Memory optimization method and device based on state classification
By subdividing memory states and load types and combining them with predictive models, resource allocation strategies are dynamically adjusted, solving the problems of simple state representation and insufficient load awareness in memory management, thereby improving system response speed and resource utilization efficiency.
CN122261818APending Publication Date: 2026-06-23SHENZHEN HEJIUGUIYI CULTURAL DEVELOPMENT CO LTD
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Patent Information
- Application Number
- CN202610360046.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-03-24
- Publication Date
- 2026-06-23
AI Technical Summary
Technical Problem
Existing memory management technologies cannot accurately describe the gradual process of memory pressure, lack load awareness, have fixed scheduling strategies, and insufficient preloading capabilities, resulting in slow system response and unreasonable resource allocation, which affects user experience.
Method used
The memory status is divided into multiple levels, the load type is divided into multiple priorities, dynamic resource allocation is achieved through a scheduling policy mapping table, and resources are preloaded by combining a behavior prediction model.
Benefits of technology
It enables fine-grained management of memory resources, improves system adaptability and smoothness, reduces response latency, and enhances user experience.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention discloses a memory optimization method and device based on state classification. The method comprises the steps that the available memory capacity of the computing device is monitored in real time, memory pressure states are divided into N levels according to a plurality of preset pressure threshold values, and N is larger than or equal to 3; obtaining a load type identifier of a currently running application program or process, and dividing a load type into at least two priority levels; querying a preset scheduling strategy mapping table according to the memory pressure state level and the load priority level to obtain a corresponding resource allocation coefficient; and generating a resource scheduling instruction according to the resource allocation coefficient, wherein the instruction comprises: adjusting the resource allocation limit of the current process, adjusting the priority of the preloading queue, and releasing the specified memory area. The dynamic optimization scheduling of the memory resources is realized by combining and classifying the memory states and the load types, the method can be widely applied to various computing devices and application scenes, and the system operation fluency and the memory utilization efficiency are improved.
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