medium. The method includes acquiring relevant information from multiple systems and integrating it into a LINKDO intelligent scheduling platform; scheduling a task processing system to process tasks, recording task processing logs, and monitoring task processing progress; identifying the processing status of the scheduled tasks based on the processing progress and task processing logs, and determining whether the scheduled tasks are in a deadlock state; determining whether to re-execute based on a preset AI intelligent processing model and a WiseOPX monitoring system; determining the deadlock state of the scheduled tasks; and identifying the resource usage and thread concurrency of the scheduled task execution queue based on the preset AI intelligent processing model, selecting an appropriate processing method to intelligently process the deadlocked scheduled tasks, which helps reduce the manual workload of operation and maintenance personnel, automatically triggers failed tasks to be rerun, and is more intelligent and efficient.

Description

Technical Field

[0001] This application relates to the fields of artificial intelligence and operation and maintenance optimization technology, and in particular to a task intelligent scheduling method, device, computer equipment and storage medium. Background Technology

[0002] Currently, a large amount of manual processing is required for reporting tasks such as big data analysis of property insurance and China Insurance Information Technology Co., Ltd., as well as task execution and monitoring.

[0003] During task scheduling, misjudgments of task status frequently occur. Currently, task scheduling relies on scripts to identify keywords in logs to distinguish whether a task has been completed or failed. Often, a task has actually been completed, but the logs still show it as running. When a running task is interrupted due to network conditions or changes, the background logs are not actually being updated, but the monitoring interface still shows it as running, requiring manual intervention to resend the task. Large data tasks often suffer from insufficient queue resources or insufficient concurrency, leading to delays in task completion and affecting task timeliness. Summary of the Invention

[0004] The purpose of this application is to provide a task intelligent scheduling method, device, computer equipment, and storage medium to reduce the manual workload of operation and maintenance personnel, automatically trigger failed tasks to rerun, and make the process more intelligent and efficient.

[0005] To address the aforementioned technical problems, this application provides an intelligent task scheduling method, employing the following technical solution:

[0006] A task intelligent scheduling method includes the following steps:

[0007] The relevant information from multiple systems is acquired and integrated into the LINKDO intelligent scheduling platform. These multiple systems include: the WiseOPX monitoring system, the scheduling log system, and the task processing system. The relevant information includes: database information, system configuration information, and script configuration path information.

[0008] Based on the preset scheduling interface and LINKDO scheduling interface of the LINKDO intelligent scheduling platform, the task processing system is scheduled to perform task processing, and the task processing log is recorded through the scheduling log system. The processing progress of the task processing system monitored by the WiseOPX monitoring system is displayed on the preset scheduling interface.

[0009] The processing status of the scheduled task is identified based on the processing progress and the task processing log;

[0010] If the processing status is in a dead state, then based on the preset AI intelligent processing model and the WiseOPX monitoring system, it is determined whether to re-execute the scheduled task.

[0011] If the processing status is deadlocked, the resource usage and thread concurrency of the scheduled task execution queue are identified based on the preset AI intelligent processing model. Based on the preset selection mechanism, a processing method is selected to intelligently process the scheduled tasks in the deadlocked state.

[0012] Furthermore, prior to the step of scheduling the task processing system to perform task processing, the method further includes:

[0013] A processing status identifier is set in advance for the task to be scheduled for execution, wherein the processing status includes completed and incomplete;

[0014] The processing status includes a suspended state. The step of identifying the processing status of the scheduled task based on the processing progress and the task processing log specifically includes:

[0015] Based on the processing progress displayed on the preset scheduling interface, identify the processing status of the task displayed on the scheduling interface;

[0016] If the processing status displayed on the scheduling interface is "completed" and the processing status of the task in the scheduling task log is "incomplete", then the task is confirmed to be in a suspended state.

[0017] Furthermore, the step of determining whether to re-execute the scheduled task based on the preset AI intelligent processing model and the WiseOPX monitoring system specifically includes:

[0018] Based on the WiseOPX monitoring system, the actual start time and actual completion time of the task when it is scheduled for execution are obtained.

[0019] Based on the preset first ratio algorithm and preset ratio threshold within the AI ​​intelligent processing model, it is determined whether the task meets the re-execution trigger condition.

[0020] If so, the task will be re-executed based on the AI ​​intelligent processing model;

[0021] If not, the processing status of the task will be set to "completed" directly in the scheduling task log.

[0022] Furthermore, the preset ratio threshold includes a first ratio threshold and a second ratio threshold, wherein the first ratio threshold is greater than the second ratio threshold; the step of determining whether the task meets the re-execution trigger condition based on the preset first ratio algorithm and the preset ratio threshold within the AI ​​intelligent processing model specifically includes:

[0023] Based on a preset first ratio algorithm: Obtain the ratio between the actual execution time and the predicted execution time of the task, where T1 represents the set execution start time, T2 represents the set execution completion time, T3 represents the actual start time when the task is executed, and T4 represents the actual completion time when the task is executed.

[0024] If the ratio is greater than a preset first ratio threshold, then the re-execution trigger condition is met;

[0025] If the ratio is greater than a preset second ratio threshold but less than a preset first ratio threshold, then the re-execution trigger condition is not met.

[0026] If the ratio is less than a preset second ratio threshold, then based on the database information monitored by the WiseOPX monitoring system, the type of change in the task data volume within a preset time period is obtained.

[0027] If the change type is a normal sudden increase, then the re-execution trigger condition is not met;

[0028] If the change type is abnormal divergence, then the re-execution trigger condition is met.

