Task processing method and device, computer storage medium and electronic device
By using a machine learning model to assign tasks in the processing system and utilizing a verification subsystem to check the implementation of tasks, the problem of low efficiency in task assignment during production was solved, achieving efficient task processing and emergency response.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- INDUSTRIAL AND COMMERCIAL BANK OF CHINA
- Filing Date
- 2023-02-22
- Publication Date
- 2026-07-14
AI Technical Summary
In existing technologies, the work assignment efficiency for production tasks is low, the implementation status is difficult to know, resulting in a waste of human and time costs, delayed emergency response efficiency, and inability to verify and handle abnormal situations in a timely manner.
The system obtains personnel information and target manuals for objectives and tasks through the task allocation subsystem. It uses machine learning models to assign indicator values to suitable personnel and verifies task implementation through the verification subsystem, including emergency handling mechanisms to ensure task completion and rollback in case of anomalies.
It enables efficient task allocation and effective monitoring of implementation, avoids verification errors, and improves production efficiency and the timeliness of emergency response.
Smart Images
Figure CN116070871B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of financial technology, and more specifically, to a task processing method, apparatus, computer storage medium, and electronic device. Background Technology
[0002] With the development of internet finance, the version iteration frequency of server-related applications is getting faster and faster, and the content being deployed in each production run is getting bigger and bigger. However, there are many problems with the current production deployment tasks.
[0003] The pre-production preparation work mainly relied on testers manually assigning tasks, manually checking and sorting them out. Each tester was responsible for a relatively fixed content module due to their level of proficiency, and their responsibilities were relatively singular. This was not conducive to the all-round development of talents and wasted a lot of human and time costs.
[0004] After deployment, version upgrades require manual review, which may result in the inability to verify in a timely manner whether the deployment manual covers all the content, thus causing the system to become unusable. If an application malfunctions and triggers an alarm, emergency operations cannot be automatically triggered, requiring manual rollback to implement the corresponding actions. This results in delayed emergency response and inability to resolve issues quickly.
[0005] There is currently no effective solution to the problems of low implementation efficiency and difficulty in obtaining information about implementation status after task assignment in related technologies. Summary of the Invention
[0006] This application provides a task processing method, apparatus, computer storage medium, and electronic device to solve the problems of low implementation efficiency and difficulty in obtaining implementation status after task assignment in related technologies.
[0007] According to one aspect of this application, a task processing method is provided. The method includes: acquiring information on multiple implementers, a target task, and a target manual for the target task through a task allocation subsystem in a processing system, and reading keyword information from the target manual, wherein the target manual is used to guide implementers in carrying out the target task; inputting the information on multiple implementers and the keyword information into the task allocation subsystem in the processing system, processing the data through a machine learning model in the task allocation subsystem to obtain an assignment index value, and assigning the target task to at least one implementer based on the assignment index value, wherein the machine learning model is trained on multiple sets of sample data, each set of sample data including keyword information from the target manual of a preset task, preset implementer information, and preset assignment index values; and using a verification subsystem in the processing system to verify the configuration information after the target task is implemented through the keyword information, obtaining a verification result, wherein the verification result is used to indicate the implementation status of the target task.
[0008] Optionally, the assignment indicator value is the working time required to complete the target task. The assignment indicator value is obtained by processing the data through a machine learning model in the task allocation subsystem. Based on the assignment indicator value, the target task is assigned to at least one implementer to carry out the target task. This includes: converting the implementer information and keyword information into numerical parameters according to a preset lookup table, and scaling the obtained numerical parameters to obtain multiple feature data. The preset lookup table is used to represent the numerical parameters corresponding to the keyword information, the memory parameters occupied by the target manual, and the implementer information; inputting the multiple feature data into the machine learning model to obtain the working time required to complete the target task; and assigning the target task to the implementer whose working time matches the target task to carry out the target task.
[0009] Optionally, the verification subsystem in the processing system verifies the configuration information after the target task is implemented by using keyword information. The verification results include: if the configuration information is the same as the keyword information in the target manual, the verification subsystem outputs a first verification result, which indicates that the target task has been completed; if the configuration information is different from the keyword information in the target manual, the verification subsystem outputs a second verification result, which indicates that the target task implementation is abnormal.
