CN112799804A - Task management method and system - Google Patents
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Abstract
The application discloses a task management method and a task management system. The task management method comprises the following steps: a new construction step: newly establishing an algorithm and a task; selecting: selecting a task type, selecting the algorithm according to the task type, configuring parameters of the algorithm, selecting a task execution strategy, and selecting the execution time of the task execution strategy; the execution steps are as follows: and after the task is executed according to the task execution strategy, acquiring an execution result of the task. The invention provides a task management method and a task management system, which can monitor the running state of each task instance, track various exceptions encountered in the task execution process through logs, and meanwhile, the invention is applied to various files of algorithm tasks, can realize centralized management of algorithm script files, algorithm model files and the like, can effectively solve the problem of the whole-flow integrated management of the algorithm model tasks, and is convenient for algorithm developers to concentrate on algorithm research and development work.
Description
Technical Field
The present application relates to the technical field of algorithm model management, and in particular, to a task management method and system.
Background
Along with the continuous development of artificial intelligence technology, algorithm models are more and more diversified, the diversification is reflected in the diversity of the algorithm, such as various machine learning algorithms, deep learning algorithms and the like, and in the diversity of application scenes of the algorithm, for the same algorithm, the algorithm models generated under different application scenes are different, and further, the effect of the algorithm models generated by multiple training of the same algorithm is greatly different. Although many problems are solved in the field of artificial intelligence, the diversity of the algorithm model brings much inconvenience to algorithm developers. In the research and development process, multiple algorithms are executed for multiple times, so that the problems of resource preemption, environmental pollution, difficulty in searching model files, unknown algorithm running states and the like are always encountered, how to ensure the independent running of algorithm tasks, the centralized management of the model files and the transparence of the algorithm task running states are ensured, the whole-flow integrated management of the algorithm model tasks is solved, developers can conveniently concentrate on the algorithm research and development work, and the method is a technical problem which needs to be solved urgently at present.
Therefore, aiming at the current situation, the invention provides a task management method and a task management system, the invention realizes the monitoring of the running state of each task instance, tracks various exceptions encountered in the task execution process through logs, is applied to various files of algorithm tasks, realizes the centralized management of algorithm script files, algorithm model files and the like, is applied to various customized task execution requirements, provides three task execution strategies, can effectively solve the problem of the whole-flow integrated management of the algorithm model tasks, and is convenient for algorithm developers to concentrate on the algorithm research and development work.
Disclosure of Invention
The embodiment of the application provides a task management method and a task management system, which are used for at least solving the problem of subjective factor influence in the related technology.
The invention provides a task management method, which comprises the following steps:
a new construction step: newly establishing an algorithm and a task;
selecting: selecting a task type, selecting the algorithm according to the task type, configuring parameters of the algorithm, selecting a task execution strategy, and selecting the execution time of the task execution strategy;
the execution steps are as follows: and after the task is executed according to the task execution strategy, acquiring an execution result of the task.
In the task management method, the newly creating step includes preparing a script file and a configuration file of the algorithm, and newly creating the algorithm and the task.
In the task management method, the selecting step includes, when the task type selects the predicted task, selecting an algorithm model first, then selecting the algorithm, and configuring the parameters of the algorithm, then selecting the task execution strategy, and then selecting the execution time of the task execution strategy, when the task type selects the training task, directly selecting the algorithm, and configuring the parameters of the algorithm, then selecting the task execution strategy, and then selecting the execution time of the task execution strategy.
In the task management method, the task execution policy includes immediate execution, timed execution and timed and repeated execution.
In the task management method, the executing step includes obtaining a prediction result of the prediction task when the prediction task is executed according to the task execution strategy, and obtaining and issuing the algorithm model when the training task is executed according to the task execution strategy.
The present invention further provides a task management system, which is suitable for the task management method described above, and the task management system includes:
newly building a unit: newly establishing an algorithm and a task;
a selection unit: selecting a task type, selecting the algorithm according to the task type, configuring parameters of the algorithm, selecting a task execution strategy, and selecting the execution time of the task execution strategy;
an execution unit: and after the task is executed according to the task execution strategy, acquiring an execution result of the task.