[0029] Furthermore, if so, the step of re-executing the task based on the AI ​​intelligent processing model specifically includes:

[0030] Obtain the task identifier corresponding to the task, and use the task identifier as input information to input the trained AI intelligent processing model to intelligently select the corresponding execution module identifier, task type identifier, scheduling unit identifier, and database identifier;

[0031] By using the execution module identifier, task type identifier, scheduling unit identifier, and database identifier, the corresponding execution module, task type, scheduling unit, and database are selected to re-execute the task.

[0032] Furthermore, before the step of re-executing the task based on the AI ​​intelligent processing model, the method further includes:

[0033] Obtain the task identifier, execution module identifier, task type identifier, scheduling unit identifier, and database identifier corresponding to each pre-set task to be scheduled in the task processing system;

[0034] The task identifier, execution module identifier, task type identifier, scheduling unit identifier, and database identifier corresponding to each scheduled task in the task processing system are used as input information to intelligently train the preset AI intelligent processing model.

[0035] Obtain the one-to-one correspondence between the task identifier, execution module identifier, task type identifier, scheduling unit identifier, and database identifier, and use it as the training result of the AI ​​intelligent processing model;

[0036] The training of the AI ​​intelligent processing model is completed when all scheduled tasks in the task processing system have obtained training results.

[0037] Furthermore, the processing state includes a deadlock state, and the step of identifying the processing state of the scheduled task based on the processing progress and the task processing log specifically includes:

[0038] If the processing status displayed on the scheduling interface is incomplete, and the processing status of the task in the scheduling task log is also incomplete, then the task identifier of the task is obtained.

[0039] The I / O blocking time when the task is scheduled for execution is obtained through the task identifier and the WiseOPX monitoring system;

[0040] Based on a preset second ratio algorithm, IO blocking time, and a preset comparison threshold, it is determined whether the task is in a deadlock state.

[0041] Furthermore, the step of determining whether the task is in a deadlock state based on a preset second ratio algorithm, IO blocking time, and a preset comparison threshold specifically includes:

[0042] Based on a preset second ratio algorithm: Obtain the ratio between the IO blocking time of the scheduled execution of the task and the predicted execution time, where T1 represents the set execution start time, T2 represents the set execution completion time, and T5 represents the IO blocking time of the scheduled execution of the task.

[0043] If the ratio is greater than a preset comparison threshold, the task is in a deadlock state.

[0044] Furthermore, the step of identifying the resource usage and thread concurrency of the scheduled task execution queue based on a preset AI intelligent processing model, and selecting a processing method to intelligently handle the scheduled tasks in a deadlock state based on a preset selection mechanism, specifically includes:

[0045] A first selection mechanism and a second selection mechanism are preset, wherein the first selection mechanism is when the resource usage of the scheduled task execution queue is greater than a preset resource usage threshold, and the second selection mechanism is when the number of concurrent threads in the scheduled task execution queue is greater than a preset concurrency threshold.

[0046] If the resource usage of the scheduled task execution queue meets the first selection mechanism, then the resource allocation of the execution queue is expanded, and after expansion, the scheduled task is re-executed.

[0047] If the number of concurrent threads in the scheduled task execution queue meets the second selection mechanism, the execution module executing the scheduled task will be allocated to another execution queue for re-execution.

[0048] To address the aforementioned technical problems, this application also provides a task intelligent scheduling device, which employs the following technical solution:

[0049] A task intelligent scheduling device, comprising:

[0050] The scheduling platform integration module is used to acquire relevant information from multiple systems and integrate it into the LINKDO intelligent scheduling platform. The multiple systems include: WiseOPX monitoring system, scheduling log system, and task processing system. The relevant information includes: database information, system configuration information, and script configuration path information.

[0051] The scheduling monitoring and logging module is used to schedule the task processing system to perform task processing based on the preset scheduling interface and LINKDO scheduling interface of the LINKDO intelligent scheduling platform, and to record the task processing log through the scheduling log system. The module also displays the processing progress of the task processing system monitored by the WiseOPX monitoring system on the preset scheduling interface.

[0052] The task status determination module is used to identify the processing status of the scheduled task based on the processing progress and the task processing log.

[0053] The dead state handling module is used to determine whether to re-execute the scheduled task based on the preset AI intelligent processing model and the WiseOPX monitoring system if the processing state is dead state.

[0054] The deadlock handling module is used to identify the resource usage and thread concurrency of the scheduled task execution queue based on a preset AI intelligent processing model if the processing state is deadlocked. Based on a preset selection mechanism, it selects a processing method to intelligently handle the scheduled tasks in the deadlock state.

[0055] To address the aforementioned technical problems, this application also provides a computer device that employs the following technical solution:

[0056] A computer device includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the intelligent task scheduling method described above.

[0057] To address the aforementioned technical problems, this application also provides a computer-readable storage medium, employing the technical solution described below:

[0058] A computer-readable storage medium storing computer-readable instructions, which, when executed by a processor, implement the steps of the intelligent task scheduling method described above.

