390 results about "Memory pool" patented technology

The invention discloses a compatible operation method for an Android camera HAL in a container based on memory access virtualization, and the method comprises the steps: taking a DMA-Buf memory heap of a Linux kernel host system as a target memory heap, taking an ION memory heap of an Android container system as a source memory heap, building a process exclusive memory management context, an FD cache table and a synchronous fence pool, and carrying out the execution of the process exclusive memory management context, the FD cache table and the synchronous fence pool; kernel registration and node binding of virtual ION equipment, pre-allocation of a first memory pool and access hook registration of kernel layer equipment are completed, and when an ION file descriptor is obtained in an HAL process, legality of a container process is verified, and context binding is initialized to the file descriptor; intercepting a memory allocation request of the HAL process, analyzing and adapting parameters, and preferentially multiplexing first memory pool resources to obtain an ION handle; when a data sharing request is processed, matching an FD cache or generating a new FD through an ION handle; when the HAL process releases the memory, resources are recycled according to a memory source, and when the HAL process exits, the context is cached or destroyed, so that cross-system memory operation compatibility is realized.

The invention discloses a multi-source data fusion type intelligent data management system, which comprises a data preprocessing module used for accessing a heterogeneous data source and generating a standardized data frame; the cognitive sub-graph construction module is used for generating cognitive sub-graphs and forming a cross-source evolutionary multi-cognitive hypergraph; the event aggregation module is used for aggregating entities and relationships according to event fingerprints and outputting a multi-source observation chain; the causal checking module is used for performing causal comparison and conflict detection and outputting a checking result, a positioning report and a treatment suggestion; the fusion updating module is used for distributing weights and performing weighted fusion, outputting fusion representation and confidence score, recording conflicts and updating the hypergraph; and the memory pool module is used for storing the hypergraph, the check result and the fusion representation, executing weight adjustment, forgetting and elimination, and outputting a service interface. According to the method, intelligent fusion and dynamic management of multi-source heterogeneous data are realized by constructing a cross-source self-evolution multi-cognitive hypergraph and an active evolution type fusion memory pool.

The invention discloses an incomplete multi-mode crisis event detection method based on a memory pool and a mode perception expert system, and relates to the field of machine learning, and the method specifically comprises the steps: building a prompt strongly related to an event instance based on the memory pool, and complementing a missing mode through a large model; a CNN encoder, a ResNet encoder and a CLIP encoder are utilized to construct a double-flow encoder, and text and visual feature extraction is achieved; capturing fine-grained event information and clues; the distribution difference between a complemented sample and a complete sample is identified based on a modal perception expert system, and the quality of fusion representation is improved; and inputting the mixed features into the classification head to complete a crisis event detection task. According to the invention, through the prompt construction module based on the memory pool, a large model is introduced to realize careful completion of a missing mode, and the distribution difference between a completion sample and a complete sample is accurately identified based on a mode perception expert system, so that a multi-mode data missing scene can be effectively coped with, and the accuracy and timeliness of crisis event detection are improved.

The invention discloses a context-aware-based gap containerization TLS compatible method, which comprises the following steps of: distinguishing a simple TLS variable and a complex TLS variable in a source gap system, and establishing a TLS variable source feature; the method comprises the following steps: constructing a dynamic telescopic TLS memory pool matched with a variable access permission in a target system, expanding a kernel state TLS segment descriptor, constructing a bidirectional mapping table of musl TLS offset and a memory pool address, and optimizing and constructing a clustering structure aligned with a CPU cache line for a hotspot variable to improve the access efficiency; tLS variable access tracks are recorded through a dynamic binary translation engine, TLS segment descriptors are updated, the descriptors are read during thread switching to recover TLS context and refresh cache to guarantee consistency, address and offset conversion is completed according to target system alignment requirements by combining source features and a bidirectional mapping table for TLS variables of nested structures, pointers and array types, and the TLS variables of the nesting structures, the pointers and the array types are aligned. And completing dynamic conversion execution of the source system application program in the target system.