[0010] Optionally, after the verification subsystem in the processing system verifies the configuration information after the target task is implemented by using keyword information to obtain the verification result, the method includes: if the verification result indicates that the target task implementation is abnormal, determining whether the emergency subsystem in the processing system has detected an abnormal transaction success rate of the target task; if the transaction success rate of the target task is abnormal, performing rollback processing on the target task; if the transaction success rate of the target task is normal, performing secondary processing on the target task.
[0011] Optionally, in the event of an abnormal transaction success rate for the target task, the rollback process for the target task includes: when the emergency subsystem detects that the transaction success rate is lower than a preset threshold, obtaining a rollback strategy from the emergency rollback task process, and rolling back the target task using the rollback strategy, wherein the emergency rollback task process is established based on the emergency rollback description keywords contained in the target manual.
[0012] Optionally, if the success rate of the target task is normal, the secondary processing of the target task includes: reassigning the target task to at least one implementer through the task allocation subsystem to re-implement the target task; verifying the configuration information of the target task after re-implementation with the keyword information of the target manual through the verification subsystem in the processing system to obtain an updated verification result, until the updated verification result indicates that the target task has been implemented.
[0013] Optionally, before obtaining the implementer information, target task, and target manual from the target task through the division of labor subsystem in the processing system, the method includes: detecting whether the target task and target manual have been generated through the division of labor subsystem, and obtaining a detection result; if the detection result indicates that the target task and target manual have been generated, sending a prompt message to the implementer's terminal device through the stored implementer communication information, wherein the prompt message is used to remind the implementer to pay attention to the content of the target manual.
[0014] According to another aspect of this application, a task processing apparatus is provided. The apparatus includes: an acquisition unit, configured to acquire information on multiple implementers, a target task, and a target manual for the target task through a task allocation subsystem in a processing system, and to read keyword information from the target manual, wherein the target manual is used to guide implementers in carrying out the target task; a first processing unit, configured to input the information on multiple implementers and the keyword information into the task allocation subsystem in the processing system, process the data through a machine learning model in the task allocation subsystem to obtain an assignment index value, and assign the target task to at least one implementer based on the assignment index value, wherein the machine learning model is trained on multiple sets of sample data, each set of sample data including keyword information from the target manual of a preset task, preset implementer information, and preset assignment index value; and a verification unit, configured to use a verification subsystem in the processing system to verify the configuration information after the target task is implemented through the keyword information, and obtain a verification result, wherein the verification result is used to indicate the implementation status of the target task.
[0015] According to another aspect of the present invention, a computer storage medium is also provided for storing a program, wherein the program, when running, controls the device where the non-volatile storage medium is located to execute a task processing method.
[0016] According to another aspect of the present invention, an electronic device is also provided, comprising a processor and a memory; the memory stores computer-readable instructions, and the processor is configured to execute the computer-readable instructions, wherein the computer-readable instructions execute a task processing method when they are run.
[0017] This application employs the following steps: First, the task allocation subsystem within the processing system acquires information on multiple implementers, target tasks, and target manuals for those tasks. Keyword information from the target manuals is then read, with the manuals guiding implementers in carrying out the target tasks. Second, the task allocation subsystem inputs the information on multiple implementers and the keyword information into the processing system. A machine learning model within the subsystem processes this information to obtain assignment index values. Based on these values, the target tasks are assigned to at least one implementer. The machine learning model is trained using multiple sets of sample data, each set including keyword information from the target manual for a preset task, preset implementer information, and preset assignment index values. Third, the verification subsystem within the processing system verifies the configuration information after the target task implementation using the keyword information, obtaining verification results. These verification results indicate the implementation status of the target task. This addresses the problems of low implementation efficiency and difficulty in obtaining implementation status after task assignment in related technologies. By rationally allocating tasks through the task allocation subsystem and verifying tasks using the verification subsystem, the system achieves efficient task deployment and avoids verification errors. Attached Figure Description
[0018] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:
[0019] Figure 1 This is a flowchart of a task processing method provided according to an embodiment of this application;
[0020] Figure 2 This is a flowchart illustrating the process of determining assignment indicators using a machine learning model in the task processing method provided in the embodiments of this application.
