CN113127198A - Task processing method, device and equipment - Google Patents
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Abstract
The embodiment of the specification provides a task processing method, a task processing device and a task processing device, which can be applied to the technical field of automatic program design. The method comprises the following steps: extracting task parameters from the target task; the task parameters are used for describing the category and the task quantity of the target task; determining at least two task processing objects corresponding to the target task based on the task parameters; the task processing objects are respectively used for processing tasks of different categories and task quantities; splitting the target task into at least two subtasks according to the task parameters and the task processing object; and distributing the subtasks to the at least two task processing objects so that the task processing objects respectively process the corresponding subtasks. The method ensures that different task processing objects can more effectively process the distributed subtasks based on the processing type and the processing capacity, thereby ensuring the efficient and accurate processing of the tasks and saving the consumption of corresponding human resources.
Description
Technical Field
The embodiment of the specification relates to the technical field of automatic programming, in particular to a task processing method, a task processing device and a task processing equipment.
Background
In the present society, in order to facilitate the management of organizations and the processing of internal transactions of organizations, corresponding task processing systems are often constructed. The task processing systems can receive various tasks and distribute the tasks to corresponding objects for processing. For example, when cost sharing is required, it is necessary to transmit each of the cost data included in the cost sharing task to a different object based on the cost data to realize the processing of the cost sharing task.
However, when a task is processed and task allocation is required, a task processing system currently only allocates tasks according to a preset template, and does not consider data of the tasks themselves. Under the conditions that the elements involved in the task are complex, the number of the allocation objects is large, and the allocation requirements are complex, the preset template is still required to be found for allocating the task, so that the problems of uneven allocation, wrong execution of the task and the like can occur to the task allocation, and the task cannot be effectively processed. Therefore, a method for distributing tasks based on the tasks itself to achieve efficient and accurate task processing is needed.
Disclosure of Invention
An object of the embodiments of the present specification is to provide a method, an apparatus, and a device for task processing, so as to solve a problem how to effectively allocate a task to implement efficient and accurate processing of the task.
To solve the foregoing technical problem, an embodiment of the present specification provides a task processing method, including: extracting task parameters from the target task; the task parameters are used for describing the category and the task quantity of the target task; determining at least two task processing objects corresponding to the target task based on the task parameters; the task processing objects are respectively used for processing tasks of different categories and task quantities; splitting the target task into at least two subtasks according to the task parameters and the task processing object; and distributing the subtasks to the at least two task processing objects so that the task processing objects respectively process the corresponding subtasks.
An embodiment of this specification further provides a task processing device, including: the task parameter extraction module is used for extracting task parameters from the target task; the task parameters are used for describing the category and the task quantity of the target task; a task processing object determination module for determining at least two task processing objects corresponding to the target task based on the task parameters; the task processing objects are respectively used for processing tasks of different categories and task quantities; the subtask splitting module is used for splitting the target task into at least two subtasks according to the task parameters and the task processing object; and the subtask allocation module is used for allocating the subtasks to the at least two task processing objects so that the task processing objects respectively process the corresponding subtasks.
The embodiment of the present specification further provides a task processing device, including a memory and a processor; the memory to store computer program instructions; the processor to execute the computer program instructions to implement the steps of: extracting task parameters from the target task; the task parameters are used for describing the category and the task quantity of the target task; determining at least two task processing objects corresponding to the target task based on the task parameters; the task processing objects are respectively used for processing tasks of different categories and task quantities; splitting the target task into at least two subtasks according to the task parameters and the task processing object; and distributing the subtasks to the at least two task processing objects so that the task processing objects respectively process the corresponding subtasks.
As can be seen from the technical solutions provided in the embodiments of the present specification, the embodiments of the present specification can determine the task processing object according to the task parameters included in the target task, split the target task into the subtasks respectively corresponding to different task processing objects, and allocate the split subtasks to corresponding task processing objects for processing. By the method, the target task is split under the condition of combining the specific data of the target task, and different task processing objects can more effectively process the allocated subtasks based on the processing type and the processing capacity, so that efficient and accurate processing of the tasks is guaranteed, and corresponding human resource consumption is saved.