[0059] Compared with the prior art, the embodiments of this application have the following main advantages:

[0060] The intelligent task scheduling method described in this application integrates relevant information from multiple systems into the LINKDO intelligent scheduling platform; the scheduling task processing system processes tasks, records task processing logs, and monitors task processing progress; based on the processing progress and task processing logs, the processing status of the scheduled tasks is identified, and the task in a deadlock state is determined; based on a preset AI intelligent processing model and the WiseOPX monitoring system, it is determined whether to re-execute; the deadlock state of the scheduled tasks is determined; based on the preset AI intelligent processing model, the resource usage and thread concurrency of the scheduled task execution queue are identified, and an appropriate processing method is selected to intelligently process the deadlocked scheduled tasks, which helps reduce the manual workload of operation and maintenance personnel, automatically triggers failed tasks to rerun, and is more intelligent and efficient. Attached Figure Description

[0061] To more clearly illustrate the solutions in this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0062] Figure 1 This is an exemplary system architecture diagram to which this application can be applied;

[0063] Figure 2 A flowchart of an embodiment of the intelligent task scheduling method according to this application;

[0064] Figure 3 yes Figure 2 A flowchart of a specific implementation of step 203 shown;

[0065] Figure 4 yes Figure 2 A flowchart of a specific implementation of step 204 shown;

[0066] Figure 5 yes Figure 4 A flowchart of a specific implementation of step 402 shown;

[0067] Figure 6 yes Figure 4 A flowchart of a specific implementation of step 403 shown;

[0068] Figure 7 This embodiment is in Figure 4 The flowchart shows a specific implementation of training the AI ​​intelligent processing model before step 403.

[0069] Figure 8 This is a schematic diagram of a structure of an embodiment of the intelligent task scheduling device according to this application;

[0070] Figure 9 yes Figure 8 A schematic diagram of a specific embodiment of 803 is shown;

[0071] Figure 10 yes Figure 8 A schematic diagram of a specific embodiment of 804 is shown;

[0072] Figure 11 This is a schematic diagram of the structure of one embodiment of the computer device according to this application. Detailed Implementation

[0073] 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.

[0074] 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.

[0075] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0076] like Figure 1As shown, system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0077] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social media platform software, etc.

[0078] Terminal devices 101, 102, and 103 can be various electronic devices with displays 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.

[0079] Server 105 can be a server that provides various services, such as a backend server that supports the pages displayed on terminal devices 101, 102, and 103.

[0080] It should be noted that the intelligent task scheduling method provided in this application embodiment is generally executed by a server / terminal device, and correspondingly, the intelligent task scheduling device is generally set in the server / terminal device.

[0081] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0082] Continue to refer to Figure 2 A flowchart of an embodiment of the intelligent task scheduling method according to this application is shown. The intelligent task scheduling method includes the following steps:

[0083] Step 201: Obtain relevant information from multiple systems and integrate it into the LINKDO intelligent scheduling platform.

[0084] In this embodiment, the multiple systems include: WiseOPX monitoring system, scheduling log system, and task processing system, and may also include Ping An email system, Happy Ping An system, Ping An cloud, and big data version management system.

[0085] In this embodiment, the relevant information includes: database information, system configuration information, script configuration path information, and may also include email alarm configuration information, host IP address information, and password information.

[0086] By linking multiple interconnected systems to a unified scheduling platform, operation and maintenance management becomes easier, as does the intelligent processing of operation and maintenance tasks, thereby improving the efficiency of operation and maintenance.

[0087] Step 202: Based on the preset scheduling interface and LINKDO scheduling interface of the LINKDO intelligent scheduling platform, the task processing system is scheduled to perform task processing, and the task processing log is recorded through the scheduling log system. The processing progress of the task processing system monitored by the WiseOPX monitoring system is displayed on the preset scheduling interface.

[0088] In this embodiment, the tasks to be scheduled in the task processing system are directly scheduled and executed through the LINKDO scheduling interface. During execution, the task processing logs are recorded by the scheduling log system, and the task processing is monitored by the WiseOPX monitoring system, and the monitoring results are displayed in the preset scheduling interface. The task processing logs and monitoring interface are provided to facilitate the intelligent AI processing model to analyze the task processing status of the scheduled tasks using different data sources, avoiding the situation where a single data source cannot be scientifically compared.

[0089] Step 203: Identify the processing status of the scheduled task based on the processing progress and the task processing log.

[0090] In this embodiment, identifying the processing status of the scheduled task based on the processing progress and the task processing log includes: before scheduling the task processing system to process the task, setting a processing status identifier for the task to be scheduled, wherein the processing status includes completed and incomplete; if the processing status displayed on the scheduling interface is completed, but the processing status of the task in the scheduling task log is incomplete, then the task is confirmed to be in a deadlock state; if the processing status displayed on the scheduling interface is incomplete, and the processing status of the task in the scheduling task log is also incomplete, then the task identifier of the task is obtained; the IO blocking time when the task is scheduled for execution is obtained through the task identifier and the WiseOPX monitoring system; and whether the task is in a deadlock state is determined based on a preset second ratio algorithm, the IO blocking time, and a preset comparison threshold.

[0091] In this embodiment, different status flags can be set according to different processing states. For example, the status flags for a processing state that is completed can be set as "SUCCESS" and "1", and the status flags for a processing state that is incomplete can be set as "FAILURE" and "0".

[0092] By setting different status labels for incomplete and completed states, it is easier for intelligent AI processing models to identify the state later.

[0093] In this embodiment, different percentages can be set for the processing progress to correspond to the processing status. For example, two percentage thresholds can be set for the processing progress, namely 0 and 100%. If the actual percentage value corresponding to the current processing progress is 0, then the processing progress is unprocessed. If the actual percentage value corresponding to the current processing progress is greater than 0 and less than 100%, then the processing progress is being processed. If the actual percentage value corresponding to the current processing progress is 100%, then the processing progress is being completed. The processing status is set as incomplete for processing progress of unprocessed and processing progress of being processed. Similarly, the processing status corresponding to processing progress of being completed is completed.