In the task management system, the new creating unit prepares a script file and a configuration file of the algorithm, and creates the algorithm and the task.
In the task management system, when the task type selects the predicted task, the selection unit selects the algorithm model first, then selects the algorithm, configures the parameters of the algorithm, then selects the task execution strategy, and selects the execution time of the task execution strategy, when the task type selects the training task, directly selects the algorithm, configures the parameters of the algorithm, then selects the task execution strategy, and selects the execution time of the task execution strategy.
In the task management system, the task execution policy includes immediate execution, timed execution and timed and repeated execution.
In the task management system, the execution unit obtains the prediction result of the prediction task when executing the prediction task according to the task execution strategy, and obtains the algorithm model and issues the algorithm model when executing the training task according to the task execution strategy.
Compared with the prior art, the invention provides a task management method and a task management system, the invention realizes the monitoring of the running state of each task instance, tracks various exceptions encountered in the task execution process through logs, is applied to various files of algorithm tasks, realizes the centralized management of algorithm script files, algorithm model files and the like, is applied to various customized task execution requirements, provides three task execution strategies, can effectively solve the problem of the whole-flow integrated management of the algorithm model tasks, and is convenient for algorithm developers to concentrate on the algorithm research and development work.
The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below to provide a more thorough understanding of the application.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a flow diagram of a task management method according to an embodiment of the application;
FIG. 2 is a system architecture framework diagram according to an embodiment of the present application;
FIG. 3 is a block diagram of a flow step according to an embodiment of the present application;
FIG. 4 is a schematic diagram of task performance according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a task management system according to the present invention;
fig. 6 is a frame diagram of an electronic device according to an embodiment of the present application.
Wherein the reference numerals are:
newly building a unit: 51;
a selection unit: 52;
an execution unit: 53;
81: a processor;
82: a memory;
83: a communication interface;
80: a bus.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments provided in the present application without any inventive step are within the scope of protection of the present application.
It is obvious that the drawings in the following description are only examples or embodiments of the present application, and that it is also possible for a person skilled in the art to apply the present application to other similar contexts on the basis of these drawings without inventive effort. Moreover, it should be appreciated that such a development effort might be complex and tedious, but would nevertheless be a routine undertaking of design, fabrication, and manufacture for those of ordinary skill having the benefit of this disclosure, and thus should not be construed as a limitation of this disclosure.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of ordinary skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms referred to herein shall have the ordinary meaning as understood by those of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar words throughout this application are not to be construed as limiting in number, and may refer to the singular or the plural. The present application is directed to the use of the terms "including," "comprising," "having," and any variations thereof, which are intended to cover non-exclusive inclusions; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to the listed steps or elements, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. Reference to "connected," "coupled," and the like in this application is not intended to be limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as referred to herein means two or more. "and/or" describes an association relationship of associated objects, meaning that three relationships may exist, for example, "A and/or B" may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. Reference herein to the terms "first," "second," "third," and the like, are merely to distinguish similar objects and do not denote a particular ordering for the objects.
The present invention is based on algorithmic model task management, which is briefly described below.
