CN114065005A - System configuration optimization parameter method and device, electronic equipment and storage medium - Google Patents
System configuration optimization parameter method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a method and a device for optimizing system configuration parameters, electronic equipment and a storage medium, relates to the technical field of network security, and can quickly acquire the system configuration optimization parameters. The method comprises the following steps: acquiring performance parameters of a current system platform; and inputting the performance parameters of the current system platform into a system configuration parameter prediction model to obtain corresponding current system configuration optimization parameters. The embodiment of the invention is applied to obtaining the system configuration optimization parameters.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method and an apparatus for optimizing a system configuration parameter, an electronic device, and a storage medium.
Background
At present, with the gradual popularization of information network technology, computers are widely applied in work and life. However, the system configuration of the computer cannot meet the needs of the user in many cases, such as the problems of too slow response speed, small capacity, and the like.
Therefore, in the current solution, the performance parameters and the system configuration parameters of the system platform are manually acquired, and after manual analysis, the system configuration is correspondingly optimized according to the association between the performance parameters and the system configuration parameters, so as to solve the technical problems. However, the currently adopted system configuration optimization method has less data for problem analysis, and the analysis result is not accurate enough, thereby resulting in a poor optimization scheme. Moreover, the process of manually acquiring data is difficult, the time is long, and the subsequent analysis process is quite complicated and time-consuming, so that the current scheme for optimizing the system configuration parameters has the problems of long time consumption and low efficiency.
Therefore, how to rapidly acquire system configuration optimization parameters becomes a technical problem to be solved urgently at present.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for system configuration optimization parameters, an electronic device, and a storage medium, which can quickly obtain system configuration optimization parameters.
In a first aspect, an embodiment of the present invention provides a method for optimizing a system configuration, where the method includes:
acquiring performance parameters of a current system platform;
and inputting the performance parameters of the current system platform into a system configuration parameter prediction model to obtain corresponding current system configuration optimization parameters.
Optionally, before the obtaining of the performance parameter of the current system platform, the method further includes:
acquiring system configuration parameters and performance parameters related to the system configuration parameters by utilizing a big data technology;
and generating the system configuration parameter prediction model based on the system configuration parameters and the performance parameters related to the system configuration parameters.
Optionally, the obtaining the system configuration parameter and the performance parameter related to the system configuration parameter includes:
network interaction data are obtained, and system configuration parameters and performance parameters related to the system configuration parameters are screened out from the network interaction data.
Optionally, the acquiring network interaction data includes:
network interaction data is obtained from the network operator side, and/or,
and acquiring the network interaction data shared by the friends.
Optionally, the generating a system configuration parameter prediction model based on the system configuration parameter and the performance parameter includes:
generating a system configuration parameter initial prediction model according to the system configuration parameters and the performance parameters;
extracting the correlation characteristics between the system configuration parameters and the performance parameters;
and training the initial prediction model according to the correlation characteristics to obtain a system configuration parameter prediction model.
Optionally, the training the initial prediction model according to the association feature to obtain a system configuration parameter prediction model includes:
vectorizing the associated features to obtain vectorized processing results;
and training the initial prediction model by adopting the vectorization processing result to obtain a system configuration parameter prediction model.
Optionally, the training the initial prediction model by using the vectorization processing result to obtain a system configuration parameter prediction model includes:
selecting partial contents in the vectorization processing result, and training the initial prediction model to obtain a system configuration parameter prediction model;
adopting the residual content in the vectorization processing result to verify the system configuration parameter prediction model;
if the verification is passed, the system configuration parameter prediction model is reserved;
and if the system configuration parameter prediction model is not verified, deleting the system configuration parameter prediction model.
Optionally, the obtaining the performance parameter of the current system platform includes:
and acquiring the performance parameters of the current system platform from the network interaction data of the current system platform.
Optionally, the inputting the performance parameters of the current system platform into a system configuration parameter prediction model to obtain corresponding current system configuration optimization parameters includes:
inputting the performance parameters of the current system platform into a system configuration parameter prediction model to obtain corresponding expected system configuration parameters;
and comparing the expected system configuration parameters with corresponding actual system configuration parameters to generate corresponding current system configuration optimization parameters.
Optionally, the inputting the performance parameters of the current system platform into the system configuration parameter prediction model to obtain corresponding current system configuration optimization parameters includes:
and displaying the current system configuration optimization parameters on a WEB page.