[0021] Figure 3 This is a schematic diagram of a task processing device provided according to an embodiment of this application;
[0022] Figure 4 This is a schematic diagram of an electronic device provided according to an embodiment of this application. Detailed Implementation
[0023] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.
[0024] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.
[0025] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application 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 for the embodiments of this application 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.
[0026] It should be noted that all information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) involved in this disclosure are information and data authorized by the user or fully authorized by all parties.
[0027] According to an embodiment of this application, a task processing method is provided.
[0028] Figure 1 This is a flowchart of a task processing method provided according to an embodiment of this application, such as... Figure 1 As shown, the method includes the following steps:
[0029] Step S102: Obtain information on multiple implementers, target tasks, and target manuals for the target tasks through the division of labor subsystem in the processing system, and read the keyword information in the target manuals. The target manuals are used to guide implementers in carrying out the target tasks.
[0030] The processing system includes a task allocation subsystem. First, the task allocation subsystem obtains information on multiple implementers, the target tasks to be processed, and the corresponding target manuals. After obtaining this information, the subsystem extracts keyword information according to the chapter content of the manuals, so that the task allocation subsystem can determine the implementers who will perform the task based on the extracted implementer information and keyword information.
[0031] The "multiple implementer information" refers to the information of the implementers who can participate in executing the target task. Each implementer's information may include their years of experience, educational background, age, and professional title. The target task may include updating the version of the application software or platform, patching the application software or platform, etc. The "target manual" is a manual issued by the testers based on the target task. This manual informs the implementers of the manual operations required for the target task and the precautions to be taken during the operation. After receiving the target manual, the implementers can perform the relevant processing work according to the contents of the manual.
[0032] The manual contains multiple keyword information items, specifically including firewall information, API information (Application Programming Interface), key creation, and PaaS parameters (Platform as a Service). It's important to note that these various keyword information items can, to some extent, indicate the type of operations required from the implementers. The keyword information can also be used to calculate the file size, which in turn indicates the difficulty and number of steps involved in that type of operation. Furthermore, the implementer information also reflects the capabilities of the implementers.
[0033] Step S104: Input multiple implementer information and keyword information into the task allocation subsystem of the processing system. The task allocation subsystem processes the data through a machine learning model to obtain the allocation index value. Based on the allocation index value, the target task is assigned to at least one implementer to carry out the target task. The machine learning model is trained with multiple sets of sample data. Each set of sample data includes the keyword information of the target manual of the preset task, the preset implementer information, and the preset allocation index value.
[0034] It should be noted that the task allocation subsystem includes a machine learning model, which is trained using the LASSO regression algorithm on multiple sets of sample data. The preset personnel information and preset keyword information in each sample data serve as the input data of the model, and the preset allocation index value serves as the output data of the model. The allocation index refers to the basis for allocating personnel to the target task based on the output data, and can be such as working time.
[0035] Specifically, Figure 2 This is a flowchart illustrating the process of determining assignment indicators using a machine learning model in the task processing method provided in the embodiments of this application, such as... Figure 2As shown, when the assignment metric is working time, the obtained keyword information and implementer information are input into a trained machine learning model to obtain the working time required for each implementer to complete the target task. Furthermore, the task allocation subsystem can assign the target task to one or more implementers based on the working time required for each implementer and the urgency of the target task, ensuring the task is completed satisfactorily within the specified time.
[0036] Step S106: The verification subsystem in the processing system verifies the configuration content information after the target task is implemented by using keyword information to obtain the verification result, wherein the verification result is used to indicate the implementation status of the target task.
[0037] Specifically, the processing system also includes a verification subsystem. After the implementers process the target task, they put the target task into production and use the verification subsystem to match and verify the relevant configuration information in the platform after production with the relevant information in the target manual to verify whether there are any abnormalities in the target task, that is, whether it can be executed.
