BIOS implementation method for optimizing computer hardware performance based on AI
By setting the hardware performance evaluation process and the AI hardware configuration optimization process at the BIOS level, and combining the AI hardware configuration optimization server, the problem of relying on manual reliance on computer hardware performance optimization is solved, and automated and efficient hardware performance tuning is achieved.
Patent Information
- Application Number
- CN202510410514.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-02
- Publication Date
- 2025-07-04
AI Technical Summary
In the prior art, computer hardware performance optimization relies on manual experience, and there are problems such as limited scope of application, error-prone and difficult to complete initialization and tuning before system loading.
Set up the hardware performance evaluation process and the AI hardware configuration optimization process at the BIOS level, combine it with the AI hardware configuration to optimize the server, and collect computer performance indicators multiple times and use big data algorithms to optimize the hardware configuration.
It realizes automatic hardware performance tuning before system startup, reduces the probability of error, improves hardware operation efficiency and stability, and adapts to a variety of application needs.
Smart Images
Figure CN120255969A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of computer technology, and particularly to a BIOS implementation method for optimizing computer hardware performance based on AI. Background Art
[0002] With the continuous evolution of hardware such as processors, memory, and storage, users' demands for the overall performance of computers are constantly increasing. However, in the prior art, the optimization of hardware parameters usually relies on manual experience or application programs at the operating system level, which has the risks of limited applicability and easy errors. Some practices require users to deeply understand processor frequencies, memory timings, or power management strategies, otherwise, problems such as instability or low performance are likely to occur. At the same time, the optimization solutions at the operating system layer are difficult to access complete hardware low-level information in a timely manner and are difficult to complete hardware initialization optimization before system loading, resulting in resource waste or insufficient operating efficiency during the startup phase. In summary, the prior art has defects such as relying on manual work, high coupling, insufficient data accumulation, and lack of optimization in the initialization stage in terms of hardware performance optimization. Summary of the Invention
[0003] In view of the above-mentioned many problems existing in the prior art, the present invention provides a BIOS implementation method for optimizing computer hardware performance based on AI. In the BIOS of the present invention, a hardware performance evaluation process and an AI hardware configuration optimization process are set. Combining the big data algorithm of the AI hardware configuration optimization server, by collecting computer processor performance indicators, storage performance indicators, and power consumption indicators multiple times, continuously iterating and comparing with preset thresholds, and finally applying them to computer hardware configuration. This method realizes multiple self-adaptations in the single-machine mode and utilizes the training results on the server side in the networked mode to improve the operating efficiency and stability of the hardware.
[0004] A BIOS implementation method for optimizing computer hardware performance based on AI includes the following:
[0005] Set a hardware performance evaluation process and an AI hardware configuration optimization process in the BIOS, and provide a hardware performance optimization interface, which distinguishes between a single-machine optimization mode and a networked optimization mode;
[0006] In the single-machine optimization mode, obtain computer hardware performance parameters and execute the AI hardware configuration optimization process to obtain configuration parameters suitable for the characteristics of the computer hardware and apply them to the computer;
[0007] Deploy an AI hardware configuration optimization server, upload the configuration parameters obtained after single-machine optimization to the AI hardware configuration optimization server, combine the stored hardware configuration parameters and performance data, and output and save configuration parameters suitable for the characteristics of the computer hardware with the help of the AI big data optimization algorithm;
[0008] In the network optimization mode, upload the current computer model and hardware information to the AI hardware configuration optimization server, receive the configuration parameters returned by the AI hardware configuration optimization server, and apply them to the computer hardware settings.
[0009] Preferably, in the single - machine optimization mode, obtain the computer hardware performance parameters multiple times and execute the AI hardware configuration optimization process. Compare the computer hardware performance parameters with the pre - set performance requirements, and apply the configuration parameters to the computer after meeting the performance requirements.
[0010] Preferably, the computer hardware performance parameters include processor performance indicators, storage performance indicators, and power consumption indicators, and the computer hardware performance parameters are used as the basis for executing the AI hardware configuration optimization process.
[0011] Preferably, when obtaining the computer hardware performance parameters multiple times, construct a performance data sequence, and compare the performance data sequence with the result obtained in the previous stage after each execution of the AI hardware configuration optimization process.
