Intelligent equipment performance self-adaption method

Abstract

The invention provides an intelligent equipment performance self-adaption method which comprises the following operation steps: S1, comprehensive performance evaluation: obtaining the overall performance grade of equipment by comprehensively evaluating CPU performance, memory capacity, storage capacity and GPU performance; s2, dynamic data preloading: dynamically adjusting a data preloading strategy according to the overall performance level of the equipment obtained in the step S1; s3, adjusting the loading size of the dynamic picture, namely dynamically adjusting the loading size of the picture according to the overall performance level of the equipment obtained in the step S1; s4, designing a system architecture; and S5, automatically adjusting the mechanism. Through comprehensive performance evaluation, a dynamic data preloading strategy, dynamic picture loading size adjustment, system architecture design and an intelligent automatic adjustment mechanism, the performance and user experience of the mobile application are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of device performance adaptation, and particularly to an intelligent device performance self - adaptation method. Background Art

[0002] An intelligent device refers to a device that can sense the environment, collect data, make decisions and perform corresponding actions through built - in sensors, processors, software and other intelligent components. Intelligent device performance adaptation refers to the ability of an intelligent device to automatically adjust its performance parameters to achieve the best operating state according to different environments and user requirements.

[0003] Currently, there are various technologies and solutions for optimizing the performance adaptation of mobile applications for intelligent devices in the market. For example, the static pre - loading strategy. Many mobile applications adopt a fixed pre - loading strategy, that is, a certain amount of data and resources are pre - loaded when the application starts. However, this strategy cannot be adjusted according to the actual performance of the device, which may lead to waste of resources on high - performance devices or lag on low - performance devices; manual configuration. Some applications allow users to manually adjust pre - loading settings, such as the amount of data and picture resolution. However, manual configuration requires users to have certain technical knowledge and cannot be adjusted in real - time according to changes in device performance, resulting in a poor user experience; optimization based on network conditions. Some applications adjust the data loading strategy according to the current network conditions (such as Wi - Fi or mobile data). However, this method ignores the hardware performance differences of the device itself and cannot comprehensively optimize the user experience; simple performance detection. Some applications determine the loading strategy through simple performance detection (such as the number of CPU cores). However, this method is too simple and fails to comprehensively consider factors such as memory, storage and GPU performance, resulting in inaccurate performance evaluation.

[0004] That is to say, in the prior art, mobile applications usually adopt fixed pre - loading strategies and picture loading sizes, which cannot adapt to devices with different performances, resulting in a poor user experience. Moreover, there are significant differences in data processing capabilities and picture rendering capabilities between high - performance devices and low - performance devices. Fixed data pre - loading strategies and picture loading sizes may lead to waste of resources on high - performance devices or lag on low - performance devices, thus affecting the user experience. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the defect that in mobile applications of the prior art, the data pre - loading strategy and picture loading size are fixed and difficult to adapt to mobile devices with different performances, which affects the user experience, and to provide an intelligent device performance self - adaptation method.

[0006] The present invention solves the above - mentioned technical problem through the following technical solutions:

[0007] The present invention provides an intelligent device performance self - adaptation method, and the self - adaptation method includes the following operating steps:

[0008] S1. Comprehensive performance evaluation. By comprehensively evaluating the CPU performance, memory capacity, storage capacity, and GPU performance, the overall performance level of the device is obtained.

[0009] S2. Dynamic data preloading. Based on the overall performance level of the device obtained in step S1, the data preloading strategy is dynamically adjusted.

[0010] S3. Dynamic adjustment of the loaded image size. Based on the overall performance level of the device obtained in step S1, the loaded image size is dynamically adjusted.

[0011] S4. System architecture design. Design a system architecture according to the information in S1, S2, and S3.

[0012] S5. Automatic adjustment mechanism. Based on the system architecture formed in step S4, through machine learning and data analysis, an intelligent automatic adjustment mechanism is implemented.

[0013] In this technical solution, through comprehensive performance evaluation, dynamic data preloading strategy, dynamic adjustment of the loaded image size, system architecture design, and intelligent automatic adjustment mechanism, the performance and user experience of mobile applications are significantly improved.

[0014] Preferably, the specific operation method of step S1 includes reading the number of CPU cores, maximum frequency, total memory, storage space, and GPU model information, and calculating a comprehensive performance score based on this information. The overall performance level of the device is obtained according to the comprehensive performance score.

[0015] In this technical solution, this part makes the evaluation result more comprehensive and accurate, and can more truly reflect the comprehensive performance of the device.

