CN114579196A - Self-learning-based computer starting disk starting sequence control method - Google Patents
Self-learning-based computer starting disk starting sequence control method Download PDFInfo
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Abstract
The invention discloses a self-learning-based computer boot disk starting sequence control method, which comprises the following steps: s100, loading a boot loader; s200, loading hardware medium information; s300, judging whether an input factor is stored in the memory, if so, executing S400, otherwise, executing S600; s400, inputting the input factors into a preset self-learning model, generating a self-learning starting sequence rule by the self-learning model, and executing S500; s500, loading the OS system according to the self-learning starting sequence rule; s600, judging whether a specified starting sequence rule exists, if so, executing S700, and otherwise, executing S800; s700, executing OS system loading according to a specified starting sequence rule; s800, executing OS system loading according to a default starting sequence rule. The invention utilizes the self-learning model to adjust the starting sequence without manual configuration of a user, thereby reducing the workload.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a self-learning-based computer boot disk starting sequence control method.
Background
Common system boot devices include a network, a USB, a hard disk, an optical disc, and a UEFI SHELL, and when hardware is replaced, the boot device will default to be loaded at the end of a boot sequence list, which results in slow system boot speed, or the system is stuck in the UEFI SHELL command line interface and cannot normally enter the operating system. At this time, the user must enter the BIOS setting interface to manually adjust the startup item, which increases the workload of the user and makes operation difficult for some users.
Disclosure of Invention
The invention aims to overcome one or more defects of the prior art and provide a self-learning-based computer boot disk starting sequence control method.
The purpose of the invention is realized by the following technical scheme: a self-learning-based computer startup disk starting sequence control method is applied to a guidance system module in a Feiteng processor, and comprises the following steps:
s100, loading a boot loader;
s200, loading hardware medium information;
s300, judging whether an input factor is stored in a memory, if so, executing S400, otherwise, executing S600;
s400, inputting the input factors into a preset self-learning model, generating a self-learning starting sequence rule by the self-learning model, and executing S500;
s500, loading the OS system according to the self-learning starting sequence rule;
s600, judging whether a specified starting sequence rule exists, if so, executing S700, otherwise, executing S800;
s700, executing OS system loading according to the specified starting sequence rule;
s800, executing OS system loading according to a default starting sequence rule.
Preferably, the default starting sequence rule is a starting sequence rule of the last successful starting.
Preferably, the specified starting sequence rule is a starting sequence rule preset by a user.
Preferably, the input factors include one or more of a type of media, a number of times of normal start-up of each type of media, and a scene application.
Preferably, the self-learning model generates a self-learning start sequence rule, including:
performing multiple linear regression on the data corresponding to each input factor to obtain a regression coefficient of each variable;
and generating a self-learning starting sequence rule according to the regression coefficients of the variables.
Preferably, the obtaining of the regression coefficient of each variable by performing multiple linear regression on the data corresponding to each input factor includes:
converting data corresponding to each input factor into standard scores;
and performing multiple linear regression according to the standard component to obtain a regression coefficient of each variable.
The invention has the beneficial effects that: the invention utilizes the self-learning model to automatically adjust the starting sequence, can learn and predict the starting scene and application purpose according to the self-used scene (such as hard disk replacement, network control, inspection test and the like), automatically provides different starting sequences for users, does not need manual configuration of the users, and reduces the workload of the users.
Drawings
FIG. 1 is a flow chart of one embodiment of a computer boot disk boot sequence control method;
FIG. 2 is a firmware block diagram of a Feiteng processor;
figure 3 is a general firmware execution block diagram of a FT processor.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood 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 obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of the present invention.
Referring to fig. 1-3, the embodiment provides a self-learning-based computer boot disk boot sequence control method, which includes:
as shown in fig. 1, the self-learning based computer boot disk start sequence control method is applied to a boot system module in a soaring processor, and comprises the following steps:
and S100, loading a boot loader.
In one embodiment, taking the FT-2000/4 processor as an example, the FT firmware is divided into two layers: a Processor Base Firmware (PBF) and a System Firmware (SFW), as shown in fig. 2. The Feiteng processor base firmware is responsible for basic initialization of the processor chip and provides related services, as well as loading the Secure OS running in Secure World.