The model is an object (the object is not equal to an object, and is not limited to a solid body and a virtual body, and is not limited to a plane and a solid body) which forms an expression purpose for objectively describing a morphological structure through subjective consciousness by means of a solid body or a virtual body. Model ≠ Commodity. Any object is defined as a model in the research and development process before the commodity, and when the model and the specification are defined and matched with corresponding prices, the model is presented in a commodity form. In a broad sense: if one thing can change as another thing changes, then that thing is a model of another thing. The model is used for expressing the properties of different concepts, one concept can enable a plurality of models to be changed to different degrees, but the properties of one concept can be expressed by only a few models, so that the expression form of the properties of one concept can be changed by referring to different models. The model forming forms are classified into a physical model (a physical form concept physical object having a volume and a weight) and a virtual model (a form formed by a digital representation using electronic data and other effective representations). The model display forms are divided into a plane display and a stereo display (the mark is one of the plane display, such as an example drawing of an album). The physical model is divided into a static model (the physical state is relatively static, a power system without energy conversion is arranged in the physical model, the integrity of the structure and the body structure is not expressed under the external acting force), a power-assisted model (the static model is used as the basis, the self expression structure is not changed under the action of external kinetic energy, and the connection relation of an object structure is detected through physical movement) and a dynamic model (the kinetic energy can be generated through an energy conversion mode, the dynamic conversion system is arranged in the self structure, and the relative continuous physical movement form is expressed in the energy conversion process). The virtual model is divided into a virtual static model, a virtual dynamic model and a virtual fantasy model. Mathematical models a class of models described in a mathematical language. The mathematical model may be one or a set of algebraic, differential, integral or statistical equations, or some suitable combination thereof, by which the interrelationship or causal relationship between the system variables is described quantitatively or qualitatively. In addition to mathematical models described by equations, there are also models described by other mathematical tools, such as algebra, geometry, topology, mathematical logic, etc. It should be noted that the mathematical model describes the behavior and characteristics of the system and not the actual structure of the system. The physical model is also called a physical model, and can be divided into a physical model and an analog model. Physical model: the real object which is manufactured according to the similarity theory and is scaled down (can be enlarged or the same as the original system in size) of the original system, such as an airplane model in a wind tunnel experiment, a hydraulic system experiment model, a building model, a ship model and the like. An analogy model: in systems in different physical fields (mechanical, electrical, thermal, hydrodynamic, etc.) the respective variables sometimes follow the same law, from which analogous and analogical models with completely different physical meanings can be produced. For example, the pressure response of a pneumatic system consisting of a throttle valve and a gas capacitor under certain conditions has a similar law to the output voltage characteristic of a circuit consisting of a resistor and a capacitor, so that the pneumatic system can be simulated by a circuit which is relatively easy to experiment. Among mathematics and computer science, algorithms (algorithms) are a specific step of computation, commonly used for computation, data processing and automatic reasoning. Precisely, an algorithm is an efficient method expressed as a long list of finite lengths. The algorithm should contain clearly defined instructions for computing the function. The algorithm classification can be classified according to the algorithm design principle, the specific application of the algorithm and other characteristics. An Algorithm (Algorithm) is an accurate and complete description of a problem solving scheme, is a series of clear instructions for solving a problem, and represents a strategy mechanism for describing the problem solving by using a systematic method. The term of algorithm classification in relevant research and scientific literature at home and abroad is not clearly defined, and the algorithm classification is simple and can be classified according to the algorithm design principle, the specific application of the algorithm and other characteristics. The algorithms can be classified into basic algorithms or classified according to specific application fields, and in machine learning, the algorithms are often classified into supervised learning algorithms, unsupervised learning algorithms and semi-supervised learning algorithms according to learning modes. The classification is carried out according to the algorithm of the graph theory, and the algorithm can be divided into Huffman coding, tree traversal, shortest path algorithm, minimum spanning tree algorithm, minimum tree graph, network flow algorithm and matching algorithm.
Along with the continuous development of artificial intelligence technology, algorithm models are more and more diversified, the diversification is reflected in the diversity of the algorithm, such as various machine learning algorithms, deep learning algorithms and the like, and in the diversity of application scenes of the algorithm, for the same algorithm, the algorithm models generated under different application scenes are different, and further, the effect of the algorithm models generated by multiple training of the same algorithm is greatly different. Although many problems are solved in the field of artificial intelligence, the diversity of the algorithm model brings much inconvenience to algorithm developers. In the research and development process, multiple algorithms are executed for multiple times, so that the problems of resource preemption, environmental pollution, difficulty in searching model files, unknown algorithm running states and the like are always encountered, how to ensure the independent running of algorithm tasks, the centralized management of the model files and the transparence of the algorithm task running states are ensured, the whole-flow integrated management of the algorithm model tasks is solved, developers can conveniently concentrate on the algorithm research and development work, and the method is a technical problem which needs to be solved urgently at present.
The invention provides a task management method and a system, which realize the monitoring of the running state of each task instance, track various exceptions encountered in the task execution process through logs, are applied to various files of algorithm tasks, realize the centralized management of algorithm script files, algorithm model files and the like, are applied to various customized task execution requirements, provide three task execution strategies, can effectively solve the problem of the whole-flow integrated management of the algorithm model tasks, and facilitate the algorithm developers to concentrate on the algorithm research and development work.
