CN116244324B - Task data relation mining method and device, electronic equipment and storage medium - Google Patents
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Abstract
The embodiment of the invention discloses a task data relation mining method, a device, electronic equipment and a storage medium. The method comprises the following steps: acquiring task data relation mining requirements, and determining a plurality of task data to be processed corresponding to the task data relation mining requirements; constructing an initial task dependency relation set according to task characteristics of each piece of task data to be processed, wherein the initial task dependency relation set comprises a plurality of initial task dependency relations; correcting each initial task dependency relationship based on a preset task dependency relationship correction rule to obtain a corrected task dependency relationship; and mining the task dependency relationship meeting the task data relationship mining requirement in each corrected task dependency relationship as a target task dependency relationship. The technical scheme of the embodiment of the invention realizes more accurate and comprehensive excavation of the dependency relationship between the task data, and further improves the excavation efficiency.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a task data relationship mining method, a device, an electronic device, and a storage medium.
Background
For task data relation mining, in the related art, task data is generally analyzed manually, so that dependency relations among the task data are mined. However, the mining method cannot comprehensively mine the dependency relationship between task data, and has the technical problems of poor accuracy and low efficiency.
Disclosure of Invention
The invention provides a task data relation mining method, a device, electronic equipment and a storage medium, which aim to mine the dependency relation among task data more accurately and comprehensively and further improve the mining efficiency.
According to an aspect of the present invention, there is provided a task data relationship mining method, including:
acquiring task data relation mining requirements, and determining a plurality of task data to be processed corresponding to the task data relation mining requirements;
constructing an initial task dependency relation set according to task characteristics of each piece of task data to be processed, wherein the initial task dependency relation set comprises a plurality of initial task dependency relations;
Correcting each initial task dependency relationship based on a preset task dependency relationship correction rule to obtain a corrected task dependency relationship;
and mining the task dependency relationship meeting the task data relationship mining requirement in each corrected task dependency relationship as a target task dependency relationship.
Optionally, the constructing an initial task dependency relationship set according to the task characteristics of each task data to be processed includes:
determining the association relation between the task data to be processed according to the task execution time of the task data to be processed;
and constructing an initial task dependency relation set based on each association relation.
Optionally, the correcting each initial task dependency relationship based on the preset task dependency relationship correction rule includes:
determining a task to be processed corresponding to each initial task dependency relationship, wherein the task to be processed comprises a first task and a second task, and the first ending time of the first task is earlier than the second starting time of the second task;
and correcting the initial task dependency relationship between the first task and the second task under the condition that the interval duration of the first ending time and the second starting time exceeds the preset interval duration.
Optionally, the correcting each initial task dependency relationship based on the preset task dependency relationship correction rule includes:
determining a first dependent resource of the first task and a second dependent resource of the second task;
and correcting the initial task dependency relationship between the first task and the second task according to the resource relationship between the first dependent resource and the second dependent resource.
Optionally, in each of the corrected task dependency relationships, a task dependency relationship meeting the task data relationship mining requirement is mined, and the task dependency relationship is used as a target task dependency relationship, including:
obtaining a scoring value of each corrected task dependency relationship;
and mining task dependency relations meeting the task data relation mining requirements based on the grading values, and taking the task dependency relations as target task dependency relations.
Optionally, after the determining a plurality of task data to be processed corresponding to the task data relation mining requirement, the method further includes:
performing data preprocessing on the task data to be processed according to each task data to be processed; the data preprocessing comprises at least one of abnormal task data filtering processing, task data sorting processing to be processed and task data format processing to be processed.
Optionally, the task data to be processed is batch task data, and the batch task data includes at least one subtask data.
According to another aspect of the present invention, a task data relationship mining apparatus is provided. The device comprises:
the task data determining module is used for acquiring task data relation mining requirements and determining a plurality of task data to be processed corresponding to the task data relation mining requirements;
the dependency relationship determining module is used for constructing an initial task dependency relationship set according to the task characteristics of each piece of task data to be processed, wherein the initial task dependency relationship set comprises a plurality of initial task dependency relationships;
the dependency relation correction module is used for correcting each initial task dependency relation based on a preset task dependency relation correction rule to obtain a corrected task dependency relation;
and the dependency relation mining module is used for mining the task dependency relation meeting the task data relation mining requirement in each corrected task dependency relation as a target task dependency relation.