[0094] By converting "unprocessed," "processing in progress," and "processed completed" to "incomplete" and "completed," it is easier to synchronize the processing status of the processing progress with the processing status of the tasks in the scheduling task log, which facilitates analysis by the intelligent AI processing model.

[0095] Continue to refer to Figure 3 , Figure 3 yes Figure 2 A flowchart of a specific implementation of step 203 shown includes the following steps:

[0096] Step 301: Before scheduling the task processing system to process the task, a processing status identifier is set in advance for the task to be scheduled for execution, wherein the processing status includes completed and incomplete;

[0097] Step 302: If the processing status displayed on the scheduling interface is "completed" and the processing status of the task in the scheduling task log is "incomplete", then it is confirmed that the task is in a suspended state.

[0098] The task processing logs and monitoring interface facilitate intelligent AI processing models to analyze the task processing status of scheduled tasks using different data sources. By analyzing the task processing status through the task processing logs and monitoring interface, it is possible to identify whether a task is in a "dead" state, making the identification of a "dead" task much simpler.

[0099] Step 303: If the processing status displayed on the scheduling interface is incomplete, and the processing status of the task in the scheduling task log is also incomplete, then obtain the task identifier of the task.

[0100] Step 304: Obtain the IO blocking time when the task is scheduled for execution using the task identifier and the WiseOPX monitoring system;

[0101] By obtaining the proportion of IO blocking time in the preset predicted time of the scheduled task, it can be determined whether the scheduled task is in a deadlock state. The algorithm is integrated into the AI ​​intelligent processing model to perform intelligent calculation and processing, reduce manual identification and processing, and improve processing efficiency.

[0102] By handling tasks in a deadlock state during the status identification and scheduling process, the AI ​​intelligent processing model can identify and process these tasks, thus avoiding prolonged deadlocks that could cause server downtime.

[0103] Step 305: Based on the preset second ratio algorithm, IO blocking time and preset comparison threshold, determine whether the task is in a deadlock state.

[0104] In this embodiment, the step of determining whether the task is in a deadlock state based on a preset second ratio algorithm, IO blocking time, and a preset comparison threshold specifically includes: based on the preset second ratio algorithm: Obtain the ratio between the IO blocking time of the scheduled execution of the task and the predicted execution time, where T1 represents the set execution start time, T2 represents the set execution completion time, and T5 represents the IO blocking time of the scheduled execution of the task; if the ratio is greater than a preset comparison threshold, the task is in a deadlock state.

[0105] Step 204: If the processing status is in a dead state, then based on the preset AI intelligent processing model and the WiseOPX monitoring system, determine whether to re-execute the scheduled task.

[0106] Continue to refer to Figure 4 , Figure 4 yes Figure 2 A flowchart of a specific implementation of step 204 shown includes the following steps:

[0107] Step 401: Based on the WiseOPX monitoring system, obtain the actual start time and actual completion time when the task is scheduled for execution;

[0108] Step 402: Based on the preset first ratio algorithm and preset ratio threshold in the AI ​​intelligent processing model, determine whether the task meets the re-execution trigger condition;

[0109] In this embodiment, the preset ratio threshold includes a first ratio threshold and a second ratio threshold, wherein the first ratio threshold is greater than the second ratio threshold.

[0110] Continue to refer to Figure 5 , Figure 5 yes Figure 4 A flowchart of a specific implementation of step 402 shown includes the following steps:

[0111] Step 501, based on the preset first ratio algorithm: Obtain the ratio between the actual execution time and the predicted execution time of the task, where T1 represents the set execution start time, T2 represents the set execution completion time, T3 represents the actual start time when the task is executed, and T4 represents the actual completion time when the task is executed.

[0112] Step 502: If the ratio is greater than the preset first ratio threshold, the re-execution trigger condition is met.

[0113] Step 503: If the ratio is greater than a preset second ratio threshold but less than a preset first ratio threshold, then the re-execution trigger condition is not met.

[0114] Step 504: If the ratio is less than a preset second ratio threshold, then based on the database information monitored by the WiseOPX monitoring system, obtain the type of change in the task data volume within a preset time period.

[0115] Step 505: If the change type is a normal sudden increase, then the re-execution trigger condition is not met;

[0116] Step 506: If the change type is abnormal divergence, then the re-execution trigger condition is met.

[0117] By judging the type of change in the amount of task data in the database, if the amount of task data increases normally or diverges abnormally, it is set as either not meeting the trigger condition or meeting the trigger condition, which is more scientific and intelligent.

[0118] The system determines whether the re-execution trigger condition is met by setting a preset ratio threshold. If it is met, the re-execution is performed. If it is not met, the corresponding task processing status is set to completed. The AI ​​intelligent processing model can identify whether the trigger condition has been met, avoiding manual intervention and improving the intelligence of the processing.

[0119] Step 403: If yes, then the task is re-executed based on the AI ​​intelligent processing model;

[0120] Continue to refer to Figure 6 , Figure 6 yes Figure 4 A flowchart of a specific implementation of step 403 shown includes the following steps:

[0121] Step 601: Obtain the task identifier corresponding to the task, and input the task identifier as input information into the trained AI intelligent processing model to intelligently select the corresponding execution module identifier, task type identifier, scheduling unit identifier and database identifier.