[0016] Preferably, the data preloading strategy in step S2 is to preload more data on high-performance devices and reduce the amount of preloaded data on low-performance devices.

[0017] In this technical solution, preloading more data on high-performance devices improves the response speed of the application; reducing the amount of preloaded data on low-performance devices avoids lagging phenomena.

[0018] Preferably, the specific operation method of step S2 includes determining the quantity and priority of the preloaded data according to the overall performance level of the device in step S1.

[0019] In this technical solution, the quantity and priority of the preloaded data for the operation in step S3 are determined.

[0020] Preferably, the rule for the picture loading size in step S3 is to load high-resolution pictures on high-performance devices and low-resolution pictures on low-performance devices.

[0021] In this technical solution, loading high-resolution pictures on high-performance devices provides a better visual experience; loading low-resolution pictures on low-performance devices reduces memory and storage occupancy.

[0022] Preferably, the specific operation method of step S3 includes determining the resolution and compression ratio of the picture according to the overall performance level of the device in step S1.

[0023] In this technical solution, determine the resolution and compression of the picture in the operation of step S3.

[0024] Preferably, the system architecture in step S4 includes a performance evaluation module, a data preloading module, and a picture loading module.

[0025] In this technical solution, each module works together to ensure that the application can dynamically adjust the loading strategy according to the device performance.

[0026] Preferably, the performance evaluation module is responsible for obtaining the hardware performance information of the mobile phone and evaluating the overall performance level.

[0027] In this technical solution, determine the function of the performance evaluation module.

[0028] Preferably, the data preloading module dynamically adjusts the data preloading strategy according to the evaluated overall performance level.

[0029] In this technical solution, determine the function of the data preloading module.

[0030] Preferably, the picture loading module dynamically adjusts the picture loading size according to the evaluated overall performance level.

[0031] In this technical solution, determine the function of the picture loading module.

[0032] Based on the common knowledge in this field, the above preferred conditions can be combined arbitrarily to obtain various preferred examples of the present invention.

[0033] The positive and progressive effects of the present invention are as follows:

[0034] The present invention comprehensively evaluates the CPU performance, memory capacity, storage capacity, and GPU performance to obtain the overall performance level of the device, which makes the evaluation result more comprehensive and accurate, can more truly reflect the comprehensive performance of the device, improves the accuracy of performance evaluation, and provides a reliable basis for subsequent dynamic adjustment;

[0035] Dynamically adjust the data preloading strategy according to the evaluated performance level, preload more data on high-performance devices to improve the application's response speed; reduce the amount of preloaded data on low-performance devices to avoid lagging, enhance the application's response speed and smoothness, reduce lagging on low-performance devices, and improve the user experience;

[0036] Dynamically adjust the image loading size according to the evaluated performance level, load high-resolution images on high-performance devices to provide a better visual experience; load low-resolution images on low-performance devices to reduce memory and storage occupancy, optimize the image loading strategy, improve the visual experience, and at the same time reduce resource occupancy on low-performance devices, enhancing the overall performance;

[0037] Designed a system architecture including a performance evaluation module, a data preloading module, and an image loading module. These modules work together to ensure that the application can dynamically adjust the loading strategy according to the device performance, making the system architecture more perfect. The modules work in coordination to improve the overall stability and reliability of the system;

[0038] Through machine learning and data analysis, an intelligent automatic adjustment mechanism is realized. The system can automatically optimize the data preloading strategy and image loading size according to the user's usage habits and device performance changes, improving the system's intelligence level, better adapting to the user's personalized needs and device performance changes, and enhancing the user experience;

[0039] By comprehensively evaluating the device performance and dynamically adjusting the loading strategy, ensure that a good user experience can be provided on devices with different performances, significantly enhancing the user experience, and enabling users to obtain a smooth and high-quality application experience on different devices;

[0040] By dynamically adjusting the loading strategy, reasonably utilize device resources, avoid resource waste and insufficiency, improve resource utilization efficiency, extend the service life of the device, and reduce energy consumption. Brief Description of the Drawings

[0041] Figure 1 It is a schematic diagram of the overall process of the intelligent device performance adaptive method according to the embodiment of the present invention. Detailed Embodiments

[0042] The present invention will be further described below by way of embodiments, but the present invention is not limited to the scope of the described embodiments.

[0043] Figure 1 Shown is a schematic structural diagram of an embodiment of the intelligent device performance adaptive method of the present invention. The intelligent device performance adaptive method includes the following operating steps:

[0044] S1. Comprehensive performance evaluation. By comprehensively evaluating the CPU performance, memory capacity, storage capacity, and GPU performance, the overall performance level of the device is obtained.

