Batch interrupt task processing method and device, equipment, medium and product

Abstract

The invention discloses a batch interrupt task processing method and device, equipment, a medium and a product, and relates to the field of distributed technologies. The method comprises the following steps: determining service types of batch services to be handled by a user; acquiring historical interrupt data of the service type; inputting the historical interruption data into a pre-trained interruption prediction model to obtain interruption prediction data of the service type in a preset duration after the current time; and performing interruption processing on the batch services to be handled based on the interruption prediction data. According to the technical scheme provided by the invention, the service interruption risk in the future preset duration can be pre-judged in advance, efficient resource configuration and cost optimization can be realized, the satisfaction degree of a user on batch task execution is improved, and the service continuity and stability are enhanced.

Description

Technical Field

[0001] The present application relates to the field of distributed technology, and in particular to a method, device, equipment, medium and product for processing batch interruption tasks. Background Art

[0002] In the distributed scenario of financial transactions, the existing technology has a lot of problems, such as the execution time of user batch tasks, the complexity of the scenario, the abnormal environment and the unevenness of data. In addition, the specific logic of batch tasks is not developed uniformly by the framework side, but developed separately by each user. Therefore, it is urgent to design a method for processing batch interruption tasks in the existing distributed scenario to predict the interruption risk of batch tasks, thereby reducing transaction problems and property losses caused by batch task interruptions, and improving user satisfaction with batch task execution. Summary of the invention

[0003] The present application provides a method, device, equipment, medium and product for processing batch interruption tasks, which can achieve early prediction of business interruption risks within a preset time period in the future, achieve efficient resource allocation and cost optimization, improve user satisfaction with batch task execution, and enhance business continuity and stability.

[0004] In a first aspect, the present application provides a method for processing batch interruption tasks, the method comprising:

[0005] Determine the business type of the user's pending batch business;

[0006] Obtaining historical interruption data of the service type;

[0007] Inputting the historical interruption data into a pre-trained interruption prediction model to obtain interruption prediction data of the service type for a preset time after the current time;

[0008] The pending batch services are interrupted based on the interruption prediction data.

[0009] Furthermore, the historical interruption data is input into a pre-trained interruption prediction model to obtain interruption prediction data of the business type for a preset time period after the current time, including: performing feature analysis on the historical interruption data to obtain interruption feature data, the interruption feature data at least including the number of interruptions, the average duration of interruptions, and the time since the last interruption; extracting feature patterns of the time dependency in the interruption feature data and the arrangement relationship between the interruption feature data; and predicting the interruption prediction data at the preset time period based on the time dependency and the feature pattern, the interruption prediction data at least including the interruption probability, the distribution of interruption time points, and the interruption duration.

[0010] Furthermore, feature analysis is performed on the historical interruption data to obtain interruption feature data, including: counting the interruption time in the historical interruption data to obtain historical interruption time series data; dividing the historical interruption time series data into multiple time steps; and extracting the interruption feature data corresponding to each time step in the historical interruption data.

[0011] Furthermore, the historical interruption data includes interruption type and interruption duration; before the historical interruption data is input into a pre-trained interruption prediction model, it also includes: when there are missing values ​​in the historical interruption data, determining the data attribute of the missing value; if the data attribute is the interruption duration, taking the mean of all interruption durations in the historical interruption data as the missing value; if the data attribute is the interruption type, taking the mode of all interruption types in the historical interruption data as the missing value.

[0012] Furthermore, interruption processing is performed on the pending batch services based on the interruption prediction data, including: determining the risk value of the interruption occurrence time point being in the peak period based on the interruption probability and the interruption time point distribution; if the risk value is greater than a preset value, obtaining the interruption processing strategy for the business type during the peak period; and interruption processing is performed on the pending batch services based on the interruption processing strategy.

[0013] Furthermore, the business types include at least big data business, large retail business, large corporate business, large asset management business and channel service business.

[0014] In a second aspect, the present application provides a batch interrupt task processing device, the device comprising:

[0015] A type determination module is used to determine the business type of the user's pending batch business;

[0016] A data acquisition module, used to acquire historical interruption data of the service type;

[0017] A data prediction module, used to input the historical interruption data into a pre-trained interruption prediction model to obtain interruption prediction data of the service type for a preset time after the current time;

[0018] A business processing module is used to perform interruption processing on the pending batch business based on the interruption prediction data.

[0019] In a third aspect, the present application provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the batch interrupt task processing method described in any embodiment of the present application.

