Batch task processing monitoring method, device and equipment and storage medium thereof

Abstract

The invention belongs to the technical field of operation and maintenance monitoring, and relates to a batch task processing monitoring method, device and equipment and a storage medium thereof. Determining all task processing nodes related to each piece of to-be-processed task data; according to a preset segmentation time efficiency configuration table, determining segmentation processing time corresponding to different task processing nodes; generating a task data set containing segmented timeliness information, and performing task processing scheduling planning; and displaying the task processing scheduling planning result in a chart form by utilizing a preset chart conversion component. The method is applied to the field of financial services or medical online services, for example, in the scene that batch users conduct service activity card / medical ID card handling, task allocation and scheduling planning are reasonably conducted in combination with processing time segment information corresponding to task processing nodes, and the actual service processing conditions of all processing parties can be conveniently obtained in a unified mode. And the auxiliary task processing party comprehensively carries out business planning in combination with segmented timeliness information.

Description

Technical Field

[0001] The present application relates to the field of operation and maintenance monitoring technology, and is applied to batch task processing monitoring scenarios under the scale of large data flow, and relates to a batch task processing monitoring method, device, equipment and storage medium thereof. Background Art

[0002] Currently, in the field of operation and maintenance monitoring, especially in scenarios where segmented time analysis needs to be processed, the batch task processing monitoring method is applied to the financial card application business or medical service online card application business processing scenario, which often involves a series of business processing processes such as user information submission, review, verification, card making, card issuance and mailing, and signing. It involves task processing nodes such as submission, review, verification, card making, card issuance and mailing, and signing. Traditional data analysis methods have obvious limitations.

[0003] The main problem is that different business indicators or nodes focus on different time periods, and these time periods often change. This variability requires a series of modifications and verifications of application scripts, table structures, data synchronization tasks, datasets, and dashboards every time requirements are adjusted. This often lacks sufficient flexibility to meet the ever-changing needs of actual business, resulting in data analysis results that cannot effectively assist task processors in business planning. Summary of the Invention

[0004] The purpose of the embodiments of the present application is to propose a batch task processing monitoring method, device, equipment and storage medium thereof, so as to achieve more reasonable analysis of task processing data and assist the task processing party in business planning.

[0005] In a first aspect, an embodiment of the present application provides a batch task processing monitoring method, which adopts the following technical solution:

[0006] A batch task processing monitoring method includes the following steps:

[0007] Get batch pending task data;

[0008] According to the preset task logic execution relationship, determine all task processing nodes involved in each task data to be processed;

[0009] Determine the segment processing times corresponding to different task processing nodes according to a preset segment time configuration table, wherein the segment time configuration table includes each time segment identifier and the executable task processing node corresponding to each time segment, and the time segments are preset time segments;

[0010] Based on all the task processing nodes involved in each piece of task data to be processed, and the segmented processing time corresponding to different task processing nodes, estimate all the time segments spanned by each piece of task data to be processed during the actual task execution process;

[0011] Merging all time segments spanned by the batch of task data to be processed during the actual task execution process to generate a task data set containing segmented timeliness information;

[0012] Performing comprehensive statistical analysis on the task data set based on the segmented timeliness information to calculate all pending task data contained in each time segment;

[0013] According to all the pending task data contained in each time segment, the executable task processing nodes corresponding to each time segment, and all the task processing nodes involved in each pending task data, task processing scheduling planning is performed to obtain the task processing scheduling planning result;

[0014] The task processing scheduling planning result is displayed in the form of a chart on a preset visual monitoring interface using a preset chart conversion component.

[0015] In a second aspect, the present application also provides a batch task processing monitoring device, which adopts the following technical solution:

[0016] A batch task processing monitoring device, comprising:

[0017] The pending task data acquisition module is used to obtain batch pending task data;

[0018] The task processing node determination module is used to determine all task processing nodes involved in each task data to be processed according to the preset task logic execution relationship;

[0019] A segment processing time determination module is used to determine the segment processing times corresponding to different task processing nodes according to a preset segment time configuration table, wherein the segment time configuration table includes each time segment identifier and the executable task processing node corresponding to each time segment, and the time segments are preset time segments;

[0020] The time segment estimation module is used to estimate all time segments that each task data to be processed will span during the actual task execution process based on all task processing nodes involved in each task data to be processed and the segment processing time corresponding to different task processing nodes;

[0021] A task data set generation module is configured to merge all time segments spanned by the batch of task data to be processed during the actual task execution process to generate a task data set containing segmented timeliness information;

[0022] A comprehensive statistical analysis module is used to perform comprehensive statistical analysis on the task data set according to the segmented timeliness information, and to calculate all the task data to be processed contained in each time segment;

[0023] The task processing scheduling planning module is used to perform task processing scheduling planning based on all pending task data contained in each time segment, the executable task processing nodes corresponding to each time segment, and all task processing nodes involved in each pending task data, and obtain the task processing scheduling planning results;

[0024] The chart conversion and display module is used to use a preset chart conversion component to display the task processing scheduling planning results in the form of a chart on a preset visual monitoring interface.

[0025] In a third aspect, an embodiment of the present application further provides a computer device that adopts the following technical solution:

[0026] A computer device includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the batch task processing monitoring method described above when executing the computer-readable instructions.

[0027] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0028] A computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the batch task processing monitoring method as described above.