According to another aspect of the present invention, there is provided an electronic apparatus including:
At least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform any one of the task data relationship mining methods of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to implement any of the task data relationship mining methods of the present invention when executed.
According to the technical scheme, the task data relation mining requirements are acquired, and a plurality of task data to be processed corresponding to the task data relation mining requirements are determined; constructing an initial task dependency relation set according to task characteristics of each piece of task data to be processed, wherein the initial task dependency relation set comprises a plurality of initial task dependency relations; correcting each initial task dependency relationship based on a preset task dependency relationship correction rule to obtain a corrected task dependency relationship; and mining the task dependency relationship meeting the task data relationship mining requirement in each corrected task dependency relationship as a target task dependency relationship. According to the technical scheme, the dependency relationship among the task data is mined more accurately and comprehensively, and the mining efficiency is further improved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a task data relation mining method according to a first embodiment of the present invention;
fig. 2 is a schematic structural diagram of a task data relationship mining apparatus according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It will be appreciated that prior to using the technical solutions disclosed in the embodiments of the present disclosure, the user should be informed and authorized of the type, usage range, usage scenario, etc. of the personal information related to the present disclosure in an appropriate manner according to the relevant legal regulations.
For example, in response to receiving an active request from a user, a prompt is sent to the user to explicitly prompt the user that the operation it is requesting to perform will require personal information to be obtained and used with the user. Thus, the user can autonomously select whether to provide personal information to software or hardware such as an electronic device, an application program, a server or a storage medium for executing the operation of the technical scheme of the present disclosure according to the prompt information.
As an alternative but non-limiting implementation, in response to receiving an active request from a user, the manner in which the prompt information is sent to the user may be, for example, a popup, in which the prompt information may be presented in a text manner. In addition, a selection control for the user to select to provide personal information to the electronic device in a 'consent' or 'disagreement' manner can be carried in the popup window.
It will be appreciated that the above-described notification and user authorization process is merely illustrative and not limiting of the implementations of the present disclosure, and that other ways of satisfying relevant legal regulations may be applied to the implementations of the present disclosure.
It will be appreciated that the data (including but not limited to the data itself, the acquisition or use of the data) involved in the present technical solution should comply with the corresponding legal regulations and the requirements of the relevant regulations.
Example 1
Fig. 1 is a flow chart of a task data relation mining method according to a first embodiment of the present invention, where the method may be applied to the case of mining the dependency relation between task data, and the method may be performed by a task data relation mining apparatus, and the task data relation mining apparatus may be implemented in the form of hardware and/or software, and the task data relation mining apparatus may be configured in an electronic device such as a computer or a server.
As shown in fig. 1, the method of the present embodiment includes:
s110, acquiring task data relation mining requirements, and determining a plurality of task data to be processed corresponding to the task data relation mining requirements.
The task data relation mining requirement can be understood as a preset requirement for mining the relation between task data. Task data relationship mining requirements may include a number of task dependencies that need to be mined. There are various ways to obtain the task data relationship mining requirements. For example, task relationship mining requirements uploaded by a user may be received; or, a configuration operation acting on a preset configuration item may be received, and a task data relationship mining requirement is generated based on the configuration operation, where the preset configuration item may be understood as a preset configuration item for mining the task data relationship. The requirement description of the task data relationship mining requirement may include the task data to be processed. Task data to be processed may be understood as task data that is needed for task data relationship mining. Alternatively, the task data to be processed may be batch task data. In an embodiment of the present invention, the batch task data may include at least one sub-task data. That is, one or more subtask data may be included in a single batch of task data. In practical applications, a single batch of task data typically includes multiple sub-task data.
Specifically, task data relationship mining requirements are obtained. After the task data relation mining requirements are obtained, the task data relation mining requirements can be analyzed. And further, a demand description of the task data relationship mining demand can be determined. Therefore, a plurality of task data to be processed, which need to be subjected to task data relation mining, can be determined based on the requirement description.
In an embodiment of the present invention, after the determining a plurality of task data to be processed corresponding to the task data relation mining requirement, the method may further include: and carrying out data preprocessing on the task data to be processed according to each task data to be processed. The data preprocessing may be used to preprocess task data to be processed. The data preprocessing comprises at least one of abnormal task data filtering processing, task data sorting processing to be processed and task data format processing to be processed. In the embodiment of the invention, the method has the advantage that the effectiveness of the task data to be processed can be ensured by filtering the abnormal task data of the task data to be processed. The aim of carrying out the sorting processing of the task data to be processed on the task data to be processed is to keep the sequence of executing the task data to be processed in the time dimension so as to reduce the difficulty of task data relation mining. The processing of the format of the task data to be processed can enable the task data to be processed to have a uniform format.