[0020] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for processing batch interrupt tasks described in any embodiment of the present application when executed.

[0021] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the method for processing batch interrupt tasks described in any embodiment of the present application.

[0022] In order to solve the defects of the prior art in the background technology, the embodiment of the present application provides a method for processing batch interruption tasks, and the execution of this method can bring the following beneficial effects: First, determine the business type of the user's pending batch business. Then, obtain the historical interruption data of the business type. After that, input the historical interruption data into the pre-trained interruption prediction model to obtain the interruption prediction data of the business type after the current time. In this way, the risk of business interruption within the preset time can be predicted in advance, and it is no longer passively waiting for the interruption to occur before responding. Instead, it is based on historical experience and data analysis of the model to know in advance which businesses may be interrupted in the future, so that corresponding preventive measures can be prepared in advance, effectively reducing the impact of business interruption on the overall operation. Finally, interruption processing is performed on the pending batch business based on the interruption prediction data; this helps to reduce a series of additional costs caused by business interruption (such as user's property loss and repair labor cost), can achieve efficient resource allocation and cost optimization, improve user satisfaction with batch task execution, and enhance business continuity and stability.

[0023] It should be noted that the above-mentioned computer instructions may be stored in whole or in part on a computer-readable storage medium. The computer-readable storage medium may be packaged together with the processor of the batch interruption task processing device, or may be packaged separately from the processor of the batch interruption task processing device, and this application does not limit this.

[0024] The description of the second, third, ..., and fifth aspects in this application can refer to the detailed description of the first aspect; and the beneficial effects of the description of the second, third, ..., and fifth aspects can refer to the beneficial effect analysis of the first aspect, which will not be repeated here.

[0025] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description.

[0026] It is understandable that before using the technical solutions disclosed in the embodiments of this application, the type, scope of use, and usage scenarios of the personal information involved in this application should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0028] Figure 1 A flowchart of a method for processing batch interruption tasks provided in an embodiment of the present application;

[0029] Figure 2 A schematic diagram of the structure of a device for processing batch interruption tasks provided in an embodiment of the present application;

[0030] Figure 3 It is a block diagram of an electronic device used to implement a method for processing batch interrupt tasks in an embodiment of the present application. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0032] It should be noted that the terms "first", "second", "target", and "original" in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including", "having", and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0033] Figure 1 A flowchart of a method for processing batch interruption tasks provided in an embodiment of the present application is provided. This embodiment can be applied to scenarios where interruption prediction data such as interruption probability, interruption time point, and interruption duration of batch tasks are predicted, and then interruption processing is performed on pending batch services. A method for processing batch interruption tasks provided in this embodiment can be executed by a processing device for batch interruption tasks provided in an embodiment of the present application, which can be implemented in software and / or hardware and integrated in an electronic device that executes this method.

[0034] See also Figure 1 The method of this embodiment includes but is not limited to the following steps:

[0035] S110: Determine the service type of the user's pending batch service.

[0036] Among them, due to the characteristics of batch business, the batch tasks run by each user every day within a month are almost the same, and the interruption reasons of batch business of the same business type are similar, then the batch business can be divided into multiple major categories according to the business type. The classification of business types varies depending on the application scenario of the method; for example, when the application scenario of the method is a financial transaction scenario, the business type includes at least big data business, large retail business, large corporate business, large asset management business and channel service business, and can also be other categories.

[0037] In one embodiment, during the process of developing the business system, a specific business type label or code is pre-set for each type of batch business. A user logs into the business system and views the list of pending batch businesses; when the system detects the execution operation of the pending batch business, it directly reads the corresponding business type label or code from the task record to determine the business type.

[0038] In another embodiment, a business type database table is pre-established, which includes fields such as business type name, business type description, and related business rules. When the system detects the execution operation of the pending batch business, the key identification information (such as business number, task name, etc.) of the pending business is obtained from the task record, and then the corresponding business type is matched by querying the business type database table.

[0039] S120: Obtain historical interruption data of the service type.

[0040] In an embodiment of the present application, historical interruption data may be stored in a preset database. In this case, a structured query language (SQL) statement is written according to the business type and interruption-related conditions. For example, records with a business type of "large retail business" and an operation status of "interruption" are filtered out, and corresponding interruption time, reason, duration and other data are obtained, thereby obtaining historical interruption data of the business type.