Drawings
In order to more clearly illustrate the embodiments of the present specification or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the specification, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of a task processing method according to an embodiment of the present disclosure;
FIG. 2 is a block diagram of a task processing device according to an embodiment of the present disclosure;
fig. 3 is a block diagram of a task processing device according to an embodiment of the present disclosure.
Detailed Description
The technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present specification without any creative effort shall fall within the protection scope of the present specification.
In order to solve the above technical problem, a task processing method according to an embodiment of the present specification is introduced. The task processing method is characterized in that an execution main body is task processing equipment, and the task processing equipment comprises but is not limited to a server, an industrial personal computer, a PC (personal computer) and the like. As shown in fig. 1, the task processing method may include the following implementation steps.
S110: extracting task parameters from the target task; the task parameters are used for describing the category and the task quantity of the target task.
The target task may be a task that needs to be processed. In practical applications, the task processing device may not have the capability of directly processing the target task, and may need to allocate the target task to other objects for processing.
In some embodiments, the target task may be a task for apportioning resources. The resource may be a resource embodied in the form of data, for example a virtual digital currency resource. The resource may also be a statistical quota for the data resource, which is acquired in advance, and is used to determine the number of resources that can be called by different objects in advance. In practical applications, when a project or an organization is managed, the corresponding resources are often called and allocated. When the cost consumption is involved, the cost is often required to be shared according to the consumption conditions of different objects on the resources; when the project progress and the actual task processing process of different objects are involved, corresponding resources are often required to be allocated to the different objects, so that the different objects can complete the processing of corresponding services by using the resources.
In some embodiments, the resources include resources corresponding to at least one of a server, storage, CPU, network device, bandwidth, equipment room, electricity charge.
In any of the above-described examples, it is necessary to determine a specific cost allocation manner according to the specific content of the task and the specific situation of the different objects responsible for the task to process the task.
In some embodiments, generic parameters may be maintained in advance. The general parameters are used for defining the data table type, the data surface layer level structure and the data table elements of the acquired target task. Based on the general parameters, the target task can be acquired.
Specifically, the general parameter may include at least one of a data table definition parameter, a data table element parameter, a dimension table definition, a financial element, an IDC parameter, a cost hierarchy parameter, and a base cost element.
The dimension table definition can be used for defining a dimension table, and the dimension table can be used for defining specific types of parameters corresponding to the target task. As shown in table 1 below, a schematic of one interface for determining the dimension tables. In the schematic diagram, the cost pool attribute, the original cost pool number, the model, the cloud type and the sub-product number are respectively used as five different dimensions, and accordingly, specific data of corresponding dimensions in each item of data are extracted based on the determined dimensions. And screening according to the data of each dimension to obtain data meeting the requirement of the target task, and processing according to the data to obtain the target task.
TABLE 1
Where the objective task is to achieve resource allocation or cost sharing, the data resources may be defined and differentiated by financial elements based on the different types of data resources involved. For example, as shown in the example of the interface diagram in table 2 below, the financial elements may be distinguished based on coding, name, type, sub-type, usage, and cloud type. From the financial elements, it may be determined whether a particular project falls within the scope defined by the target task.
TABLE 2
The IDC parameter may be used to represent the initial direct cost, i.e. to achieve cost accounting and amortization from the initial dimension. Specifically, as shown in the example of the interface diagram in table 3 below, the IDC parameter may be restricted based on different levels of parameter numbers and parameter names. The IDC parameters may be limited based on different levels of parameter numbers and parameter names, and it can also be seen based on the example in the figure that the IDC parameters may be limited to more refined data types, so that the target task can be determined more accurately according to different types of data.
TABLE 3
The cost hierarchy and parameters may be used to define cloud type data related to costs, and as shown in the example corresponding to the interface diagram in table 4 below, the cost hierarchy and parameters divide costs into three categories and differentiate parameters according to the specific types corresponding to the costs, such as fixed and intangible assets, hardware devices, software products, and so on. Based on the determination of the cost level and the parameters, the attribution type of the cost is limited, so that the object to which the cost belongs can be determined according to different types to which the cost belongs, and further, the cost is effectively allocated.