In the embodiment of the present invention, the manner of acquiring the task data to be processed may specifically be that the task data to be processed is read from a database for storing the task data to be processed. Alternatively, the database may be a local database and/or a remote database.
S120, constructing an initial task dependency relation set according to task characteristics of each piece of task data to be processed, wherein the initial task dependency relation set comprises a plurality of initial task dependency relations.
The task features of the task data to be processed may be used to characterize the data features of the task data to be processed. The initial task dependency relationship set can be a set constructed based on task characteristics of each task data to be processed, and can be used for storing dependency relationships among the task data to be processed. The initial task dependency relationship may be understood as a dependency relationship between each of the task data to be processed stored in the initial task dependency set. Alternatively, the initial set of task dependencies may be presented in the form of a graph. At this time, the nodes in the graph may be task data to be processed. The links between nodes may be used to represent dependencies between task data to be processed. That is, the links between nodes can be understood as initial task dependencies.
Specifically, according to the task characteristics of each piece of task data to be processed, determining the dependency relationship among the pieces of task data to be processed, and taking each dependency relationship as an initial task dependency relationship. And then the initial task dependency relationship set can be obtained based on each initial task dependency relationship.
In the embodiment of the present invention, between the construction of the initial task dependency relationship set according to the task characteristics of each task data to be processed, the method further includes: after determining each task data to be processed, feature extraction can be performed on each task data to be processed. Thus, the task characteristics of each task data to be processed can be obtained.
In the embodiment of the present invention, the constructing an initial task dependency relationship set according to the task characteristics of each task data to be processed may include: determining the association relation between the task data to be processed according to the task execution time of the task data to be processed; and constructing an initial task dependency relation set based on each association relation.
The task execution time is understood to be the execution time of the task data to be processed. In the embodiment of the invention, the task execution time can comprise a task start execution time and a task end execution time. The association relationship between the task data to be processed can be parallel relationship or serial relationship.
Specifically, after the task data to be processed are obtained, the task execution time of the task data to be processed can be determined. And further, the association relationship between the task data to be processed can be determined according to the task execution time of the task data to be processed. The association relationship between the task data to be processed can be a parallel relationship or a serial relationship. And then the initial task dependency relation set can be obtained according to the parallel relation or serial relation among the task data to be processed.
Optionally, in an embodiment of the present invention, the constructing an initial task dependency relationship set according to task characteristics of each piece of task data to be processed may include: mining each task data to be processed based on an algorithm model for data mining; thus, an initial set of task dependencies may be obtained. Alternatively, the algorithm model for data mining may be a Belta algorithm model.
S130, correcting each initial task dependency relationship based on a preset task dependency relationship correction rule to obtain a corrected task dependency relationship.
In the embodiment of the present invention, the correcting the initial task dependency relationship based on the preset task dependency relationship correction rule includes: determining a task to be processed corresponding to each initial task dependency relationship, wherein the task to be processed comprises a first task and a second task, and the first ending time of the first task is earlier than the second starting time of the second task; and correcting the initial task dependency relationship between the first task and the second task under the condition that the interval duration of the first ending time and the second starting time exceeds the preset interval duration.
The first task and the second task can be understood as tasks to be processed having an initial task dependency relationship. The first end time may be understood as a task end execution time of the first task. The second start time may be understood as a task start execution time of the second task. In an embodiment of the invention, the first end time of the first task is earlier than the second start time of the second task. The preset interval period may be a preset time, and may be set according to actual requirements, for example, 10 seconds, 30 seconds, or 3 minutes, which is not specifically limited herein.
In the embodiment of the present invention, when the interval duration of the first ending time and the second starting time exceeds the preset interval duration, correcting the initial task dependency relationship with the first task and the second task may include: and under the condition that the interval duration of the first ending time and the second starting time exceeds the preset interval duration, the initial task dependency relationship with the first task and the second task can be canceled or deleted.
It may be appreciated that, in a case where the interval duration of the first ending time and the second starting time does not exceed the preset interval duration, correcting the initial task dependency relationship with the first task and the second task may include: and under the condition that the interval duration of the first ending time and the second starting time does not exceed the preset interval duration, establishing an initial task dependency relationship of the first task and the second task.