[0029] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0030] The batch task processing monitoring method described in the embodiment of the present application obtains batches of pending task data; determines all task processing nodes involved in each pending task data according to a preset task logic execution relationship; determines the segmented processing time corresponding to different task processing nodes according to a preset segmented time configuration table; generates a task data set containing segmented time information, performs task processing scheduling planning, and obtains task processing scheduling planning results; utilizes a preset chart conversion component to display the task processing scheduling planning results in the form of a chart to a preset visual monitoring interface. The batch task processing monitoring method is applied to the field of financial business, especially in commodity or service purchase scenarios involving monetary transactions. It can perform reasonable and unified task allocation and scheduling planning based on the scale of large quantities of pending task data, combined with the processing time segmentation information corresponding to different task processing nodes, and also facilitates the unified acquisition of the actual business processing status of each processing party involved in the actual business processing process. It assists the task processing party in comprehensively conducting business planning in combination with the segmented time information. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the solutions in this application, a brief introduction will be given below to the drawings required for use in the description of the embodiments of this application. Obviously, the drawings described below are some embodiments of this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0032] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0033] Figure 2 is a flow chart of an embodiment of a batch task processing monitoring method according to the present application;

[0034] Figure 3 This is a flowchart of a specific embodiment of the batch task processing monitoring method described in this application for preliminary division and processing of task data to be processed;

[0035] Figure 4 yes Figure 2 A flowchart of a specific embodiment of step 302 is shown;

[0036] Figure 5 yes Figure 4 A flowchart of a specific embodiment of step 402 is shown;

[0037] Figure 6 yes Figure 2 A flowchart of a specific embodiment of step 207 is shown;

[0038] Figure 7is a structural diagram of an embodiment of a batch task processing monitoring device according to the present application;

[0039] Figure 8 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0040] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art to which this application belongs. The terms used in the specification of the application are for the purpose of describing specific embodiments only and are not intended to limit this application. The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of this application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0041] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0042] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0043] like Figure 1 As shown, system architecture 100 may include a terminal device 101, a network 102, and a server 103. Terminal device 101 may be a laptop computer 1011, a tablet computer 1012, or a mobile phone 1013. Network 102 is a medium for providing a communication link between terminal device 101 and server 103. Network 102 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0044] The user can use the terminal device 101 to interact with the server 103 via the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0045] The terminal device 101 can be various electronic devices with a display screen and supporting web browsing. In addition to the laptop computer 1011, tablet computer 1012 or mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV) player, a laptop computer and a desktop computer, etc.

[0046] The server 103 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal device 101 .

[0047] It should be noted that the batch task processing monitoring method provided in the embodiment of the present application is generally executed by a server, and accordingly, a batch task processing monitoring device is generally set in the server.

[0048] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0049] Continue to refer Figure 2 , shows a flow chart of an embodiment of a batch task processing monitoring method according to the present application. The batch task processing monitoring method includes the following steps:

[0050] Step 201: Obtain batches of pending task data.

[0051] Specifically, the batch of task data to be processed, for example, batch order data, or batch alarm prompt data, batch migration data, etc. In short, the batch of task data to be processed varies based on different actual processing scenarios. For example, when the batch data to be processed is batch order data, the subsequent corresponding actual processing scenario or the task processing node involved is the actual processing scenario or the task processing node involved corresponding to the completion of order sending; when the batch data to be processed is batch alarm prompt data, the subsequent corresponding actual processing scenario or the task processing node involved is the actual processing scenario or the task processing node involved when performing task-related optimization or exception processing according to the alarm data; furthermore, when the batch data to be processed is batch migration data, the subsequent corresponding actual processing scenario or the task processing node involved is the actual processing scenario or the task processing node involved in migrating data from the existing data lake / library / platform to the target data lake / library / platform.

[0052] Specifically, in a financial business scenario, for example, when a financial business service receives bank cards or business activity cards sent by several users for online business processing, if the number of users is large, it will involve batches of related card (bank card, business activity card) processing task data. At this time, it often involves a series of business processing processes such as user information submission, review, verification, card making, card issuance and mailing, and receipt, which involve task processing nodes such as submission, review, verification, card making, card issuance and mailing, and receipt. Applying the batch task processing monitoring method to the financial card processing business or medical service online card processing business processing scenario can, in the scenario of large-scale task processing, uniformly process multiple tasks under the same segmented time limit for the same task processing node, and also facilitate separate processing for task processing nodes involved in different segmented time limits, so as to facilitate specific actual business processing planning in combination with the actual task processing period, and ensure the rationality and unified scheduling of actual business processing. Of course, the batch task processing monitoring method can also be applied to medical service scenarios, for example: before making an appointment, the user applies for a hospital ID card through the online card processing function.

[0053] Step 202: Determine all task processing nodes involved in each piece of task data to be processed according to the preset task logic execution relationship.

[0054] Specifically, according to the preset task logic execution relationship, all task processing nodes involved in each task data to be processed are determined. For example: according to the task logic execution relationship corresponding to the order sending business processing scenario, all order sending business nodes involved in the current order data are determined; for another example, according to the task logic execution relationship corresponding to the abnormal tuning business processing scenario, all abnormal tuning processing business nodes involved in the current alarm prompt data are determined; for another example, according to the task logic execution relationship corresponding to the cross-platform data migration business processing scenario, all data migration processing business nodes involved in the data currently being migrated are determined.

[0055] Through the preset task logic execution relationship, all task processing nodes involved in each task data to be processed are determined, so as to clarify the task execution process involved in each task data to be processed.

[0056] Step 203: Determine the segment processing times corresponding to different task processing nodes according to a preset segment time configuration table, wherein the segment time configuration table includes each time segment identifier and the executable task processing node corresponding to each time segment, and the time segment is a preset time segment.

[0057] Specifically, the preset segmented time configuration table is pre-set according to the specific business processing scenario and the task logic execution relationship. Specifically, the segmented time configuration table includes each time segment identifier and the executable task processing node corresponding to each time segment. For example: for order data, the merchant's stocking task involves the stocking time period, and when the subsequent delivery task involves air or land transportation, the express delivery task corresponds to the processing time period and in-transit transportation time period.