[0122] Step 602: Select the corresponding execution module, task type, scheduling unit, and database using the execution module identifier, task type identifier, scheduling unit identifier, and database identifier to re-execute the task.

[0123] By pre-training the AI ​​intelligent processing model, the model can quickly identify the task identifier, execution module identifier, task type identifier, scheduling unit identifier, and database identifier corresponding to each task to be scheduled. Thus, in handling false deadlocks and deadlocks, the corresponding execution module, task type, scheduling unit, and database can be quickly located directly through the task identifier, saving time to a certain extent.

[0124] Continue to refer to Figure 7 , Figure 7 This embodiment is in Figure 4 The flowchart shown is a specific implementation of training the AI ​​intelligent processing model before step 403, including the following steps:

[0125] Step 701: Obtain the task identifier, execution module identifier, task type identifier, scheduling unit identifier, and database identifier corresponding to each scheduled task preset in the task processing system;

[0126] Step 702: Use the task identifier, execution module identifier, task type identifier, scheduling unit identifier, and database identifier corresponding to each scheduled task in the task processing system as input information to perform intelligent training on the preset AI intelligent processing model.

[0127] Step 703: Obtain the one-to-one correspondence between the task identifier, execution module identifier, task type identifier, scheduling unit identifier, and database identifier, as the training result of the AI ​​intelligent processing model;

[0128] Step 704: The AI ​​intelligent processing model training is completed when all scheduled tasks in the task processing system have obtained training results.

[0129] By using the AI ​​intelligent processing model with a preset first ratio algorithm and preset ratio threshold, and associating the set execution start time, execution completion time, and actual start time with the actual completion time, the model determines whether to re-execute a seemingly dead task or directly set the processing status to completed through a simple ratio relationship. This avoids the previous practice of uniformly re-executing all seemingly dead tasks, which is both unscientific and consumes a lot of processing time.

[0130] Step 404: If not, then directly set the processing status of the task to "completed" in the scheduling task log.

[0131] Step 205: If the processing status is deadlocked, the resource usage and thread concurrency of the scheduling task execution queue are identified based on the preset AI intelligent processing model. Based on the preset selection mechanism, a processing method is selected to intelligently process the scheduling task in deadlocked state.

[0132] In this embodiment, the step of identifying the resource usage and thread concurrency of the scheduling task execution queue based on a preset AI intelligent processing model, and selecting a processing method to intelligently handle the scheduling task in a deadlock state based on a preset selection mechanism, specifically includes: preset setting a first selection mechanism and a second selection mechanism, wherein the first selection mechanism is that the resource usage of the scheduling task execution queue is greater than a preset resource usage threshold, and the second selection mechanism is that the thread concurrency of the scheduling task execution queue is greater than a preset concurrency threshold; if the resource usage of the scheduling task execution queue meets the first selection mechanism, the resource allocation of the execution queue is expanded, and after expansion, the scheduling task is re-executed; if the thread concurrency of the scheduling task execution queue meets the second selection mechanism, the execution module executing the scheduling task is allocated to other execution queues for re-execution.

[0133] By obtaining information on resource usage and thread concurrency in the task execution queue, different deadlock handling methods can be selected to diversify deadlock handling and improve processing efficiency.

[0134] This application integrates relevant information from multiple systems into the LINKDO intelligent scheduling platform. The scheduling task processing system processes tasks, records task processing logs, and monitors task processing progress. Based on the processing progress and task processing logs, it identifies the processing status of the scheduled tasks and determines if the scheduled tasks are in a deadlock state. Based on a preset AI intelligent processing model and the WiseOPX monitoring system, it determines whether to re-execute the task. It also determines if the scheduled task is in a deadlock state. Based on the preset AI intelligent processing model, it identifies the resource usage and thread concurrency of the scheduled task execution queue, selects the appropriate processing method to intelligently handle the deadlocked scheduled tasks, reduces the manual workload of maintenance personnel, and automatically triggers failed tasks to be rerun, making it more intelligent and efficient.

[0135] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0136] Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0137] For example, in this embodiment of the application, artificial intelligence technology can be used to train the AI ​​intelligent processing model. At the same time, artificial intelligence technology can also be used to identify the processing status of the scheduled tasks in the big data log file, avoiding the problem of long time consumption for manual identification or conventional identification.

[0138] Further reference Figure 8 As a response to the above Figure 2 The implementation of the method shown in this application provides an embodiment of a task intelligent scheduling device, which is similar to... Figure 2 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0139] like Figure 8 As shown, the intelligent task scheduling device 800 described in this embodiment includes: a scheduling platform integration module 801, a scheduling monitoring and log recording module 802, a task status judgment module 803, a false dead handling module 804, and a deadlock handling module 805. Wherein:

[0140] The scheduling platform integration module 801 is used to acquire relevant information from multiple systems and integrate it into the LINKDO intelligent scheduling platform. The multiple systems include: WiseOPX monitoring system, scheduling log system and task processing system. The relevant information includes: database information, system configuration information and script configuration path information.

[0141] The scheduling monitoring and logging module 802 is used to schedule the task processing system to perform task processing based on the preset scheduling interface and LINKDO scheduling interface of the LINKDO intelligent scheduling platform, and to record the task processing log through the scheduling log system. The module also displays the processing progress of the task processing system monitored by the WiseOPX monitoring system on the preset scheduling interface.

[0142] The task status judgment module 803 is used to identify the processing status of the scheduled task based on the processing progress and the task processing log.

[0143] The dead state handling module 804 is used to determine whether to re-execute the scheduled task based on the preset AI intelligent processing model and the WiseOPX monitoring system if the processing state is a dead state.

