CN111459588A - Big data model setting method, terminal device and computer readable storage medium - Google Patents
Big data model setting method, terminal device and computer readable storage medium Download PDFInfo
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- CN111459588A CN111459588A CN202010235351.6A CN202010235351A CN111459588A CN 111459588 A CN111459588 A CN 111459588A CN 202010235351 A CN202010235351 A CN 202010235351A CN 111459588 A CN111459588 A CN 111459588A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/451—Execution arrangements for user interfaces
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/445—Program loading or initiating
- G06F9/44505—Configuring for program initiating, e.g. using registry, configuration files
Abstract
The invention discloses a big data model setting method, which comprises the following steps: after receiving a model setting request, outputting a model setting interface; obtaining configuration parameters and model data of the model through the model setting interface; and generating an operation script of the model according to the configuration parameters of the model and the model data. The invention also discloses a terminal device and a computer readable storage medium. The invention aims to improve the management experience of a user on a big data model.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a big data model setting method, terminal equipment and a computer readable storage medium.
Background
With the continuous development of big data, the types of models involved in big data services are more and more, the management of the models deployed on the platform is more disordered, the models are generally deployed and run by relying on the memory of a user, and the management of the models deployed on the platform is very limited.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a big data model setting method, terminal equipment and a computer readable storage medium, and aims to improve the management experience of a user on a big data model.
In order to achieve the above object, the present invention provides a big data model setting method, including the following steps:
after receiving a model setting request, outputting a model setting interface;
obtaining configuration parameters and model data of the model through the model setting interface;
and generating an operation script of the model according to the configuration parameters of the model and the model data.
Preferably, the step of generating a running script of the model according to the configuration parameters of the model and the model data comprises:
judging whether the model needs to be decompressed according to the model type in the configuration parameters;
if the model needs to be decompressed, decompressing the model data, and generating the running script according to the configuration parameters and the decompressed model data;
and if the model does not need to be decompressed, generating the running script according to the configuration parameters of the model and the model data.
Preferably, before the step of generating the running script of the model according to the configuration parameters of the model and the model data, the method further includes:
judging whether the configuration parameters are legal or not;
if the configuration parameters are legal, executing a step of generating an operation script of the model according to the configuration parameters of the model and the model data;
and if the configuration parameters are illegal, outputting prompt information.
Preferably, after the step of generating the running script of the model according to the configuration parameters of the model and the model data, the method further includes:
acquiring a starting time point or a starting period according to the configuration parameters;
and starting the running script according to the starting time point or the starting period so as to run the model.
Preferably, the step of starting the running script to run the model according to the starting time point or the starting period includes:
detecting a state of the model;
and if the model is in the available state, starting the running script according to the starting time point or the starting period so as to run the model.
Preferably, after the step of starting the running script according to the starting time point or the starting period to run the model if the model is in the available state, the method further includes:
acquiring an operation log file of the model;
and acquiring the state information of the model according to the operation log file.
Preferably, after the step of obtaining the state information of the model according to the operation log file, the method further includes:
and stopping running the running script when receiving a running stop instruction of the model.
Preferably, after the step of stopping running the script when the running stop instruction of the model is received, the method further includes:
and acquiring an output interface corresponding to the model, and displaying the state information of the model through the output interface.
In order to achieve the above object, the present invention further provides a terminal device, including:
the terminal equipment comprises a memory, a processor and a control program of the big data model setting method stored on the memory and capable of running on the processor, and the control program of the big data model setting method realizes the steps of the big data model setting method when being executed by the processor.
To achieve the above object, the present invention also provides a computer-readable storage medium having stored thereon a control program of a big data model setting method, the control program of the big data model setting method, when executed by a processor, implementing the steps of the big data model setting method as described above.
According to the big data model setting method, the terminal device and the computer readable storage medium, after a model setting request is received, a model setting interface is output, the configuration parameters and the model data of the model are obtained through the model setting interface, the configuration parameters of the model are input into the model setting interface by a user, and the running script of the model is generated according to the configuration parameters of the model and the model data, so that the user can manage the big data model more conveniently, and the management experience of the user on the big data model is improved.
