41 results about "Dynamic memory management" patented technology

A computer system implements a unified framework integrating an adaptive elastic funnel (AEF) with a convergent intelligence fabric (CIF) for multi-agent AI collaboration. The system provides a universal multi-modal key-value subsystem for sharing partial computations, implements hybrid placement strategies for dynamic memory management, and incorporates quantum-resistant secure enclaves. The architecture integrates hardware acceleration through GPU-FPGA hybrid caching and neuromorphic processors, applies adaptive energy and thermal management across hardware generations, and implements autonomous flash resource orchestration with multi-dimensional wear management. The system orchestrates tensor workflows using hierarchical scheduling, enables cross-agent collaboration with privacy preservation, and supports continuous learning without catastrophic forgetting. This integration delivers unprecedented computational efficiency and security in high-dimensional decision-making environments while supporting incremental adoption through modular interfaces.

The invention provides a large language model long-term memory method based on human cognition inspiration, which comprises the following steps of: calculating semantic similarity distribution of historical interaction text vectors according to a sliding window, and calculating local information entropy of the window; determining an event boundary based on the local information entropy difference of the adjacent windows; taking each discrete event as the memory of the model, and performing partition management on the memory; when the event memory partition is full, the memory event with the low retention rate is transferred to a long-term memory area; when the interactive content relates to historical information, retrieving related historical events in the event memory partition by using a contextualized memory retrieval method; and the large language model generates content for the user in combination with the current context and the retrieved related historical events. According to the method, memory coding and storage, multi-level dynamic memory management and event-level situational memory retrieval methods based on the event cutting theory are adopted, and therefore the problem that an existing large language model long-term memory method lacks dynamic memory and situational memory is solved.

A computer system implements a unified framework integrating an adaptive elastic funnel (AEF) with a convergent intelligence fabric (CIF) for flexible and contextualized multi-agent AI and human collaboration at scale. The system provides a universal multi-modal key-value subsystem for sharing partial computations across agents, implements a hybrid greedy / non-greedy placement strategy for dynamic memory management, orchestrates dynamic computational workflows and tensor workflows using hierarchical tensor-fragment scheduling, enables cross-agent orchestration with policy-based privacy preservation, and incorporates quantum-resistant secure memory enclaves. The architecture supports continuous learning without catastrophic forgetting, compositional reasoning across modalities, and secure task execution in distributed environments. This integration enables unprecedented computational efficiency, secure collaboration, and adaptive intelligence in high-dimensional decision-making environments while supporting incremental adoption through modular interfaces.

The invention relates to the technical field of embedded systems, in particular to a memory management method for dynamic allocation of an operating system and a storage medium, and dynamic memory management is realized by transforming a memory allocation interface and an operation interface of the operating system. A memory applicant adds a flag bit when initiating a demand application, an operating system executes allocation operation according to the flag bit, when the flag bit indicates that the demand is determined, allocation is performed according to a specified length, and when the flag bit indicates that the demand is not determined, a fixed-length memory block is allocated firstly. And when the memory operation is executed, detecting the idle state of the subsequent memory block according to the memory head flag bit, and expanding the original memory block or redistributing and copying data. According to the method, the memory waste can be remarkably reduced, the memory use efficiency under resource-limited scenes such as an embedded scene and the like is improved, the system flexibility is enhanced through configuration file adjustment and a flag bit strategy while stable operation of a program is guaranteed, and memory management and maintenance are facilitated in combination with detailed memory information records.

The invention discloses a large model training dynamic memory management method based on a multilayer hierarchical cache mechanism, and relates to the technical field of computer system architecture and storage. The method is managed on the basis of a dynamic heterogeneous memory unloading system, and comprises the following steps of: in an initial stage of a training task, constructing a user state access path, establishing a TensorID-LBA mapping table, and performing hierarchical caching and threshold presetting; in the training task running stage, the access popularity of each tensor is evaluated in real time; when occupation of the GPU video memory cache layer exceeds a threshold value or cold data is detected, a dynamic migration strategy is triggered, and the low-frequency tensor is unloaded to a CPU memory cache layer or an SSD storage layer; when the tensor access popularity is improved, automatically migrating back to a high-performance layer through a prediction mechanism; in the process, tensor self-adaptive migration is executed by a user mode I / O thread pool. According to the method, the high-performance I / O stack is constructed in the user mode, and the adaptive tensor unloading and hierarchical caching mechanism of the multi-layer heterogeneous memory is combined, so that the universal deployment of the system is realized.