[0012] Preferably, the AI hardware configuration optimization server integrates the configuration parameters and performance data uploaded in the single - machine optimization mode, and generates configuration parameters suitable for the same type of computer through the AI big - data optimization algorithm.
[0013] Preferably, after receiving the new configuration parameters, the AI hardware configuration optimization server updates the performance data, and executes a new round of training at fixed intervals to improve the generated configuration parameters.
[0014] Preferably, the hardware performance optimization interface displays the computer hardware performance status information in the network optimization mode. After the user makes a selection in the hardware performance optimization interface, upload the current computer model and hardware information to the AI hardware configuration optimization server.
[0015] Preferably, after completing the configuration operation, compare the information before and after the configuration parameter adjustment, and record the comparison result in the log file.
[0016] Preferably, the AI hardware configuration optimization server stores the performance data generated in the single - machine optimization mode and the network optimization mode respectively. When the number of stored data reaches the specified value, start the AI big - data optimization algorithm to calculate the updated configuration parameters.
[0017] Preferably, after obtaining the updated configuration parameters in the network optimization mode, call the hardware performance evaluation process for verification, and upload the verification result to the AI hardware configuration optimization server.
[0018] Compared with the prior art, the advantages and beneficial effects of the present invention are as follows:
[0019] Through setting the hardware performance evaluation process, the AI hardware configuration optimization process, and deploying the AI hardware configuration optimization server at the BIOS level, the present invention realizes automated and data-driven hardware performance tuning, and solves the problems of manual operation and insufficient efficiency in the prior art.
[0020] By invoking the hardware performance evaluation process within the BIOS, the present invention realizes obtaining the actual indicators of the processor and storage before the system starts. Through the AI hardware configuration optimization server, large-scale training and iteration are realized, overcoming the limitations of manual tuning.
[0021] By allocating the single-machine optimization mode and the networked optimization mode, the present invention realizes local adaptive configuration in a network-free scenario and rapid parameter acquisition in a network scenario.
[0022] Through the technical means of the present invention, automatic adjustment of hardware performance and multi-machine data fusion are realized, meeting various application requirements and reducing the error probability. BRIEF DESCRIPTION OF THE DRAWINGS
[0023] Figure 1 is a schematic flowchart of the method of the present invention;
[0024] Figure 2 is a schematic structural diagram of the BIOS and the AI server in the present invention;
[0025] Figure 3 is a schematic flowchart of the server training process in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0026] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the following detailed description, for the sake of explanation, many specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure. However, obviously, one or more embodiments can also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessarily confusing the concepts of the present disclosure.
[0027] The terms used herein are merely for describing specific embodiments and are not intended to limit the present disclosure. The terms "including", "comprising", etc. used herein indicate the presence of the described features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.
[0028] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.
[0029] The following further describes the BIOS implementation method for optimizing computer hardware performance based on AI in combination with the accompanying drawings and specific examples. The core of the present invention lies in using the hardware performance evaluation process and AI hardware configuration optimization process set in the BIOS, combining the AI hardware configuration optimization server and the corresponding big data optimization algorithm, providing configuration parameters suitable for the current hardware characteristics for the computer, and dividing it into two paths: single-machine optimization mode and networked optimization mode. Thus, after meeting the set performance requirements, the corresponding configuration parameters are applied to optimize the hardware performance.
[0030] As Figure 1 shown, the present invention mainly aims at the need to evaluate, automatically configure the computer hardware performance and use artificial intelligence algorithms to improve the hardware performance at the BIOS level. In the traditional environment, the adjustment of computer hardware parameters often depends on the user's understanding of the hardware configuration options, and incorrect or improper configurations are likely to lead to system instability or performance degradation. By embedding the hardware performance evaluation process and AI hardware configuration optimization process described in the present invention at the BIOS level, and combining with the independently deployed AI hardware configuration optimization server, more suitable hardware configuration parameters for the actual usage environment can be quickly obtained without the need for in-depth user intervention. And by networking, the configuration parameters and performance data of multiple computers are accumulated, and then the accuracy of the big data optimization algorithm is continuously strengthened.