Optionally, the correcting the initial task dependency relationship between the first task and the second task may include: determining a first dependent resource of the first task and a second dependent resource of the second task; and correcting the initial task dependency relationship between the first task and the second task according to the resource relationship between the first dependent resource and the second dependent resource.
Wherein a first dependent resource may be understood as a resource on which the first task is executed. The second dependent resource may be understood as a resource on which the second task is performed. The number of first dependent resources may be one, two or more than two. The number of second dependent resources may be one, two or more than two. In an embodiment of the present invention, the first dependent resource and the second dependent resource may be all the same, partially the same, or all different.
In one embodiment, modifying the initial task dependency relationship with the first task and the second task according to the resource relationship of the first dependent resource and the second dependent resource may include: if the first dependent resource and the second dependent resource are the same, if the initial task dependency relationship between the first task and the second task does not exist, the dependency relationship between the first task and the second task can be established.
In another embodiment, modifying the initial task dependency relationship with the first task and the second task according to the resource relationship of the first dependent resource and the second dependent resource may include: when the first dependent resource and the second dependent resource are the same, if there is no initial task dependency relationship between the first task and the second task, the job units in the first task and the second task may be determined respectively. Further, the job units in the first task and the job units in the second task corresponding to the common dependent resource can be provided. Thus, a dependency relationship of the job units in the first task and the job units in the second task can be established.
The job unit can be understood as a subtask in the first or second task. Alternatively, the first task may be a batch task and the second task may be a batch task. A common dependent resource may be understood as the same resource in the resources that are relied upon to perform a first task and the resources that are relied upon to perform a second task. Illustratively, the first dependent resource may include resource 1, resource 2, and resource 3, and the second dependent resource may include resource 2, resource 4, resource 5, and resource 6. At this point, the common dependent resource may be resource 2.
In another embodiment, modifying the initial task dependency relationship with the first task and the second task according to the resource relationship of the first dependent resource and the second dependent resource may include: if the first dependency resource and the second dependency are all different, the dependency relationship between the first task and the second task may be deleted if there is an initial task dependency relationship between the first task and the second task.
S140, mining task dependency relationships meeting the task data relationship mining requirements in the corrected task dependency relationships as target task dependency relationships.
The target task dependency relationship can be understood as a task dependency relationship meeting the task data relationship mining requirement.
Optionally, in each of the corrected task dependency relationships, a task dependency relationship meeting the task data relationship mining requirement is mined, and the task dependency relationship is used as a target task dependency relationship, including: obtaining a scoring value of each corrected task dependency relationship; and mining task dependency relations meeting the task data relation mining requirements based on the grading values, and taking the task dependency relations as target task dependency relations.
The scoring value of the corrected task dependency relationship may be a scoring value obtained by scoring the corrected task dependency relationship. The scoring value of the corrected task dependency may be used to reflect the accuracy of the corrected task dependency.
Specifically, the scoring value of each corrected task dependency relationship is obtained. And then, based on each scoring value, the task dependency relationship meeting the task data relationship mining requirement can be mined from the corrected task dependency relationship. Therefore, the mined task dependency relationship can be obtained, and the mined task dependency relationship is used as a target task dependency relationship.
In the embodiment of the invention, the task data relation mining requirement can be the number of task dependency relations to be mined; based on each grading value, mining task dependency relationships meeting the task data relationship mining requirements as target task dependency relationships, which can include: and sorting the corrected task dependency relations according to the order of the grading values from large to small. And then a sequencing result can be obtained. After the sorting result is obtained, selecting the task dependency relationship with the number of task dependency relationships to be mined as a target task dependency relationship according to the sorting result.
According to the technical scheme, the task data relation mining requirements are acquired, and a plurality of task data to be processed corresponding to the task data relation mining requirements are determined; constructing an initial task dependency relation set according to task characteristics of each piece of task data to be processed, wherein the initial task dependency relation set comprises a plurality of initial task dependency relations; correcting each initial task dependency relationship based on a preset task dependency relationship correction rule to obtain a corrected task dependency relationship; and mining the task dependency relationship meeting the task data relationship mining requirement in each corrected task dependency relationship as a target task dependency relationship. According to the technical scheme, the dependency relationship among the task data is mined more accurately and comprehensively, and the mining efficiency is further improved.