[0045] Compared with most of the prior arts that only focus on a single performance metric, such as the number of CPU cores or memory capacity, the present invention obtains the overall performance level of the device by comprehensively evaluating the CPU performance, memory capacity, storage capacity, and GPU performance, improving the accuracy of performance evaluation and providing a reliable basis for subsequent dynamic adjustment.

[0046] S2. Dynamic data preloading. Based on the overall performance level of the device obtained in step S1, the data preloading strategy is dynamically adjusted.

[0047] Compared with the prior art that uses a fixed preloading strategy and cannot be adjusted according to the device performance, the present invention dynamically adjusts the data preloading strategy according to the evaluated performance level, preloading more data on high-performance devices to improve the application response speed; reducing the amount of preloaded data on low-performance devices to avoid lagging, enhancing the application response speed and fluency, reducing the lagging phenomenon on low-performance devices, and improving the user experience.

[0048] S3. Dynamic adjustment of image loading size. Based on the overall performance level of the device obtained in step S1, the image loading size is dynamically adjusted.

[0049] Compared with the prior art that uses a fixed image loading size and cannot be adjusted according to the device performance, the present invention dynamically adjusts the image loading size according to the evaluated performance level, loading high-resolution images on high-performance devices to provide a better visual experience; loading low-resolution images on low-performance devices to reduce memory and storage occupancy, optimizing the image loading strategy, improving the visual experience, and at the same time reducing the resource occupancy on low-performance devices and enhancing the overall performance.

[0050] S4. System architecture design. Design a system architecture according to the information in S1, S2, and S3.

[0051] Compared with the prior art that has a relatively simple system architecture and lacks a comprehensive performance evaluation and dynamic adjustment mechanism, the present invention designs a system architecture including a performance evaluation module, a data preloading module, and an image loading module. The modules work together to ensure that the application can dynamically adjust the loading strategy according to the device performance. The system architecture is more perfect, and the modules work together to improve the overall stability and reliability of the system.

[0052] S5. Automatic adjustment mechanism. Based on the system architecture formed in step S4, through machine learning and data analysis, an intelligent automatic adjustment mechanism is realized.

[0053] Compared with the problem in the prior art of relying on simple rules or manual user configuration and lacking an intelligent automatic adjustment mechanism, the present invention realizes an intelligent automatic adjustment mechanism through machine learning and data analysis. The system can automatically optimize the data preloading strategy and the picture loading size according to the user's usage habits and changes in device performance, improving the intelligence level of the system, better adapting to the personalized needs of users and changes in device performance, and enhancing the user experience.

[0054] Compared with the problem in the prior art that resources may be wasted on high-performance devices and there may be serious lags on low-performance devices, resulting in poor user experience, the present invention comprehensively evaluates the device performance and dynamically adjusts the loading strategy to ensure a good user experience on devices with different performances, significantly enhancing the user experience, and users can obtain a smooth and high-quality application experience on different devices;

[0055] The present invention dynamically adjusts the loading strategy, rationally utilizes device resources, avoids resource waste and insufficiency, improves the resource utilization efficiency, extends the service life of the device, and reduces energy consumption.

[0056] The present invention significantly improves the performance and user experience of mobile applications through comprehensive performance evaluation, dynamic data preloading strategy, dynamic picture loading size adjustment, system architecture design, and an intelligent automatic adjustment mechanism;

[0057] Compared with the prior art, the present invention has obvious advantages and positive effects in multiple aspects, can better adapt to mobile devices with different performances, and meet the diverse needs of users.

[0058] In this technical solution, the performance and user experience of mobile applications are significantly improved through comprehensive performance evaluation, dynamic data preloading strategy, dynamic picture loading size adjustment, system architecture design, and an intelligent automatic adjustment mechanism.

[0059] The specific operation method of the S1 step includes reading information such as the number of CPU cores, maximum frequency, total memory, storage space, and GPU model information, calculating a comprehensive performance score based on this information, and obtaining the overall performance level of the device according to the comprehensive performance score.

[0060] In this technical solution, this part makes the evaluation result more comprehensive and accurate, and can more truly reflect the comprehensive performance of the device.

[0061] The data preloading strategy in the S2 step is to preload more data on high-performance devices and reduce the amount of preloaded data on low-performance devices.

[0062] In this technical solution, more data is pre-loaded on high-performance devices to improve the response speed of the application; on low-performance devices, the amount of pre-loaded data is reduced to avoid lagging.

[0063] The specific operation method of step S2 includes determining the quantity and priority of pre-loaded data according to the overall device performance level in step S1.