[0038] The task processing method provided in this application embodiment obtains information on multiple implementers, target tasks, and target manuals for the target tasks through a task allocation subsystem in the processing system, and reads keyword information from the target manuals. The target manuals guide implementers in carrying out the target tasks. The information on multiple implementers and the keyword information are input into the task allocation subsystem. A machine learning model in the task allocation subsystem processes the data to obtain assignment index values, and the target tasks are assigned to at least one implementer based on these values. The machine learning model is trained using multiple sets of sample data, each set including keyword information from the target manual of a preset task, preset implementer information, and preset assignment index values. A verification subsystem in the processing system verifies the configuration information after the target task is implemented using the keyword information, obtaining verification results. These verification results indicate the implementation status of the target tasks. This method solves the problems of low implementation efficiency and difficulty in knowing the implementation status after task allocation in related technologies. By rationally allocating tasks through the task allocation subsystem and verifying tasks using the verification subsystem, it achieves efficient task deployment and avoids verification errors.
[0039] Optionally, in the task processing method provided in this application embodiment, the assignment index value is the working time required to complete the target task. The assignment index value is obtained by processing the data through a machine learning model in the task allocation subsystem, and the target task is assigned to at least one implementer according to the assignment index value to implement the target task. This includes: converting the implementer information and keyword information into numerical parameters according to a preset lookup table, and scaling the obtained numerical parameters to obtain multiple feature data. The preset lookup table is used to represent the numerical parameters corresponding to the keyword information, the memory parameters occupied by the target manual, and the implementer information; inputting the multiple feature data into the machine learning model to obtain the working time required to complete the target task; and assigning the target task to the implementer whose working time matches the target task to implement the target task.
[0040] In order for the model to recognize and process the input information, the information needs to be transformed. Specifically, the division of labor subsystem stores a preset lookup table for each piece of information. Using the preset lookup table, each piece of input information can be assigned a numerical parameter. The numerical parameter of each piece of information can be obtained by converting the preset lookup table. Since the multiple numerical parameters are relatively discrete, all numerical parameters need to be scaled.
[0041] For example, if the age and years of service of the personnel input into the machine learning model are discrete values, it will affect the stability of the model results. Therefore, before inputting the data into the model, the age information is processed in three-year intervals, such as discretizing 20-23 years old as 20 years old and 24-26 years old as 25 years old. Inputting the discretized data into the model can yield more accurate allocation index values.
[0042] Optionally, in the task processing method provided in this application embodiment, the verification subsystem in the processing system verifies the configuration content information after the target task is implemented by using keyword information to obtain the verification result. This includes: if the configuration content information is the same as the keyword information in the target manual, the verification subsystem outputs a first verification result, wherein the first verification result indicates that the target task has been completed; if the configuration content information is different from the keyword information in the target manual, the verification subsystem outputs a second verification result, wherein the second verification result indicates that the target task implementation is abnormal.
[0043] Specifically, after the target task is implemented in production, the relevant configuration information on the platform can be matched and verified against the information in the target manual to obtain the verification results. When the verification shows that the configuration information matches the keyword information in the target manual, the verification subsystem outputs the first verification result, i.e., the output value 0. The configuration information may include the platform's version number, parameter information, and the import status of the monitoring platform template, etc. Matching the two pieces of information can mean that the version number is the same, the parameter information is the production value information in the manual, and the monitoring platform template has been successfully imported, etc.
[0044] When the configuration information differs from the keyword information in the target manual, a second verification result is output, which is the value 1. For example, the version number of the platform is different from the version number in the target manual, or the parameter information does not match the production value information in the target manual.
[0045] Optionally, in the task processing method provided in this application embodiment, after the verification subsystem in the processing system verifies the configuration content information after the target task is implemented by using keyword information to obtain the verification result, the method includes: if the verification result indicates that the target task implementation is abnormal, determining whether the emergency subsystem in the processing system detects that the transaction success rate of the target task is abnormal; if the transaction success rate of the target task is abnormal, performing rollback processing on the target task; if the transaction success rate of the target task is normal, performing secondary processing on the target task.