[0031] As Figure 2 shown, to achieve the above goals, the present invention is carried out through the following important points:
[0032] 1. Set the hardware performance evaluation process and AI hardware configuration optimization process in the BIOS, and on this basis, establish a hardware performance optimization interface to provide users with access to the single-machine optimization mode and networked optimization mode.
[0033] 2. In the single-machine optimization mode, obtain the computer hardware performance parameters multiple times and execute the AI hardware configuration optimization process; compare the computer hardware performance parameters with the pre-set performance requirements, and apply the obtained configuration parameters to the computer after meeting the performance requirements.
[0034] 3. If the AI hardware configuration optimization server is deployed, after completing the single-machine optimization mode, the generated configuration parameters can be uploaded to the AI hardware configuration optimization server; the server generates configuration parameters suitable for the same type of computer by fusing the configuration parameters and performance data, and realizes continuous optimization with the help of the AI big data optimization algorithm.
[0035] 4. When the user selects the network connection optimization mode, the hardware performance optimization interface is responsible for obtaining the current computer model and hardware information and uploading them to the AI hardware configuration optimization server. Then, the AI hardware configuration optimization server returns the configuration parameters, which are finally applied to the computer hardware settings.
[0036] 5. For different hardware environments and the requirements of multiple iterations, after receiving the new configuration parameters, the AI hardware configuration optimization server will update the performance data and perform a new round of training within a certain period, so that the configuration scheme provided by the server can adapt to diverse hardware combinations and usage scenarios.
[0037] 6. In the specific implementation process of the present invention, the operation strategies in the single-machine optimization mode and the network connection optimization mode are strictly distinguished, and the computer hardware performance evaluation process and the AI hardware configuration optimization process are improved, so as to ensure that in the single-machine environment, the optimization effect on the hardware performance can also be fully exerted; when the network environment is available, the targeted configuration parameters can be directly obtained from the AI hardware configuration optimization server to achieve a more convenient optimization process.
[0038] The settings of the hardware performance evaluation process and the AI hardware configuration optimization process at the BIOS level include:
[0039] 1. The particularity at the BIOS level: The BIOS is the basic input / output system and belongs to the relatively low-level firmware. When the operating system has not been loaded, the BIOS has already controlled the initialization of the hardware and the configuration of some hardware parameters. On most computer motherboards, the BIOS will set several adjustable options for the processor, memory, storage controller, bus frequency, and power management. In the traditional method, if the user wants to fine-tune the hardware performance parameters by himself, he needs to enter the manual adjustment interface of the BIOS and have sufficient knowledge of the relevant options. Otherwise, there will be potential risks of system incompatibility or instability.
[0040] 2. Setting the hardware performance evaluation process: The present invention sets a hardware performance evaluation process in the BIOS to detect the processor performance index, storage performance index, and power consumption index. This process will call several low-level instructions to read the operation status values of various hardware during the detection. For example, the processor performance index can include the instruction execution throughput capacity, temperature and frequency readings, and cache management efficiency. The storage performance index can include the read / write speed, latency of the memory, and the workload status of the memory controller. The power consumption index can include the real-time current and voltage readings and the power limit strategy to measure the overall energy consumption situation.
[0041] 3. Set the AI hardware configuration optimization process: The present invention further adds an AI hardware configuration optimization process at the BIOS level. This process is used to generate or iterate new configuration parameters through a specified AI algorithm based on the computer hardware performance parameters obtained from the hardware performance evaluation process. In the single-machine optimization mode, this process mainly adjusts the allowed hardware parameters multiple times and re-executes the hardware performance evaluation process after each adjustment to obtain new hardware performance parameters. When the AI hardware configuration optimization process detects that the computer hardware performance parameters meet the pre-set performance requirements, it will write the current configuration parameters into the BIOS and apply them to the computer.
[0042] 4. Interface design: Set a hardware performance optimization interface in the BIOS. The hardware performance optimization interface provides a single-machine optimization mode and an online optimization mode. There are two main entrances at the interface level:
[0043] (1) Select the single-machine optimization mode: At this time, it mainly relies on the hardware performance evaluation process and the AI hardware configuration optimization process provided by the local machine and does not need to communicate with an external server.