TABLE 4
The basic cost element may obtain specific numerical values of different items of the cost data based on the cost data type determined by the parameters. As shown in table 5 below, for different products in the cloud type, the first class classification, the second class classification, and the third class classification, the data of the original value, the depreciated value, and the attributed device value corresponding to the different products can be determined. Based on the basic cost elements, specific data information can be extracted and obtained, and then a target task is constructed according to the specific data.
TABLE 5
Although the above examples define and describe the general parameters from different aspects of the general parameters, in practical applications, according to different types of the target tasks and different requirements for data, other types of data may be set as the general parameters, and are not limited to the above examples, and are not described herein again.
After the target task is obtained, task parameters may be extracted from the target task. The task parameter may be a parameter for describing a category and a task amount of the target task.
Since the processing task capacity and the processing task type of different objects have certain differences when allocating the target task to different objects for processing, in order to ensure the best effect of task processing, the task allocation can be performed based on the specific situation of the target task. After the task parameters are obtained, the specific category and the task amount of the target task can be determined through the task parameters, so that the corresponding task processing object can be found based on the task parameters, and the difference of the task is realized.
It should be noted that, in a case that the target task needs to be split into different task processing objects for processing, the task parameters may be refined, classified and described based on the content in the target task, so as to determine at least two task processing objects corresponding to the target task in the subsequent steps.
In practical applications, the task parameters can be determined based on the differences between different task processing objects, so that the optimal allocation mode of the tasks can be determined based on the differences between different task processing objects.
The specific task parameter extraction from the target task may be set based on the specific type of the target task. In a specific example, assuming that the target task is a cost sharing service, the target task may correspond to at least one of a cost pool maintenance parameter, a cost pool cost structure, and a cost pool cost detail.
The cost pool maintenance parameters may be used mainly for defining and maintaining the product involved in the target task and the properties of the cost pool itself. As shown in table 6 below, is a schematic view of an interface for performing maintenance on products involved in a target task. Wherein the product itself is restricted based on different levels of coding and names.
TABLE 6
Correspondingly, as shown in table 7 below, the parameters are corresponding parameters of the cost pool itself, including parameters of different attributes such as a cost pool number, a cost pool name, a cost pool attribute, an original cost pool number, a model code, a model name, and a cloud type, so as to distinguish the cost pool itself. After the cost pools with different detailed classifications are obtained, the cost pools can be refined and classified based on the corresponding relation between the products and the cost pools, so that the corresponding target tasks can be divided into different subtasks to be distributed to different objects.
TABLE 7
The cost pool cost structure may be a structure for distinguishing when the cost pool classifies costs. Specifically, as shown in the example corresponding to table 8 below, the cost pool may correspond to a corresponding cost pool attribute, and the costs are classified in detail in the aspects of hardware devices, software products, machine room environment, energy consumption and electricity charge, network communication, and the like, so that the costs of the cost pool in different categories are directly determined. When the target task does not involve cost sharing, the structure corresponding to the target task may also be subdivided with reference to this example, which is not described herein again.
TABLE 8
The cost pool cost detail can be a further refined classification of the cost pools based on the attributes and hierarchical structure of the respective cost pools. Specifically, as shown in table 9 below, for different cost pools, under the limitation of the attributes corresponding to the cost pools and the classification conditions of each level, the cost pools are further refined, so that the cost pools can be more accurately distinguished to implement allocation of target tasks in subsequent steps.
TABLE 9
The foregoing example is only based on that the target task is a cost allocation task, and an exemplary introduction is performed on the target task, and in practical application, when the target task is another task that needs to be split, adaptive adjustment may also be performed according to a practical application situation, and the situation in the foregoing example is not absolutely limited, and details of other situations are not described here.
S120: determining at least two task processing objects corresponding to the target task based on the task parameters; the task processing objects are respectively used for processing tasks of different categories and task volumes.
After the task parameters corresponding to the target task are obtained, the task processing object can be searched based on the task parameters. The task processing object is an object responsible for processing the target task. The task processing object may be a specific user client, for example, the split sub-tasks are displayed to the user based on the user client, or may be a group or an individual in an abstract sense, for example, when performing cost sharing, the shared cost is distributed to different users. In practical application, the task processing object may also be set according to requirements, and is not limited to the above example, and is not described herein again.