The following describes embodiments of the present application with algorithmic model task management as an example.
Example one
The embodiment provides a task management method. Referring to fig. 1-3, fig. 1 is a flowchart illustrating a task management method according to an embodiment of the present disclosure; FIG. 2 is a system architecture framework diagram according to an embodiment of the present application; fig. 3 is a flowchart of a flowchart step framework according to an embodiment of the present application, and as shown in the figure, the task management method includes the following steps:
new step S1: newly establishing an algorithm and a task;
selection step S2: selecting a task type, selecting the algorithm according to the task type, configuring parameters of the algorithm, selecting a task execution strategy, and selecting the execution time of the task execution strategy;
step S3 is executed: and after the task is executed according to the task execution strategy, acquiring an execution result of the task.
In an embodiment, the new creating step S1 includes preparing a script file and a configuration file of the algorithm, and creating the algorithm and the task.
In specific implementation, the system architecture framework diagram is shown in fig. 2, wherein the algorithm management module is used for uploading, displaying, modifying and deleting the algorithm. The uploading of the algorithm includes two parts, namely a script file and a configuration file in the new step S1, where the script file includes all codes required for running the algorithm, and the configuration file includes fixed configuration parameters required for running the algorithm script.
In an embodiment, the selecting step S2 includes, when the task type selects a predicted task, selecting an algorithm model first, then selecting the algorithm, configuring the parameters of the algorithm, then selecting the task execution policy, and selecting the execution time of the task execution policy, when the task type selects a training task, directly selecting the algorithm, configuring the parameters of the algorithm, then selecting the task execution policy, and selecting the execution time of the task execution policy.
In an embodiment, the task execution policy includes immediate execution, timed execution, and timed repetitive execution.
In specific implementation, the immediate execution means that the task is immediately executed after the creation is completed; the timed execution means that the task starts to be executed at a time point after the task is created, specifically, a timed execution strategy is selected and the execution time of the timed task is set, the execution time is greater than or equal to the current time, and if the set execution time of the task is less than the current time, the system defaults to be automatically executed after the task is created; the timed repetitive execution means that the task is repeatedly executed in a time period after the creation is completed, for example, a timed task executed a little in the morning every day is set, specifically, a timed repetitive execution strategy is selected and the execution time of the timed repetitive task is set, for example, the timed repetitive execution is repeatedly executed a little in the morning every day.
In an embodiment, the executing step S3 includes acquiring a prediction result of the prediction task when the prediction task is executed according to the task execution policy, and acquiring the algorithm model and issuing the algorithm model when the training task is executed according to the task execution policy.
In a specific implementation, the system architecture diagram is shown in fig. 2, where the task management module is used for creating, starting and stopping, modifying, updating, and viewing a log of a task. Specifically, the task types are divided into a training task and a prediction task, after an execution strategy is determined, the task is executed by a scheduler to generate a task instance, the instance runs in an independent environment and is distributed with fixed resources, the scheduler can monitor the running state of each instance and can check the specific execution condition of the current task through a log, and particularly, if the task is abnormally stopped, the abnormal reason can be checked through the log. The specific information of the task instance comprises an instance ID, a starting time, an ending time and a task state, and particularly for a training task, the information of the task instance further comprises an algorithm model name and a model state generated by the task. Wherein the task state comprises completion, running, termination and abnormal stop, and the model state comprises unpublished state and published state. And the model management module is used for carrying out centralized management on the algorithm model, including generation, deletion and release of the model. Specifically, for the same algorithm, multiple task instances may be generated by training for multiple times, an algorithm model is generated after each instance is completed, and the name of the algorithm model is automatically generated by the system. Due to the uncertainty of the algorithm model, even if the same algorithm is used, the model effect generated by each training is different, and not all models are suitable for prediction tasks. Further, for the generated algorithm model, only the publishing operation is performed for the prediction task. Specifically, step S3 is executed, the running state of the current task instance may be checked in the task instance list, if the task instance is running, the running is displayed, if the task instance runs successfully, the display is completed, if the task instance runs unsuccessfully due to an algorithm or configuration, the state is displayed as an abnormal stop, and if the task instance is manually stopped due to a human factor, the state is displayed as a stop; if the training task is completed, an algorithm model is generated, the training result is checked to determine whether the current model needs to be issued, and the issued model can be checked in a model list; and if the execution of the prediction task is finished, generating a prediction result and checking the prediction result.