In a second aspect, an embodiment of the present invention provides a device for optimizing a system configuration, where the device includes:
the system comprises an acquisition unit, a processing unit and a control unit, wherein the acquisition unit is used for acquiring performance parameters of a current system platform;
and the input unit is used for inputting the performance parameters of the current system platform into a system configuration parameter prediction model to obtain corresponding current system configuration optimization parameters.
Optionally, the apparatus further comprises:
the system comprises a collecting unit, a processing unit and a processing unit, wherein the collecting unit is used for acquiring system configuration parameters and performance parameters related to the system configuration parameters by utilizing a big data technology;
and the generating unit is used for generating the system configuration parameter prediction model based on the system configuration parameters and the performance parameters related to the system configuration parameters.
Optionally, the collecting unit includes:
the acquisition subunit is used for acquiring network interaction data;
and the screening subunit is used for screening out the system configuration parameters and the performance parameters related to the system configuration parameters from the network interaction data.
Optionally, the obtaining subunit is specifically configured to:
network interaction data is obtained from the network operator side, and/or,
and acquiring the network interaction data shared by the friends.
Optionally, the generating unit includes:
the initial prediction model generation subunit is used for generating a system configuration parameter initial prediction model according to the system configuration parameters and the performance parameters;
the extracting subunit is used for extracting the correlation characteristics between the system configuration parameters and the performance parameters;
and the training subunit is used for training the initial prediction model according to the association characteristics to obtain a system configuration parameter prediction model.
Optionally, the training subunit includes:
the processing module is used for carrying out vectorization processing on the associated characteristics to obtain a vectorization processing result;
and the acquisition module is used for training the initial prediction model by adopting the vectorization processing result to obtain a system configuration parameter prediction model.
Optionally, the obtaining module is specifically configured to:
selecting partial contents in the vectorization processing result, and training the initial prediction model to obtain a system configuration parameter prediction model;
adopting the residual content in the vectorization processing result to verify the system configuration parameter prediction model;
if the verification is passed, the system configuration parameter prediction model is reserved;
and if the system configuration parameter prediction model is not verified, deleting the system configuration parameter prediction model.
Optionally, the obtaining unit is specifically configured to:
and acquiring the performance parameters of the current system platform from the network interaction data of the current system platform.
Optionally, the input unit is specifically configured to:
inputting the performance parameters of the current system platform into a system configuration parameter prediction model to obtain corresponding expected system configuration parameters;
and comparing the expected system configuration parameters with corresponding actual system configuration parameters to generate corresponding current system configuration optimization parameters.
Optionally, the apparatus includes:
and the display unit is used for displaying the current system configuration optimization parameters on a WEB page.
In a third aspect, an embodiment of the present invention provides an electronic device, where the electronic device includes: the device comprises a shell, a processor, a memory, a circuit board and a power circuit, wherein the circuit board is arranged in a space enclosed by the shell, and the processor and the memory are arranged on the circuit board; a power supply circuit for supplying power to each circuit or device of the electronic apparatus; the memory is used for storing executable program codes; the processor executes a program corresponding to the executable program code by reading the executable program code stored in the memory, and is used for executing the system configuration optimization parameter method provided by any embodiment of the invention.
In a fourth aspect, embodiments of the present invention also provide a computer-readable storage medium storing one or more programs, the one or more programs being executable by one or more processors to implement a method as in any one of the preceding implementations.
The system configuration optimization parameter method, the device, the electronic equipment and the storage medium provided by the embodiment of the invention can acquire the performance parameters of the current system platform; and inputting the performance parameters of the current system platform into a system configuration parameter prediction model to obtain corresponding current system configuration optimization parameters. In this way, the performance parameters of the current system platform are input into the system configuration parameter prediction model, so that the current system configuration optimization parameters, that is, how to optimize the system configuration parameters of the current system, can be directly obtained. The performance parameters and the associated system configuration parameters do not need to be collected manually, and the complicated analysis process is carried out, so that the system configuration optimization parameters can be obtained quickly.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flowchart of a method for configuring optimized parameters of a system according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a system configuration parameter optimizing apparatus according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
It should be understood that the described embodiments are only some embodiments of the invention, and not all 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.
An embodiment of the present invention provides a method for configuring optimized parameters of a system, and referring to fig. 1, the method may include:
s11, acquiring performance parameters of the current system platform;
in particular, the current system platform is an electronic device, such as a computer, that a user needs to optimize system configuration. The performance parameters may be response speed, capacity size, etc. of the system platform.