[0058] Through the preset segmented time configuration table, the segmented processing time corresponding to different task processing nodes is determined, so that in the subsequent scenario of large-scale task processing, multiple tasks under the same segmented time can be uniformly processed for the same task processing node, and it is also convenient to process the task processing nodes involved in different segmented time limits separately.

[0059] Step 204 : Based on all task processing nodes involved in each piece of task data to be processed and the segment processing times corresponding to different task processing nodes, all time segments spanned by each piece of task data to be processed during the actual task execution are estimated.

[0060] Specifically, continuing to take the above order data as an example, assuming that the task processing nodes involved in the current order data include the stocking task processing node, the transportation task processing node, and the receipt task processing node, assuming that the order time is 10:00 am on May 2, and the stocking time reserved for the merchant is half an hour, then the time segment corresponding to the stocking task processing node is between 10:00 am on May 2 and 10:30 am on May 2; then, the time reserved for logistics transportation is 3 days, then the time segment corresponding to the transportation task processing node is between 10:30 am on May 2 and 10:30 am on May 5; finally, The signing time reserved by the buyer is 1 day, so the time segment corresponding to the signing task processing node is between 10:30 am on May 5 and 10:30 am on May 6. It can be estimated that the current order data, that is, all the time segments spanned by the current task data to be processed during the actual task execution process, at least include the preparation time segment between 10:00 am on May 2 and 10:30 am on May 2, the transportation time segment between 10:30 am on May 2 and 10:30 am on May 5, and the signing time segment between 10:30 am on May 5 and 10:30 am on May 6.

[0061] By using all the task processing nodes involved in each piece of task data to be processed, as well as the segmented processing time corresponding to different task processing nodes, all the time segments spanned by each piece of task data to be processed during the actual task execution process are estimated, so as to allocate and plan reasonable business relaxation time for the actual task execution, so that all the docking parties in the entire task processing process have sufficient task processing time.

[0062] Step 205: Merge all time segments spanned by the batch of task data to be processed during the actual task execution process to generate a task data set containing segmented timeliness information.

[0063] In this embodiment, all time segments spanned by the batch of pending task data during the actual task execution process are merged to generate a task data set containing segmented timeliness information. For example, there are three order data, namely order data A, B and C. Due to the differences in purchased goods, production inventory and address distances, there are certain differences in the preparation time segments, transportation time segments and receipt time segments involved in order data A, B and C. Specifically, the goods involved in order data A are ordinary clothes with sufficient inventory and a short address distance. The order time is 10:00 on May 3rd, the preparation time is 30 minutes, the transportation time is 2 days, the receipt method is to simply confirm whether there are quality problems with the clothes, and the receipt time is within 30 minutes after receipt. All time segments spanned by order data A during the actual task execution process are merged to generate a task dataset containing segmented aging information. The JSON format array data can be formed with the identification information of order data A as the JSON array identification information, the stocking processing node as the first key value and the stocking time segment as the first value value, the transportation processing node as the second key value and the transportation time segment as the second value value, the receipt processing node as the third key value and the receipt time segment as the third value value. Similarly, corresponding JSON format array data is generated for order data B and C; finally, the JSON format array data corresponding to all order data are added to the same dataset to generate the task dataset containing segmented aging information.

[0064] By merging all time segments spanned by the batch of pending task data during the actual task execution process, a task data set containing segment timeliness information is generated, so as to facilitate the subsequent unified processing of the batch of pending task data in the time segment dimension.

[0065] Step 206 : Perform comprehensive statistical analysis on the task data set according to the segmented timeliness information, and calculate all the to-be-processed task data contained in each time segment.

[0066] Step 207 , based on all pending task data contained in each time segment, the executable task processing nodes corresponding to each time segment, and all task processing nodes involved in each pending task data, perform task processing scheduling planning to obtain a task processing scheduling planning result.

[0067] Specifically, a comprehensive statistical analysis of the task data set is performed through the segmented time information, and all the pending task data contained in each time segment are counted. Task processing scheduling planning is performed based on all the pending task data contained in each time segment, the executable task processing nodes corresponding to each time segment, and all the task processing nodes involved in each pending task data, so as to make specific actual business processing planning in combination with the actual task processing time period, and ensure the rationality and unified scheduling of actual business processing.

[0068] Step 208: Using a preset chart conversion component, the task processing scheduling planning result is displayed in a chart form on a preset visual monitoring interface.

[0069] Specifically, by utilizing a preset chart conversion component, the task processing scheduling planning results are displayed in a chart form on a preset visual monitoring interface, so that each processing party involved in the actual business processing flow can know the specific business processing status in real time.

[0070] In this embodiment, the batch task processing monitoring method is applied to the financial business field, especially in scenarios involving the purchase of goods or services involving monetary transactions. It can combine the processing time segmentation information corresponding to different task processing nodes with the scale of large quantities of task data to be processed to perform reasonable and unified task allocation and scheduling planning. It also facilitates unified acquisition of the actual business processing status of each processor involved in the actual business processing flow. This assists the task processor in comprehensively conducting business planning based on the segmented time information.

[0071] In this embodiment, by obtaining batches of pending task data; determining all task processing nodes involved in each pending task data according to the preset task logic execution relationship; determining the segmented processing time corresponding to different task processing nodes according to the preset segmented time configuration table; generating a task data set containing segmented time information, performing task processing scheduling planning, and obtaining task processing scheduling planning results; utilizing a preset chart conversion component, displaying the task processing scheduling planning results in the form of a chart to a preset visual monitoring interface. The batch task processing monitoring method is applied to the financial business field, especially in commodity or service purchase scenarios involving monetary transactions. It can perform reasonable and unified task allocation and scheduling planning based on the scale of large batches of pending task data, combined with the processing time segmentation information corresponding to different task processing nodes, and also facilitate the unified acquisition of the actual business processing status of each processing party involved in the actual business processing process. It assists the task processing party in comprehensively conducting business planning in combination with the segmented time information. The batch task processing monitoring method is applied to the financial card application business or medical service online card application business processing scenario. In the scenario of large-scale task processing, it can uniformly process multiple tasks of the same task processing node under the same segment time limit, and it is also convenient to separately process the task processing nodes involved in different segment time limits, so as to make specific actual business processing planning in combination with the actual task processing time period, and ensure the rationality and unified scheduling of actual business processing.