[0044] (2) Select the online optimization mode: At this time, the interface will prompt the user to upload the current computer model and hardware information to the AI hardware configuration optimization server. After the server returns the corresponding configuration parameters, they will be written into the BIOS and applied to the current computer.
[0045] The adjustment and application of hardware performance in the single-machine optimization mode include:
[0046] 1. Obtain computer hardware performance parameters multiple times: In the single-machine optimization mode, the change trend of the current hardware metrics is judged by obtaining computer hardware performance parameters multiple times. The computer hardware performance parameters at least include processor performance metrics, storage performance metrics, and power consumption metrics. Each time the hardware performance evaluation process is started, it can be compared with the hardware performance parameters obtained in the previous stage to construct a performance data sequence for tracking the impact of each adjustment.
[0047] 2. AI hardware configuration optimization process: After obtaining the initial hardware performance parameters, the AI hardware configuration optimization process will try to make changes to the adjustable hardware configuration according to the pre-written strategy. The adjustable range may include the upper limit of the processor frequency, the memory timing strategy, or other BIOS adjustable switches related to performance. After each adjustment, the hardware performance evaluation process is called again to obtain new hardware performance parameters.
[0048] This process will compare the new hardware performance parameters with the previous ones and determine whether the preset performance requirements are met. If not, continue with new configuration adjustments and enter the hardware performance evaluation process again. If the set performance requirements are met, write the configuration parameters at this time to the BIOS and apply them to the computer to complete the hardware performance setting in the single-machine optimization mode.
[0049] 3. Preset performance requirements: To ensure the simplicity of the operation of the present invention, the user can set one or more performance thresholds in the hardware performance optimization interface, such as the processor throughput index, the memory peak index, the power consumption control upper limit value, etc. Once the system detects that the current comprehensive index is not lower than these set values, it is considered that the performance requirements are met. One of the original features of the present invention is to integrate this set of hardware performance evaluation and configuration adjustment at the BIOS level, enabling users to complete relatively complex parameter optimization operations without much professional knowledge.
[0050] Such as Figure 3 As shown, the deployment and functions of the AI hardware configuration optimization server include:
[0051] 1. Key points of server deployment: In the scenario relying on the network, one or more AI hardware configuration optimization servers can be deployed in the present invention. This server mainly receives the configuration parameters and performance data uploaded from each computer, and fuses, screens, and stores them in the database. The server can use the AI big data optimization algorithm to train the performance information of multiple computers and multiple hardware environments collected, so as to form configuration results for various motherboard models, processor specifications, memory capacities, or other hardware features.
[0052] 2. Fusion of configuration parameters generated by the server for the single-machine optimization mode: After the single-machine optimization mode is completed, the BIOS will automatically upload the generated configuration parameters and the corresponding performance data to the AI hardware configuration optimization server. This server will compare the newly received data with the stored historical data and generate configuration parameters suitable for the same type of computer with the help of the AI big data optimization algorithm. Here, the same type refers to a group of computers with the same or similar hardware infrastructure, such as those with similar motherboard models, processor series, and memory controller characteristics. Through data fusion and algorithm training, the hardware configuration strategy for this type of computer can be continuously improved.
[0053] To ensure the timeliness of training, the server often updates the performance data after receiving new configuration parameters and triggers a new round of training at fixed intervals. After the training is completed, the corresponding configuration strategy will be saved on the server side, and any computer in the subsequent networked optimization mode can directly obtain these newly trained configuration parameters.
[0054] 3. Network Optimization Mode and Configuration Parameter Acquisition: When the user selects the network optimization mode in the hardware performance optimization interface, the current computer model and hardware information (such as motherboard model, processor model, and memory capacity) will be uploaded to the AI Hardware Configuration Optimization Server. The server searches for the most suitable configuration parameters in the same or similar hardware environments based on the stored data and returns them to the computer for application.
[0055] Compared with the single - machine optimization mode, the network optimization mode can eliminate the process of repeatedly performing hardware performance evaluation and configuration adjustment on the local machine. If the server has trained the same model of computer multiple times, the configuration parameters can usually meet the actual performance requirements and complete the hardware optimization in a short time. In addition, after connecting to the network, the latest performance data can be synchronized to the server, further strengthening the server's database resources.