[0046] Specifically, the processing system also includes an emergency subsystem. When the verification subsystem outputs a second verification result, that is, when the target task is found to be abnormal, the emergency subsystem needs to be used to determine the transaction status of the target task after it is put into production. If the determination indicates that the transaction success rate after the target task is implemented is abnormal, the emergency subsystem needs to be used to perform emergency processing on the target task. For example, the emergency processing can be rollback. If the determination indicates that the transaction success rate after the target task is implemented is normal, since the verification result indicates that the target task is abnormal, the verification subsystem needs to be used to perform secondary processing on the target task.
[0047] Optionally, in the task processing method provided in the embodiments of this application, when the transaction success rate of the target task is abnormal, the rollback processing of the target task includes: when the emergency subsystem detects that the transaction success rate is lower than a preset threshold, obtaining the rollback processing strategy from the emergency rollback task process, and performing rollback processing on the target task through the rollback processing strategy, wherein the emergency rollback task process is established based on the emergency rollback description keywords contained in the target manual.
[0048] Specifically, after the target task is put into production, the monitoring unit of the emergency subsystem in the processing system will monitor the transaction success rate of the platform in real time. When an anomaly is detected in a certain transaction indicator belonging to the target manual, that is, when the transaction success rate is lower than the preset threshold, the emergency subsystem will trigger the emergency handling strategy in the emergency rollback task process. The emergency handling strategy will be used to handle the target task. The emergency handling strategy may include refreshing the parameters stored in the server memory and rolling back the version or rollback the patch. For example, when an abnormal transaction success rate is detected in a certain region, the connection to the main database in that region can be turned off by modifying the script to prevent data loss in the database.
[0049] It should be noted that the emergency rollback task process is established by the division of labor subsystem. Specifically, when the division of labor subsystem identifies keywords containing emergency rollback descriptions in the target manual, it establishes an emergency rollback task process that can store processing strategies, so that the task can be handled in a timely manner when implementation anomalies occur.
[0050] Optionally, in the task processing method provided in this application embodiment, when the transaction success rate of the target task is normal, the secondary processing of the target task includes: reassigning the target task to at least one implementer through the task allocation subsystem to re-implement the target task; verifying the configuration content information of the target task after re-implementation with the keyword information of the target manual through the verification subsystem in the processing system to obtain an updated verification result, until the updated verification result indicates that the target task has been completed.
[0051] Specifically, when the verification subsystem outputs a second verification result, i.e., when the target task implementation is found to be abnormal, the target task is processed a second time through the task allocation subsystem and the verification subsystem. This second processing involves using the task allocation subsystem to reselect the personnel responsible for implementing the target task, so that the reselected personnel can execute the target task. For example, the required operation times for each person are sorted in descending order, and the second-ranked person is selected as the person to re-execute the target task.
[0052] Furthermore, after the target tasks are reassigned and processed, a second phase of task implementation is carried out, and the verification subsystem is used to perform a second verification of the configuration content information and the keyword information in the target manual, so as to ensure the successful completion of the target tasks.
[0053] Optionally, in the task processing method provided in this application embodiment, before obtaining the implementer information, target task, and target manual of the target task through the division of labor subsystem in the processing system, the method includes: detecting whether the target task and target manual have been generated through the division of labor subsystem, and obtaining a detection result; if the detection result indicates that the target task and target manual have been generated, issuing a prompt message to the implementer's terminal device through the stored implementer communication information, wherein the prompt message is used to prompt the implementer to pay attention to the content of the target manual.
[0054] Specifically, before assigning personnel to implement the target task, it is necessary to first determine whether a new target task and corresponding target manual have been generated, and obtain the detection result. When the detection result obtained by the task allocation subsystem indicates that a new target task and corresponding target manual have been generated, it first retrieves the implementation personnel communication information stored on the server, and then sends a prompt message to the terminal device of each implementation personnel based on the communication information, prompting the implementation personnel to pay attention to the task assignment result of the target manual in a timely manner, so that the implementation personnel can process the task assignment result in a timely manner. The terminal device can include the implementation personnel's email and mobile device.
[0055] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.
[0056] This application also provides a task processing apparatus. It should be noted that the task processing apparatus of this application can be used to execute the task processing method provided in this application. The task processing apparatus provided in this application is described below.