[0072] Continue to refer Figure 3 In some optional implementations, the method further includes the step of performing preliminary division processing on the task data to be processed. Figure 3 This is a flowchart of a specific embodiment of performing preliminary division processing on task data to be processed in the batch task processing monitoring method described in this application, including the following steps:

[0073] Step 301: Obtaining a task processing control field corresponding to each piece of pending task data from a task processing request end corresponding to each piece of pending task data, wherein the task processing control field includes an identification field for processing time control that is pre-reserved on the task request initiation interface and can be clicked and selected or manually entered;

[0074] Step 302 : Preliminary division processing is performed on the batch of task data to be processed according to the identification field for processing time control corresponding to each piece of task data to be processed.

[0075] By obtaining the task processing control field corresponding to each piece of task data to be processed from the task processing request end corresponding to each piece of task data to be processed, and performing preliminary division processing on the batch of task data to be processed according to the identification field for processing time control corresponding to each piece of task data to be processed, it is easy to identify the task data to be processed that has been task processed controlled by the task processing request end, and perform preliminary division according to different settings of task processing control.

[0076] Continue to refer Figure 4 , Figure 4 yes Figure 2 The flowchart of a specific embodiment of step 302 shown includes the following steps:

[0077] Step 401: parse the task processing control field selection or task processing control field input corresponding to each to-be-processed task data at the corresponding task processing request end to obtain a parsing result;

[0078] Step 402: Determine corresponding identification fields according to the analysis results, and perform preliminary division processing on the batch of task data to be processed based on the identification fields.

[0079] Specifically, by parsing the task processing control field selection or task processing control field input corresponding to each piece of to-be-processed task data at the corresponding task processing request end, a parsing result is obtained, and according to the parsing result, a corresponding identification field is determined, and based on the identification field, a preliminary division and processing of the batch of to-be-processed task data is performed. For example, the control mechanism of the task processing control field includes two modes, one is a processing time control mode based on a default processing time point, and the other is a processing time mode of a directly specified specific control;

[0080] Accordingly, the processing time control mode based on the default processing time point includes delaying the processing by 5 hours or processing by 30 minutes in advance; the directly specified specific control processing time mode includes executing task processing at 9:00 on May 20.

[0081] By determining the corresponding identification field based on the analysis results, and performing preliminary division and processing on the batch of pending task data based on the identification field, that is, according to the corresponding task processing control field, filtering out the pending task data whose task processing time corresponds to the default situation, delayed situation and early situation respectively.

[0082] In this embodiment, the identification field for processing time regulation includes a default identification field, a delay identification field and an advance identification field. Specifically, the default identification field refers to the default field corresponding to the case where the task processing request end has not made a selection or input. Generally, the task processing time corresponding to the default field is immediate processing; and the delay identification field refers to the field selected or input by the task processing request end, representing the delay of the task processing time, and the field corresponds to the corresponding delay time information; similarly, the advance identification field refers to the field selected or input by the task processing request end, representing the advance of the task processing time, and the field corresponds to the corresponding advance time information.

[0083] Continue to refer Figure 5 , Figure 5 yes Figure 2 The flowchart of a specific embodiment of step 402 includes the following steps:

[0084] Step 501: If, according to the analysis result, it is identified that the task processing control field selection or task processing control field input corresponding to the current task data to be processed at the corresponding task processing request end is an empty value or a NULL value, then the identification field corresponding to the current task data to be processed is directly marked as a default identification field;

[0085] Specifically, if the task processing control field selection or task processing control field input corresponding to the corresponding task processing request end of the current task data to be processed is an empty value or a NULL value, it means that the task processing request end does not require a specified processing time control, and directly uses the instant time or the original task processing time as the task processing time.

[0086] Step 502: If, based on the analysis result, it is identified that the task processing control field selection corresponding to the current task data to be processed at the corresponding task processing request end or the task processing control field input is neither an empty value nor a NULL value, then an identification field corresponding to the current task data to be processed is identified based on a preset field matching strategy;

[0087] Step 503: Perform preliminary division processing on the batch of task data to be processed based on the identification field.

[0088] In this embodiment, the preset field matching strategy includes a field matching strategy based on a preset dictionary and a field matching strategy based on semantic parsing. Specifically, the step of identifying the identification field corresponding to the current task data to be processed according to the preset field matching strategy includes: if it is identified that the task processing control field corresponding to the current task data to be processed at the corresponding task processing request end is a selected field, then based on the field matching strategy of the preset dictionary, the identification field corresponding to the task processing control field is filtered out from the preset dictionary, wherein the preset dictionary summarizes the default identification field, delay identification field and advance identification field for processing time control; if it is identified that the task processing control field corresponding to the current task data to be processed at the corresponding task processing request end is an input field, then based on the field matching strategy of semantic parsing, the task processing control field is semantically parsed, and the corresponding identification field is determined according to the semantic parsing result.

[0089] Specifically, the field matching strategy based on semantic analysis performs semantic analysis on the task processing control field and determines the corresponding identification field based on the semantic analysis results. The corresponding identification field can be determined by identifying relevant keywords in the input field, such as: delay, postpone, advance, direct, default, immediate, etc.