[0056] 4. Hierarchical Management and Regular Training of Performance Data: The server stores the performance data of the single - machine optimization mode and the network optimization mode separately for more refined analysis in the subsequent training stage. When the number of stored data reaches the specified value, the system will start the AI big data optimization algorithm to recalculate the configuration parameters. This algorithm involves clustering, regression analysis, or multi - dimensional feature extraction of the existing data, and finally forms a more perfect hardware configuration mapping relationship. If updated configuration parameters are obtained in the network optimization mode, the system can actively call the hardware performance evaluation process for verification and upload the verification results to the server to further improve the server's performance data.
[0057] Through this hierarchical management and regular training mechanism, each new set of configuration parameters can be used as the basis for improving the server's configuration strategy. For computers of the same type, newly networked computers will directly benefit from the previous training work. For computers with different hardware combinations, appropriate configuration strategy suggestions will also be obtained at the server level as more data accumulates.
[0058] Specific implementation process example:
[0059] 1. Startup Phase: After the computer boots up, the BIOS loads and checks whether the user calls out the hardware performance optimization interface. The user can select the "single - machine optimization mode" or "network optimization mode" in this interface. If the network optimization mode is selected, it is necessary to connect to the AI Hardware Configuration Optimization Server.
[0060] 2. Specific operations of the single - machine optimization mode: After the user selects the single - machine optimization mode, the system first obtains a set of computer hardware performance parameters through the hardware performance evaluation process and compares them with the pre - set performance requirements. If the corresponding threshold is not met, it enters the AI hardware configuration optimization process. The AI hardware configuration optimization process will adjust the processor frequency, memory control, and other BIOS - adjustable switches within a certain range, and then call the hardware performance evaluation process again. The hardware performance parameters obtained this time will be compared. If they reach or exceed the required threshold, the configuration parameters will be written and the optimization process will exit. If they still do not meet the requirements, further adjustment and continuous detection will be carried out.
[0061] After completing the single - machine optimization, if the network is available, the system will upload all the final configuration parameters and the detected performance data to the AI hardware configuration optimization server for reference in subsequent server training and other computers.
[0062] 3. Specific operations of the network - connected optimization mode: In the network - connected optimization mode, the hardware performance optimization interface will automatically read the local machine model and hardware information and submit this information to the AI hardware configuration optimization server. The server matches or newly calculates the configuration parameters suitable for the local machine through the big - data optimization algorithm and returns the configuration parameters to the computer. After the computer writes and applies the configuration parameters from the BIOS level, it can execute the hardware performance evaluation process again during the subsequent operation phase and upload the results back to the server to update the performance data. This process is generally relatively fast and can reduce the duration of the local machine's self - adjustment.
[0063] 4. Optimization and update on the server side: The AI hardware configuration optimization server archives and integrates each newly uploaded configuration parameter and performance data to form a unified analysis of the same - model or similar hardware environments. For example, if a large number of computers all use the same motherboard and processor and complete the single - machine optimization, then the server can extract a relatively stable and reliable adjustment range from these data. As the types and quantities of received computers increase, the server side continuously updates the hardware model, so as to provide more direct configuration parameters for new computers in the network - connected optimization mode.
[0064] If the server detects that the quantity of stored configuration parameters and performance data reaches the specified threshold, it will automatically trigger the big - data optimization algorithm to perform deeper calculations and categorizations on all the current parameters in the library, which may include means such as cluster analysis and regression training of different hardware combinations to form a new adjustment strategy. After that, all network - connected requests can be given updated configuration parameters according to the new strategy to further fit the actual load scenarios and hardware structure characteristics of the computer. If some configurations still need to be verified in the actual environment, the hardware performance evaluation process on the BIOS side reports the corresponding results again to continuously iteratively improve the accuracy of the server's strategy.
[0065] Data management and logging include:
[0066] 1. Management of performance data sequences: In the single - machine optimization mode, the performance data sequence is a list or matrix established after multiple acquisitions of computer hardware performance parameters, used to record the performance changes brought about by each adjustment. For processor performance metrics, storage performance metrics, and power consumption metrics, a set of values is generated in each evaluation cycle, and the BIOS can temporarily save these values through internal variables or specified spaces. After completing the single - machine optimization process, the final configuration parameters and the performance data of each stage will form a relatively complete data set for uploading to the AI hardware configuration optimization server.