The specific task processing object may be determined by matching according to the type of the target task and the type of the task processing object, for example, corresponding tags may be set in advance for the task processing object, and each tag is used to indicate the type of the task that can be processed. And matching according to the type of the task processing object and the type of the target task. Or, the corresponding task processing capacity can be marked in advance for each task processing object, and the division is performed according to the task processing capacity of different objects and the total task amount of the target task. In practical applications, the task processing object may be determined according to the condition corresponding to the result of the specific situation, and is not limited to the above example.
In some embodiments, the task parameter corresponds to a parameter right, and the parameter right is used to limit a task processing object allocated by the target task, that is, the task processing object can be selected as the task processing object only on the basis of having the right to process the parameter. Therefore, after the parameter permission is acquired, the corresponding object can be further screened based on the parameter permission, and the screened object is used as a task processing object.
In practical application, the parameter authority can be directly set manually by a manager, or set according to a preset rule based on the type of the parameter, which is not limited to this.
S130: and splitting the target task into at least two subtasks according to the task parameters and the task processing object.
After the task processing object is obtained, the target task may be split to obtain corresponding subtasks. The subtasks may be tasks assigned to different task processing objects for processing. For example, the subtasks may be tasks that are processed by different task processing targets using their own resources, or may be resource amounts allocated to different task processing targets such that each task processing target realizes processing of another transaction based on the divided subtasks. Specifically, the target task may be split based on the task parameter corresponding to the target task according to the type and capability of the task processing object to process the task.
In some embodiments, corresponding split rules and split weight values may be maintained in advance. The splitting rule is used for expressing a rule for splitting the target task into subtasks corresponding to the task processing objects, and the splitting weight value is used for limiting the weight value of the task quantity distributed to each task processing object, so that the subtasks corresponding to different task quantities can be obtained through splitting based on the splitting weight value.
In some embodiments, the splitting rule may be used to indicate a manner of splitting the target task into minimum units, for example, defining a task amount corresponding to the minimum task unit, or defining a parameter corresponding to the minimum task unit.
After the minimum task unit is determined according to the splitting rule, the number of the minimum task units contained in each subtask can be determined according to the splitting weight value of each subtask, and then the subtasks are split from the target task according to the splitting rule and the splitting weight value.
Specifically, when the target task is a cost allocation task, the corresponding splitting rule and the splitting weight value may be a cost pool allocation rule and an allocation cause, and correspondingly, may further include a unit price calculation rule.
The base contribution factor may be a factor indicating a parameter for which a contribution operation is required. A schematic of the interface for the underlying contribution is shown in table 10 below. In the figure, two basic factors of the number of servers and the depreciation amount of hardware equipment are set, and corresponding parameters such as cloud types, sub-product numbers and names, numerical values, occupation ratios and the like are set.
Watch 10
In the product unit price query, the unit price can be used to refer to the task amount or the task resource amount contained in the minimum task capable of being divided. As shown in table 11 below, for the schematic diagram of the product unit price query interface, for the numbers and names of different tasks, there are corresponding product families, product categories, product names, sub-product names, product item names, product detail item names, capacity units, yields, and product unit prices, that is, corresponding unit prices can be determined according to the difference of the parameters corresponding to the tasks.
TABLE 11
Based on the corresponding contribution factors and the contribution rules, a specific cost pool cost structure may be determined. As shown in table 12 below, after the different cost pools are distinguished based on the cost pool numbers and the cost pool names, specific parameters of the cost pools, including specific data corresponding to hardware devices, software products, machine room environments, energy consumption and electricity charges, network communications, human resources, and the like, can be obtained, so that effective cost sharing can be achieved.
TABLE 12
In some embodiments, the task parameters may further include task-level parameters and task processing logic. The task level parameter is used to describe a level result of the target task, and a specific manner for defining the level of the target task may refer to the description in step S120, which is not described herein again. The task processing logic may be used to represent task relationships between different task hierarchies. According to the task processing logic, the target tasks can be sequentially split into lower levels, so that the split lower levels have more tasks based on a level division mode, and the final subtasks are obtained after the split of a certain level is achieved.