[0090] Continue to refer Figure 6 , Figure 6 yes Figure 2 The flowchart of a specific embodiment of step 207 shown includes the following steps:

[0091] Step 601: Calculate the task execution amount corresponding to each time segment based on all pending task data contained in each time segment;

[0092] Step 602: Calculate the processing frequencies of different task processing nodes in each time segment based on the executable task processing nodes corresponding to each time segment.

[0093] Step 603: Based on all task processing nodes involved in each piece of task data to be processed and the processing frequencies corresponding to different task processing nodes in each time segment, the high-frequency task processing nodes and low-frequency task processing nodes involved in the task processing flow corresponding to each piece of task data to be processed are counted.

[0094] Step 604: pre-allocate task processing for the task processing flows corresponding to all pending task data based on the task execution volume corresponding to each time segment and the high-frequency task processing nodes and low-frequency task processing nodes involved in the task processing flows corresponding to each pending task data.

[0095] Step 605 : generating the task processing scheduling planning result by combining the correspondence between the task processing nodes and the task processing time in the pre-allocation result.

[0096] In this embodiment, the steps of pre-allocating task processing for the task processing flows corresponding to all pending task data based on the task execution volume corresponding to each time segment and the high-frequency task processing nodes and low-frequency task processing nodes involved in the task processing flow corresponding to each pending task data specifically include: if the task execution volume corresponding to the current time segment exceeds the preset task execution volume threshold, the service node expansion method is adopted to expand the service nodes within the current time segment and allocate task execution volume to the newly expanded service nodes; if the high-frequency task processing nodes involved in task processing under the current time segment exceed the preset node number threshold, the task processing volume is allocated according to the preset processing resources. The source first allocation mechanism allocates more task processing resources to the high-frequency task processing node, wherein the preset processing resource first allocation mechanism includes dividing more task processing resources from the non-high-frequency task processing nodes under the same service cluster to the high-frequency task processing node; if the low-frequency task processing nodes involved in task processing in the current time segment exceed the preset node quantity threshold, then according to the preset processing resource second allocation mechanism, fewer task processing resources are allocated to the low-frequency task processing node, wherein the preset processing resource second allocation mechanism includes dividing more task processing resources from the low-frequency task processing node to the non-low-frequency task processing node under the same service cluster.

[0097] Task processing scheduling is carried out through all pending task data contained in each time segment, the executable task processing nodes corresponding to each time segment, and all task processing nodes involved in each pending task data, so as to make specific actual business processing planning in combination with the actual task processing time period, and ensure the rationality and unified scheduling of actual business processing.

[0098] In this embodiment, by obtaining batches of pending task data; determining all task processing nodes involved in each pending task data according to the preset task logic execution relationship; determining the segmented processing time corresponding to different task processing nodes according to the preset segmented time configuration table; generating a task data set containing segmented time information, performing task processing scheduling planning, and obtaining task processing scheduling planning results; utilizing a preset chart conversion component, displaying the task processing scheduling planning results in the form of a chart to a preset visual monitoring interface. The batch task processing monitoring method is applied to the financial business field, especially in commodity or service purchase scenarios involving monetary transactions. It can perform reasonable and unified task allocation and scheduling planning based on the scale of large batches of pending task data, combined with the processing time segmentation information corresponding to different task processing nodes, and also facilitate the unified acquisition of the actual business processing status of each processing party involved in the actual business processing process. It assists the task processing party in comprehensively conducting business planning in combination with the segmented time information.

[0099] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to achieve optimal results.

[0100] Fundamental AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interaction systems, and mechatronics. AI software technologies primarily encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0101] In this embodiment, by obtaining batches of pending task data; determining all task processing nodes involved in each pending task data according to the preset task logic execution relationship; determining the segmented processing time corresponding to different task processing nodes according to the preset segmented time configuration table; generating a task data set containing segmented time information, performing task processing scheduling planning, and obtaining task processing scheduling planning results; utilizing a preset chart conversion component, displaying the task processing scheduling planning results in the form of a chart to a preset visual monitoring interface. The batch task processing monitoring method is applied to the field of financial business, especially in commodity or service purchase scenarios involving currency transactions, and can perform reasonable and unified task allocation and scheduling planning based on the scale of large batches of pending task data, combined with the processing time segmentation information corresponding to different task processing nodes, and also facilitates the unified acquisition of the actual business processing status of each processor involved in the actual business processing process. Assists the task processor to comprehensively carry out business planning in combination with the segmented time information. Further reference Figure 7 , as a response to the above Figure 2 The present application provides an embodiment of a batch task processing monitoring device. Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0102] like Figure 7 As shown, the batch task processing monitoring device 700 of this embodiment includes: a pending task data acquisition module 701, a task processing node determination module 702, a segment processing time determination module 703, a time span segment estimation module 704, a merged task data set generation module 705, a comprehensive statistical analysis module 706, a task processing scheduling planning module 707, and a chart conversion and display module 708. Among them:

[0103] The pending task data acquisition module 701 is used to acquire batch pending task data;

[0104] The task processing node determination module 702 is used to determine all task processing nodes involved in each piece of task data to be processed according to a preset task logic execution relationship;

[0105] The segment processing time determination module 703 is used to determine the segment processing time corresponding to different task processing nodes according to a preset segment aging configuration table, wherein the segment aging configuration table includes each time segment identifier and the executable task processing node corresponding to each time segment, and the time segments are preset time segments;

[0106] The time segment estimation module 704 is used to estimate all time segments that each piece of task data to be processed will span during the actual task execution process based on all task processing nodes involved in each piece of task data to be processed and the segment processing times corresponding to different task processing nodes;

[0107] The merging and generating task data set module 705 is used to merge all time segments spanned by the batch of task data to be processed during the actual task execution process to generate a task data set containing segmented timeliness information;

[0108] Comprehensive statistical analysis module 706, used to perform comprehensive statistical analysis on the task data set according to the segment timeliness information, and calculate all the to-be-processed task data contained in each time segment;

[0109] The task processing scheduling planning module 707 is used to perform task processing scheduling planning based on all pending task data contained in each time segment, the executable task processing nodes corresponding to each time segment, and all task processing nodes involved in each pending task data, and obtain a task processing scheduling planning result;

[0110] The chart conversion and display module 708 is used to use a preset chart conversion component to display the task processing scheduling planning result in the form of a chart on a preset visual monitoring interface.