Example two
Fig. 2 is a schematic structural diagram of a task data relationship mining apparatus according to a second embodiment of the present invention. As shown in fig. 2, the apparatus includes: a task data determination module 210, a dependency determination module 220, a dependency correction module 230, and a dependency mining module 240.
The task data determining module 210 is configured to obtain task data relation mining requirements, and determine a plurality of task data to be processed corresponding to the task data relation mining requirements; the dependency relationship determining module 220 is configured to construct an initial task dependency relationship set according to task characteristics of each task data to be processed, where the initial task dependency relationship set includes a plurality of initial task dependency relationships; the dependency relation correction module 230 is configured to correct each initial task dependency relation based on a preset task dependency relation correction rule, so as to obtain a modified task dependency relation; the dependency relation mining module 240 is configured to mine, as a target task dependency relation, a task dependency relation that meets the task data relation mining requirement, from the corrected task dependency relations.
According to the technical scheme, a task data relation mining requirement is acquired through a task data determining module, and a plurality of task data to be processed corresponding to the task data relation mining requirement are determined; constructing an initial task dependency relationship set according to task characteristics of each piece of task data to be processed by a dependency relationship determining module, wherein the initial task dependency relationship set comprises a plurality of initial task dependency relationships; correcting each initial task dependency relationship based on a preset task dependency relationship correction rule through a dependency relationship correction module to obtain a corrected task dependency relationship; and excavating a task dependency relationship meeting the task data relationship excavation requirement in each corrected task dependency relationship by a dependency relationship excavation module to serve as a target task dependency relationship. According to the technical scheme, the dependency relationship among the task data is mined more accurately and comprehensively, and the mining efficiency is further improved.
Optionally, the dependency relationship determining module 220 is configured to determine an association relationship between the task data to be processed according to a task execution time of the task data to be processed; and constructing an initial task dependency relation set based on each association relation.
Optionally, the dependency correction module 230 includes a relationship correction unit; wherein the relation correction unit is used for:
determining a task to be processed corresponding to each initial task dependency relationship, wherein the task to be processed comprises a first task and a second task, and the first ending time of the first task is earlier than the second starting time of the second task; and correcting the initial task dependency relationship between the first task and the second task under the condition that the interval duration of the first ending time and the second starting time exceeds the preset interval duration.
Optionally, the relationship correction unit is specifically configured to determine a first dependent resource of the first task and a second dependent resource of the second task, and correct an initial task dependency relationship between the first task and the second task according to a resource relationship between the first dependent resource and the second dependent resource.
Optionally, the dependency relation mining module 240 is configured to obtain a scoring value of each of the modified task dependency relations; and mining task dependency relations meeting the task data relation mining requirements based on the grading values, and taking the task dependency relations as target task dependency relations.
Optionally, the device further comprises a task data preprocessing module; the task data preprocessing module is used for:
after the plurality of task data to be processed corresponding to the task data relation mining requirements are determined, carrying out data preprocessing on the task data to be processed according to each task data to be processed; the data preprocessing comprises at least one of abnormal task data filtering processing, task data sorting processing to be processed and task data format processing to be processed.
Optionally, the task data to be processed is batch task data, and the batch task data includes at least one subtask data.
The task data relation mining device provided by the embodiment of the invention can execute the task data relation mining method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
It should be noted that, each unit and module included in the task data relation mining apparatus are only divided according to the functional logic, but not limited to the above division, so long as the corresponding functions can be implemented; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the embodiments of the present invention.
Example III
Fig. 3 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 3, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the task data relationship mining method.