Drawings
Fig. 1 is a schematic diagram of a hardware operating environment of a terminal according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a big data model setup method according to a first embodiment of the present invention;
FIG. 3 is a flowchart illustrating a big data model setup method according to a second embodiment of the present invention;
FIG. 4 is a flowchart illustrating a big data model setup method according to a third embodiment of the present invention;
FIG. 5 is a flowchart illustrating a big data model setup method according to a fourth embodiment of the present invention;
FIG. 6 is a flowchart illustrating a big data model setup method according to a fifth embodiment of the present invention;
fig. 7 is a flowchart illustrating a big data model setting method according to a sixth embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In addition, technical solutions between various embodiments may be combined with each other, but must be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present invention.
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The main solution of the embodiment of the invention is as follows: after receiving a model setting request, outputting a model setting interface; obtaining configuration parameters and model data of the model through the model setting interface; and generating an operation script of the model according to the configuration parameters of the model and the model data.
The invention provides a big data model setting method, aiming at improving the management experience of a user on a big data model.
As shown in fig. 1, fig. 1 is a schematic diagram of a hardware operating environment of a terminal according to an embodiment of the present invention;
the terminal of the embodiment of the invention can be a server or terminal equipment with data analysis.
As shown in fig. 1, the terminal may include: a processor 1001, such as a Central Processing Unit (CPU), a memory 1002, a communication bus 1003, and a network interface 1004. The communication bus 1003 is used for implementing connection communication between the components in the terminal. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., a WiFi interface). The memory 1002 may be a random-access memory (RAM-random-access memory) or a non-volatile memory (non-volatile memory), such as a disk memory. The memory 1002 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration of the terminal shown in fig. 1 is not intended to be limiting of the terminal of embodiments of the present invention and may include more or less components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, a control program of the big data model setting method may be included in the memory 1002 as a kind of computer storage medium.
In the terminal shown in fig. 1, the network interface 1004 is mainly used for connecting to a backend server and performing data communication with the backend server, and the processor 1001 may be configured to invoke a control program of the big data model setting method stored in the memory 1002 and perform the following operations:
after receiving a model setting request, outputting a model setting interface;
obtaining configuration parameters and model data of the model through the model setting interface;
and generating an operation script of the model according to the configuration parameters of the model and the model data.
Further, the processor 1001 may call a control program of the big data model setting method stored in the memory 1002, and also perform the following operations:
judging whether the model needs to be decompressed according to the model type in the configuration parameters;
if the model needs to be decompressed, decompressing the model data, and generating the running script according to the configuration parameters and the decompressed model data;
and if the model does not need to be decompressed, generating the running script according to the configuration parameters of the model and the model data.
Further, the processor 1001 may call a control program of the big data model setting method stored in the memory 1002, and also perform the following operations:
judging whether the configuration parameters are legal or not;
if the configuration parameters are legal, executing a step of generating an operation script of the model according to the configuration parameters of the model and the model data;
and if the configuration parameters are illegal, outputting prompt information.
Further, the processor 1001 may call a control program of the big data model setting method stored in the memory 1002, and also perform the following operations:
acquiring a starting time point or a starting period according to the configuration parameters;
and starting the running script according to the starting time point or the starting period so as to run the model.
Further, the processor 1001 may call a control program of the big data model setting method stored in the memory 1002, and also perform the following operations:
detecting a state of the model;
and if the model is in the available state, starting the running script according to the starting time point or the starting period so as to run the model.
Further, the processor 1001 may call a control program of the big data model setting method stored in the memory 1002, and also perform the following operations:
acquiring an operation log file of the model;
and acquiring the state information of the model according to the operation log file.
Further, the processor 1001 may call a control program of the big data model setting method stored in the memory 1002, and also perform the following operations:
and stopping running the running script when receiving a running stop instruction of the model.