[0111] This application obtains batches of pending task data; determines all task processing nodes involved in each pending task data according to the preset task logic execution relationship; determines the segmented processing time corresponding to different task processing nodes according to the preset segmented time configuration table; generates a task data set containing segmented time information, performs task processing scheduling planning, and obtains task processing scheduling planning results; uses a preset chart conversion component to display the task processing scheduling planning results in the form of a chart to a preset visual monitoring interface. The batch task processing monitoring method is applied to the financial business field, especially in commodity or service purchase scenarios involving currency transactions. It can perform reasonable and unified task allocation and scheduling planning under the scale of large quantities of pending task data, combined with the processing time segmentation information corresponding to different task processing nodes, and also facilitates the unified acquisition of the actual business processing status of each processor involved in the actual business processing process. Assists the task processor to comprehensively carry out business planning in combination with the segmented time information. In this embodiment, the batch task processing monitoring device 700 also includes a task processing control field acquisition module and a task data preliminary division module. Among them:

[0112] A task processing control field acquisition module is used to obtain the task processing control field corresponding to each piece of to-be-processed task data from the task processing request end corresponding to each piece of to-be-processed task data, wherein the task processing control field includes an identification field for processing time control that is reserved in advance on the task request initiation interface and can be clicked and selected or manually entered;

[0113] The task data preliminary division module is used to perform preliminary division processing on the batch of task data to be processed according to the identification field for processing time regulation corresponding to each piece of task data to be processed.

[0114] In this embodiment, the task data preliminary division module includes a task processing control field parsing unit and a task data preliminary division unit.

[0115] The task processing control field parsing unit is used to parse the task processing control field selection or task processing control field input corresponding to each to-be-processed task data at the corresponding task processing request end to obtain a parsing result;

[0116] The task data preliminary division unit is used to determine the corresponding identification field according to the analysis result, and perform preliminary division processing on the batch of task data to be processed based on the identification field.

[0117] In this embodiment, the task data preliminary division module further includes a first analysis result recognition processing unit and a second analysis result recognition processing unit.

[0118] a first identification processing unit for the parsing result, configured to directly mark the identification field corresponding to the current task data to be processed as a default identification field if, based on the parsing result, it is identified that the selection of the task processing control field corresponding to the current task data to be processed at the corresponding task processing request end or the input of the task processing control field is an empty value or a NULL value;

[0119] The second identification processing unit of the analysis result is used to identify the identification field corresponding to the current task data to be processed according to the preset field matching strategy if it is identified based on the analysis result that the task processing control field selection corresponding to the current task data to be processed at the corresponding task processing request end or the task processing control field input is neither an empty value nor a NULL value.

[0120] In this embodiment, the second recognition processing unit for the analysis result includes a first recognition processing subunit and a second recognition processing subunit.

[0121] The first identification and processing subunit is configured to, if it is identified that the task processing control field corresponding to the current to-be-processed task data at the corresponding task processing request end is a selected field, filter out an identification field corresponding to the task processing control field from the preset dictionary based on a field matching strategy of the preset dictionary, wherein the preset dictionary summarizes a default identification field, a delay identification field, and an advance identification field for processing time control;

[0122] The second identification and processing sub-unit is used to perform semantic analysis on the task processing control field corresponding to the task processing request end of the current task data to be processed, if it is identified as an input field, based on the field matching strategy of semantic analysis, and determine the corresponding identification field according to the semantic analysis result.

[0123] In this embodiment, the task processing scheduling and planning module 707 includes a task execution amount statistics unit, a node processing frequency statistics unit, a task processing node classification statistics unit, a task processing pre-allocation unit and a task processing scheduling and planning unit.

[0124] The task execution amount statistics unit is used to calculate the task execution amount corresponding to each time segment based on all the pending task data contained in each time segment;

[0125] The node processing frequency statistics unit is used to count the processing frequencies of different task processing nodes in each time segment according to the executable task processing nodes corresponding to each time segment;

[0126] The task processing node classification and statistics unit is used to count the high-frequency task processing nodes and low-frequency task processing nodes involved in the task processing flow corresponding to each piece of task data to be processed based on all the task processing nodes involved in each piece of task data to be processed and the processing frequencies corresponding to different task processing nodes in each time segment;

[0127] A task processing pre-allocation unit is used to pre-allocate task processing for the task processing flows corresponding to all pending task data based on the task execution amount corresponding to each time segment and the high-frequency task processing nodes and low-frequency task processing nodes involved in the task processing flows corresponding to each pending task data;

[0128] The task processing scheduling planning unit is used to generate the task processing scheduling planning result by combining the correspondence between the task processing nodes and the task processing time in the pre-allocation result.

[0129] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware via computer-readable instructions. The computer-readable instructions can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes in the above-described method embodiments. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0130] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified 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 of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0131] To solve the above technical problems, the present application also provides a computer device. Figure 8 , Figure 8 This is a basic structural block diagram of the computer device in this embodiment.

[0132] The computer device 8 includes a memory 8a, a processor 8b, and a network interface 8c that are interconnected via a system bus. Figure 8 Only a computer device 8 having components such as a memory 8a, a processor 8b, and a network interface 8c is shown. However, it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead. It should be understood by those skilled in the art that a computer device herein is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to a microprocessor, an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), an embedded device, etc.