The invention discloses an auditing data security management and control system based on big data, and relates to the field of data management and control, and the system comprises a distributed crawler which collects auditing logs, operation records and network traffic in real time. And the stream batch integrated engine performs metadata alignment and timestamp synchronization on heterogeneous data to generate a unified audit event stream. The intelligent desensitization module carries out gradient desensitization on the sensitive fields and constructs reversible blood relationship mapping. According to the hierarchical storage, data are stored in an encrypted memory pool, an SSD and a cold standby cluster according to data sensitivity and access frequency, and consistent hash load balancing is achieved. Multi-modal safety monitoring analyzes data in real time through a three-level assembly line. And the hierarchical response module writes a permission chain according to the risk trigger action and the key audit event hash value, and constructs a tamper-proof evidence chain. The method has the advantages that the data security, privacy protection and efficient emergency response are ensured through intelligent desensitization, hierarchical storage and security analysis of the audit data, and the non-tampering property of the data is ensured by utilizing the block chain technology.

The invention discloses an operating system containerization kernel compatibility method based on shadow structure mapping, which comprises the following steps of: constructing a multi-dimensional mapping rule base by analyzing the difference of data structures between a container system and a standard system, and creating a shadow structure comprising a data area, a metadata area and an expansion area when the container system runs on a host system; the host system allocates a shadow memory space and a memory pool, establishes mapping between a shadow structure and a physical memory page and bidirectional mapping between the shadow structure and a host structure, intercepts system call of a container application, obtains a target structure and allocates a shadow structure and a host structure memory, and sends the shadow structure and the host structure memory to the host system; according to the method, conversion from the target structure to the host structure is completed according to the multi-dimensional mapping rule base, the host machine completes calling through the host structure, then the calling result is reversely converted into the target structure and returned to the container application, and seamless compatibility of the container system and the host system is achieved under the condition that a user mode program of the container system is not modified.

The invention discloses a resource management method and device, equipment and a medium, and relates to the technical field of storage, the resource management method comprises the following steps: carrying out performance test and data analysis on different storage architectures according to an I / O path of a distributed storage system and an asynchronous programming model, and determining processor static parameters under the different storage architectures according to the results of the performance test and the data analysis; the method comprises the following steps: collecting performance indexes of a distributed storage system through an asynchronous programming model, predicting load change conditions of the system according to the performance indexes of the distributed storage system, and dynamically adjusting processor resources according to the load change conditions of the system; managing a large-page memory pool and a cache according to the large-page memory management model and the business model type of the distributed storage system; and optimizing a memory application and release mechanism in the storage service of the distributed storage system. Through dynamic scheduling of processor resources and a large-page memory pool data sharing mechanism, balanced allocation of storage and calculation resources is realized, and the system performance and the resource utilization rate are improved.

The invention discloses a method and a system for tracking and detecting an occluded target in an electric power scene based on thunder-vision integrated multi-mode spatio-temporal adversarial learning, and relates to the field of intelligent inspection of electric power equipment, and the method comprises the steps: inputting a structured point cloud matrix and a visual image into a double-branch feature extraction model, respectively extracting geometric features and semantic features by using a double-branch feature extraction model; inputting the bimodal fusion features into a dynamic weight adaptive fusion mechanism based on an adversarial training strategy to obtain a fusion weight coefficient of the target in a short-time shielding state in the power scene; and a double-buffer memory pool is constructed, and a three-level progressive trajectory prediction compensation mechanism is combined to compensate a target trajectory position in a long-time shielding state in a power scene. According to the method, the shielding state quantitative model is constructed, the fusion weight distribution proportion can be effectively and dynamically adjusted in real time according to the visual sensor, and when it is detected that the target equipment enters the shielding state, the visual data weight is automatically reduced to the safety threshold value.

A real-time target detection method and system based on RTSP flow and NPU collaborative optimization belong to the technical field of computer vision and artificial intelligence, and are characterized by comprising the following steps: adopting dynamic memory optimization management of a hybrid pipeline architecture, and performing single-time continuous copying through a CPU (Central Processing Unit); through deep integration of innovative technologies such as DMA direct transmission NPU continuous memory pool management, dynamic batch processing scheduling, hybrid assembly line processing, intelligent equipment load balancing, parallel preprocessing optimization and vectorization post-processing, RTSP flow collaborative optimization and the like, the NPU utilization rate is improved to 85% or above, the overall average FPS is improved by 200% or above, the assembly line parallelism degree achieves three times of performance gain, and the production efficiency is greatly improved. The data transmission delay is reduced by 80%, the system stability is remarkably improved, performance degradation is avoided after long-time operation, and the method is suitable for various scenes such as edge calculation and cloud reasoning.

