Batch task execution method and device, storage medium and program product

Abstract

The invention relates to the technical field of artificial intelligence, and discloses a batch task execution method and device, a storage medium and a program product. The method comprises the following steps: when a selection operation on an automatic mode is detected, obtaining a transaction associated parameter value of a target time period; obtaining a first transaction load corresponding to the target time period according to the transaction association parameter value through a pre-trained transaction prediction model; and obtaining a first execution frequency corresponding to the target time period according to the first transaction load, and executing the batch tasks of the target time period based on the first execution frequency. According to the scheme of the embodiment, the transaction load is predicted in real time according to the transaction associated parameter value through the transaction prediction model, the execution frequency of the batch tasks is dynamically adjusted according to the transaction load, the execution efficiency of the batch tasks can be improved, the resource utilization rate can be improved, and meanwhile safe operation of production services can be guaranteed.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, and in particular to a method, device, storage medium, and program product for executing batch tasks. Background Technology

[0002] With the rapid development of internet technology, and to cope with the ever-changing needs of market operations, there are often instances of WeChat official accounts migrating or merging, or applications or mini-programs being migrated. Customer information from the original channels and platforms needs to be updated or re-maintained on the new platform. Alternatively, in daily operations, for more precise targeting, customer information needs to be regularly maintained and updated. For these scenarios, a large amount of customer data needs to be cleaned, requiring various internet maintenance channels to achieve seamless updating and maintenance of customer information without affecting customer functionality.

[0003] Currently, for dirty data generated during the daily operation of WeChat Official Accounts or Mini Programs, or data requiring re-cleaning in other special scenarios, data processing is generally achieved through multi-threaded batch scheduled tasks to avoid affecting normal production operations. This involves creating batch scheduled tasks to process database data in batches on a regular schedule, separating this process from online business transactions. However, existing technologies typically set a fixed execution frequency for batch tasks. This approach cannot adjust the execution frequency in real-time according to business needs or peak production periods, resulting in low execution efficiency and low resource utilization. Furthermore, it lacks protection mechanisms against sudden traffic surges. When production business transaction volume increases dramatically, it cannot quickly adjust the batch execution frequency to release database connections, compromising the safe operation of production processes. Summary of the Invention

[0004] This invention provides a method, device, storage medium, and program product for executing batch tasks, which can improve the execution efficiency of batch tasks, increase resource utilization, and ensure the safe operation of production processes.

[0005] According to one aspect of the present invention, a method for executing batch tasks is provided, comprising:

[0006] When an automatic mode selection operation is detected, the transaction-related parameter values ​​for the target time period are obtained;

[0007] Using a pre-trained transaction prediction model, the first transaction load corresponding to the target time period is obtained based on the transaction association parameter values;

[0008] Based on the first transaction load, obtain the first execution frequency corresponding to the target time period, and execute the batch tasks of the target time period based on the first execution frequency.

[0009] According to another aspect of the present invention, a batch task execution apparatus is provided, comprising:

[0010] The parameter value acquisition module is used to acquire transaction-related parameter values ​​for the target time period when an automatic mode selection operation is detected.

[0011] The transaction load acquisition module is used to acquire the first transaction load corresponding to the target time period based on the transaction association parameter values ​​using a pre-trained transaction prediction model.

[0012] The execution frequency acquisition module is used to acquire the first execution frequency corresponding to the target time period based on the first transaction load, and execute the batch tasks of the target time period based on the first execution frequency.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] At least one processor; and

[0015] A memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the batch task execution method described in any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program configured to cause a processor to execute a batch task execution method as described in any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the batch task execution method described in any embodiment of the present invention.