[0133] The computer device may be a desktop computer, notebook computer, PDA, cloud server, etc. The computer device may interact with the user via a keyboard, mouse, remote control, touchpad, or voice control device.

[0134] The memory 8a 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 8a can be an internal storage unit of the computer device 8, such as the hard disk or memory of the computer device 8. In other embodiments, the memory 8a can also be an external storage device of the computer device 8, such as a plug-in hard disk equipped on the computer device 8, a smart memory card (SMC), a secure digital (SD) card, a flash memory card, etc. Of course, the memory 8a can also include both the internal storage unit of the computer device 8 and its external storage device. In this embodiment, the memory 8a is generally used to store the operating system and various application software installed on the computer device 8, such as computer-readable instructions of a batch task processing monitoring method. In addition, the memory 8a can also be used to temporarily store various types of data that have been output or are to be output.

[0135] In some embodiments, the processor 8b may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 8b is generally used to control the overall operation of the computer device 8. In this embodiment, the processor 8b is used to execute computer-readable instructions stored in the memory 8a or process data, such as executing computer-readable instructions for the batch task processing monitoring method.

[0136] The network interface 8c may include a wireless network interface or a wired network interface. The network interface 8c is generally used to establish a communication connection between the computer device 8 and other electronic devices.

[0137] The computer device proposed in this embodiment belongs to the field of operation and maintenance monitoring technology, and is applied to batch task processing monitoring scenarios under the scale of large data flow. This application obtains batch task data to be processed; determines all task processing nodes involved in each task data to be processed according to the preset task logic execution relationship; determines the segmented processing time corresponding to different task processing nodes according to the preset segmented time configuration table; generates a task data set containing segmented time information, performs task processing scheduling planning, and obtains task processing scheduling planning results; uses a preset chart conversion component to display the task processing scheduling planning results in the form of a chart to a preset visual monitoring interface. The batch task processing monitoring method is applied to the field of financial business, especially in the purchase scenario of goods or services involving currency transactions. It can perform reasonable and unified task allocation and scheduling planning under the scale of large batches of task data to be processed, combined with the processing time segmentation information corresponding to different task processing nodes, and also facilitates the unified acquisition of the actual business processing status of each processing party involved in the actual business processing process. It assists the task processing party in comprehensively carrying out business planning in combination with the segmented time information. The present application also provides another embodiment, namely, providing a computer-readable storage medium, which stores computer-readable instructions. The computer-readable instructions can be executed by a processor to enable the processor to perform the steps of a batch task processing monitoring method as described above.

[0138] The computer-readable storage medium proposed in this embodiment belongs to the field of operation and maintenance monitoring technology and is applied to batch task processing monitoring scenarios under the scale of large data flow. This application obtains batch task data to be processed; determines all task processing nodes involved in each task data to be processed according to the preset task logic execution relationship; determines the segmented processing time corresponding to different task processing nodes according to the preset segmented time configuration table; generates a task data set containing segmented time information, performs task processing scheduling planning, and obtains task processing scheduling planning results; uses a preset chart conversion component to display the task processing scheduling planning results in the form of a chart to a preset visual monitoring interface. The batch task processing monitoring method is applied to the financial business field, especially in the commodity or service purchase scenario involving currency transactions. It can perform reasonable and unified task allocation and scheduling planning under the scale of large batches of task data to be processed, combined with the processing time segmentation information corresponding to different task processing nodes, and also facilitates the unified acquisition of the actual business processing status of each processing party involved in the actual business processing process. It assists the task processing party in comprehensively carrying out business planning in combination with the segmented time information. Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course 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 the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0139] Obviously, the embodiments described above are only some of the embodiments of the present application, rather than all of the embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific embodiments, or to make equivalent replacements for some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is also within the scope of patent protection of this application. The non-company software tools or components that appear in the embodiments of this application are merely examples and do not represent actual use.

Claims

1. A batch task processing monitoring method, characterized in that: The steps include: Get batch pending task data; According to the preset task logic execution relationship, determine all task processing nodes involved in each task data to be processed; Determine the segment processing times corresponding to different task processing nodes according to a preset segment time configuration table, wherein the segment time configuration table includes each time segment identifier and the executable task processing node corresponding to each time segment, and the time segments are preset time segments; Based on all the task processing nodes involved in each piece of task data to be processed, and the segmented processing time corresponding to different task processing nodes, estimate all the time segments spanned by each piece of task data to be processed during the actual task execution process; Merging all time segments spanned by the batch of task data to be processed during the actual task execution process to generate a task data set containing segmented timeliness information; Performing comprehensive statistical analysis on the task data set based on the segmented timeliness information to calculate all pending task data contained in each time segment; According to all the pending task data contained in each time segment, the executable task processing nodes corresponding to each time segment, and all the task processing nodes involved in each pending task data, task processing scheduling planning is performed to obtain the task processing scheduling planning result; The task processing scheduling planning result is displayed in the form of a chart on a preset visual monitoring interface using a preset chart conversion component.

2. The batch task processing monitoring method according to claim 1, characterized in that: The method further comprises: Obtaining, from the task processing request end corresponding to each piece of to-be-processed task data, a task processing control field corresponding to each piece of to-be-processed task data, wherein the task processing control field includes an identification field for processing time control that is pre-reserved on the task request initiation interface and can be clicked and selected or manually entered; The batch of task data to be processed is preliminarily divided and processed according to the identification field for processing time control corresponding to each piece of task data to be processed.

3. The batch task processing monitoring method according to claim 2, characterized in that: The step of preliminarily dividing the batch of task data to be processed according to the identification field for processing time control corresponding to each piece of task data to be processed specifically includes: Analyze the task processing control field selection or task processing control field input corresponding to each to-be-processed task data at the corresponding task processing request end to obtain the analysis result; According to the analysis result, a corresponding identification field is determined, and the batch of task data to be processed is preliminarily divided and processed based on the identification field.