[0057] Figure 3 This is a schematic diagram of a task processing device provided according to an embodiment of this application, such as... Figure 3 As shown, the device includes: an acquisition unit 30, a first processing unit 31, and a verification unit 32.
[0058] The acquisition unit 30 is used to acquire information on multiple implementers, target tasks, and target manuals for the target tasks through the division of labor subsystem in the processing system, and to read keyword information in the target manuals. The target manuals are used to guide implementers in carrying out the target tasks.
[0059] The first processing unit 31 is used to input multiple implementer information and keyword information into the division of labor subsystem of the processing system, process the information through the machine learning model in the division of labor subsystem to obtain the assignment index value, and assign the target task to at least one implementer according to the assignment index value to implement the target task. The machine learning model is trained with multiple sets of sample data, and each set of sample data includes the keyword information of the target manual of the preset task, the preset implementer information, and the preset assignment index value.
[0060] The verification unit 32 is used to verify the configuration content information after the implementation of the target task by using the verification subsystem in the processing system through keyword information, and obtain the verification result, wherein the verification result is used to indicate the implementation status of the target task.
[0061] Optionally, in the task processing device provided in this application embodiment, the first processing unit 31 includes: a conversion module, used to convert the implementer information and keyword information into numerical parameters according to a preset lookup table, and to scale the obtained numerical parameters to obtain multiple feature data, wherein the preset lookup table is used to represent the numerical parameters corresponding to the keyword information, the memory parameters occupied by the target manual, and the implementer information; an input module, used to input the multiple feature data into a machine learning model and process it to obtain the working time required to complete the target task; and a dispatch module, used to dispatch the target task to the implementer matching the working time to implement the target task.
[0062] Optionally, in the task processing apparatus provided in this application embodiment, the verification unit 32 includes: a first output module, used to verify the subsystem to output a first verification result when the configuration content information is the same as the keyword information of the target manual, wherein the first verification result indicates that the target task has been completed; and a second output module, used to verify the subsystem to output a second verification result when the configuration content information is different from the keyword information of the target manual, wherein the second verification result indicates that the target task has been abnormally implemented.
[0063] Optionally, in the task processing apparatus provided in this application embodiment, the apparatus includes: a judgment unit, configured to, after the verification subsystem in the processing system verifies the configuration content information after the target task is implemented by using keyword information, and after obtaining the verification result, determine whether the emergency subsystem in the processing system has detected an abnormal transaction success rate of the target task if the verification result indicates that the target task implementation is abnormal; a second processing unit, configured to perform rollback processing on the target task if the transaction success rate of the target task is abnormal; and a third processing unit, configured to perform secondary processing on the target task if the transaction success rate of the target task is normal.
[0064] Optionally, in the task processing device provided in this application embodiment, the verification unit 32 includes: an acquisition module, used to acquire a rollback processing strategy from the emergency rollback task process when the emergency subsystem detects that the transaction success rate is lower than a preset threshold, and to perform rollback processing on the target task through the rollback processing strategy, wherein the emergency rollback task process is established based on the emergency rollback description keywords contained in the target manual.
[0065] Optionally, in the task processing apparatus provided in this application embodiment, the verification unit 32 includes: a dispatch module, used to re-dispatch the target task to at least one implementer through the task allocation subsystem to re-implement the target task; and a verification module, used to verify the configuration content information of the re-implemented target task with the keyword information of the target manual through the verification subsystem in the processing system to obtain an updated verification result until the updated verification result indicates that the target task has been implemented.
[0066] Optionally, in the task processing apparatus provided in this application embodiment, the apparatus includes: a detection unit, configured to detect whether a target task and a target manual have been generated by the division of labor subsystem before obtaining the implementer information, target task, and target manual of the target task through the division of labor subsystem in the processing system, and obtain a detection result; and a sending unit, configured to send a prompt message to the implementer's terminal device through stored implementer communication information when the detection result indicates that a target task and a target manual have been generated, wherein the prompt message is used to prompt the implementer to pay attention to the content of the target manual.