The invention discloses an interactive response method, device and equipment for multi-modal sensing data and a storage medium, and relates to the technical field of intelligent interaction.The interactive response method for the multi-modal sensing data comprises the steps that the multi-modal sensing data is obtained, and a user mental evaluation result is constructed based on the multi-modal sensing data; identifying user identity information according to the multi-mode sensing data, and determining short-term memory information and long-term memory information according to the user identity information; when a demand instruction of a user is received, performing retrieval in the short-term memory information and the long-term memory information according to the demand instruction to obtain a retrieval result; and generating interaction response content according to the retrieval result and the user mental evaluation result, and performing interaction. Through multi-modal perception and dynamic memory management, long-term continuous personalized interaction is realized, and the user interaction experience is improved.

The application discloses a large language model offline reasoning task reasoning acceleration method and system under limited resources, a model construction module, a dynamic batch processing reorganization module, a dynamic memory management module and an unloading strategy adjustment module; based on the lexGen large language model offline reasoning system, dynamic batch processing reorganization is performed on the reasoning task, dynamic memory management design is combined, reasoning tasks are managed in an iterative level fine-grained manner, occupied memory of completed reasoning tasks is released in real time, and dynamic arrangement is performed on uncompleted reasoning tasks, so that the waste of reasoning resources in the reasoning process is reduced, and the offline reasoning system throughput based on the unloading technology is improved. The application efficiently utilizes the idle memory of hardware, improves the throughput of offline reasoning, reduces the influence of unloading on the reasoning process, and transfers part of tensors in a device with low bandwidth to the idle memory, so that the I / O overhead during transmission is reduced.

The invention discloses a method and a system for realizing a security acquisition software plug-in with dynamic characteristics for an electric power monitoring system, and aims to solve the problems of difficulty in function extension, insufficient security and poor resource isolation of a traditional electric power monitoring system. According to the system, through a plug-in architecture design, a plug-in function module is realized in a dynamic library form, and dynamic loading, unloading and security isolation operation are supported. The plug-in is started through the sub-process, the main program ensures the legality of the plug-in through SM2 certificate verification, and efficient interaction between the main program and the plug-in is achieved through a callback function mechanism. The plug-in supports dynamic memory allocation, and potential safety hazards caused by predefined field length are avoided. Core rights points of the method comprise plug-in expansion capability, sub-process isolation, SM2 certificate verification, a transparent transmission mechanism and dynamic memory management, and the function expansibility, the safety and the operation stability of the power monitoring system can be remarkably improved.

The invention relates to the technical field of memory management, in particular to a dynamic memory management method based on a bidirectional queue, electronic equipment and a medium. A first pointer and a second pointer are set to orderly control bidirectional insertion or deletion of queue elements, a queue main memory is started firstly, and when the storage requirement of the queue elements is not met, areas stored with the queue elements are reordered and then a queue auxiliary memory is started; when the queue main memory and the queue auxiliary memory are not enough to meet the storage requirements of the queue elements, updating the sizes of the queue main memory and the queue auxiliary memory to expand the queue memory, reordering discrete regions, in which the queue elements are stored, in the original queue memory, generating a continuous region, and writing the continuous region into a new queue main memory; the frequency of queue element copying and memory releasing in the dynamic change process of the bidirectional queue is reduced, and the memory utilization rate of the bidirectional queue is increased.

This invention relates to a dynamic memory management device and method for high-level synthesis. The device includes at least several search cache modules and several modification write-back modules. The search cache modules are connected to DRAM storage modules and BRAM cache modules, respectively. The modification write-back modules are also connected to DRAM storage modules and BRAM cache modules, respectively. The BRAM cache modules cache node information on the search path and temporarily store modification information for nodes. The search cache modules read node data from the DRAM storage modules and write it to the BRAM cache modules based on received operators and node addresses. The modification write-back modules read node data from the BRAM cache modules and write it back to the DRAM storage modules. Addressing the low execution efficiency of directly porting traditional operating systems to FPGAs, this invention leverages the large capacity of DRAM on FPGAs to achieve efficient dynamic memory allocation and reclamation, improving the ease of use and code reusability for high-level synthesis.