4. The batch task processing monitoring method according to claim 3, characterized in that: The identification fields for processing time control include a default identification field, a delay identification field, and an advance identification field. The steps of determining the corresponding identification fields according to the analysis results and performing preliminary division processing on the batch of task data to be processed based on the identification fields specifically include: If, according to the analysis result, it is identified that the task processing control field selection corresponding to the current task data to be processed at the corresponding task processing request end or the task processing control field input is an empty value or a NULL value, then the identification field corresponding to the current task data to be processed is directly marked as a default identification field; If, according to the analysis result, it is identified that the task processing control field selection corresponding to the current task data to be processed at the corresponding task processing request end or the task processing control field input is neither an empty value nor a NULL value, then the identification field corresponding to the current task data to be processed is identified according to the preset field matching strategy; The batch of task data to be processed is preliminarily divided and processed based on the identification field.

5. The batch task processing monitoring method according to claim 4, characterized in that: The preset field matching strategy includes a field matching strategy based on a preset dictionary and a field matching strategy based on semantic analysis. The step of identifying the identification field corresponding to the current task data to be processed according to the preset field matching strategy specifically includes: If it is identified that the task processing control field corresponding to the current task data to be processed at the corresponding task processing request end is a selected field, then based on the field matching strategy of the preset dictionary, the identification field corresponding to the task processing control field is filtered out from the preset dictionary, wherein the preset dictionary summarizes the default identification field, the delay identification field, and the advance identification field for processing time control; If it is identified that the task processing control field corresponding to the current task data to be processed at the corresponding task processing request end is the input field, then based on the field matching strategy of semantic analysis, the task processing control field is semantically analyzed, and the corresponding identification field is determined according to the semantic analysis result.

6. The batch task processing monitoring method according to claim 1, characterized in that: The step of performing task processing scheduling planning based on all pending task data contained in each time segment, the executable task processing nodes corresponding to each time segment, and all task processing nodes involved in each pending task data to obtain the task processing scheduling planning result specifically includes: According to all the pending task data contained in each time segment, the task execution volume corresponding to each time segment is counted; According to the executable task processing nodes corresponding to each time segment, the processing frequencies of different task processing nodes corresponding to each time segment are counted; According to all the task processing nodes involved in each piece of task data to be processed, and the processing frequencies corresponding to different task processing nodes in each time segment, the high-frequency task processing nodes and low-frequency task processing nodes involved in the task processing flow corresponding to each piece of task data to be processed are counted; Based on the task execution volume corresponding to each time segment and the high-frequency task processing nodes and low-frequency task processing nodes involved in the task processing flow corresponding to each pending task data, pre-assign task processing to the task processing flows corresponding to all pending task data; The task processing scheduling planning result is generated by combining the correspondence between the task processing nodes and the task processing time in the pre-allocation result.

7. The batch task processing monitoring method according to claim 2, characterized in that: The step of pre-allocating task processing for the task processing flows corresponding to all pending task data based on the task execution amount corresponding to each time segment and the high-frequency task processing nodes and low-frequency task processing nodes involved in the task processing flows corresponding to each pending task data specifically includes: If the task execution volume corresponding to the current time segment exceeds the preset task execution volume threshold, the service node expansion method is adopted to expand the service nodes within the current time segment and allocate task execution volume to the newly expanded service nodes; If the number of high-frequency task processing nodes involved in task processing in the current time segment exceeds a preset node quantity threshold, more task processing resources are allocated to the high-frequency task processing nodes according to a preset first allocation mechanism for processing resources, wherein the preset first allocation mechanism for processing resources includes allocating more task processing resources from non-high-frequency task processing nodes under the same service cluster to the high-frequency task processing nodes; If the number of low-frequency task processing nodes involved in task processing in the current time segment exceeds the preset node quantity threshold, fewer task processing resources will be allocated to the low-frequency task processing nodes according to the preset second processing resource allocation mechanism, wherein the preset second processing resource allocation mechanism includes allocating more task processing resources from the low-frequency task processing nodes to non-low-frequency task processing nodes under the same service cluster.

8. A batch task processing monitoring device, characterized in that: The batch task processing monitoring device is used to implement the steps of the batch task processing monitoring method according to any one of claims 1 to 7, and the batch task processing monitoring device includes: The pending task data acquisition module is used to obtain batch pending task data; The task processing node determination module is used to determine all task processing nodes involved in each task data to be processed according to the preset task logic execution relationship; A segment processing time determination module is used to determine the segment processing times corresponding to different task processing nodes according to a preset segment time configuration table, wherein the segment time configuration table includes each time segment identifier and the executable task processing node corresponding to each time segment, and the time segments are preset time segments; The time segment estimation module is used to estimate all time segments that each task data to be processed will span during the actual task execution process based on all task processing nodes involved in each task data to be processed and the segment processing time corresponding to different task processing nodes; A task data set generation module is configured to merge all time segments spanned by the batch of task data to be processed during the actual task execution process to generate a task data set containing segmented timeliness information; A comprehensive statistical analysis module is used to perform comprehensive statistical analysis on the task data set according to the segmented timeliness information, and to calculate all the task data to be processed contained in each time segment; The task processing scheduling planning module is used to perform task processing scheduling planning based on all pending task data contained in each time segment, the executable task processing nodes corresponding to each time segment, and all task processing nodes involved in each pending task data, and obtain the task processing scheduling planning results; The chart conversion and display module is used to use a preset chart conversion component to display the task processing scheduling planning results in the form of a chart on a preset visual monitoring interface.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the batch task processing monitoring method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the batch task processing monitoring method according to any one of claims 1 to 7.

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