[0041] Historical interruption data can also be stored in business system logs. In this case, according to the identification information of the business type, write corresponding filtering rules to extract the log records of the business type corresponding to the pending batch business, that is, historical interruption data. For example, you can use a general text processing tool or a general programming language to use regular expressions combined with file reading operations to filter out log lines containing the "large retail business" and "interruption" fields, and organize them into structured historical interruption data.

[0042] S130: Input the historical interruption data into a pre-trained interruption prediction model to obtain interruption prediction data of a service type for a preset time period after the current time.

[0043] Specifically, historical interruption data are input into a pre-trained interruption prediction model to obtain interruption prediction data of a business type within a preset time after the current time, including: performing feature analysis on the historical interruption data to obtain interruption feature data, the interruption feature data at least including the number of interruptions, the average duration of interruptions, and the time since the last interruption; extracting feature patterns of time dependencies in the interruption feature data and arrangement relationships between the interruption feature data; predicting interruption prediction data within a preset time based on the time dependencies and feature patterns, the interruption prediction data at least including the interruption probability, the distribution of interruption time points, and the interruption duration.

[0044] Furthermore, feature analysis is performed on the historical interruption data to obtain interruption feature data, including: counting the interruption time in the historical interruption data to obtain historical interruption time series data; dividing the historical interruption time series data into multiple time steps; and extracting the interruption feature data corresponding to each time step in the historical interruption data.

[0045] In an embodiment of the present application, the pre-trained interruption prediction model can be a model based on time series analysis, a machine learning model (such as a random forest, a support vector machine) or a deep learning model (such as a long short-term memory network (Long Short-term Memory, LSTM)). Taking the LSTM model as an example, its architecture includes an input layer, a hidden layer and an output layer. The input layer receives historical interruption data, such as dividing the historical interruption time series data according to a certain time step, and each time step contains multiple features (such as the number of interruptions in the past period of time, the average duration of the interruption, the time since the last interruption, etc.). The hidden layer learns and processes the input data through multiple LSTM units to capture the time dependency and feature patterns in the data. The output layer outputs the prediction results according to the training objectives of the model, such as predicting whether an interruption will occur within a preset time in the future and the relevant parameters of the interruption (such as the interruption probability, the possible interruption duration range, etc.).

[0046] In the model training phase, a large amount of historical interruption data is used for training, and the data is divided into training set, validation set and test set. The model is trained using appropriate loss functions (such as cross entropy loss function for classification tasks, mean square error loss function for numerical tasks such as prediction of interruption duration) and optimization algorithms (such as gradient descent algorithm and its variants, etc.). During the training process, the model performance is evaluated through the validation set, and the model hyperparameters (such as the number of LSTM units, the number of hidden layers, the learning rate, etc.) are adjusted according to performance indicators (such as accuracy, recall rate, root mean square error, etc.) to optimize the model performance and ensure that the model can accurately predict the interruption of different business types.

[0047] When historical interruption data is input into the trained model, the model makes predictions based on the patterns and rules it has learned. For example, for the interruption data of a certain business type in the past year, the model will analyze the periodicity, trend, and correlation with other related factors of the interruption, so as to predict the interruption of this business type within a preset time after the current time (such as the next week, month, etc.). If it is a binary classification task to predict whether there will be an interruption, the model outputs the probability value of the interruption. If the probability is greater than the set threshold (such as 0.5), it is predicted that an interruption will occur; for numerical tasks such as predicting the duration of an interruption, the model directly outputs the predicted duration value or duration range.

[0048] Optionally, the historical interruption data includes the interruption type and the interruption duration.

[0049] The historical interruption data obtained may have problems such as noise, missing values ​​or inconsistent formats. Data cleaning and preprocessing operations are required. Therefore, before executing this step, the method also includes: determining whether there are missing values ​​in the historical interruption data; when there are missing values ​​in the historical interruption data, determining the data attributes of the missing values; if the data attribute is the interruption duration, the mean of all interruption durations in the historical interruption data is used as the missing value; if the data attribute is the interruption type, the mode of all interruption types in the historical interruption data is used as the missing value.

[0050] In addition, noise data can be filtered by setting reasonable thresholds. For example, if a certain interruption duration data is obviously beyond the normal range (such as several times higher than the average interruption duration of similar services), it can be regarded as noise data and corrected or deleted. At the same time, the data is standardized and data of different dimensions are converted to a unified standard for subsequent model processing, such as using Min-Max standardization to map the interruption duration data to a range between 0 and 1.

[0051] S140 , interrupting the pending batch business based on the interruption prediction data.