[0019] The technical solution of this invention, when detecting the selection operation of automatic mode, obtains the transaction-related parameter value of the target time period; through a pre-trained transaction prediction model, obtains the first transaction load corresponding to the target time period based on the transaction-related parameter value; based on the first transaction load, obtains the first execution frequency corresponding to the target time period, and executes batch tasks of the target time period based on the first execution frequency; by using the transaction prediction model to predict the transaction load in real time based on the transaction-related parameter value, and dynamically adjusting the execution frequency of batch tasks based on the transaction load, the execution efficiency of batch tasks can be improved, resource utilization can be improved, and the safe operation of production business can be guaranteed.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a batch task execution method provided according to Embodiment 1 of the present invention;

[0023] Figure 2 This is a flowchart of a method for obtaining execution frequency according to Embodiment 1 of the present invention;

[0024] Figure 3 This is a flowchart of a batch task execution method provided according to Embodiment 2 of the present invention;

[0025] Figure 4 This is a schematic diagram of the structure of a batch task execution device provided according to Embodiment 3 of the present invention;

[0026] Figure 5 This is a schematic diagram of the structure of an electronic device that implements the batch task execution method of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first," "second," "modification," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] Example 1

[0030] Figure 1 This is a flowchart illustrating a batch task execution method according to Embodiment 1 of the present invention. This embodiment is applicable to the automatic scheduling and execution of batch tasks. The method can be executed by a batch task execution device, which can be implemented in hardware and / or software. Typically, the batch task execution device can be configured in an electronic device, such as a computer or server. Figure 1 As shown, the method includes:

[0031] S110. When an operation to select automatic mode is detected, obtain the transaction-related parameter values ​​for the target time period.

[0032] It should be noted that, in this embodiment, the information collected is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data all comply with the relevant laws, regulations and standards of the relevant countries and regions, necessary confidentiality measures have been taken, and they do not violate public order and good morals. Corresponding operation entry points are provided for users to choose to authorize or refuse.

[0033] In this embodiment, the execution of batch tasks supports a dual-mode control mechanism, meaning users can choose between automatic or manual mode. In automatic mode, the transaction monitoring platform uses a transaction prediction model based on machine learning algorithms to automatically predict the corresponding transaction load based on service transaction data. The execution frequency is then adaptively adjusted and distributed to the parameter platform based on the transaction load, while simultaneously being hot-synchronized to the batch scheduling server. In manual mode, users can manually configure the execution frequency on the parameter platform, and the execution frequency is hot-synchronized to the batch scheduling server in real time without requiring a server restart.

[0034] The target time period can be a time interval where the execution frequency needs to be determined, such as 08:00 to 18:00, 18:01 to 07:59 the next day, etc. Transaction-related parameter values ​​can be values ​​of indicators or characteristic parameters related to transaction load, such as promotional activities, the number of pre-booked transactions, historical transaction data, etc. In this embodiment, the transaction-related parameter values ​​for different time periods can be monitored in real time through a monitoring platform.

[0035] S120. Using a pre-trained transaction prediction model, obtain the first transaction load corresponding to the target time period based on the transaction association parameter values.

[0036] The input to the transaction prediction model can be transaction-related parameter values ​​from different time periods, and the output can be the transaction load. In this embodiment, the transaction prediction model can be built based on machine learning algorithms, such as Long Short-Term Memory (LSTM) network algorithms and decision tree algorithms, and can be trained using historical transaction data and corresponding transaction loads.

[0037] Specifically, firstly, the transaction-related parameter values ​​can be preprocessed, for example, by filling in missing values ​​and deleting duplicate / outlier values, to obtain transaction-related parameter values ​​in a standard format. Then, these standard-formatted transaction-related parameter values ​​are input into the transaction prediction model to obtain the transaction load output by the model. The transaction load can be a traffic pressure value (such as concurrent request volume, data throughput, burst peaks, etc.) or a resource consumption value (such as computing resources, network resources, storage resources, etc.).

[0038] S130. Based on the first transaction load, obtain the first execution frequency corresponding to the target time period, and execute the batch tasks of the target time period based on the first execution frequency.

[0039] In this embodiment, a pre-defined correspondence between transaction load ranges and execution frequencies can be established. Therefore, after obtaining the first transaction load, the corresponding transaction load range can be determined first. Then, based on the determined transaction load range, a matching first execution frequency can be found within the pre-defined correspondence. Finally, batch tasks can be executed according to this first execution frequency during the target time period.

[0040] Optionally, obtaining the first execution frequency corresponding to the target time period based on the first transaction load may include:

[0041] Obtain resource usage data of the batch scheduling server, and based on the resource usage data and the first transaction load, obtain the first execution frequency corresponding to the target time period.