[0144] The deadlock handling module 805 is used to identify the resource usage and thread concurrency of the scheduling task execution queue based on a preset AI intelligent processing model if the processing state is deadlocked, and select a processing method to intelligently process the scheduling task in deadlocked state based on a preset selection mechanism.

[0145] The intelligent task scheduling device 800 described in this embodiment further includes: an AI intelligent processing model training module, used to obtain the task identifier, execution module identifier, task type identifier, scheduling unit identifier, and database identifier corresponding to each pre-set task to be scheduled in the task processing system; to use the task identifier, execution module identifier, task type identifier, scheduling unit identifier, and database identifier corresponding to each pre-set task to be scheduled in the task processing system as input information to intelligently train the pre-set AI intelligent processing model; to obtain the one-to-one correspondence between the task identifier, execution module identifier, task type identifier, scheduling unit identifier, and database identifier as the training result of the AI ​​intelligent processing model; and to complete the training of the AI ​​intelligent processing model until all tasks to be scheduled in the task processing system have obtained training results.

[0146] This application integrates relevant information from multiple systems into the LINKDO intelligent scheduling platform. The scheduling task processing system processes tasks, records task processing logs, and monitors task processing progress. Based on the processing progress and task processing logs, it identifies the processing status of the scheduled tasks and determines if the scheduled tasks are in a deadlock state. Based on a preset AI intelligent processing model and the WiseOPX monitoring system, it determines whether to re-execute the task. It also determines if the scheduled task is in a deadlock state. Based on the preset AI intelligent processing model, it identifies the resource usage and thread concurrency of the scheduled task execution queue, selects the appropriate processing method to intelligently handle the deadlocked scheduled tasks, reduces the manual workload of maintenance personnel, and automatically triggers failed tasks to be rerun, making it more intelligent and efficient.

[0147] Continue to refer to Figure 9 , Figure 9 yes Figure 8 The schematic diagram shown is a structural diagram of a specific embodiment of the task status judgment module 803. The task status judgment module 803 includes a false death judgment submodule 8031 ​​and a deadlock judgment submodule 8032, wherein,

[0148] The "dead state" judgment submodule 8031 ​​is used to confirm that the task is in a dead state if the processing status displayed on the scheduling interface is "completed" and the processing status of the task in the scheduling task log is "incomplete".

[0149] The deadlock detection submodule 8032 is used to obtain the task identifier of the task if the processing status displayed on the scheduling interface is incomplete and the processing status of the task in the scheduling task log is also incomplete; obtain the IO blocking time when the task is scheduled to be executed through the task identifier and the WiseOPX monitoring system; and determine whether the task is in a deadlock state based on a preset second ratio algorithm, the IO blocking time, and a preset comparison threshold.

[0150] In this embodiment, the deadlock detection submodule 8032 includes an acquisition subunit and an algorithm subunit, wherein,

[0151] The acquisition subunit is used to acquire the IO blocking time when the task is scheduled for execution through the task identifier and the WiseOPX monitoring system;

[0152] Algorithm subunit, used for algorithms based on a preset second ratio: Obtain the ratio between the IO blocking time of the scheduled execution of the task and the predicted execution time, where T1 represents the set execution start time, T2 represents the set execution completion time, and T5 represents the IO blocking time of the scheduled execution of the task; if the ratio is greater than a preset comparison threshold, the task is in a deadlock state.

[0153] Continue to refer to Figure 10 , Figure 10 yes Figure 8 The schematic diagram shown is a structural diagram of a specific embodiment of the 804. The apparent death handling module 804 includes an acquisition submodule 8041, an algorithm submodule 8042, and a judgment submodule 8043, wherein...

[0154] The acquisition submodule 8041 is used to acquire the actual start time and actual completion time of the task when it is scheduled to be executed, based on the WiseOPX monitoring system.

[0155] Algorithm submodule 8042 is used for algorithms based on a preset first ratio: Obtain the ratio between the actual execution time and the predicted execution time of the task;

[0156] The judgment submodule 8043 is used to determine whether the re-execution trigger condition is met if the ratio is greater than a preset first ratio threshold; if the ratio is greater than a preset second ratio threshold but less than the preset first ratio threshold, the re-execution trigger condition is not met; if the ratio is less than the preset second ratio threshold, the change type of the task data volume within a preset time period is obtained based on the database information monitored by the WiseOPX monitoring system; if the change type is a normal surge, the re-execution trigger condition is not met; if the change type is an abnormal divergence, the re-execution trigger condition is met.

[0157] 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 through computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0158] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures 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, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0159] To address the aforementioned technical problems, embodiments of this application also provide a computer device. Please refer to [link / reference needed]. Figure 11 , Figure 11 This is a basic structural block diagram of the computer device in this embodiment.

[0160] The computer device 11 includes a memory 11a, a processor 11b, and a network interface 11c that are interconnected via a system bus. It should be noted that only the computer device 11 with components 11a-11c is shown in the figure; however, it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described here is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and 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.

[0161] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0162] The memory 11a includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., 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 11a may be an internal storage unit of the computer device 11, such as the hard disk or memory of the computer device 11. In other embodiments, the memory 11a may also be an external storage device of the computer device 11, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 11. Of course, the memory 11a may include both the internal storage unit and its external storage device of the computer device 11. In this embodiment, the memory 11a is typically used to store the operating system and various application software installed on the computer device 11, such as computer-readable instructions for intelligent task scheduling methods. In addition, the memory 11a can also be used to temporarily store various types of data that have been output or will be output.