As shown in fig. 3, the firmware boot execution flow of the soar processor is as follows: after the Feiteng processor is powered on, the PBF in Flash is operated firstly, the initialization of the most basic processor chip is completed, and then the System Firmware in Flash is jumped to. The System Firmware calls the initialization service of each module provided by the PBF in turn according to the System requirement. The method specifically comprises the following steps: a. the PLL (CPU frequency multiplication controller) is embodied in a PlatformPerei.c under a Feiteng pack directory and a PlatformPeri file, a function prototype is VOID PLlInit (UINT16 MaxDrfrequency), and the PLL mainly embodies that an initialization user sets related parameters such as frequency multiplication rate and the like; b. the memory controller: the method is characterized in that a function prototype of platform before Perform Package (PlatformPai.c) under a platform before Perform Package directory is VOID Ddrinit (UINT 32S 3Flag), and related parameters such as a memory channel and speed are mainly set by an initialization user; c. a PCIe controller: the method is characterized in that a function prototype is VOID PceiInit (VOID) under a PlatformPeri file under a Feitening pack directory, and related parameters such as PCIe channels and speed are set by an initialization user mainly.
After the initialization of the corresponding module is completed, the PBF also provides some query resets, such as a reason for the query system Reset (Reset Source). The reset reason is that in platformpei.c, the source code is embodied in a PlatformPei file, and the function prototype is UINT32 GetResetSource (), which is mainly the reason for acquiring the reset of the CPU. When the PBF enters the System Firmware for the first time, the memory is not initialized, and the PBF has no available memory and is limited in function. Therefore, the System Firmware is based on the memory initialization service of the PBF, after completing the memory initialization, the Relocation service (Relocation) provided by the PBF must be explicitly called, the PBF Relocation service loads the PBF into the memory, and the memory Relocation of the PBF service is completed. And after the System Firmware completes the related System initialization, loading the operating System.
S200, loading hardware medium information. The hardware medium mainly comprises storage media such as flash memories, CD discs, solid state disks, mechanical hard disks and the like, Feiteng UEFI firmware is stored in hardware medium information and used for initializing CPU starting and calling functions of services and the like, and medium contents, medium hardware information and the like are stored in the internal flash of the hardware medium. After the rule is set, the rule making class is stored in the storage medium specified area class, and the rule making is completed.
S300, judging whether an input factor is stored in a memory, if so, executing S400, otherwise, executing S600.
In one embodiment, the input factors include one or more of media type, number of normal starts of each type of media, and scene application.
And S400, inputting the input factors into a preset self-learning model, generating a self-learning starting sequence rule by the self-learning model, and executing S500.
In one embodiment, the self-learning model generates self-learning startup sequence rules, including: performing multiple linear regression on the data corresponding to each input factor to obtain a regression coefficient of each variable; and generating a self-learning starting sequence rule according to the regression coefficients of the variables. Performing multiple linear regression on data corresponding to each input factor to obtain a regression coefficient of each variable, wherein the method comprises the following steps: converting data corresponding to each input factor into standard scores; and performing multiple linear regression according to the standard component to obtain a regression coefficient of each variable. In this embodiment, all variables (times, medium, scene, etc.) and dependent variables are first converted into standard scores, and then linear regression is performed, so that the obtained regression coefficients can reflect the importance degrees of corresponding independent variables, the regression equation is referred to as a standard regression equation, and the regression coefficients are referred to as standard regression coefficients, which are expressed as follows: zy = beta 0 + beta 1X1+ beta 2X2+ … … + beta kXk + gamma, and a scene model is established through association rule analysis. The model is established to mainly realize BdsEntry.c under an MdeModulePkg packet directory, and the prototype of the realized method is VOID BootFormFv (VOID).
In one embodiment, the self-learning based computer boot disk boot sequence control method further comprises: and recording data corresponding to each input factor when the starting is successfully carried out each time.