[0042] The batch scheduling server can be the specific execution device for batch tasks. Resource usage data can include storage resource utilization, network resource utilization, and computing resource utilization. This embodiment also supports a resource protection mechanism, which combines the resource usage data of the batch scheduling server with the predicted transaction load to generate the execution frequency. In a specific example, the correspondence between resource utilization range, transaction load range, and execution frequency can be preset. Then, the current resource utilization of the batch scheduling server is collected, and the corresponding resource utilization range and the transaction load range corresponding to the first transaction load are determined. Finally, based on the determined resource utilization range and transaction load range, a matching execution frequency is found in the preset correspondence to serve as the first execution frequency for the target time period.

[0043] For example, the process of obtaining the execution frequency can be as follows: Figure 2 As shown, firstly, the transaction monitoring platform collects metrics related to transaction load, and then uses a pre-trained large language model to predict transaction load, such as transaction volume, transaction response time, and transaction results, based on these metrics. Secondly, the resource utilization rate of the batch scheduling server is collected. Finally, the execution frequency scheme is generated by combining the resource utilization rate and the predicted transaction load.

[0044] The advantages of the above settings are that they can improve the resource utilization of the batch scheduling server, prevent the execution frequency from exceeding the capacity of the batch scheduling server, and ensure the safe execution of batch tasks.

[0045] Optionally, executing batch tasks for the target time period based on the current execution frequency may include:

[0046] The target time period and the current execution frequency are hot-synchronized to the batch scheduling server, and the batch scheduling server executes the batch tasks of the target time period based on the current execution frequency.

[0047] In this embodiment, after generating the execution frequency, the target time period and execution frequency can be automatically synchronized to the batch scheduling server simultaneously with the execution frequency being sent to the parameter platform. Then, when the target time period arrives, the batch scheduling server can automatically execute batch tasks according to the current execution frequency.

[0048] The advantage of the above settings is that they can automatically adjust the execution frequency, thereby improving the execution efficiency of batch tasks.

[0049] The technical solution of this invention, when detecting the selection operation of automatic mode, obtains the transaction-related parameter value of the target time period; through a pre-trained transaction prediction model, obtains the first transaction load corresponding to the target time period based on the transaction-related parameter value; based on the first transaction load, obtains the first execution frequency corresponding to the target time period, and executes batch tasks of the target time period based on the first execution frequency; by using the transaction prediction model to predict the transaction load in real time based on the transaction-related parameter value, and dynamically adjusting the execution frequency of batch tasks based on the transaction load, the execution efficiency of batch tasks can be improved, resource utilization can be improved, and the safe operation of production business can be guaranteed.

[0050] Example 2

[0051] Figure 3 This is a flowchart illustrating a batch task execution method according to Embodiment 2 of the present invention. This embodiment is a further refinement of the above technical solution, and the technical solution in this embodiment can be combined with one or more of the above implementation methods. For example... Figure 3 As shown, the method includes:

[0052] S210, Start, execute S220.

[0053] S220. When an operation to select automatic mode is detected, obtain the transaction-related parameter value for the target time period and execute S230.

[0054] S230. Using a pre-trained transaction prediction model, obtain the first transaction load corresponding to the target time period based on the transaction association parameter values, and execute S240.

[0055] S240. Based on the first transaction load, obtain the first execution frequency corresponding to the target time period, and execute S250.

[0056] S250. Determine whether the first execution frequency is within the preset frequency range.

[0057] If the first execution frequency is determined to be within the preset frequency range, then step S260 is executed; if the first execution frequency is determined to be outside the preset frequency range, then step S270 is executed. This embodiment supports dual-mode control, achieving a collaborative mechanism between manual and automatic modes by creating a dual-mode controller. Specifically, a frequency range is preset. When the predicted execution frequency exceeds the preset frequency range, it is considered that the deviation is too high, and the system automatically switches to manual mode. When the predicted execution frequency is within the preset frequency range, the predicted execution frequency can be directly used. The preset frequency range can be set based on historical experience.

[0058] S260. Execute the batch tasks for the target time period based on the first execution frequency, and then execute S280.

[0059] Specifically, when the predicted execution frequency is within the preset frequency range, a batch scheduling server can be used to schedule batch tasks for execution within the target time period based on this execution frequency.

[0060] S270, switch from automatic mode to manual mode, and in response to the user's setting operation of the execution frequency, obtain the second execution frequency, and execute the batch tasks of the target time period based on the second execution frequency, and then execute S280.