This application discloses a memory management method and electronic device, relating to the field of computer technology. The method includes configuring the memory of any controller in a target cluster based on its current memory management parameters, running preset test cases on the controller to obtain performance indicators under the current memory configuration, determining the fitness function value of the controller under the current memory configuration, and then determining the controller's historically optimal memory management parameters. If the current iteration count has not reached an iteration count threshold, the memory management parameters are updated based on the target update speed, and iteration continues based on the updated memory management parameters. If the current iteration count reaches the iteration count threshold, the controller is configured with appropriate memory based on its historically optimal memory management parameters. This achieves dynamic memory management of the controller, resulting in improved memory utilization.

The invention discloses a memory dynamic management method and terminal equipment, and relates to the field of terminals, the terminal equipment is deployed with a large language model, the large language model comprises a large number of operators, and the first operator is any one of the operators. After the target model receives the first input data; if it is determined that a first memory block applied for the input tensor and the output tensor of the first operator in the historical round exists, and the size of the first memory block meets the requirement of data generated in the processing process of the round, the first memory block is reused to store the data generated in the processing process of the round; and a large number of memory blocks do not need to be released and applied in each round of processing process. The number of times of memory block application and release is reduced, and the processing speed of the large language model is improved.

The present application relates to a kind of coal mining operation multi-source time series data attention perception and dynamic memory method, belong to coal mine intelligent technology field.It includes: the three-dimensional feature dynamic extraction and scene information attention fusion of the multi-source time series data of coal mining face, generate scene multi-dimensional feature dataset;Multi-agent driven coal mining scene multi-dimensional dynamic information hybrid storage mode is used to store the multi-source time series data of coal mining face and scene multi-dimensional feature dataset, form data base;When obtaining coal mining scene task instruction, based on data base, in combination with equipment, process, environmental information, the related coal mining scene of it is mapped out, and the content indicated by the coal mining scene task instruction is completed by the cooperation of various types of Agent.The present application introduces Agent intelligent agent architecture and attention mechanism, in combination with dynamic memory management strategy, can provide core technical support for coal mining operation state real-time monitoring, fault intelligent diagnosis and production optimization decision.

The invention discloses a large model memory management and interaction method based on a dynamic cognitive map. The interaction method comprises the following steps: receiving information input by a user; performing dialogue generation, intention recognition and information extraction by adopting a large language model inference engine to obtain an information recognition result; and performing dynamic memory management on the information identification result. And through contradiction detection and an active verification mechanism of the reflection engine, continuous pollution of wrong memory is effectively avoided, so that the system memory becomes more and more reliable over time.

The invention relates to a coal mining operation multi-source time series data attention perception and dynamic memory method, and belongs to the technical field of coal mine intellectualization. Comprising the following steps: performing three-dimensional feature dynamic extraction and scene information attention fusion on multi-source time sequence data of a coal face to generate a scenarized multi-dimensional feature data set; the method comprises the following steps of: storing multi-source time sequence data and a scenarized multi-dimensional feature data set of a coal face by adopting a multi-agent driven coal mining scene multi-element dynamic information mixed storage mode to form a data base; after a coal mining scene task instruction is obtained, coal mining scenes related to the coal mining scene task instruction are mapped on the basis of a data base in combination with equipment, process and environment information, and contents indicated by the coal mining scene task instruction are completed through cooperation of various types of Agents. According to the method, an Agent agent architecture and an attention mechanism are introduced, and a dynamic memory management strategy is combined, so that core technical support can be provided for coal mining operation state real-time monitoring, fault intelligent diagnosis and production optimization decision making.

The embodiments of the present application disclose a memory management method, apparatus, memory manager, device and storage medium, which relate to the field of memory management. The method comprises: determining a memory slice according to the business type corresponding to different business data; adjusting the slice capacity of the memory slice based on the interruption frequency of the memory slice during business processing, wherein the interruption frequency refers to the frequency at which the memory usage of the memory slice is greater than the memory waterline, and the memory waterline is the threshold value for triggering an interruption of the memory slice. The method provided in the embodiments of the present application can reasonably adjust the slice capacity of the memory slice corresponding to different business types through a dynamic memory management mechanism, thereby improving memory utilization.

The embodiment of the invention discloses a memory management method and device for a secure container. The specific implementation mode of the method comprises the steps that a mounting point of the virtual file system is created according to preset mounting parameters, and the mounting parameters comprise an identifier used for indicating whether a large-page memory is started or not; creating a security container, and setting the virtual file system as a memory rear-end carrier of the security container through the virtual machine simulator; detecting mounting parameters through the virtual machine simulator to determine whether a large page memory is started or not; in response to determining to enable the large page memory, allocation of the large page memory is requested. According to the implementation mode, refined application of a transparent large page technology in a secure container environment is realized, and performance benefits close to static large pages are obtained while dynamic memory management advantages are kept.