[0163] In some embodiments, the processor 11b may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. The processor 11b is typically used to control the overall operation of the computer device 11. In this embodiment, the processor 11b is used to execute computer-readable instructions stored in the memory 11a or to process data, for example, to execute computer-readable instructions of the intelligent task scheduling method.

[0164] The network interface 11c may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between the computer device 11 and other electronic devices.

[0165] The computer device proposed in this embodiment belongs to the field of operation and maintenance optimization technology. This application integrates relevant information from multiple systems into the LINKDO intelligent scheduling platform; the scheduling task processing system processes tasks, records task processing logs, and monitors task processing progress; based on the processing progress and task processing logs, it identifies the processing status of the scheduled tasks and determines whether the scheduled tasks are in a deadlock state; based on a preset AI intelligent processing model and the WiseOPX monitoring system, it determines whether to re-execute; it determines the deadlock state of the scheduled tasks; based on the preset AI intelligent processing model, it identifies the resource usage and thread concurrency of the scheduled task execution queue, selects the appropriate processing method to intelligently process the scheduled tasks in a deadlock state, which helps reduce the manual workload of operation and maintenance personnel, automatically triggers failed tasks to rerun, and is more intelligent and efficient.

[0166] This application also provides another embodiment, namely, providing a computer-readable storage medium storing computer-readable instructions that can be executed by a processor to cause the processor to perform the steps of the task intelligent scheduling method described above.

[0167] The computer-readable storage medium proposed in this embodiment belongs to the field of operation and maintenance optimization technology. This application integrates relevant information from multiple systems into the LINKDO intelligent scheduling platform; the scheduling task processing system processes tasks, records task processing logs, and monitors task processing progress; based on the processing progress and task processing logs, it identifies the processing status of the scheduled tasks and determines if the scheduled tasks are in a deadlock state; based on a preset AI intelligent processing model and the WiseOPX monitoring system, it determines whether to re-execute; it also determines if the scheduled tasks are in a deadlock state; based on the preset AI intelligent processing model, it identifies the resource usage and thread concurrency of the scheduled task execution queue, selects an appropriate processing method to intelligently handle the deadlocked scheduled tasks, thus reducing the manual workload of operation and maintenance personnel, automatically triggering failed tasks to rerun, making it more intelligent and efficient.

[0168] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0169] Obviously, the embodiments described above are only some embodiments of this application, not all embodiments. The accompanying drawings show preferred embodiments of this application, but do not limit the patent scope of this application. This application can be implemented in many different forms; rather, the purpose of providing these embodiments is to provide a more thorough and comprehensive understanding of the disclosure of this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this application's specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of patent protection of this application.

Claims

1. A method for intelligent scheduling of tasks, characterized in that, The method comprises the following steps: obtaining relevant information of a plurality of systems and integrating the information into a LINKDO intelligent scheduling platform, wherein the plurality of systems comprise a WiseOPX monitoring system, a scheduling log system and a task processing system, and the relevant information comprises database information, system configuration information and script configuration path information; scheduling the task processing system to perform task processing based on a preset scheduling interface and a LINKDO scheduling interface of the LINKDO intelligent scheduling platform, recording task processing logs through the scheduling log system, and displaying processing progress of the task processing system monitored by the WiseOPX monitoring system on the preset scheduling interface; identifying a processing state of the scheduled task based on the processing progress and the task processing logs; if the processing state is a false death state, determining whether to perform task re-execution on the scheduled task based on a preset AI intelligent processing model and the WiseOPX monitoring system; if the processing state is a deadlock state, identifying resource usage and thread concurrency of a scheduling task execution queue based on a preset AI intelligent processing model, and selecting a processing mode to intelligently process the scheduling task in the deadlock state based on a preset selection mechanism, specifically comprising: if the resource usage of the scheduling task execution queue meets a first selection mechanism, expanding the resource allocation of the execution queue, and re-executing the scheduling task after the expansion; if the thread concurrency of the scheduling task execution queue meets a second selection mechanism, assigning an execution module executing the scheduling task to other execution queues for re-execution, wherein the first selection mechanism is that the resource usage of the scheduling task execution queue is greater than a preset resource usage threshold, and the second selection mechanism is that the thread concurrency of the scheduling task execution queue is greater than a preset concurrency threshold.

2. The method of claim 1, wherein, Before the step of scheduling the task processing system to perform task processing, the method further comprises: previously setting a processing state identifier for a task to be scheduled and executed, wherein the processing state comprises completed and not completed; the processing state comprises a false death state, and the step of identifying the processing state of the scheduled task based on the processing progress and the task processing logs specifically comprises: identifying the processing state of the task displayed on the scheduling interface based on the processing progress displayed on the preset scheduling interface; if the processing state displayed on the scheduling interface is completed and the processing state of the task in the scheduling task log is not completed, it is determined that the task is in a false death state.