[0061] Specifically, after successfully switching to manual mode, users can manually set the execution frequency through the parameter platform. At this time, a second execution frequency can be obtained based on the user's parameter settings, and the batch scheduling server can then schedule batch tasks for execution during the target time period according to this second execution frequency. For example, manual mode can involve manually and dynamically setting different execution frequency parameters, such as setting a batch cleaning task to execute once per minute from 08:00 to 18:00 daily, and five times per minute from 18:01 to 07:59 the next day.

[0062] Optionally, to ensure emergency response in production, this embodiment also supports priority manual fuse tripping (set switch), with manual mode configuration as the standard. That is, under any circumstances, as long as a user's selection operation of the manual fuse tripping switch is detected, it will immediately switch to manual mode.

[0063] Optionally, switching from automatic mode to manual mode may include:

[0064] The frequency deviation value is calculated based on the first execution frequency and the preset frequency range;

[0065] If the frequency deviation is greater than or equal to a preset threshold, the system switches from automatic mode to manual mode.

[0066] In this embodiment, after determining that the first execution frequency is not within the preset frequency range, the frequency deviation value of the first execution frequency relative to the preset frequency range can be further calculated. For example, based on the preset frequency range, the minimum frequency boundary value and the maximum frequency boundary value can be obtained, and a first difference between the first execution frequency and the minimum frequency boundary value, and a second difference between the first execution frequency and the maximum frequency boundary value can be calculated. Then, the absolute values ​​of the first and second differences are compared, and the larger absolute value is determined as the frequency deviation value. Finally, if the frequency deviation value is greater than or equal to a preset threshold, the deviation is considered large, and the automatic mode is switched to manual mode; if the frequency deviation value is less than the preset threshold, the deviation is considered small, and the automatic mode is maintained.

[0067] The advantage of the above settings is that they can improve the accuracy of mode switching and avoid accidental mode switching.

[0068] Optionally, after executing the batch tasks for the target time period based on the second execution frequency, the process may further include:

[0069] Based on the second execution frequency, the second transaction load is obtained, and based on the transaction correlation parameter values ​​of the target time period and the second transaction load, the transaction prediction model is retrained to obtain an updated prediction model.

[0070] Switch from manual mode back to automatic mode, and then use the updated prediction model to perform subsequent transaction load prediction.

[0071] In this embodiment, continuous optimization of the transaction prediction model is also supported. Specifically, when the deviation of the execution frequency obtained by the current transaction prediction model is large, a second transaction load corresponding to a second execution frequency manually set by the user can be obtained based on the preset correspondence between execution frequency and transaction load. Then, a correspondence between transaction-related parameter values ​​and the second transaction load can be generated as a training sample, and the transaction prediction model can be retrained using this training sample until a preset training termination condition is detected, such as reaching a preset number of iterations or the loss function being less than a preset threshold. The trained transaction prediction model is then obtained and used as the updated prediction model. Finally, the manual mode is automatically switched back to the automatic mode, and the updated prediction model is applied for a new round of transaction load prediction.

[0072] The advantage of the above settings is that they enable continuous model optimization and improve the accuracy of transaction load prediction.

[0073] S280, End.

[0074] This embodiment provides a batch execution scheme with dynamically adjustable execution frequency, which can effectively improve the execution efficiency of batch tasks while ensuring the security of customer services. The scheme in this embodiment can dynamically adjust the execution frequency of batch tasks based on automatic monitoring of online business transactions. Simultaneously, through a collaborative manual mode, it supports manual dynamic configuration of peak business periods and batch execution frequency parameters, with parameters automatically issued and taking effect in real time. Through the manual-collaborative-automatic intelligent control mode, batch execution efficiency can be flexibly improved, effectively ensuring the safe operation of production.

[0075] Moreover, the solution in this embodiment can achieve dynamic adaptability, breaking through the limitations of fixed frequency and improving the response speed to changes in production transaction volume and business from minutes to seconds. Additionally, it enables intelligent resource management; by integrating the resource usage of the batch scheduling server into automatic mode, it can effectively improve resource utilization, reduce idle resources, and avoid the risks associated with excessive resource usage. Finally, it features security, controllability, and scalability; the dual-mode collaborative mechanism effectively ensures the system is always online and can be applied to any batch-stream mixed scenario.