The invention belongs to the technical field of computer software, and particularly relates to a collaborative review management system based on PDF (Portable Document Format) structured preview and precise annotation, which is characterized in that structural analysis is carried out on an uploaded PDF through a PDF JavaScript analysis engine, paragraph structure information is extracted, and hierarchical and paging quick preview is realized; providing a text-level precise annotation tool to firmly bind approval and original text character positions and perform reverse highlight positioning; a Web-based real-time cooperation platform is constructed, multi-user synchronous review is supported, and full-life-cycle management including states and persons in charge is carried out on review opinions; and a large PDF file is efficiently processed by adopting a paging loading and dynamic memory management strategy. According to the method, the problems of difficult cooperation, rough annotation, poor traceability, large file performance bottleneck and the like in a traditional review mode are effectively solved, and the efficiency and quality of scenes such as technical review and contract auditing are remarkably improved.

The invention provides a method and a system for synchronously hiding multiple parameters in a memory address. The method comprises the following steps of: creating a number parameter mapping table and a parameter type mapping table; responding to the parameter generation instruction, generating a splitting position based on a timestamp seed of a current parameter in the parameter-seed mapping table, and determining a splitting function; splitting the original values of the parameters, distributing splitting values for splitting positions, and updating the parameter mapping table; responding to the parameter modification instruction, searching all splitting positions of the current parameter, and preferentially selecting the splitting position corresponding to a parameter set with a plurality of parameters as an associated splitting position; the method comprises the following steps: extracting an associated parameter set from a data parameter mapping table, triggering a parameter generation instruction of each parameter in the set, repeatedly executing a split position generation and numerical value distribution process, and synchronously updating the data parameter mapping table, thereby sharing a split position through multiple parameters, converting the cracking difficulty from linearity to nonlinearity, and combining dynamic memory management to improve the cracking efficiency. And data concealment and operation efficiency are considered in a high-frequency scene.

The invention relates to an agent memory compression method based on a large-model attention mechanism, and the method comprises the following steps: obtaining the attention weight of each memory in a historical memory sequence for a current conversation, and calculating the correlation degree of the attention weight; determining a demarcation point for distinguishing the early content from the recent content from the historical memory sequence based on the correlation degree and the system constraint condition; dividing the historical memory into an early memory section and a recent memory section by the demarcation point C; performing semantic extraction on the early memory segment to generate summary information, and replacing the original early memory segment with the summary information; and combining the abstracted early memory segment with the complete recent memory segment to form compressed memory input required by the next round of model reasoning. According to the dynamic memory management method based on early-stage content abstract extraction and recent content complete retention, the demarcation point of the early-stage content and the recent content is automatically determined through an attention weight calculation mode, and controllable compression and effective fidelity of the memory content are achieved.

The invention provides a dynamic hierarchical semantic memory method. The dynamic hierarchical semantic memory method comprises a hierarchical encoder, a dynamic memory management and access mechanism and context-based semantic resolution and fusion. A multi-level and structured dynamic semantic memory system can be constructed, surface sequence information of a text can be captured at the same time, the method is used for local coherence and deep semantic architecture and global understanding, and the most relevant part can be dynamically activated according to a current task.

The description relates to dynamic memory management. One example includes an assembly that entails processing elements and memory. A dynamic UMA / NUMA configuration module is configured to facilitate managing a first region of the memory based upon a Uniform Memory Access (UMA) architecture and a second region of the memory based upon a Non-Uniform Memory Access (NUMA) architecture. The dynamic UMA / NUMA configuration module is configured to dynamically adjust ratios of the memory in the first region and the second region based upon workload changes on the processing elements.

A memory controller is configured to receive segment pointers that identify physical memory devices and slices for associated segments. The memory controller is configured to identify a most-full first one of the physical memory devices, to identify a most-empty second one of the physical memory devices, and to identify one of the segments in the most-full first one of the physical memory devices. The memory controller is configured to move a slice from the identified one of the segments in the most-full first one of the physical memory devices to the most-empty second one of the physical memory devices. The memory controller is configured to update one of the segment pointers for the identified segment. The memory controller is configured to update one of the segment pointers for the identified segment by removing a previous segment pointer to the most-full first one of the physical memory devices.