In this step, the user may determine the performance parameters of the system platform to be acquired based on the specific problems of the system platform according to actual needs. For example, the customer considers that the response speed of the current system platform is slow and cannot meet the office needs, and may obtain a specific value of the response speed, for example, 1 s.
And S12, inputting the performance parameters of the current system platform into a system configuration parameter prediction model to obtain corresponding current system configuration optimization parameters.
In this step, the system configuration parameter prediction model may be a pre-established model, and the system configuration parameters to be optimized may be determined according to the performance parameters of the current system platform through the model. Based on the foregoing example, for example, the performance parameter is a response speed, and the system configuration parameter prediction model may obtain parameters related to the response speed according to the response speed of 1s, such as CPU processing capacity, memory capacity, and network bandwidth value, and further measure and calculate how to improve the three parameters, so as to achieve the performance parameter of improving the response speed. Similarly, the system configuration optimization parameters may further include hard disk capacity expansion, hard disk capacity reduction, data structure modification, middleware addition, deletion function addition, parameter adjustment, and the like.
Therefore, after the user inputs the performance parameters needing to be improved into the system configuration parameter prediction model, the system configuration parameter prediction model can automatically calculate which system configuration parameters need to be optimized and how to optimize. Compared with the prior art, when a certain performance parameter of the system platform needs to be improved, the performance parameter and the system configuration parameter related to the performance parameter need to be obtained in advance and manually analyzed, and then corresponding measures can be provided for improving the system configuration parameter. The embodiment of the invention can directly obtain the optimized parameters of the current system configuration, namely, how to optimize the parameters of the current system, thereby being capable of quickly obtaining the optimized parameters of the system configuration.
The system configuration parameter optimizing method provided by the embodiment of the invention can acquire the performance parameters of the current system platform; and inputting the performance parameters of the current system platform into a system configuration parameter prediction model to obtain corresponding current system configuration optimization parameters. In this way, the performance parameters of the current system platform are input into the system configuration parameter prediction model, so that the current system configuration optimization parameters, that is, how to optimize the system configuration parameters of the current system, can be directly obtained. The performance parameters and the associated system configuration parameters do not need to be collected manually, and the complicated analysis process is carried out, so that the system configuration optimization parameters can be obtained quickly.
Optionally, in an embodiment of the present invention, before the obtaining the performance parameter of the current system platform in step S11, the embodiment of the present invention may further include: acquiring system configuration parameters and performance parameters related to the system configuration parameters by utilizing a big data technology; and generating the system configuration parameter prediction model based on the system configuration parameters and the performance parameters related to the system configuration parameters.
In the embodiment of the present invention, before using the system configuration parameter prediction model, the system configuration parameters and the performance parameters related to the system configuration parameters need to be obtained by a big data technology, so as to generate the system configuration parameter prediction model.
Specifically, mass data about performance parameters and system configuration parameters can be rapidly acquired through a big data technology, and the data do not need to be collected manually, so that the data collection efficiency is greatly improved. And from another perspective, the system configuration parameter prediction model generated through mass data has better prediction performance, and compared with a small amount of manually collected data, the system configuration parameter prediction model can obtain more accurate system configuration optimization parameters when the prediction model is used, so that a better optimization effect on the performance of a system platform is achieved.
Optionally, in an embodiment of the present invention, obtaining the system configuration parameter and the performance parameter related to the system configuration parameter may be implemented in the following specific manner: network interaction data are obtained, and system configuration parameters and performance parameters related to the system configuration parameters are screened out from the network interaction data.
In the embodiment of the invention, a large amount of data of the system platform interacts with other devices through the network, so that system configuration parameters and performance parameters of the system platform exist in the large amount of data in the network interaction data. Through filtering and screening and analyzing of data, a large number of performance parameters and system configuration parameters related to the performance parameters can be obtained.
For example, in order to obtain the network interaction data, in an embodiment of the present invention, the following specific manner may be adopted to obtain the network interaction data: and acquiring the network interaction data from the network operator side and/or acquiring the network interaction data shared by the friends.
In the embodiment of the present invention, a bypass traffic detection device may be deployed in a network operator to acquire network interaction data of a system platform. Since many network interaction data of the friends are the same as or similar to the data of the system platform in the embodiment of the invention, the data shared by the friends can be referred to expand the collected data volume.