[0076] The technical solution of this invention, after obtaining the first execution frequency corresponding to the target time period based on the first transaction load, determines whether the first execution frequency is within a preset frequency range. If so, the batch tasks for the target time period are executed based on the first execution frequency. By only using the predicted execution frequency when it is within the preset frequency range, the stability of batch task execution can be improved. Secondly, if it is determined that the first execution frequency is not within the preset frequency range, the system switches from automatic mode to manual mode, and in response to the user's setting operation of the execution frequency, obtains the second execution frequency, and executes the batch tasks for the target time period based on the second execution frequency. This enables automatic switching between different modes and improves the flexibility of batch task execution.

[0077] Example 3

[0078] Figure 4 This is a schematic diagram of the structure of a batch task execution device provided in Embodiment 3 of the present invention. Figure 4 As shown, the device includes: a parameter value acquisition module 310, a transaction load acquisition module 320, and an execution frequency acquisition module 330; wherein,

[0079] The parameter value acquisition module 310 is used to acquire the transaction-related parameter values ​​for the target time period when an operation to select the automatic mode is detected.

[0080] The transaction load acquisition module 320 is used to acquire the first transaction load corresponding to the target time period based on the transaction association parameter values ​​using a pre-trained transaction prediction model.

[0081] The execution frequency acquisition module 330 is used to acquire the first execution frequency corresponding to the target time period based on the first transaction load, and execute the batch tasks of the target time period based on the first execution frequency.

[0082] The technical solution of this invention, when detecting the selection operation of automatic mode, obtains the transaction-related parameter value of the target time period; through a pre-trained transaction prediction model, obtains the first transaction load corresponding to the target time period based on the transaction-related parameter value; based on the first transaction load, obtains the first execution frequency corresponding to the target time period, and executes batch tasks of the target time period based on the first execution frequency; by using the transaction prediction model to predict the transaction load in real time based on the transaction-related parameter value, and dynamically adjusting the execution frequency of batch tasks based on the transaction load, the execution efficiency of batch tasks can be improved, resource utilization can be improved, and the safe operation of production business can be guaranteed.

[0083] Optionally, the execution frequency acquisition module 330 is specifically used to determine whether the first execution frequency is within a preset frequency range. If so, the batch tasks for the target time period are executed based on the first execution frequency.

[0084] Optionally, the execution frequency acquisition module 330 is further configured to switch from automatic mode to manual mode if it is determined that the first execution frequency is not within the preset frequency range;

[0085] In response to the user's setting of the execution frequency, a second execution frequency is obtained, and batch tasks for the target time period are executed based on the second execution frequency.

[0086] Optionally, the execution frequency acquisition module 330 is further configured to calculate a frequency deviation value based on the first execution frequency and the preset frequency range;

[0087] If the frequency deviation is greater than or equal to a preset threshold, the system switches from automatic mode to manual mode.

[0088] Optionally, the execution frequency acquisition module 330 is specifically used to hot synchronize the target time period and the current execution frequency to the batch scheduling server, and execute the batch tasks of the target time period based on the current execution frequency through the batch scheduling server.

[0089] Optionally, the execution frequency acquisition module 330 is further configured to acquire the second transaction load based on the second execution frequency, and retrain the transaction prediction model based on the transaction association parameter value of the target time period and the second transaction load to acquire an updated prediction model;

[0090] Switch from manual mode back to automatic mode, and then use the updated prediction model to perform subsequent transaction load prediction.

[0091] Optionally, the execution frequency acquisition module 330 is specifically used to acquire resource usage data of the batch scheduling server, and acquire the first execution frequency corresponding to the target time period based on the resource usage data and the first transaction load.