A dynamic memory management circuit includes interfaces configured to receive data from a memory subsystem, the data describing memory allocation requests from accelerator processing units during a first time period; and one or more processors coupled to the interfaces and configured to: determine memory space to be allocated to a CPU and the accelerator processing units in accordance with a memory allocation policy. The policy includes: allocating a minimum memory space required for an operating system executed by the CPU; allocating a minimum memory space required by the CPU to fulfill a predefined quality of service requirement; one or more rules to amend memory space to be allocated to the CPU and the accelerator processing units based on the data. The processors are further configured to send an instruction to allocate memory for the CPU and the accelerator processing units in accordance with the determined memory space to be allocated.

The invention provides a multi-shot consistency video generation method based on dynamic memory management, and the method comprises the steps: carrying out the coding and feature extraction of a story script to obtain semantic information, and mapping the semantic information into a structured shot description; entity categories and attribute vectors are extracted according to the shot description; retrieving a matched target entity reference image in a dynamic memory library according to the entity category and the attribute vector; when the matched entity reference image does not exist in the dynamic memory bank, generating a new entity reference image according to the attribute vector and the historical image, and writing the new entity reference image into the dynamic memory bank; generating key frames according to the shot description and the target entity reference image, and synthesizing a video according to a plurality of key frames; the performance of role consistency, prop consistency and background consistency in long video generation can be remarkably improved, and the method has high robustness and expandability.

A computer system implements a unified framework integrating an adaptive elastic funnel (AEF) with a convergent intelligence fabric (CIF) for multi-agent AI collaboration. The system provides a multi-layer key-value subsystem for sharing computations across GPU partitions, applying hybrid placement strategies for dynamic memory management. It unifies physical GPU sub-allocation and virtual GPU time-slicing through a common abstraction layer while maintaining isolation through policy-based multi-tenancy. A predictive resource orchestration system forecasts computational needs, while speculative data scheduling proactively manages memory access patterns. The architecture includes risk-based scheduling for uncertain workloads, test-time compute scaling, and federated learning for edge collaboration. This framework delivers computational efficiency, security, and adaptive intelligence in high-dimensional environments while supporting incremental adoption through modular interfaces.

The application relates to the technical field of memory management, in particular to a dynamic memory management method based on a one-way queue, electronic equipment and a medium. The application stores queue elements in the mode of setting a queue main memory and a queue auxiliary memory, first activates the queue main memory, then activates the queue auxiliary memory, and when the queue main memory and the queue auxiliary memory cannot meet the storage requirements of the queue elements, the size of the queue main memory and the queue auxiliary memory is updated to expand the capacity, the frequency of copying and releasing the memory of the queue elements in the dynamic change process of the one-way queue is reduced, and the queue memory utilization rate of the one-way queue is improved.

The invention relates to a database security migration method and device based on memory protection, and aims to solve the problems of memory overflow (OOM) and migration interruption caused by rate mismatching under a read-write thread separation architecture. The method comprises the steps that the memory occupation amount of data read by a read thread is counted in real time and accumulated to a cache accumulated value, and the value is deducted to dynamically update the memory occupation after write-in by a write thread; when the cache accumulated value exceeds a preset backlog threshold value, pausing a read thread and scheduling the read thread to assist a write task; and continuously checking the accumulated value in the assistance mode, and recovering the original task of the read thread if the accumulated value reaches the standard. According to the method, through dynamic memory management and control and thread scheduling, excessive accumulation of the memory is accurately inhibited, and OOM faults are avoided; the efficient migration advantage of read-write separation is reserved, migration stability is guaranteed through closed-loop regulation and control, collaborative improvement of efficiency, stability and data safety is achieved, and the method is suitable for high-concurrency and large-data-volume migration scenes of the database.

The invention relates to the technical field of memory management, in particular to a dynamic memory management method based on a one-way queue, electronic equipment and a medium. Queue elements are stored by setting a queue main memory and a queue auxiliary memory, the queue main memory is started firstly, and then the queue auxiliary memory is started; and when the queue main memory and the queue auxiliary memory are not enough to meet the queue element storage requirement, the sizes of the queue main memory and the queue auxiliary memory are updated to expand the capacity, so that the frequency of copying the queue elements and releasing the memories in the dynamic change process of the one-way queue is reduced, and the queue memory utilization rate of the one-way queue is improved.