The invention realizes the monitoring of the running state of each task instance, tracks various exceptions encountered in the task execution process through logs, is applied to various files of algorithm tasks, realizes the centralized management of algorithm script files, algorithm model files and the like, is applied to various customized task execution requirements, provides three task execution strategies, can effectively solve the problem of the whole-flow integrated management of the algorithm model tasks, and is convenient for algorithm developers to concentrate on the algorithm research and development work.
Example two
Referring to fig. 4, fig. 4 is a schematic diagram illustrating a task execution situation according to an embodiment of the present application. The training task with an industrial algorithm as an embodiment comprises the following steps:
a new construction step: preparing an executable python script file required by an algorithm, wherein other optional script files also comprise a shell script; preparing an algorithm configuration file, wherein the file format is json, the configuration parameters are displayed in the form of json, newly establishing an uploading algorithm, newly establishing a task, selecting the task type as a training task, and selecting the uploading algorithm.
Selecting: configuring parameters required by the task, wherein the configurable parameters comprise a time range, and the data set in the time range is used for task prediction; optionally, the parameter configuration may also be customized, and in this embodiment, due to the relationship of the industrial context in which the algorithm is located, other parameter configurations further include an apparatus instance, a last prediction result, and the like. And selecting an execution strategy, wherein the selectable execution strategy comprises immediate execution, timed execution and timed repeated execution. The immediate execution task is suitable for the disposable task which needs to be executed immediately, the timing execution task is suitable for the disposable task which needs to be executed at a certain future time point, and the timing repeated task is suitable for the task which needs to be executed repeatedly in a certain future time. In the embodiment, the prediction task is expected to dynamically obtain the prediction result according to the change of the data set in a future period of time, the selected execution strategy is to execute repeatedly at a fixed time, and the execution time of the fixed time is set, and the execution time is set to be executed once every day at 09:30:00 in the embodiment.
The execution steps are as follows: the training task is executed, and fig. 4 is a schematic diagram of the task execution in the next five days.
EXAMPLE III
Referring to fig. 5, fig. 5 is a schematic structural diagram of a task management system according to the present invention. As shown in fig. 5, the task management system according to the present invention is applied to the task management method described above, and includes:
the new unit 51: newly establishing an algorithm and a task;
the selection unit 52: selecting a task type, selecting the algorithm according to the task type, configuring parameters of the algorithm, selecting a task execution strategy, and selecting the execution time of the task execution strategy;
the execution unit 53: and after the task is executed according to the task execution strategy, acquiring an execution result of the task.
In this embodiment, the new creation unit 51 prepares a script file and a configuration file of the algorithm, and creates the algorithm and the task.
In this embodiment, in the selecting unit 52, when the task type selects a predicted task, an algorithm model is selected first, then the algorithm is selected, the parameter of the algorithm is configured, then the task execution policy is selected, and the execution time of the task execution policy is selected, when the task type selects a training task, the algorithm is directly selected, the parameter of the algorithm is configured, then the task execution policy is selected, and the execution time of the task execution policy is selected.
In this embodiment, the task execution policy includes immediate execution, timed execution, and timed and repeated execution.
In this embodiment, the executing unit 53 obtains the prediction result of the prediction task when executing the prediction task according to the task execution policy, and obtains the algorithm model and issues the algorithm model when executing the training task according to the task execution policy.
Example four
Referring to fig. 6, this embodiment discloses a specific implementation of an electronic device. The electronic device may include a processor 81 and a memory 82 storing computer program instructions.
Specifically, the processor 81 may include a Central Processing Unit (CPU), or A Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.
The memory 82 may be used to store or cache various data files for processing and/or communication use, as well as possible computer program instructions executed by the processor 81.
The processor 81 implements any of the task management methods in the above embodiments by reading and executing computer program instructions stored in the memory 82.