[0092] The batch task execution device provided in the embodiments of the present invention can execute the batch task execution method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0093] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0094] Example 4

[0095] Figure 5 A schematic diagram of an electronic device 40 that can be used to implement embodiments of the present invention is shown. The electronic device 40 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device 40 may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0096] like Figure 5 As shown, the electronic device 40 includes at least one processor 41 and a memory, such as a read-only memory (ROM) 42 or a random access memory (RAM) 43, communicatively connected to the at least one processor 41. The memory stores computer programs executable by the at least one processor. The processor 41 can perform various appropriate actions and processes based on the computer program stored in the read-only memory 42 or loaded from the storage unit 48 into the random access memory 43. The RAM 43 can also store various programs and data required for the operation of the electronic device 40. The processor 41, ROM 42, and RAM 43 are interconnected via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0097] Multiple components in electronic device 40 are connected to I / O interface 45, including: input unit 46, such as keyboard, mouse, etc.; output unit 47, such as various types of monitors, speakers, etc.; storage unit 48, such as disk, optical disk, etc.; and communication unit 49, such as network card, modem, wireless transceiver, etc. Communication unit 49 allows electronic device 40 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0098] Processor 41 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, central processing units, graphics processing units, various special-purpose artificial intelligence computing chips, various processors running machine learning model algorithms, digital signal processors, and any suitable processor, controller, microcontroller, etc. Processor 41 performs the various methods and processes described above, such as batch task execution methods.

[0099] In some embodiments, the batch task execution method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the batch task execution method described above may be performed. Alternatively, in other embodiments, processor 41 may be configured to execute the batch task execution method by any other suitable means (e.g., by means of firmware).

[0100] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays, application-specific integrated circuits (ASICs), application-specific standard products (ASICs), systems-on-a-chip (SoCs), payload programmable logic devices, computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0101] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0102] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory, read-only memory, erasable programmable read-only memory, optical fibers, portable compact disk read-only memory, optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0103] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device 40, which includes: a display device (e.g., a cathode ray tube or liquid crystal display) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device 40. Other types of devices can also be used to provide interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0104] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0105] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact via a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server.

[0106] This embodiment may also include a computer program product, which includes a computer program that, when executed by a processor, implements the batch task execution method provided in any embodiment of the present invention.

[0107] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0108] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for executing batch tasks, characterized in that, include: When an automatic mode selection operation is detected, the transaction-related parameter values ​​for the target time period are obtained; Using a pre-trained transaction prediction model, the first transaction load corresponding to the target time period is obtained based on the transaction association parameter values; Based on the first transaction load, obtain the first execution frequency corresponding to the target time period, and execute the batch tasks of the target time period based on the first execution frequency.

2. The method according to claim 1, characterized in that, Executing batch tasks for the target time period based on the first execution frequency includes: Determine whether the first execution frequency is within a preset frequency range. If so, execute the batch tasks for the target time period based on the first execution frequency.

3. The method according to claim 2, characterized in that, After determining whether the first execution frequency is within a preset frequency range, the method further includes: If it is determined that the first execution frequency is not within the preset frequency range, then switch from automatic mode to manual mode; In response to the user's setting of the execution frequency, a second execution frequency is obtained, and batch tasks for the target time period are executed based on the second execution frequency.

4. The method according to claim 3, characterized in that, Switching from automatic mode to manual mode includes: The frequency deviation value is calculated based on the first execution frequency and the preset frequency range; If the frequency deviation is greater than or equal to a preset threshold, the system switches from automatic mode to manual mode.

5. The method according to any one of claims 1-3, characterized in that, Execute batch tasks for the target time period based on the current execution frequency, including: The target time period and the current execution frequency are hot-synchronized to the batch scheduling server, and the batch scheduling server executes the batch tasks of the target time period based on the current execution frequency.

6. The method according to claim 3, characterized in that, After executing the batch tasks for the target time period based on the second execution frequency, the process further includes: Based on the second execution frequency, the second transaction load is obtained, and based on the transaction correlation parameter values ​​of the target time period and the second transaction load, the transaction prediction model is retrained to obtain an updated prediction model. Switch from manual mode back to automatic mode, and then use the updated prediction model to perform subsequent transaction load prediction.

7. The method according to claim 1, characterized in that, Based on the first transaction load, the first execution frequency corresponding to the target time period is obtained, including: Obtain resource usage data of the batch scheduling server, and based on the resource usage data and the first transaction load, obtain the first execution frequency corresponding to the target time period.

8. An electronic device, characterized in that, The electronic device includes: At least one processor, and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the batch task execution method according to any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method for executing batch tasks according to any one of claims 1-7.

10. A computer program product, characterized in that, It includes a computer program that, when executed by a processor, implements the method for executing batch tasks according to any one of claims 1-7.

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