[0067] The task processing apparatus provided in this application embodiment, through the acquisition unit 30, is used to acquire information on multiple implementers, target tasks, and target manuals for the target tasks through the task allocation subsystem in the processing system, and to read keyword information from the target manuals. The target manuals are used to guide implementers in carrying out the target tasks. The first processing unit 31 is used to input the information on multiple implementers and keyword information into the task allocation subsystem in the processing system, process the data through a machine learning model in the task allocation subsystem to obtain assignment index values, and assign the target tasks to at least one implementer based on the assignment index values. The machine learning model is trained using multiple sets of sample data, each set of sample data including keyword information from the target manual of a preset task, preset implementer information, and preset assignment index values. The verification unit 32 is used to verify the configuration information after the implementation of the target task by using the verification subsystem in the processing system through keyword information to obtain the verification result. The verification result is used to indicate the implementation status of the target task, which solves the problems of low implementation efficiency and difficulty in knowing the implementation status after the work task is assigned in related technologies. By rationally allocating tasks through the division of labor subsystem and verifying tasks through the verification subsystem, the goal of efficiently completing the task and avoiding verification errors is achieved.
[0068] The aforementioned task processing device includes a processor and a memory. The aforementioned acquisition unit 30, first processing unit 31, and verification unit 32 are all stored in the memory as program units. The processor executes the aforementioned program units stored in the memory to realize the corresponding functions.
[0069] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and adjusting kernel parameters can address the problems of low implementation efficiency and difficulty in obtaining implementation status after task assignment in related technologies.
[0070] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.
[0071] This application also provides a computer storage medium for storing a program, wherein the program, when running, controls the device where the non-volatile storage medium is located to execute a task processing method.
[0072] This application also provides an electronic device. Figure 4 This is a schematic diagram of an electronic device provided according to an embodiment of this application, such as... Figure 4As shown, electronic device 40 includes a processor and a memory; the memory stores computer-readable instructions, and the processor executes the computer-readable instructions, wherein the computer-readable instructions perform a task processing method when executed. The electronic device in this document can be a server, PC, PAD, mobile phone, etc.
[0073] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0074] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0075] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0076] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0077] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.
[0078] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.
[0079] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.
[0080] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.
[0081] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.
Claims
1. A task processing method, characterized in that, include: The system obtains information on multiple implementers, target tasks, and target manuals for the target tasks through the division of labor subsystem in the processing system, and reads keyword information from the target manuals. The target manuals are used to guide the implementers in carrying out the target tasks. The information of the multiple implementers and the keyword information are input into the task allocation subsystem of the processing system. The machine learning model in the task allocation subsystem processes the data to obtain the allocation index value. Based on the allocation index value, the target task is allocated to at least one implementer to carry out the target task. The machine learning model is trained with multiple sets of sample data. Each set of sample data includes the keyword information of the target manual of the preset task, the preset implementer information, and the preset allocation index value. The keyword information includes: firewall information, application programming interface information, key creation, and service parameters. The assignment index value is the working time required to complete the target task. The assignment index value is obtained through processing by a machine learning model in the task allocation subsystem. Based on the assignment index value, the target task is assigned to at least one implementer to carry out the target task. This includes: converting the implementer information and the keyword information into numerical parameters according to a preset lookup table; scaling the obtained numerical parameters to obtain multiple feature data, where the preset lookup table represents the keyword information, the memory parameters occupied by the target manual, and the numerical parameters corresponding to the implementer information; inputting the multiple feature data into the machine learning model to obtain the working time required to complete the target task; and assigning the target task to an implementer matching the working time to carry out the target task. The verification subsystem in the processing system verifies the configuration information after the implementation of the target task using the keyword information to obtain a verification result, wherein the verification result is used to indicate the implementation status of the target task; After the verification subsystem in the processing system verifies the configuration information after the implementation of the target task using the keyword information and obtains the verification result, the method includes: if the verification result indicates that the implementation of the target task is abnormal, determining whether the emergency subsystem in the processing system has detected an abnormal transaction success rate of the target task; if the transaction success rate of the target task is abnormal, performing a rollback process on the target task; if the transaction success rate of the target task is normal, performing secondary processing on the target task.