Further, the processor 1001 may call a control program of the big data model setting method stored in the memory 1002, and also perform the following operations:
and acquiring an output interface corresponding to the model, and displaying the state information of the model through the output interface.
Referring to fig. 2, in an embodiment, the big data model setting method includes:
and step S10, outputting a model setting interface after receiving the model setting request.
And step S20, obtaining configuration parameters of the model and model data through the model setting interface.
And step S30, generating an operation script of the model according to the configuration parameters of the model and the model data.
In this embodiment, when uploading a big data model, a user sends a model setting request to a system through a big data model management interface, the system outputs a model setting interface after receiving the model setting request, the user can configure configuration parameters of the model on the model setting interface, it should be noted that the configuration parameters of the model include basic information of the model, user parameters, common parameters, operation parameters and the like required by model operation and scheduling, where the basic information of the model includes the name of the model, the type of the model, the starting operation time of the model, and whether the model operates in a single instance, the single instance operation means that when the model set by the user is in a single instance, the system cannot concurrently operate multiple models, in the process of operating the model, only one model will be operated until the current model is operated, the user parameters refer to specific parameters that a user can set for each model according to requirements, for example, the user parameters may be running time intervals of the models, when the running time intervals of the models set by the user in the user parameters are 10min, the models will run periodically every 10min after being started, the common parameters refer to path parameters for obtaining model data, the model data can be obtained by setting hdfs paths for obtaining model data, it should be noted that hdfs refers to a Hadoop distributed file system, the model data is obtained through a data dictionary in the Hadoop distributed file system, the data dictionary refers to definitions of data items, data structures, data streams, data storage and processing logic of the data, the running parameters include queues where the set models are located during running, paths stored in logs generated during model running are set, after configuration of each parameter is completed, the system detects whether the parameters configured by the user are legal, and if the configured parameters are legal, an operation script of the model is generated according to the configured parameters of the model and the model data, wherein the operation script is used for operating the model uploaded by the user.
In this embodiment, after receiving a model setting request, a model setting interface is output; obtaining configuration parameters and model data of the model through the model setting interface; and generating an operation script of the model according to the configuration parameters of the model and the model data. Therefore, after a setting request of the model is received, the configuration parameters and the model data of the model are obtained through the output model setting interface, the model is operated through the operation script of the model, and the management experience of a user on the big data model is improved through visual interface configuration operation.
In the second embodiment, as shown in fig. 3, step S30 in fig. 2 includes, on the basis of the embodiment shown in fig. 2 described above:
and S310, judging whether the model needs to be decompressed according to the model type in the configuration parameters.
And S320, if the model needs to be decompressed, decompressing the model data, and generating the running script according to the configuration parameters and the decompressed model data.
And S330, if the model does not need to be decompressed, generating the running script according to the configuration parameters of the model and the model data.
In this embodiment, since the big data model is developed from different languages according to the user's requirements in practical applications, for example, a Java language development model, a Python language development model, some model data in the model developed by the Python language is acquired in a compressed packet form, therefore, in the model uploaded by the user, the judgment needs to be carried out according to the model type in the configuration parameters to determine whether the uploaded model needs to be decompressed or not, if the model data acquired from the data dictionary is in the form of a compressed packet, the decompression processing is carried out on the model, and after the model is decompressed, generating an operation script of the model according to the configuration parameters of the model and the decompressed model data, and if the model data acquired from the data dictionary does not need to be decompressed, directly generating the operation script according to the configuration parameters of the model and the model data.
In this embodiment, whether the model needs to be decompressed is judged according to the model type in the configuration parameters of the model, if the model needs to be decompressed, the model is decompressed, and then the running script of the model is generated according to the configuration parameters of the model and the decompressed model data, so that the system can automatically decompress the model according to the obtained model type, and does not need a user to manually decompress the model data and then upload the model data, thereby improving the management experience of the user on the big data model.
In the third embodiment, as shown in fig. 4, on the basis of the embodiment shown in fig. 2, before step S30 in fig. 2, the method further includes:
and step S40, judging whether the configuration parameters are legal.