Specifically, the target task may be adjusted to at least one intermediate task of a next hierarchy based on the task processing logic according to different data in the target task, the corresponding intermediate task is adjusted to at least one intermediate task of a next hierarchy based on the obtained intermediate task and based on the task processing logic again, and the above steps are repeated until the hierarchy adjusted by the task meets the requirement of the task hierarchy parameter, and accordingly, the intermediate task in the hierarchy corresponding to the task hierarchy parameter is obtained as the sub-task.
In some embodiments, the task processing logic may include at least one of inherited superior task logic, associated task logic, and inherited causal task logic.
The inheritance superior task logic directly inherits the dimension information and the structure information corresponding to the intermediate task of the previous task level, keeps consistent with the intermediate task of the previous task level, and can adopt the task logic when the intermediate task does not need to be continuously split but only the level of the intermediate task is ensured to extend to the requirement of the task level parameters.
And the associated task logic indicates that the intermediate task is obtained based on the corresponding task assignment weight value and the intermediate task of the previous task level. For example, under the condition that a corresponding weight value is preset, the intermediate task of the previous level can be split directly by the corresponding weight value, so that the intermediate task of the next level is obtained by splitting.
The inherited cause task logic may be related only to the corresponding cause. Before dividing the intermediate task, a minimum subtask, i.e., a minimum unit of dividing the target task, may be determined based on the target task. An actor may be used to represent the number of minimum subtasks. The inheritance cause task logic can obtain the minimum subtasks with corresponding quantity based on the causes directly without considering the task quantity of the corresponding intermediate tasks.
Of course, in practical application, other task logics may also be set according to requirements, for example, parameters such as dimensions and structures of a certain layer of intermediate tasks are directly set by force, and the logic of the intermediate tasks is directly specified based on a certain rule or an instruction of a user. The specific manner of setting the task logic may be set according to the requirements of the actual application, and is not described herein again.
The task processing logic corresponding to the task is selected in the specific task execution process, which may be that one of the three task logics is selected as the task logic in the specific implementation process, and the specific selected task logic needs to be set according to the actual application condition, which is not described herein again.
In some embodiments, the task processing logic includes task processing logic fed back by the user terminal after the target task is pushed to the user terminal.
In some embodiments, the target task includes a task resource, which may be, for example, a corresponding data resource, and is directly allocated with different objects to complete the processing of corresponding transactions; or resource quota in abstract form, so that different task processing objects can call the resource of the corresponding quota based on the corresponding resource quota in the subsequent execution process. In practical application, the setting mode of the task resource is not limited, and the setting can be carried out according to the requirement of practical application.
When the target task includes task resources, the task resources may be divided into sub-task resources respectively corresponding to each task processing object according to the task parameters, and sub-tasks corresponding to each task processing object may be respectively constructed based on the sub-task resources.
Correspondingly, when the task resources are split, the weight values corresponding to the task processing objects can be determined according to the task parameters, and the task resources are divided into subtask resources corresponding to the task processing objects respectively according to the weight values.
In some embodiments, the manner of acquiring the subtasks may also be directly sending the target task, the task parameter, and the task processing object to a manager terminal, and after the manager acquires the information through the manager terminal, the manager may determine corresponding subtask division information and feed back the subtask division information through the manager terminal. After receiving the subtask division information, the target task may be divided into at least two subtasks using the subtask division information.
S140: and distributing the subtasks to the at least two task processing objects so that the task processing objects respectively process the corresponding subtasks.
After the sub tasks are obtained by splitting, the sub tasks can be distributed to the task processing objects to respectively complete the corresponding sub tasks, so that the processing efficiency of the tasks is improved.
And when the target task is a cost allocation task, the obtained subtasks all contain resources corresponding to the allocated cost. Under the condition that each subtask is distributed to each task processing object, each task processing object is enabled to have a corresponding cost limit, and corresponding transactions are correspondingly processed.
The task allocation results are explained below using two specific examples. In the case where the target task is a cost sharing task, as shown in table 13 below, the target task is a schematic diagram of a cost pool sharing rule configuration. Corresponding to each cost pool, information such as corresponding numbers, names, attributes and the like exists.