[0067] 2. Log file recording: In the network - connected optimization mode, after the hardware performance optimization interface completes the configuration operation, it can compare the information before and after the configuration parameter adjustment and record the comparison results in the log file. This log file can be provided for subsequent maintenance or user reading to help understand the change process of key configuration items in the BIOS. The server side can also archive these log messages together to provide data support for future tracing and research.
[0068] The present invention directly implements hardware performance adjustment at the BIOS bottom layer without relying on professional tools or drivers at the operating system level, and is suitable for a wide range of hardware platforms; the AI hardware configuration optimization process enables non - professional users to improve hardware performance with fewer operations and reduces stability problems caused by incorrect adjustment; the single - machine optimization mode and the network - connected optimization mode complement each other; if the network conditions are not available, the configuration that meets the performance requirements can still be obtained through the single - machine adaptive method; if the network is unobstructed, the existing results of the AI hardware configuration optimization server can be quickly obtained, and the new data obtained from its own training can be fed back to the server to achieve collective wisdom; the AI hardware configuration optimization server can accumulate configuration parameters and performance data in a large - scale computer group, and adapt to more diverse hardware combinations through big - data optimization algorithms, enabling subsequent computers accessing the network to directly enjoy the existing optimization results; log recording and performance data sequence management make each configuration adjustment traceable, facilitating subsequent research or troubleshooting when problems occur.
[0069] The applications of the present invention include but are not limited to:
[0070] (1) Enterprise computing centers: When deploying large - scale servers or workstations, it is necessary to quickly allocate the most suitable parameters for hardware performance. The present invention can assist administrators in batch optimization.
[0071] (2) Game or rendering workstations: Such computers have higher requirements for processor and video memory frequencies and power consumption management. The BIOS adaptive adjustment mode described in the present invention can make a flexible balance between stability and performance.
[0072] (3) Educational and research institutions: In some teaching or research scenarios, diverse tests and optimizations of hardware parameters are required. The present invention allows for quick switching between single - machine optimization and networked optimization in the same environment, and continuously collects data to improve performance.
[0073] (4) Ordinary users: For conventional consumer - grade computers, the present invention can also obtain better hardware performance through simple selection in the BIOS interface, without the need to deeply understand numerous hardware options.
[0074] Through the method provided by the present invention, both the hardware performance evaluation process and the AI - based hardware configuration optimization process are implemented in the BIOS, reducing the need for complex installations or dependencies at the system layer. In the single - machine optimization mode, by repeatedly reading the performance metrics of the processor, storage performance metrics, and power consumption metrics, and gradually adjusting the configuration in combination with the AI - based hardware configuration optimization process, the performance requirements set in advance are finally met. In the networked optimization mode, the local hardware information is connected to the AI - based hardware configuration optimization server, and the server can quickly give practical configuration suggestions based on models or strategies trained from data collected globally, avoiding the repeated self - adaptation process on the local machine. This solution is flexible and efficient in both the hardware and network dimensions, and through the big - data integration of the server, it can adapt to more hardware combinations and application requirements.
[0075] Compared with the traditional method of manually adjusting BIOS parameters, the present invention significantly improves the automation level of hardware performance scheduling by means of the AI - based hardware configuration optimization process, overcoming the high requirement for users' professional knowledge. Compared with some methods that only perform tuning at the operating - system level, the present invention directly collects and switches performance parameters at the BIOS bottom layer, so it can better fit the characteristics of the hardware itself and determine the optimized hardware state before the system starts, which is more helpful for system stability and overall power consumption control. At the same time, by deploying the AI - based hardware configuration optimization server and the networked optimization mode, the present invention can continuously accumulate and optimize hardware configuration methods in a large - scale environment, forming a more comprehensive hardware tuning system.