And step S50, if the configuration parameters are legal, executing the step of generating the running script of the model according to the configuration parameters of the model and the model data.
And step S60, if the configuration parameters are illegal, outputting prompt information.
In this embodiment, a user configures configuration parameters of a model through a model setting interface, where the configuration parameters of the model all have corresponding rules to represent the role of the parameters, when the user inputs the configuration parameters of the model, a representation form error of the parameters may occur, when the configuration parameters input by the user do not conform to the rules corresponding to the parameters, the system determines that the parameters are illegal parameters, if the parameters input by the user are illegal, the system outputs prompt information to remind the user that the currently input parameters are illegal parameters, and to remind the user that the configuration parameters need to be re-input, and if the configuration parameters input by the user are legal, the system generates an operation script of the model according to the configuration parameters of the model and the model data.
In this embodiment, whether the configuration parameters of the model are legal is judged, if the configuration parameters are legal, the running script of the model is generated according to the configuration parameters and the model data, and if the configuration parameters are illegal, prompt information is output, so that the user is reminded of the fact that the current configuration parameters are illegal by outputting the prompt information, the user can input the legal configuration parameters again in time, and the management experience of the user on the big data model is improved.
In the fourth embodiment, as shown in fig. 5, on the basis of the embodiment shown in fig. 2, after step S30 in fig. 2, the method further includes:
and step S70, acquiring a starting time point or a starting period according to the configuration parameters.
And step S80, starting the running script according to the starting time point or the starting period so as to run the model.
In this embodiment, when a user uploads a model and creates a model running task, a starting time point or a starting period about the beginning of the model running needs to be configured on a model setting interface, after the model is uploaded, a daemon thread of a model in a system detects whether the current time reaches the time for starting the model running within a preset time interval, when the running time of the model is reached, a running script of the model is started, the model is run through the running script, of course, the running script of the model can be run by the running script, and as for the running period of the model in the system, the user can set the running period of the model in configuration parameters according to the requirement, for example, when the running time interval of the model set in the configuration parameters by the user is 10min, the model is started and then periodically operated every 10min, the system continuously updates the next operation time of the model according to the operation time interval of the model, and when the next operation time of the model is reached, the operation script operation model of the model is started.
In this embodiment, a starting time point or a starting period is obtained according to the configuration parameters, and an operation script is started according to the starting time point or the starting period, so that a user can conveniently start the model to operate at regular time by operating the script operation model.
In the fifth embodiment, as shown in fig. 6, step S80 in fig. 5 includes, on the basis of the embodiment shown in fig. 5 described above:
and step S810, detecting the state of the model.
And S820, if the model is in the available state, starting the running script according to the starting time point or the starting period so as to run the model.
In this embodiment, a model uploaded by a user is in a default shutdown state in a system, before the model is started to run, the user may switch a model to be run into a usable state according to a requirement through a corresponding output interface, so as to ensure that the system can start the model to run when detecting a start time point of the model, and for a model that is not switched into the usable state, the model is not started even when the system detects that a current time is a run time of the model, so that, after the start time point or the start period of the model is obtained, the state of the model is detected, and if the model is in the usable state, a run script of the model is started according to the start time point or the start period of the model, so as to run the model through the run script.
In this embodiment, the state of the model is detected, and if the model is in the available state, the running script is started according to the starting time point or the starting period to run the model, so that a user can determine the model to be run according to requirements and switch the model to the available state, thereby improving the management experience of the user on the big data model.
In the sixth embodiment, as shown in fig. 7, on the basis of the embodiment shown in fig. 6, after step S80 in fig. 6, the method further includes:
and step S830, acquiring an operation log file of the model.
And step 840, acquiring the state information of the model according to the operation log file.
And step S850, stopping running the running script when the running stop instruction of the model is received.
And S860, acquiring an output interface corresponding to the model, and displaying the state information of the model through the output interface.