[0052] In the embodiment of the present application, corresponding interruption handling strategies are formulated in advance for different service types and interruption occurrence periods (such as peak period and non-peak period) and stored in the strategy library. According to the interruption prediction data output by the interruption prediction model, the corresponding interruption handling strategy is matched in the strategy library.

[0053] Specifically, interruption processing is performed on the pending batch business based on the interruption prediction data, including: determining the risk value of the interruption occurrence time point being in the peak period based on the interruption probability and the interruption time point distribution; if the risk value is greater than a preset value, obtaining the interruption processing strategy of the business type in the peak period; and performing interruption processing on the pending batch business based on the interruption processing strategy.

[0054] For example, the interruption handling strategy during peak hours can be: before the high-incidence period of interruptions, dynamically adjust resource allocation and optimize resource configuration. For example, increase the processing capacity of the server (such as starting more virtual machine instances), expand the network bandwidth, etc., to cope with possible interruption pressure. For example, for a video streaming service business, during the evening traffic peak and the predicted interruption risk period, temporarily rent more cloud server resources to ensure the smoothness of video playback.

[0055] The technical solution provided by this embodiment first determines the business type of the user's pending batch business. Then, the historical interruption data of the business type is obtained. After that, the historical interruption data is input into the pre-trained interruption prediction model to obtain the interruption prediction data of the business type within a preset time after the current time; in this way, the risk of business interruption within the preset time in the future can be predicted in advance, and it is no longer a passive wait for the interruption to occur before responding, but based on historical experience and model data analysis, it is known in advance which businesses may be interrupted in the future, so that corresponding preventive measures can be prepared in advance, effectively reducing the impact of business interruption on the overall operation. Finally, the pending batch business is interrupted based on the interruption prediction data; this helps to reduce a series of additional costs caused by business interruption (such as user's property loss and repair labor costs), can achieve efficient resource allocation and cost optimization, improve user satisfaction with batch task execution, and enhance business continuity and stability.

[0056] Figure 2 A schematic diagram of the structure of a batch interrupt task processing device provided in an embodiment of the present application, such as Figure 2 As shown, the apparatus 200 may include:

[0057] A type determination module 210, used to determine the service type of the user's pending batch service;

[0058] A data acquisition module 220, used to acquire historical interruption data of the service type;

[0059] The data prediction module 230 is used to input the historical interruption data into a pre-trained interruption prediction model to obtain interruption prediction data of the service type for a preset time after the current time;

[0060] The business processing module 240 is used to perform interruption processing on the pending batch business based on the interruption prediction data.

[0061] Furthermore, the above-mentioned data prediction module 230 can be specifically used to: perform feature analysis on the historical interruption data to obtain interruption feature data, and the interruption feature data at least includes the number of interruptions, the average duration of the interruption, and the time since the last interruption; extract the feature pattern of the time dependency in the interruption feature data and the arrangement relationship between the interruption feature data; predict the interruption prediction data at the preset duration based on the time dependency and the feature pattern, and the interruption prediction data at least includes the interruption probability, the distribution of interruption time points and the interruption duration.

[0062] Furthermore, the above-mentioned data prediction module 230 can also be specifically used to: count the interruption time in the historical interruption data to obtain historical interruption time series data; divide the historical interruption time series data into multiple time steps; and extract the interruption feature data corresponding to each time step in the historical interruption data.

[0063] In one embodiment, the historical interruption data includes the interruption type and the interruption duration;

[0064] Furthermore, the above-mentioned batch interruption task processing device may also include: a data preprocessing module;

[0065] The data preprocessing module is used to determine the data attribute of the missing value when there is a missing value in the historical interruption data before inputting the historical interruption data into a pre-trained interruption prediction model; if the data attribute is the interruption duration, the mean of all interruption durations in the historical interruption data is used as the missing value; if the data attribute is the interruption type, the mode of all interruption types in the historical interruption data is used as the missing value.

[0066] Furthermore, the above-mentioned business processing module 240 can be specifically used to: determine the risk value of the interruption occurrence time point being in the peak period based on the interruption probability and the interruption time point distribution; if the risk value is greater than a preset value, obtain the interruption processing strategy of the business type in the peak period; and perform interruption processing on the pending batch business based on the interruption processing strategy.

[0067] In one embodiment, the business types include at least big data business, large retail business, large corporate business, large asset management business and channel service business.

[0068] The device for processing batch interruption tasks provided in this embodiment can be applied to the method for processing batch interruption tasks provided in any of the above embodiments, and has corresponding functions and beneficial effects.