The invention discloses a CXL protocol extension method, device and system based on the Ethernet. The method comprises the following steps of: carrying out long-distance expansion interconnection on a CXL (Compute Express Link) protocol by using Ethernet as a physical transmission medium; by bearing the CXL protocol on the Ethernet, the range of memory pooling is expanded from the rack level to the whole data center (kilometer level), and the potential of the CXL technology is thoroughly released; through protocol translation, label management and flow control realized by pure hardware, an operating system kernel and a software protocol stack are bypassed, so that the end-to-end memory access delay is far lower than that of any software-based network storage scheme, and a reliable request-response tracking and timeout retransmission mechanism is realized through a hardware label manager. By distinguishing the control flow and the data flow, differentiated QoS guarantee is provided for different types of memory flows, and the stability of the system under high load is ensured.

The invention relates to the technical field of memory expansion, and discloses a memory access system and method for a memory pool, the system is connected with PC I e equipment and CXL equipment, the PC I e equipment is used for sending an access request to a host so as to access the CXL memory pool through the host; the CXL equipment is used for sending a read-write command to the CXL switch so as to access the CXL memory pool through the CXL switch, the system comprises a host which is respectively connected with the PC I e equipment and the CXL switch, and the host is used for receiving an access request sent by the PC I e equipment, converting the access request into the read-write command and sending the read-write command to the CXL switch so as to access the CXL memory pool through the CXL switch, cXL equipment and PC I e equipment which are short in memory are expanded through the CXL memory pool, the memory access system of the memory pool can be compatible with the CXL equipment and the PC I e equipment at the same time, unified memory distribution and memory management are provided for the CXL equipment and the PC I e equipment, memory sharing between the CXL equipment and the PC I e equipment is achieved, memories of the CXL equipment and the PC I e equipment are expanded, and therefore the memory access efficiency and the memory utilization rate are improved.

The invention provides a real-time scheduling method for a wireless network based on a large language model, the wireless network comprises a scheduling node and a plurality of user nodes, and the scheduling node is provided with a scheduling agent, a historical memory pool, an reflection agent and a suggestion buffer area. The method comprises the following steps executed in each time slot: step S1, the scheduling node acquires global information of a wireless network under the current time slot and calculates states of all data streams under the current time slot according to a preset calculation rule; and S2, the scheduling agent generates a scheduling decision of the current time slot according to a preset scheduling prompt word by using a large language model configured by the scheduling agent based on the states of all data streams under the current time slot and decision suggestions stored in the suggestion buffer area and by taking a maximized reward as a target. According to the technical scheme of the invention, by introducing the large language model, the problem of insufficient adaptability in a dynamic network environment in the prior art is solved.

The invention discloses a data transmission method and device, equipment, a storage medium and a computer program product, and the method comprises the steps that first equipment responds to first system call of an application process, and divides a first data buffer area into a plurality of sections; the first data buffer area is used for buffering first data written by an application process; allocating a first mbuf and a plurality of second mbufs from the first memory pool based on the plurality of sections; the first mbuf is used for storing message header information corresponding to a first message, and the first message represents a message used for transmitting first data; each second mbuf in the plurality of second mbufs is used for indicating one section; linking one first mbuf and a plurality of second mbufs into a first linked list, and mapping the first linked list into a network card, so that the network card sends a first message to the second equipment; the first linked list is used for the network card to read the first data from the first data buffer area and encapsulate the first data into a first message.

Provided is a system and method for memory pool management, the method including receiving, by a first control circuit of a memory device controller, a first request to create a first logical memory space, the first control circuit being associated with a first memory zone, based on determining that a first memory unit, at a first physical address range of the first memory zone is available, allocating the first memory unit to the first logical memory space, based on determining that a second memory unit of the first memory zone is occupied, sending a memory-donation request to a second control circuit of the memory device controller, the second control circuit being associated with a second memory zone, and based on determining that a third memory unit of the second memory zone is available, allocating the third memory unit to the first logical memory space.

The invention discloses a building material transportation scheduling method based on edge calculation, and the method comprises the following steps: S1, deploying an edge sensing terminal, and collecting and standardizing multi-source state data; s2, detecting an abnormal event at an edge node and outputting event information; s3, inputting the event information into a disturbance attention affine transformation unit to generate scheduling input; s4, generating and broadcasting a local scheduling scheme by adopting a distributed asynchronous game mechanism; s5, establishing a multi-order memory pool, recording data and schemes, and retrieving similar scene adjustment parameters; and S6, the scheme and the optimization result are uploaded to the cloud, and the edge node is optimized through strategy return. According to the invention, through edge calculation, multi-source data fusion and a distributed cooperative scheduling mechanism, real-time anomaly perception and adaptive optimization scheduling in a building material transportation process are realized.