Optionally, in an embodiment of the present invention, the generating a system configuration parameter prediction model based on the system configuration parameter and the performance parameter may include: generating a system configuration parameter initial prediction model according to the system configuration parameters and the performance parameters; extracting the correlation characteristics between the system configuration parameters and the performance parameters; and training the initial prediction model according to the correlation characteristics to obtain a system configuration parameter prediction model.
In the embodiment of the present invention, according to the multiple performance parameters and the system configuration parameters acquired in the foregoing steps, an initial prediction model of the system configuration parameters related to the two parameters can be generated. In order to further establish the association relationship between the initial prediction model of the system configuration parameters and the initial prediction model of the system configuration parameters, the association characteristics between the two can be obtained, and the initial prediction model of the system configuration parameters is trained by adopting a special algorithm according to the association characteristics. Generally speaking, the more training data used in the training process, the stronger the predictive power of the established system configuration parameter prediction model.
The training process is a process of performing machine learning on the system configuration parameter initial prediction model. Machine learning is a multi-field cross discipline, and relates to a plurality of disciplines such as probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and the like. The special research on how a computer simulates or realizes the learning behavior of human beings so as to acquire new knowledge or skills and reorganize the existing knowledge structure to continuously improve the performance of the computer. Specifically, in the embodiment of the present invention, the system configuration parameter initial prediction model may learn the association relationship between the performance parameter and the system configuration parameter through machine learning, so as to generate the system configuration parameter initial model.
Since it is difficult for a computer program to directly process the correlation characteristics between the system configuration parameters and the performance parameters, in an embodiment of the present invention, the training the initial prediction model according to the correlation characteristics to obtain a system configuration parameter prediction model may include: vectorizing the associated features to obtain vectorized processing results; and training the initial prediction model by adopting the vectorization processing result to obtain a system configuration parameter prediction model.
In the embodiment of the present invention, in order to enable the computer program to directly process the correlation characteristics between the system configuration parameters and the performance parameters, the correlation characteristics may be vectorized, and specifically, the correlation characteristics may be characterized by using a code of a specific rule, so as to generate a vectorization processing result. And then the initial prediction model of the system configuration parameters can be trained according to the vectorization processing result, so that the generation efficiency of the prediction model of the system configuration parameters is improved.
In order to ensure the correctness of the system configuration parameter prediction model, in an embodiment of the present invention, the training the initial prediction model by using the vectorization processing result to obtain the system configuration parameter prediction model may include: selecting partial contents in the vectorization processing result, and training the initial prediction model to obtain a system configuration parameter prediction model; adopting the residual content in the vectorization processing result to verify the system configuration parameter prediction model; if the verification is passed, the system configuration parameter prediction model is reserved; and if the system configuration parameter prediction model is not verified, deleting the system configuration parameter prediction model.
In the embodiment of the present invention, the vectorization processing result may be divided into two parts, where the first part of the processing result is used to train the initial prediction model generated in the previous step, so as to obtain a system configuration parameter prediction model; then, the performance parameters and the system configuration parameters with corresponding relations can be searched in the second part of processing results, the performance parameters are input into the system configuration parameter prediction model, and corresponding system configuration parameter output values are obtained from the output results of the system configuration parameter prediction model. And comparing the output value with the system configuration parameters, and judging whether the system configuration parameter prediction model is trained successfully according to the consistency of the output value and the system configuration parameters. If the training is successful, which indicates that the prediction accuracy of the prediction model can meet the requirement, the system configuration parameter prediction model can be used in the subsequent steps. If the training is unsuccessful, the prediction accuracy of the prediction model is not met, and the system configuration parameter prediction model cannot be used in the subsequent steps. In this case, the system configuration parameter prediction model may be retrained and validated until a satisfactory system configuration parameter prediction model is obtained.
In order to obtain the performance parameters of the current system platform, in an embodiment of the present invention, the obtaining the performance parameters of the current system platform may include: and acquiring the performance parameters of the current system platform from the network interaction data of the current system platform.
In the embodiment of the present invention, in order to obtain the performance parameter to be improved, a value of the performance parameter, for example, a response speed of a system platform, needs to be obtained first, specifically, the value of the performance parameter may be obtained from network interaction data of the system platform, and then the obtained performance parameter value may be input into a system configuration parameter prediction model.