[0064] In this technical solution, determine the quantity and priority of pre-loaded data for the operation of step S3.

[0065] The rule for the picture loading size in step S3 is to load high-resolution pictures on high-performance devices and low-resolution pictures on low-performance devices.

[0066] In this technical solution, high-resolution pictures are loaded on high-performance devices to provide a better visual experience; low-resolution pictures are loaded on low-performance devices to reduce memory and storage occupancy.

[0067] The specific operation method of step S3 includes determining the resolution and compression ratio of the pictures according to the overall device performance level in step S1.

[0068] In this technical solution, determine the resolution and compression of the pictures for the operation in step S3.

[0069] The system architecture in step S4 includes a performance evaluation module, a data pre-loading module, and a picture loading module.

[0070] In this technical solution, each module works in coordination to ensure that the application can dynamically adjust the loading strategy according to the device performance.

[0071] The performance evaluation module is responsible for obtaining the hardware performance information of the mobile phone and evaluating the overall performance level.

[0072] In this technical solution, determine the function of the performance evaluation module.

[0073] The data pre-loading module dynamically adjusts the data pre-loading strategy according to the evaluated overall performance level.

[0074] In this technical solution, determine the function of the data pre-loading module.

[0075] The picture loading module dynamically adjusts the picture loading size according to the evaluated overall performance level.

[0076] In this technical solution, determine the function of the picture loading module.

[0077] The present invention realizes the performance evaluation and dynamic adjustment functions through Java code, including the specific logics of obtaining hardware performance information, evaluating the overall performance level, pre-loading data, and loading pictures;

[0078] The following is a simplified example code that demonstrates how to dynamically adjust the data preloading strategy and the image loading size according to the mobile phone performance, implemented in Java:

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[0086] Although the specific embodiments of the present invention have been described above, those skilled in the art should understand that this is only an example. The protection scope of the present invention is defined by the appended claims. Without departing from the principles and essence of the present invention, those skilled in the art can make various changes or modifications to these embodiments, but these changes and modifications all fall within the protection scope of the present invention.

Claims

1. A method for self-adapting performance of intelligent devices, characterized in that: The adaptive method comprises the following steps: S1. Comprehensive performance evaluation: By comprehensively evaluating CPU performance, memory capacity, storage capacity and GPU performance, the overall performance level of the device is obtained; S2, dynamic data preloading, dynamically adjust the data preloading strategy according to the overall performance level of the device obtained in step S1; S3, dynamic image loading size adjustment, dynamically adjust the image loading size according to the overall performance level of the device obtained in step S1; S4, system architecture design, design a system architecture based on the information in S1, S2 and S3; S5: Automatic adjustment mechanism. Based on the system architecture formed in step S4, an intelligent automatic adjustment mechanism is implemented through machine learning and data analysis.

2. The method for self-adapting performance of an intelligent device according to claim 1, characterized in that: The specific operation method of the S1 step includes reading the number of CPU cores, maximum frequency, total memory, storage space and GPU model information, and calculating a comprehensive performance score based on this information, and obtaining the overall performance level of the device based on the comprehensive performance score.

3. The method for self-adapting performance of an intelligent device according to claim 1, characterized in that: The data preloading strategy in step S2 is to preload more data on high-performance devices and reduce the amount of preloaded data on low-performance devices.

4. The method for self-adapting performance of an intelligent device according to claim 3, characterized in that: The specific operation method of step S2 includes determining the quantity and priority of preloaded data according to the overall performance level of the device in step S1.

5. The method for self-adapting performance of an intelligent device according to claim 1, characterized in that: The rule for the image loading size in the S3 step is to load high-resolution images on high-performance devices and low-resolution images on low-performance devices.

6. The method for self-adapting performance of an intelligent device according to claim 5, characterized in that: The specific operation method of step S3 includes determining the resolution and compression rate of the image according to the overall performance level of the device in step S1.

7. The method for self-adapting performance of an intelligent device according to claim 1, characterized in that: The system architecture in step S4 includes a performance evaluation module, a data preloading module and a picture loading module.

8. The method for self-adapting performance of an intelligent device according to claim 7, characterized in that: The performance evaluation module is responsible for obtaining the hardware performance information of the mobile phone and evaluating the overall performance level.

9. The method for self-adapting performance of an intelligent device according to claim 7, characterized in that: The data preloading module dynamically adjusts the data preloading strategy according to the evaluated overall performance level.

10. The method for self-adapting performance of an intelligent device according to claim 7, characterized in that: The image loading module dynamically adjusts the image loading size according to the evaluated overall performance level.

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