In this embodiment, a corresponding log file is generated during the running process of a model, wherein the state information of the model during running is recorded in the log file of the model, the state information of the model is obtained according to the running log file, a system obtains the running log file of each model in a running state within a preset time interval, whether the running log file has an APP number corresponding to the model and keywords such as success or abnormality in running are detected, the state information of the model is updated, a user can send a stop instruction for stopping the running of the model to the system through an output interface, when the system receives the stop instruction, the APP number corresponding to the model is obtained, the APP number corresponding to the model is transmitted as a parameter to a stop instruction module, and a stop instruction is executed to stop the running of the running script of the model corresponding to the APP number, after the running script is stopped, and acquiring an output interface corresponding to the model, and displaying the state information of the model through the output interface.
In this embodiment, an operation log file of the model is obtained, the state information of the model is obtained according to the operation log file, so that a user can know the state information of the model in time through the operation log file of the model, when an operation stop instruction of the model is received, an operation script of the model is stopped to operate, an output interface corresponding to the model is obtained, the state information of the model is displayed through the output interface, and the user can conveniently send instruction information related to the model and obtain the state information of the model through the output visual interface, thereby improving the management experience of the user on a big data model.
In addition, the present invention further provides a terminal device, where the terminal device includes a memory, a processor, and a control program of the big data model setting method stored in the memory and executable on the processor, and the processor implements the steps of the big data model setting method according to the above embodiment when executing the control program of the big data model setting method.
Furthermore, the present invention also proposes a computer-readable storage medium including a control program of a big data model setting method, which realizes the steps of the big data model setting method as described in the above embodiments when executed by a processor.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (e.g., a television, a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A big data model setting method is characterized by comprising the following steps:
after receiving a model setting request, outputting a model setting interface;
obtaining configuration parameters and model data of the model through the model setting interface;
and generating an operation script of the model according to the configuration parameters of the model and the model data.
2. The big data model setup method of claim 1, wherein the step of generating a running script of the model according to the configuration parameters of the model and the model data comprises:
judging whether the model needs to be decompressed according to the model type in the configuration parameters;
if the model needs to be decompressed, decompressing the model data, and generating the running script according to the configuration parameters and the decompressed model data;
and if the model does not need to be decompressed, generating the running script according to the configuration parameters of the model and the model data.
3. The big data model setup method according to claim 1, wherein the step of generating the running script of the model according to the configuration parameters of the model and the model data is preceded by the step of:
judging whether the configuration parameters are legal or not;
if the configuration parameters are legal, executing a step of generating an operation script of the model according to the configuration parameters of the model and the model data;
and if the configuration parameters are illegal, outputting prompt information.
4. The big data model setup method according to claim 1, wherein after the step of generating the running script of the model according to the configuration parameters of the model and the model data, further comprising:
acquiring a starting time point or a starting period according to the configuration parameters;
and starting the running script according to the starting time point or the starting period so as to run the model.
5. The big data model setup method according to claim 4, wherein the step of starting the run script to run the model according to the starting time point or the starting period comprises:
detecting a state of the model;
and if the model is in the available state, starting the running script according to the starting time point or the starting period so as to run the model.
6. The big data model setting method according to claim 5, wherein after the step of starting the running script according to the starting time point or the starting period to run the model if the model is in the available state, the method further comprises:
acquiring an operation log file of the model;
and acquiring the state information of the model according to the operation log file.
7. The big data model setup method according to claim 6, wherein after the step of obtaining the state information of the model from the execution log file, further comprising:
and stopping running the running script when receiving a running stop instruction of the model.
8. The big data model setup method according to claim 7, wherein after the step of stopping running the running script upon receiving the running stop instruction of the model, further comprising:
and acquiring an output interface corresponding to the model, and displaying the state information of the model through the output interface.
9. A terminal device characterized by comprising a memory, a processor, and a control program of a big data model setting method stored on the memory and executable on the processor, the control program of the big data model setting method realizing the steps of the big data model setting method according to any one of claims 1 to 8 when executed by the processor.
10. A computer-readable storage medium, characterized in that a control program of a big data model setup method is stored thereon, which when executed by a processor implements the steps of the big data model setup method according to any one of claims 1 to 8.
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