Optionally, in an embodiment of the present invention, the inputting the performance parameter of the current system platform into a system configuration parameter prediction model to obtain a corresponding current system configuration optimization parameter includes: inputting the performance parameters of the current system platform into a system configuration parameter prediction model to obtain corresponding expected system configuration parameters; and comparing the expected system configuration parameters with corresponding actual system configuration parameters to generate corresponding current system configuration optimization parameters.
In the embodiment of the present invention, in order to obtain a specific optimized parameter value, a performance parameter value of the system platform, for example, a response speed of 1s, may be input into the system configuration parameter prediction model. Because the prediction model obtains the incidence relation between the parameter of the response speed and the system configuration parameter related to the parameter in the training process through a machine learning mode, a target optimization value, for example 0.1s, can be determined through the system configuration parameter prediction model according to the parameter value of the response speed of 1s, and then expected system configuration parameter values, for example an expected bandwidth value A, an expected CPU processing capacity B and an expected memory size C, are calculated through the system configuration parameter prediction model according to the target optimization value. And comparing the three values with an actual bandwidth value a, an actual CPU processing capacity b and an actual memory size c of the system platform, and calculating the difference value of the three values to obtain a corresponding optimized parameter value. For example, A-a is a bandwidth optimization parameter value, B-B is a CPU processing capacity optimization parameter value, and C-C is a memory capacity optimization parameter value.
In order to intuitively and clearly display the current system configuration optimization parameters to the user, in an embodiment of the present invention, the inputting the performance parameters of the current system platform into the system configuration parameter prediction model, and after obtaining the corresponding current system configuration optimization parameters, may include: and displaying the current system configuration optimization parameters on a WEB page.
In the embodiment of the present invention, the current system configuration optimization parameters may be converted into a JSON (JavaScript Object Notation) format, and transmitted to a WEB page, and the optimization parameters are displayed to a user in a chart or graph manner, so that the user can visually and clearly obtain the relevant optimization parameters, and further, the user can review the optimization parameters and decide how to optimize the system platform according to the optimization parameters.
Correspondingly, as shown in fig. 2, an embodiment of the present invention further provides a system configuration optimization parameter device 2, including:
an obtaining unit 21, configured to obtain performance parameters of a current system platform;
and the input unit 22 is configured to input the performance parameters of the current system platform into a system configuration parameter prediction model, so as to obtain corresponding current system configuration optimization parameters.
The system configuration optimization parameter device provided by the embodiment of the invention can acquire the performance parameters of the current system platform; and inputting the performance parameters of the current system platform into a system configuration parameter prediction model to obtain corresponding current system configuration optimization parameters. In this way, the performance parameters of the current system platform are input into the system configuration parameter prediction model, so that the current system configuration optimization parameters, that is, how to optimize the system configuration parameters of the current system, can be directly obtained. The performance parameters and the associated system configuration parameters do not need to be collected manually, and the complicated analysis process is carried out, so that the system configuration optimization parameters can be obtained quickly.
Optionally, the apparatus further comprises:
the system comprises a collecting unit, a processing unit and a processing unit, wherein the collecting unit is used for acquiring system configuration parameters and performance parameters related to the system configuration parameters by utilizing a big data technology;
and the generating unit is used for generating the system configuration parameter prediction model based on the system configuration parameters and the performance parameters related to the system configuration parameters.
Optionally, the collecting unit includes:
the acquisition subunit is used for acquiring network interaction data;
and the screening subunit is used for screening out the system configuration parameters and the performance parameters related to the system configuration parameters from the network interaction data.
Optionally, the obtaining subunit is specifically configured to:
network interaction data is obtained from the network operator side, and/or,
and acquiring the network interaction data shared by the friends.
Optionally, the generating unit includes:
the initial prediction model generation subunit is used for generating a system configuration parameter initial prediction model according to the system configuration parameters and the performance parameters;
the extracting subunit is used for extracting the correlation characteristics between the system configuration parameters and the performance parameters;
and the training subunit is used for training the initial prediction model according to the association characteristics to obtain a system configuration parameter prediction model.
Optionally, the training subunit includes:
the processing module is used for carrying out vectorization processing on the associated characteristics to obtain a vectorization processing result;
and the acquisition module is used for training the initial prediction model by adopting the vectorization processing result to obtain a system configuration parameter prediction model.