In some embodiments, the task data relationship mining method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more of the steps of the task data relationship mining method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the task data relationship mining method in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (8)
1. A method for mining task data relationships, comprising:
acquiring task data relation mining requirements, and determining a plurality of task data to be processed corresponding to the task data relation mining requirements;
constructing an initial task dependency relation set according to task characteristics of each piece of task data to be processed, wherein the initial task dependency relation set comprises a plurality of initial task dependency relations;
Correcting each initial task dependency relationship based on a preset task dependency relationship correction rule to obtain a corrected task dependency relationship;
in each corrected task dependency relationship, mining a task dependency relationship meeting the task data relationship mining requirement as a target task dependency relationship;
the correcting the initial task dependency relationship based on the preset task dependency relationship correction rule includes:
determining a task to be processed corresponding to each initial task dependency relationship, wherein the task to be processed comprises a first task and a second task, and the first ending time of the first task is earlier than the second starting time of the second task;
correcting the initial task dependency relationship between the first task and the second task under the condition that the interval duration of the first ending time and the second starting time exceeds the preset interval duration;
the correcting the initial task dependency relation between the first task and the second task comprises the following steps:
determining a first dependent resource of the first task and a second dependent resource of the second task;
Correcting the initial task dependency relationship between the first task and the second task according to the resource relationship between the first dependent resource and the second dependent resource;
the correcting the initial task dependency relationship with the first task and the second task according to the resource relationship of the first dependent resource and the second dependent resource includes:
if the initial task dependency relationship between the first task and the second task does not exist under the condition that the first dependent resource and the second dependent resource are all the same, establishing the dependency relationship between the first task and the second task;
if the first dependent resource and the second dependent resource are partially the same, if an initial task dependency relationship between the first task and the second task does not exist, determining a job unit in the first task and a job unit in the second task respectively, and establishing a dependency relationship between the job unit in the first task and the job unit in the second task corresponding to a common dependent resource;
And if the initial task dependency relationship between the first task and the second task exists under the condition that the first dependent resource and the second dependent resource are all different, deleting the dependency relationship between the first task and the second task.
2. The method of claim 1, wherein constructing an initial set of task dependencies based on task characteristics of each of the task data to be processed comprises:
determining the association relation between the task data to be processed according to the task execution time of the task data to be processed;
and constructing an initial task dependency relation set based on each association relation.
3. The method according to claim 1, wherein mining, as a target task dependency, a task dependency that meets the task data relationship mining requirement from the corrected task dependencies, includes:
obtaining a scoring value of each corrected task dependency relationship;
and mining task dependency relations meeting the task data relation mining requirements based on the grading values, and taking the task dependency relations as target task dependency relations.
4. The method of claim 1, wherein after said determining a plurality of pending task data corresponding to said task data relationship mining requirements, the method further comprises:
Performing data preprocessing on the task data to be processed according to each task data to be processed; the data preprocessing comprises at least one of abnormal task data filtering processing, task data sorting processing to be processed and task data format processing to be processed.
5. A method as defined in claim 1, wherein the task data to be processed is batch task data, the batch task data including at least one sub-task data.
6. A task data relationship mining apparatus, comprising:
the task data determining module is used for acquiring task data relation mining requirements and determining a plurality of task data to be processed corresponding to the task data relation mining requirements;
the dependency relationship determining module is used for constructing an initial task dependency relationship set according to the task characteristics of each piece of task data to be processed, wherein the initial task dependency relationship set comprises a plurality of initial task dependency relationships;
the dependency relation correction module is used for correcting each initial task dependency relation based on a preset task dependency relation correction rule to obtain a corrected task dependency relation;
The dependency relation mining module is used for mining task dependency relations meeting the task data relation mining requirements in each corrected task dependency relation as target task dependency relations;
the dependency relation correction module comprises a relation correction unit;
the relation correction unit is used for determining a task to be processed corresponding to each initial task dependency relation, wherein the task to be processed comprises a first task and a second task, and the first ending time of the first task is earlier than the second starting time of the second task; correcting the initial task dependency relationship between the first task and the second task under the condition that the interval duration of the first ending time and the second starting time exceeds the preset interval duration;
the relation correction unit is specifically configured to determine a first dependent resource of the first task and a second dependent resource of the second task, and correct an initial task dependency relation between the first task and the second task according to a resource relation between the first dependent resource and the second dependent resource;
The correcting the initial task dependency relationship with the first task and the second task according to the resource relationship of the first dependent resource and the second dependent resource includes:
if the initial task dependency relationship between the first task and the second task does not exist under the condition that the first dependent resource and the second dependent resource are all the same, establishing the dependency relationship between the first task and the second task;
if the first dependent resource and the second dependent resource are partially the same, if an initial task dependency relationship between the first task and the second task does not exist, determining a job unit in the first task and a job unit in the second task respectively, and establishing a dependency relationship between the job unit in the first task and the job unit in the second task corresponding to a common dependent resource;
and if the initial task dependency relationship between the first task and the second task exists under the condition that the first dependent resource and the second dependent resource are all different, deleting the dependency relationship between the first task and the second task.
7. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the task data relationship mining method of any of claims 1-5.
8. A computer readable storage medium storing computer instructions for causing a processor to implement the task data relationship mining method of any one of claims 1-5 when executed.
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