2. The method according to claim 1, characterized in that, The verification subsystem in the processing system verifies the configuration information after the target task is implemented using the keyword information, and the verification results include: If the configuration content information is the same as the keyword information in the target manual, the verification subsystem outputs a first verification result, wherein the first verification result indicates that the target task has been completed. If the configuration content information differs from the keyword information in the target manual, the verification subsystem outputs a second verification result, wherein the second verification result indicates that the target task implementation is abnormal.
3. The method according to claim 1, characterized in that, In the event of an abnormally high transaction success rate for the target task, the rollback process for the target task includes: If the emergency subsystem detects that the transaction success rate is lower than a preset threshold, it obtains a rollback processing strategy from the emergency rollback task process and performs rollback processing on the target task using the rollback processing strategy. The emergency rollback task process is established based on the emergency rollback description keywords contained in the target manual.
4. The method according to claim 1, characterized in that, If the transaction success rate of the target task is normal, the secondary processing of the target task includes: The target task is reassigned to at least one implementer through the task allocation subsystem, so as to re-implement the target task; The verification subsystem in the processing system verifies the configuration information of the re-implemented target task against the keyword information in the target manual to obtain an updated verification result, until the updated verification result indicates that the target task has been completed.
5. The method according to claim 1, characterized in that, Before obtaining the implementer information, target tasks, and target manuals for the target tasks through the division of labor subsystem in the processing system, the method includes: The detection result is obtained by checking whether the target task and the target manual have been generated by the division of labor subsystem. If the detection result indicates the generation of the target task and the target manual, a prompt message is sent to the implementer's terminal device through the stored implementer communication information, wherein the prompt message is used to remind the implementer to pay attention to the contents of the target manual.
6. A task processing device, characterized in that, include: The acquisition unit is used to acquire information on multiple implementers, target tasks, and target manuals for the target tasks through the division of labor subsystem in the processing system, and to read keyword information from the target manuals, wherein the target manuals are used to guide the implementers in carrying out the target tasks; The first processing unit is used to input the information of the multiple implementers and the keyword information into the task allocation subsystem of the processing system, process the data through a machine learning model in the task allocation subsystem to obtain an allocation index value, and allocate the target task to at least one implementer according to the allocation index value to implement the target task. The machine learning model is trained using multiple sets of sample data, each set of sample data including keyword information of the target manual for the preset task, preset implementer information, and preset allocation index value; the keyword information includes: firewall information, application programming interface information, key creation, and service parameters. The assignment index value is the working time required to complete the target task. The first processing unit includes: a conversion module, used to convert the implementer information and the keyword information into numerical parameters according to a preset lookup table, and to scale the obtained numerical parameters to obtain multiple feature data, wherein the preset lookup table is used to represent the keyword information, the memory parameters occupied by the target manual, and the numerical parameters corresponding to the implementer information; an input module, used to input the multiple feature data into the machine learning model and process it to obtain the working time required to complete the target task; and a dispatch module, used to dispatch the target task to implementers matching the working time to carry out the target task. The verification unit is used to verify the configuration content information after the implementation of the target task by using the verification subsystem in the processing system through the keyword information, and obtain the verification result, wherein the verification result is used to indicate the implementation status of the target task; The task processing device further includes: a judgment unit, used to determine whether the transaction success rate of the target task is abnormal if the verification subsystem in the processing system detects it after the target task is implemented by verifying the configuration content information through keyword information, and after obtaining the verification result, the emergency subsystem in the processing system detects it after the verification result indicates that the target task is implemented abnormally; a second processing unit, used to perform rollback processing on the target task if the transaction success rate of the target task is abnormal; and a third processing unit, used to perform secondary processing on the target task if the transaction success rate of the target task is normal.
7. A computer storage medium, characterized in that, The computer storage medium is used to store a program, wherein the program, when running, controls the device where the computer storage medium is located to execute the task processing method according to any one of claims 1 to 5.
8. An electronic device, characterized in that, The device includes a processor and a memory, the memory storing computer-readable instructions, and the processor being configured to execute the computer-readable instructions, wherein the computer-readable instructions, when executed, perform the task processing method according to any one of claims 1 to 5.