Optionally, the obtaining module is specifically configured to:
selecting partial contents in the vectorization processing result, and training the initial prediction model to obtain a system configuration parameter prediction model;
adopting the residual content in the vectorization processing result to verify the system configuration parameter prediction model;
if the verification is passed, the system configuration parameter prediction model is reserved;
and if the system configuration parameter prediction model is not verified, deleting the system configuration parameter prediction model.
Optionally, the obtaining unit 21 is specifically configured to:
and acquiring the performance parameters of the current system platform from the network interaction data of the current system platform.
Optionally, the input unit 22 is specifically configured to:
inputting the performance parameters of the current system platform into a system configuration parameter prediction model to obtain corresponding expected system configuration parameters;
and comparing the expected system configuration parameters with corresponding actual system configuration parameters to generate corresponding current system configuration optimization parameters.
Optionally, the apparatus includes:
and the display unit is used for displaying the current system configuration optimization parameters on a WEB page.
The system configuration optimization parameter device provided in the embodiment of the present invention and the system configuration optimization parameter method belong to the same inventive concept, and the technical details that are not described in the system configuration optimization parameter device may refer to the related descriptions in the system configuration optimization parameter method, and are not described herein again.
Correspondingly, an embodiment of the present invention further provides an electronic device, and fig. 3 is a schematic structural diagram of an embodiment of the electronic device of the present invention, which can implement the process of the embodiment shown in fig. 1 of the present invention. As shown in fig. 3, the electronic device may include: the device comprises a shell 31, a processor 32, a memory 33, a circuit board 34 and a power circuit 35, wherein the circuit board 34 is arranged inside a space enclosed by the shell 31, and the processor 32 and the memory 33 are arranged on the circuit board 34; a power supply circuit 35 for supplying power to each circuit or device of the electronic apparatus; the memory 33 is used for storing executable program codes; the processor 32 executes a program corresponding to the executable program code by reading the executable program code stored in the memory 33, for executing the method described in any of the foregoing embodiments.
The specific execution process of the above steps by the processor 32 and the steps further executed by the processor 32 by running the executable program code may refer to the description of the embodiment shown in fig. 1 of the present invention, and are not described herein again.
The electronic device exists in a variety of forms, including but not limited to:
(1) a mobile communication device: such devices are characterized by mobile communications capabilities and are primarily targeted at providing voice, data communications. Such terminals include: smart phones (e.g., iphones), multimedia phones, functional phones, and low-end phones, among others.
(2) Ultra mobile personal computer device: the equipment belongs to the category of personal computers, has calculation and processing functions and generally has the characteristic of mobile internet access. Such terminals include: PDA, MID, and UMPC devices, etc., such as ipads.
(3) A portable entertainment device: such devices can display and play multimedia content. This type of device comprises: audio, video players (e.g., ipods), handheld game consoles, electronic books, and smart toys and portable car navigation devices.
(4) A server: the device for providing the computing service comprises a processor, a hard disk, a memory, a system bus and the like, and the server is similar to a general computer architecture, but has higher requirements on processing capacity, stability, reliability, safety, expandability, manageability and the like because of the need of providing high-reliability service.
(5) And other electronic equipment with data interaction function.
In addition, an embodiment of the present invention further provides a computer program medium, where one or more programs are stored, and the one or more programs may be executed by one or more processors to implement the method provided in any embodiment of the present invention, so that corresponding beneficial technical effects may also be achieved, and corresponding descriptions have been given above, and are not repeated here.