Watch 13
As shown in table 14 below, a schematic diagram of an interface configured for a unit price calculation rule. And setting corresponding information such as unit price numbers, unit price names, cost pool attributes, signals, cloud types, sub-products and the like aiming at cost pools with different numbers and names. Based on the interface schematic diagram, the cost pool is determined.
TABLE 14
The specific process of processing the subtasks may be set based on the specific type of the target task and the process of processing the tasks in the actual application, which is not described herein again.
In some embodiments, after the task processing objects are determined, the subtasks may be pushed to the user devices corresponding to the at least two task processing objects, so that the user devices display the corresponding subtasks, and thus, the corresponding subtasks are displayed by using different user devices, so as to better implement the processing of the tasks.
Through the introduction of the above embodiment, it can be seen that the method can determine the task processing object according to the task parameters included in the target task, split the target task into the subtasks respectively corresponding to different task processing objects, and allocate the split subtasks to the corresponding task processing objects for processing. By the method, the target task is split under the condition of combining the specific data of the target task, and different task processing objects can more effectively process the allocated subtasks based on the processing type and the processing capacity, so that efficient and accurate processing of the tasks is guaranteed, and corresponding human resource consumption is saved.
A task processing device according to an embodiment of the present description is introduced based on a task processing method corresponding to fig. 1. The task processing device is arranged on the task processing equipment. As shown in fig. 2, the task processing device includes the following modules.
A task parameter extraction module 210, configured to extract task parameters from the target task; the task parameters are used for describing the category and the task quantity of the target task.
A task processing object determining module 220, configured to determine at least two task processing objects corresponding to the target task based on the task parameters; the task processing objects are respectively used for processing tasks of different categories and task volumes.
And a subtask splitting module 230, configured to split the target task into at least two subtasks according to the task parameter and the task processing object.
A sub-task allocating module 240, configured to allocate the sub-tasks to the at least two task processing objects, so that the task processing objects process the corresponding sub-tasks respectively.
Based on the task processing method corresponding to fig. 1, an embodiment of the present specification provides a task processing device. As shown in fig. 3, the task processing device may include a memory and a processor.
In this embodiment, the memory may be implemented in any suitable manner. For example, the memory may be a read-only memory, a mechanical hard disk, a solid state disk, a U disk, or the like. The memory may be used to store computer program instructions.
In this embodiment, the processor may be implemented in any suitable manner. For example, the processor may take the form of, for example, a microprocessor or processor and a computer-readable medium that stores computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, an embedded microcontroller, and so forth. The processor may execute the computer program instructions to perform the steps of: extracting task parameters from the target task; the task parameters are used for describing the category and the task quantity of the target task; determining at least two task processing objects corresponding to the target task based on the task parameters; the task processing objects are respectively used for processing tasks of different categories and task quantities; splitting the target task into at least two subtasks according to the task parameters and the task processing object; and distributing the subtasks to the at least two task processing objects so that the task processing objects respectively process the corresponding subtasks.
It should be noted that the task processing method, the task processing device, and the task processing apparatus may be applied to the technical field of automatic programming, and may also be applied to other technical fields, which are not limited to this.
In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Hardware Description Language), traffic, pl (core universal Programming Language), HDCal (jhdware Description Language), lang, Lola, HDL, laspam, hardward Description Language (vhr Description Language), vhal (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
From the above description of the embodiments, it is clear to those skilled in the art that the present specification can be implemented by software plus the necessary first hardware platform. Based on such understanding, the technical solutions of the present specification may be essentially or partially implemented in the form of software products, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and include instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments of the present specification.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The description is operational with numerous first or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.
This description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
While the specification has been described with examples, those skilled in the art will appreciate that there are numerous variations and permutations of the specification that do not depart from the spirit of the specification, and it is intended that the appended claims include such variations and modifications that do not depart from the spirit of the specification.
Claims (15)
1. A task processing method, comprising:
extracting task parameters from the target task; the task parameters are used for describing the category and the task quantity of the target task;
determining at least two task processing objects corresponding to the target task based on the task parameters; the task processing objects are respectively used for processing tasks of different categories and task quantities;
splitting the target task into at least two subtasks according to the task parameters and the task processing object;
and distributing the subtasks to the at least two task processing objects so that the task processing objects respectively process the corresponding subtasks.