In some of these embodiments, the electronic device may also include a communication interface 83 and a bus 80. As shown in fig. 6, the processor 81, the memory 82, and the communication interface 83 are connected via the bus 80 to complete communication therebetween.
The communication interface 83 is used for implementing communication between modules, devices, units and/or equipment in the embodiment of the present application. The communication port 83 may also be implemented with other components such as: the data communication is carried out among external equipment, image/task management equipment, a database, external storage, an image/data processing workstation and the like.
The bus 80 includes hardware, software, or both to couple the components of the electronic device to one another. Bus 80 includes, but is not limited to, at least one of the following: data Bus (Data Bus), Address Bus (Address Bus), Control Bus (Control Bus), Expansion Bus (Expansion Bus), and Local Bus (Local Bus). By way of example, and not limitation, Bus 80 may include an Accelerated Graphics Port (AGP) or other Graphics Bus, an Enhanced Industry Standard Architecture (EISA) Bus, a Front-Side Bus (FSB), a HyperTransport (HT) Interconnect, an ISA (ISA) Bus, an InfiniBand (InfiniBand) Interconnect, a Low Pin Count (LPC) Bus, a memory Bus, a Microchannel Architecture (MCA) Bus, a PCI (Peripheral Component Interconnect) Bus, a PCI-Express (PCI-X) Bus, a Serial Advanced Technology Attachment (AGP) Bus, a Local Video Association (Video Electronics Association), abbreviated VLB) bus or other suitable bus or a combination of two or more of these. Bus 80 may include one or more buses, where appropriate. Although specific buses are described and shown in the embodiments of the application, any suitable buses or interconnects are contemplated by the application.
The electronic device may be connected to a task management system to implement the methods described in connection with fig. 1-3.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method for task management, comprising:
a new construction step: newly establishing an algorithm and a task;
selecting: selecting a task type, selecting the algorithm according to the task type, configuring parameters of the algorithm, selecting a task execution strategy, and selecting the execution time of the task execution strategy;
the execution steps are as follows: and after the task is executed according to the task execution strategy, acquiring an execution result of the task.
2. The task management method according to claim 1, wherein the newly creating step comprises preparing a script file and a configuration file of the algorithm, and newly creating the algorithm and the task.
3. The task management method according to claim 1, wherein the selecting step includes, when the task type selects a predicted task, selecting an algorithm model first and then the algorithm, and configuring the parameters of the algorithm, then selecting the task execution policy, and selecting the execution time of the task execution policy, when the task type selects a training task, directly selecting the algorithm, and configuring the parameters of the algorithm, then selecting the task execution policy, and selecting the execution time of the task execution policy.
4. The task management method according to claim 1, wherein the task execution policy includes immediate execution, timed execution, and timed repeat execution.
5. The task management method according to claim 3, wherein the executing step includes acquiring a prediction result of the prediction task when the prediction task is executed according to the task execution policy, and acquiring the algorithm model and issuing the algorithm model when the training task is executed according to the task execution policy.
6. A task management system adapted to the task management method according to any one of claims 1 to 5, the task management system comprising:
newly building a unit: newly establishing an algorithm and a task;
a selection unit: selecting a task type, selecting the algorithm according to the task type, configuring parameters of the algorithm, selecting a task execution strategy, and selecting the execution time of the task execution strategy;
an execution unit: and after the task is executed according to the task execution strategy, acquiring an execution result of the task.
7. The task management system according to claim 6, wherein the new creation unit prepares a script file and a configuration file of the algorithm and creates the algorithm and the task.
8. The task management system according to claim 7, wherein in the selection unit, when the task type selects a predicted task, an algorithm model is selected first, then the algorithm is selected, and after the parameters of the algorithm are configured, the task execution policy is selected, and the execution time of the task execution policy is selected, when the task type selects a training task, the algorithm is directly selected, and after the parameters of the algorithm are configured, the task execution policy is selected, and the execution time of the task execution policy is selected.
9. The task management system of claim 8, wherein the task execution policy comprises immediate execution, timed execution, and timed repeat execution.
10. The task management system according to claim 9, wherein the execution unit obtains a prediction result of the prediction task when executing the prediction task according to the task execution policy, and obtains the algorithm model and issues the algorithm model when executing the training task according to the task execution policy.
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