The system configuration optimization parameter method, the device, the electronic equipment and the medium provided by the embodiment of the invention can acquire the performance parameters of the current system platform; and inputting the performance parameters of the current system platform into a system configuration parameter prediction model to obtain corresponding current system configuration optimization parameters. In this way, the performance parameters of the current system platform are input into the system configuration parameter prediction model, so that the current system configuration optimization parameters, that is, how to optimize the system configuration parameters of the current system, can be directly obtained. The performance parameters and the associated system configuration parameters do not need to be collected manually, and the complicated analysis process is carried out, so that the system configuration optimization parameters can be obtained quickly. Further, in the embodiment of the present invention, before the system configuration parameter prediction model is used, a big data technology may be used to obtain the system configuration parameters and the performance parameters related to the system configuration parameters, and the system configuration parameter prediction model may be generated in a specific manner based on the system configuration parameters and the performance parameters related to the system configuration parameters. Specifically, the embodiment of the present invention may obtain network interaction data, and screen out system configuration parameters and performance parameters related to the system configuration parameters from the network interaction data, so as to obtain a large number of performance parameters and system configuration parameters related to the performance parameters. More specifically, the embodiment of the invention can acquire the network interaction data from the network operator side and/or acquire the network interaction data shared by the friends, thereby quickly and conveniently acquiring the network interaction data. Further, an embodiment of the present invention further provides a specific manner for obtaining a system configuration parameter prediction model, which is as follows: generating a system configuration parameter initial prediction model according to the system configuration parameters and the performance parameters; extracting the correlation characteristics between the system configuration parameters and the performance parameters; and training the initial prediction model according to the correlation characteristics to obtain a system configuration parameter prediction model. In addition, the embodiment of the invention can also carry out vectorization processing on the associated characteristics to obtain a vectorization processing result, and further can train the system configuration parameter initial prediction model according to the vectorization processing result, so that a computer program can directly process the associated characteristics between the system configuration parameters and the performance parameters, and the generation efficiency of the system configuration parameter prediction model is improved. In order to ensure that a successful system configuration parameter prediction model can be obtained, the embodiment of the invention selects part of contents in the vectorization processing result, trains the initial prediction model and obtains the system configuration parameter prediction model; adopting the residual content in the vectorization processing result to verify the system configuration parameter prediction model; if the verification is passed, the system configuration parameter prediction model is reserved; and if the system configuration parameter prediction model is not verified, deleting the system configuration parameter prediction model. The embodiment of the invention can also quickly acquire the performance parameters of the current system platform from the network interaction data of the current system platform. Furthermore, an embodiment of the present invention further provides a convenient way for obtaining the current system configuration optimization parameters, which is as follows: acquiring and inputting the performance parameters of the current system platform into a system configuration parameter prediction model to acquire corresponding expected system configuration parameters; and comparing the expected system configuration parameters with corresponding actual system configuration parameters to generate corresponding current system configuration optimization parameters. According to the embodiment of the invention, after the current system configuration optimization parameters are obtained, the current system configuration optimization parameters can be displayed on the WEB page, so that a user can visually and clearly obtain the relevant optimization parameters, and further, a system platform is optimized according to the optimization parameters.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The term "and/or" in the embodiments of the present invention describes an association relationship of associated objects, and indicates that three relationships may exist, for example, a and/or B may indicate: 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.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments.
In particular, as for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
For convenience of description, the above devices are described separately in terms of functional division into various units/modules. Of course, the functionality of the units/modules may be implemented in one or more software and/or hardware implementations of the invention.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (22)
1. A method for predicting system configuration optimization parameters, the method comprising:
acquiring performance parameters of a current system platform;
and inputting the performance parameters of the current system platform into a system configuration parameter prediction model to obtain corresponding current system configuration optimization parameters.
2. The method of claim 1, wherein prior to said obtaining performance parameters of the current system platform, the method further comprises:
acquiring system configuration parameters and performance parameters related to the system configuration parameters by utilizing a big data technology;
and generating the system configuration parameter prediction model based on the system configuration parameters and the performance parameters related to the system configuration parameters.
3. The method of claim 2, wherein the obtaining the system configuration parameters and the performance parameters related to the system configuration parameters comprises:
network interaction data are obtained, and system configuration parameters and performance parameters related to the system configuration parameters are screened out from the network interaction data.
4. The method of claim 3, wherein the obtaining network interaction data comprises:
network interaction data is obtained from the network operator side, and/or,
and acquiring the network interaction data shared by the friends.
5. The method of claim 2, wherein generating a system configuration parameter prediction model based on the system configuration parameters and the performance parameters comprises:
generating a system configuration parameter initial prediction model according to the system configuration parameters and the performance parameters;
extracting the correlation characteristics between the system configuration parameters and the performance parameters;
and training the initial prediction model according to the correlation characteristics to obtain a system configuration parameter prediction model.
6. The method of claim 5, wherein training the initial prediction model to obtain a system configuration parameter prediction model according to the associated features comprises:
vectorizing the associated features to obtain vectorized processing results;
and training the initial prediction model by adopting the vectorization processing result to obtain a system configuration parameter prediction model.
7. The method of claim 6, wherein the training the initial prediction model using the vectorization processing result to obtain a system configuration parameter prediction model comprises:
selecting partial contents in the vectorization processing result, and training the initial prediction model to obtain a system configuration parameter prediction model;
adopting the residual content in the vectorization processing result to verify the system configuration parameter prediction model;
if the verification is passed, the system configuration parameter prediction model is reserved;
and if the system configuration parameter prediction model is not verified, deleting the system configuration parameter prediction model.