2. The method of claim 1, wherein the target task comprises a task for apportioning resources.
3. The method of claim 2, wherein the resources comprise resources corresponding to at least one of servers, storage, CPUs, network devices, bandwidth, equipment rooms, electricity charges.
4. The method of claim 1, wherein extracting task parameters from the target task comprises:
calling the target task based on general parameters; the general parameters are used for defining the data table type, the data surface layer level structure and the data table elements of the acquired target task.
5. The method of claim 4, wherein the generic parameters comprise at least one of data table definition parameters, data table element parameters, IDC parameters, cost hierarchy parameters.
6. The method of claim 1, wherein the task parameter corresponds to a parameter privilege; the parameter authority is used for limiting a task processing object distributed by a target task; the determining at least two task processing objects corresponding to the target task based on the task parameters includes:
and screening to obtain a task processing object based on the parameter authority.
7. The method of claim 1, wherein the target task comprises a task resource; the splitting the target task into at least two subtasks according to the task parameter and the task processing object includes:
dividing the task resources into subtask resources respectively corresponding to each task processing object according to the task parameters;
and constructing the subtasks based on the subtask resources.
8. The method of claim 7, wherein the dividing the task resources into sub-task resources respectively corresponding to the task processing objects according to the task parameters comprises:
determining a weight value corresponding to each task processing object according to the task parameters;
and dividing the task resources into subtask resources respectively corresponding to each task processing object according to the weight value.
9. The method of claim 1, wherein the task parameters further include task level parameters and task processing logic; the task processing logic is used for representing task relationships among different task hierarchies; the splitting the target task into at least two subtasks according to the task parameter and the task processing object includes:
adjusting a target task to at least one next level intermediate task based on the task processing logic;
repeating the step of adjusting an intermediate task to at least one next level of intermediate tasks based on task processing logic until the adjusted level meets the requirements of the task level parameters;
and acquiring an intermediate task in a hierarchy corresponding to the task hierarchy parameter as a subtask.
10. The method of claim 9, wherein the task processing logic comprises at least one of inherited superior task logic, associated task logic, and inherited causal task logic;
the inheritance superior task logic comprises: directly taking the task of the previous level as the logic of the task of the next level;
the associated task logic comprises: logic for obtaining a next level task based on a previous level task and a corresponding split weight;
the inheritance cause task logic comprises: and acquiring the logic of the task of the next level based on the minimum task unit divided by the target task and the specified number of the minimum task units.
11. The method of claim 9, wherein the task processing logic comprises task processing logic fed back by the user terminal after pushing the target task to the user terminal.
12. The method of claim 1, wherein the splitting the target task into at least two subtasks according to the task parameters and task processing objects comprises:
sending the target task, the task parameter and the task processing object to a manager terminal;
receiving subtask division information fed back by a manager terminal;
splitting the target task into at least two subtasks based on the subtask division information.
13. The method of claim 1, wherein the task processing objects correspond to different user devices; the assigning the subtasks to the at least two task processing objects includes:
and pushing the subtasks to user equipment corresponding to at least two task processing objects so that the user equipment displays the subtasks.
14. A task processing apparatus, comprising:
the task parameter extraction module is used for extracting task parameters from the target task; the task parameters are used for describing the category and the task quantity of the target task;
a task processing object determination module for determining at least two task processing objects corresponding to the target task based on the task parameters; the task processing objects are respectively used for processing tasks of different categories and task quantities;
the subtask splitting module is used for splitting the target task into at least two subtasks according to the task parameters and the task processing object;
and the subtask allocation module is used for allocating the subtasks to the at least two task processing objects so that the task processing objects respectively process the corresponding subtasks.
15. A task processing device comprising a memory and a processor;
the memory to store computer program instructions;
the processor to execute the computer program instructions to implement the steps of: extracting task parameters from the target task; the task parameters are used for describing the category and the task quantity of the target task; determining at least two task processing objects corresponding to the target task based on the task parameters; the task processing objects are respectively used for processing tasks of different categories and task quantities; splitting the target task into at least two subtasks according to the task parameters and the task processing object; and distributing the subtasks to the at least two task processing objects so that the task processing objects respectively process the corresponding subtasks.
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