[0076] In summary, the BIOS implementation method for optimizing computer hardware performance based on AI proposed by the present invention breaks the traditional dilemma of setting hardware parameters in the BIOS manually, and at the same time, the combination with the AI - based hardware configuration optimization server improves the scalability and accuracy of the algorithm. The single - machine optimization mode and the networked optimization mode can be applied in various environments to meet different network conditions and requirements. The process of the present invention is simple and clear. Users only need to perform a small number of operations during the BIOS startup phase to complete the optimization of the local machine's hardware performance. When the network environment is improved, the AI - based hardware configuration optimization server can be used to uniformly collect and process configuration parameters and performance data, and feedback the large - scale learning results to more computers, thus forming a virtuous cycle. The present invention has significant practical value and broad promotion prospects.
[0077] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects.
[0078] The above are only the embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.
Claims
1. A BIOS implementation method for optimizing computer hardware performance based on AI, characterized in that, It includes the following: Set the hardware performance evaluation process and the AI hardware configuration optimization process in the BIOS, and provide a hardware performance optimization interface, which distinguishes between the single - machine optimization mode and the network - connected optimization mode; In the single - machine optimization mode, obtain the computer hardware performance parameters and execute the AI hardware configuration optimization process, obtain the configuration parameters suitable for the computer hardware characteristics and apply them to the computer; Deploy an AI hardware configuration optimization server, upload the configuration parameters obtained after single - machine optimization to the AI hardware configuration optimization server, combine the stored hardware configuration parameters and performance data, and output and save the configuration parameters suitable for the computer hardware characteristics with the help of the AI big - data optimization algorithm; In the network - connected optimization mode, upload the current computer model and hardware information to the AI hardware configuration optimization server, and receive the configuration parameters returned by the AI hardware configuration optimization server and apply them to the computer hardware settings.
2. The BIOS implementation method for optimizing computer hardware performance based on AI according to claim 1, wherein: In the single - machine optimization mode, obtain the computer hardware performance parameters multiple times and execute the AI hardware configuration optimization process, compare the computer hardware performance parameters with the pre - set performance requirements, and apply the configuration parameters to the computer after meeting the performance requirements.
3. The BIOS implementation method for optimizing computer hardware performance based on AI according to claim 2, characterized in that: The computer hardware performance parameters include processor performance indicators, storage performance indicators, and power consumption indicators, and the computer hardware performance parameters are used as the basis for executing the AI hardware configuration optimization process.
4. The BIOS implementation method for optimizing computer hardware performance based on AI according to claim 3, characterized in that: Construct a performance data sequence when obtaining the computer hardware performance parameters multiple times, and compare the performance data sequence with the result obtained in the previous stage after each execution of the AI hardware configuration optimization process.
5. The BIOS implementation method for optimizing computer hardware performance based on AI according to claim 1, characterized in that: The AI hardware configuration optimization server integrates the configuration parameters and performance data uploaded in the single - machine optimization mode, and generates configuration parameters suitable for the same type of computer through the AI big - data optimization algorithm.
6. The BIOS implementation method for optimizing computer hardware performance based on AI according to claim 5, wherein: After receiving new configuration parameters, the AI hardware configuration optimization server updates the performance data, and executes a new round of training at fixed intervals to improve the generated configuration parameters.
7. The BIOS implementation method for optimizing computer hardware performance based on AI according to claim 1, wherein: The hardware performance optimization interface displays the computer hardware performance status information in the network - connected optimization mode. The user selects and uploads the current computer model and hardware information to the AI hardware configuration optimization server in the hardware performance optimization interface.
8. The BIOS implementation method for optimizing computer hardware performance based on AI according to claim 7, characterized in that: After completing the configuration operation, compare the information before and after the configuration parameter adjustment, and record the comparison result in the log file.
9. The BIOS implementation method for optimizing computer hardware performance based on AI according to claim 1, characterized in that: The AI hardware configuration optimization server stores the performance data generated in the single - machine optimization mode and the network - connected optimization mode respectively. When the number of stored data reaches the specified value, start the AI big - data optimization algorithm to calculate the updated configuration parameters.
10. The BIOS implementation method for optimizing computer hardware performance based on AI according to claim 9, characterized in that: After obtaining the updated configuration parameters in the network - connected optimization mode, call the hardware performance evaluation process for verification, and upload the verification result to the AI hardware configuration optimization server.