8. The method of claim 1, wherein the obtaining performance parameters of the current system platform comprises:
and acquiring the performance parameters of the current system platform from the network interaction data of the current system platform.
9. The method of claim 1, wherein inputting the performance parameters of the current system platform into a system configuration parameter prediction model to obtain corresponding current system configuration optimization parameters comprises:
inputting the performance parameters of the current system platform into a system configuration parameter prediction model to obtain corresponding expected system configuration parameters;
and comparing the expected system configuration parameters with corresponding actual system configuration parameters to generate corresponding current system configuration optimization parameters.
10. The method according to any one of claims 1 to 9, wherein the inputting the performance parameters of the current system platform into a system configuration parameter prediction model, and after obtaining the corresponding current system configuration optimization parameters, comprises:
and displaying the current system configuration optimization parameters on a WEB page.
11. An apparatus for predicting system configuration optimization parameters, the apparatus comprising:
the system comprises an acquisition unit, a processing unit and a control unit, wherein the acquisition unit is used for acquiring performance parameters of a current system platform;
and the input unit is used for inputting the performance parameters of the current system platform into a system configuration parameter prediction model to obtain corresponding current system configuration optimization parameters.
12. The apparatus of claim 11, further comprising:
the system comprises a collecting unit, a processing unit and a processing unit, wherein the collecting unit is used for acquiring system configuration parameters and performance parameters related to the system configuration parameters by utilizing a big data technology;
and the generating unit is used for generating the system configuration parameter prediction model based on the system configuration parameters and the performance parameters related to the system configuration parameters.
13. The apparatus of claim 12, wherein the collection unit comprises:
the acquisition subunit is used for acquiring network interaction data;
and the screening subunit is used for screening out the system configuration parameters and the performance parameters related to the system configuration parameters from the network interaction data.
14. The apparatus according to claim 13, wherein the obtaining subunit is specifically configured to:
network interaction data is obtained from the network operator side, and/or,
and acquiring the network interaction data shared by the friends.
15. The apparatus of claim 12, wherein the generating unit comprises:
the initial prediction model generation subunit is used for generating a system configuration parameter initial prediction model according to the system configuration parameters and the performance parameters;
the extracting subunit is used for extracting the correlation characteristics between the system configuration parameters and the performance parameters;
and the training subunit is used for training the initial prediction model according to the association characteristics to obtain a system configuration parameter prediction model.
16. The apparatus according to claim 15, wherein the training subunit comprises:
the processing module is used for carrying out vectorization processing on the associated characteristics to obtain a vectorization processing result;
and the acquisition module is used for training the initial prediction model by adopting the vectorization processing result to obtain a system configuration parameter prediction model.
17. The apparatus of claim 16, wherein the obtaining module is specifically configured to:
selecting partial contents in the vectorization processing result, and training the initial prediction model to obtain a system configuration parameter prediction model;
adopting the residual content in the vectorization processing result to verify the system configuration parameter prediction model;
if the verification is passed, the system configuration parameter prediction model is reserved;
and if the system configuration parameter prediction model is not verified, deleting the system configuration parameter prediction model.
18. The apparatus according to claim 11, wherein the obtaining unit is specifically configured to:
and acquiring the performance parameters of the current system platform from the network interaction data of the current system platform.
19. The apparatus of claim 11, wherein the input unit is specifically configured to:
inputting the performance parameters of the current system platform into a system configuration parameter prediction model to obtain corresponding expected system configuration parameters;
and comparing the expected system configuration parameters with corresponding actual system configuration parameters to generate corresponding current system configuration optimization parameters.
20. The apparatus according to any one of claims 11 to 19, characterized in that it comprises:
and the display unit is used for displaying the current system configuration optimization parameters on a WEB page.
21. An electronic device, characterized in that the electronic device comprises: the device comprises a shell, a processor, a memory, a circuit board and a power circuit, wherein the circuit board is arranged in a space enclosed by the shell, and the processor and the memory are arranged on the circuit board; a power supply circuit for supplying power to each circuit or device of the electronic apparatus; the memory is used for storing executable program codes; the processor reads the executable program code stored in the memory to run the program corresponding to the executable program code, and is used for executing the method of any one of the claims 1-10.
22. A computer program medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 10.
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