The input factors comprise one or more of parameters such as media types, times corresponding to the media types and scene applications, the self-learning model stores data of the parameters in the multi-level cache, and after data recording is completed, multiple linear regression is performed on the data. In this embodiment, the multiple linear regression is to convert all variables (times, medium, scene, etc.) and dependent variables into standard scores, and then perform linear regression, where the obtained regression coefficients can reflect the importance of the corresponding independent variables (implementing a self-learning engine). The regression equation at this time is called a standard regression equation, and the regression coefficient is called a standard regression coefficient, and is expressed as follows: zy = β 0 + β 1X1+ β 2X2+ … … + β kXk + γ, and the association rule is analyzed to establish a scene model. The model is established to mainly realize BdsEntry.c under an MdeModulePkg packet directory, and the prototype of the realized method is VOID BootFormFv (VOID).
S500, loading the OS system according to the self-learning starting sequence rule.
S600, judging whether a specified starting sequence rule exists, if so, executing S700, otherwise, executing S800.
Generally, the specified starting sequence rule is a starting sequence rule preset by a user. For example, the user selects priorities of the media such as USB, DVD, Shell, and the like to sort, thereby generating a specified startup order rule.
And S700, executing OS system loading according to the specified starting sequence rule.
S800, executing OS system loading according to a default starting sequence rule.
Generally, the default starting sequence rule is a starting sequence rule of the last successful starting.
When the OS system is loaded, scanning and screening hardware media IN a starting sequence rule (self-learning starting sequence rule, specified starting sequence rule or default starting sequence rule and the like), judging whether a guidable OS system exists IN the media IN the rule, and finally performing quick and rule fusion matching according to the scanned media with the guidable OS system, wherein BdsEntry.c under an MdeModulePkg package directory is mainly realized, and the realized function prototype is BOOLEAN BOT OPOTOPpositions (IN EFI _ BOOT _ MANAGER _ LOAD _ OPTIONS, IN UINTN BOT OPTIONCount, INEFI _ BOOT _ MANAGER _ LOAD _ OPTIONS) and correctly guides the OS system.
The foregoing is illustrative of the preferred embodiments of this invention, and it is to be understood that the invention is not limited to the precise form disclosed herein and that various other combinations, modifications, and environments may be resorted to, falling within the scope of the concept as disclosed herein, either as described above or as apparent to those skilled in the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (6)
1. A self-learning-based computer startup disk starting sequence control method is applied to a guidance system module in a Feiteng processor, and is characterized by comprising the following steps of:
s100, loading a boot loader;
s200, loading hardware medium information;
s300, judging whether an input factor is stored in a memory, if so, executing S400, otherwise, executing S600;
s400, inputting the input factors into a preset self-learning model, generating a self-learning starting sequence rule by the self-learning model, and executing S500;
s500, loading the OS system according to the self-learning starting sequence rule;
s600, judging whether a specified starting sequence rule exists, if so, executing S700, otherwise, executing S800;
s700, executing OS system loading according to the specified starting sequence rule;
s800, executing OS system loading according to a default starting sequence rule.
2. The self-learning based computer boot disk startup sequence control method of claim 1, wherein the default startup sequence rule is a startup sequence rule of a last successful startup.
3. The self-learning based computer boot disk startup sequence control method of claim 1, wherein the specified startup sequence rule is a startup sequence rule preset by a user.
4. The self-learning based computer boot disk startup sequence control method of claim 1, wherein the input factors include one or more of media type, number of normal starts of each type of media, and scene application.
5. The self-learning based computer boot disk startup sequence control method of claim 1, wherein the self-learning model generates self-learning startup sequence rules comprising:
performing multiple linear regression on the data corresponding to each input factor to obtain a regression coefficient of each variable;
and generating a self-learning starting sequence rule according to the regression coefficients of the variables.
6. The self-learning based computer-initiated disk startup sequence control method of claim 5, wherein performing multiple linear regression on the data corresponding to each input factor to obtain the regression coefficient for each variable comprises:
converting data corresponding to each input factor into standard scores;
and performing multiple linear regression according to the standard component to obtain a regression coefficient of each variable.
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