3. The method of claim 2, wherein, The step of determining whether to perform task re-execution on the scheduled task based on the preset AI intelligent processing model and the WiseOPX monitoring system specifically comprises: obtaining an actual start time and an actual completion time of the task based on the WiseOPX monitoring system; determining whether the task meets a re-execution trigger condition based on a preset first proportional algorithm and a preset proportion threshold in the AI intelligent processing model; if yes, performing re-execution on the task based on the AI intelligent processing model; If not, the processing state corresponding to the task in the scheduling task log is directly set as completed. 4.The task intelligent scheduling method of claim 3, wherein, The preset proportion threshold includes a first proportion threshold and a second proportion threshold, where the first proportion threshold is greater than the second proportion threshold; the step of determining whether the task meets the re-execution triggering condition based on the preset first proportion algorithm and the preset proportion threshold in the AI intelligent processing model specifically includes: Based on a preset first proportional algorithm: , a proportional relationship between an actual time and a predicted time for the task to be executed is obtained, wherein, represents a set execution start time, represents a set execution completion time, represents the actual start time when executed, represents the actual completion time when executed; If the proportion relationship is greater than the preset first proportion threshold, the re-execution triggering condition is met; If the proportion relationship is greater than the preset second proportion threshold but less than the preset first proportion threshold, the re-execution triggering condition is not met; If the proportion relationship is less than the preset second proportion threshold, the change type of the task data amount within a preset time is obtained based on the database information monitored by the WiseOPX monitoring system; If the change type is normal burst, the re-execution triggering condition is not met; If the change type is abnormal divergence, the re-execution triggering condition is met.

5. The method of claim 3, wherein, The step of re-executing the task based on the AI intelligent processing model if yes specifically includes: The task identifier corresponding to the task is obtained, and the task identifier is input as input information into the trained AI intelligent processing model to intelligently select the corresponding execution module identifier, task type identifier, scheduling unit identifier and database identifier; The corresponding execution module, task type, scheduling unit and database are selected through the execution module identifier, task type identifier, scheduling unit identifier and database identifier to perform the task re-execution.

6. The method of claim 5, wherein, Before the step of re-executing the task based on the AI intelligent processing model, the method further includes: The task identifier, execution module identifier, task type identifier, scheduling unit identifier and database identifier corresponding to each to-be-scheduled task in the task processing system are obtained; The task identifier, execution module identifier, task type identifier, scheduling unit identifier and database identifier corresponding to each to-be-scheduled task in the task processing system are input as input information into the preset AI intelligent processing model for intelligent training; The one-to-one correspondence relationship among the task identifier, execution module identifier, task type identifier, scheduling unit identifier and database identifier is obtained as the training result of the AI intelligent processing model; Until each to-be-scheduled task in the task processing system obtains the training result, the AI intelligent processing model is trained to be completed.

7. The method of claim 2, wherein, The processing state includes a deadlock state, and the step of identifying the processing state of the scheduled task based on the processing progress and the task processing log specifically includes: If the processing state displayed on the scheduling interface is not completed, and the processing state of the task in the scheduling task log is also not completed, the task identifier of the task is obtained; The IO blocking time when the task is scheduled to be executed is obtained through the task identifier and the WiseOPX monitoring system; Whether the task is in a deadlock state is determined based on a preset second proportion algorithm, the IO blocking time and a preset comparison threshold. 8.The task intelligent scheduling method of claim 7, wherein, The step of judging whether the task is in a deadlock state based on the preset second proportional algorithm, the IO blocking time and the preset comparison threshold value specifically comprises: Based on a preset second proportional algorithm: , a proportional relationship between the IO blocking time and the predicted time of the task being scheduled to execute is obtained, wherein, represents a set execution start time, represents a set execution completion time, represents the IO blocking time of the task being scheduled to execute; If the proportional relationship is greater than the preset comparison threshold value, the task is in a deadlock state.

9. A task intelligent scheduling apparatus characterized by comprising: Comprise: The scheduling platform integration module is configured to obtain relevant information of multiple systems and integrate the information into a LINKDO intelligent scheduling platform, wherein the multiple systems comprise a WiseOPX monitoring system, a scheduling log system and a task processing system, and the relevant information comprises database information, system configuration information and script configuration path information. The scheduling monitoring and log recording module is configured to schedule the task processing system to process tasks based on a preset scheduling interface and a LINKDO scheduling interface of the LINKDO intelligent scheduling platform, record task processing logs through the scheduling log system, and display processing progress of the task processing system monitored by the WiseOPX monitoring system on the preset scheduling interface. The task state judgment module is configured to identify a processing state of a scheduled task based on the processing progress and the task processing logs. The false death processing module is configured to determine whether to perform task re-execution on the scheduling task based on a preset AI intelligent processing model and the WiseOPX monitoring system if the processing state is a false death state. The deadlock processing module is configured to identify resource usage and thread concurrency of a scheduling task execution queue based on a preset AI intelligent processing model if the processing state is a deadlock state, and select a processing mode to intelligently process the scheduling task in the deadlock state based on a preset selection mechanism, specifically comprising: if the resource usage of the scheduling task execution queue meets a first selection mechanism, expanding the resource allocation of the execution queue, and re-executing the scheduling task after the expansion; if the thread concurrency of the scheduling task execution queue meets a second selection mechanism, assigning an execution module executing the scheduling task to another execution queue for re-execution, wherein the first selection mechanism is that the resource usage of the scheduling task execution queue is greater than a preset resource usage threshold value, and the second selection mechanism is that the thread concurrency of the scheduling task execution queue is greater than a preset concurrency threshold value.

10. A computer device comprising a memory and a processor, wherein the memory stores computer readable instructions, and the processor executes the computer readable instructions to implement the steps of the task intelligent scheduling method according to any one of claims 1 to 8.

11. A computer readable storage medium, characterized in that, The computer readable storage medium stores computer readable instructions, and the computer readable instructions are executed by the processor to implement the steps of the task intelligent scheduling method according to any one of claims 1 to 8.

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