[0069] Figure 3 It is a block diagram of an electronic device for implementing a method for processing a batch interruption task of an embodiment of the present application. Electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0070] like Figure 3 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0071] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0072] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a method for processing batch interrupt tasks.

[0073] In some embodiments, the processing method of batch interrupt tasks can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the processing method of batch interrupt tasks described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the processing method of batch interrupt tasks in any other appropriate manner (for example, by means of firmware).

[0074] 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 (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0075] The computer programs for implementing the methods of the present application 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, so that when the computer programs are executed by the processor, the functions / operations specified in the flow charts and / or block diagrams are implemented. The computer programs may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0076] In the context of the present application, a computer readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. Alternatively, a computer readable storage medium may be a machine readable signal medium. A more specific example of a machine readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0077] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0078] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server) or a computing system that includes middleware components (e.g., an application server) or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein) or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0079] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0080] Note that the above are only preferred embodiments of the present application and the technical principles used. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and that various obvious changes, readjustments, and substitutions can be made by those skilled in the art without departing from the scope of protection of the present application. For example, those skilled in the art can use the various forms of processes shown above to reorder, add, or delete steps; the steps recorded in the present application can be performed in parallel, sequentially, or in different orders, as long as the desired results of the technical solution of the present application can be achieved, and this document does not limit them here.

[0081] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art 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 application should be included in the protection scope of this application.

Claims

1. A method for processing batch interruption tasks, characterized in that: The method comprises: Determine the business type of the batch business to be processed by the user; Obtaining historical interruption data of the service type; Inputting the historical interruption data into a pre-trained interruption prediction model to obtain interruption prediction data of the service type for a preset time after the current time; The pending batch services are interrupted based on the interruption prediction data.

2. The method for processing batch interruption tasks according to claim 1, characterized in that: The inputting the historical interruption data into a pre-trained interruption prediction model to obtain interruption prediction data of the service type for a preset time after the current time includes: Performing feature analysis on the historical interruption data to obtain interruption feature data, wherein the interruption feature data at least includes the number of interruptions, the average duration of interruptions, and the time since the last interruption; Extracting a characteristic pattern of a time dependency relationship in the interruption feature data and an arrangement relationship between the interruption feature data; The interruption prediction data at the preset duration is predicted based on the time dependency and the characteristic pattern, and the interruption prediction data at least includes an interruption probability, an interruption time point distribution and an interruption duration.

3. The method for processing batch interruption tasks according to claim 2, characterized in that: Performing feature analysis on the historical interruption data to obtain interruption feature data includes: Counting the interruption time in the historical interruption data to obtain historical interruption time series data; Splitting the historical interruption time series data into a plurality of time steps; The interruption feature data corresponding to each time step in the historical interruption data is extracted.

4. The method for processing batch interruption tasks according to claim 1, characterized in that: The historical interruption data includes the interruption type and the interruption duration; before the historical interruption data is input into the pre-trained interruption prediction model, it also includes: When there are missing values ​​in the historical interruption data, determining the data attributes of the missing values; If the data attribute is the interruption duration, the mean of all interruption durations in the historical interruption data is used as the missing value; If the data attribute is an interruption type, the mode of all interruption types in the historical interruption data is used as the missing value.

5. The method for processing batch interruption tasks according to claim 2, characterized in that: Performing interruption processing on the pending batch services based on the interruption prediction data includes: Determine the risk value of the interruption occurrence time point being in the peak period based on the interruption probability and the interruption time point distribution; If the risk value is greater than a preset value, obtaining an interruption handling strategy for the service type during peak hours; The pending batch services are interrupted based on the interruption processing strategy.

6. The method for processing batch interruption tasks according to claim 1, characterized in that: The business types include at least big data business, large retail business, large corporate business, large asset management business and channel service business.

7. A processing device for batch interruption tasks, characterized in that: The device comprises: A type determination module is used to determine the business type of the user's pending batch business; A data acquisition module, used to acquire historical interruption data of the service type; A data prediction module, used to input the historical interruption data into a pre-trained interruption prediction model to obtain interruption prediction data of the service type for a preset time after the current time; A business processing module is used to perform interruption processing on the pending batch business based on the interruption prediction data.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for processing batch interrupt tasks described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for processing batch interrupt tasks according to any one of claims 1 to 6 when executed. 10 . A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method for processing batch interrupt tasks according to any one of claims 1 to 6.

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