The invention discloses a chip prototype verification method and device, a medium and a product, relates to the technical field of chip design, is applied to a hardware debugger of a simulation test tool, and comprises the following steps: analyzing a capability maturity model script; the analyzed script and the execution mode switching condition are sent to a central processing unit of the field programmable gate array, so that the central processing unit determines a current execution mode according to the analyzed script and the execution mode switching condition; test case codes of different execution modes in the capability maturity model script are stored in different data areas of a preset memory pool respectively, so that a central processing unit obtains the corresponding test case codes based on the current execution mode, verification data are generated according to the test case codes and peripheral equipment of a pre-configured chip, and the verification data are stored in the preset memory pool; returning the verification data to a hardware debugger of the simulation test tool; and generating a prototype verification report based on the verification data and the expected data to complete chip prototype verification. And chip prototype verification can be realized by dynamically switching verification modes.

The invention discloses a closed-loop transcranial magnetic stimulation brain regulation and control method based on multi-model fusion and individualized learning. Dynamic optimization and accurate regulation and control of TMS stimulation parameters are achieved. According to the technical scheme, the method comprises the following steps: collecting and preprocessing a multi-guide EEG signal in real time, and extracting frequency domain and time domain features and brain network topology indexes; the EEG state in the future 0.5-2 seconds is predicted by using depth sequential networks such as LSTM and the like, and an individualized stimulation parameter strategy is generated in combination with reinforcement learning and Bayesian optimization; tMS stimulation is triggered based on a prediction result, brain network indexes and reward evaluation, and long-term adaptive adjustment is realized through an individual memory pool. The traditional passive feedback mode is broken through, an active closed-loop mechanism of prediction-learning-regulation is realized, the TMS regulation efficiency and the individual adaptability are remarkably improved, and the method can be widely applied to scenes such as mental disease intervention, cognitive regulation and nerve rehabilitation.

The invention provides an asynchronous communication system, method and device based on Netty and a medium, and belongs to the technical field of communication. The system comprises: an asynchronous network communication module, which is used for establishing asynchronous non-blocking network communication connection between a client and a server by using a Netty asynchronous non-blocking I / O model; the memory management module is used for carrying out allocation and recovery management on the memory in the communication process through a memory pool and object pool technology; the protocol adaptation module is used for adapting to different protocols through a self-defined codec and carrying out encoding and decoding processing on data of different protocols; and the back pressure control module is used for introducing a machine learning model, predicting the processing capability of the server and the request trend of the client, dynamically adjusting a semaphore threshold value or the length of a request queue, and realizing adaptive flow control. According to the invention, the communication efficiency is improved, the memory management is optimized, the protocol adaptation is flexible, and the adaptive flow control is realized, so that the performance and service adaptability of a communication system are enhanced.

The invention discloses a memory optimization system for a large-scale language model, and the system comprises a dynamic semantic memory modeling module which is used for generating a context-aware memory vector through a bidirectional Transform, and supporting the increment to be updated to one hundred million entity records; the self-adaptive memory resource scheduling module dynamically allocates memory pool partitions based on inference load, high-frequency memory is stored in a working area, and quantitative compression is started in an archiving area; the memory attenuation and enhancement module adopts a two-factor attenuation mechanism to execute enhanced storage and regular pruning on the high-value memory; the cross-scene memory migration module is used for realizing cross-domain adaptation through a domain adapter and comparative learning; and the memory conflict resolution module identifies conflicts based on the rule base and adopts a multi-factor voting mechanism to resolve the conflicts. According to the method, the memory retention integrity and the retrieval efficiency are improved, the resource utilization is optimized, the cross-scene adaptation capability is enhanced, the memory conflict is eliminated, and the model reliability is improved.

The invention provides an RDMA-based big data transmission and storage system and method and a semiconductor detection device.The big data transmission and storage system comprises a hardware layer and a software layer, the hardware layer comprises an NVMe SSD storage array, an NVMe RAID acceleration card based on a GPU architecture and an RDMA network card, and the software layer comprises an NVMe SSD storage array, an NVMe RAID acceleration card based on the GPU architecture and an RDMA network card; the software layer comprises a multi-thread transmission module, a self-adaptive memory pool module, a transmission control module, a data verification and retransmission module, a scheduling and optimizing module and a disk falling and storage management module. According to the method, multi-channel transmission parallelization is achieved through data direct connection, zero copy transmission, RAID acceleration and multi-thread parallel RDMA transmission in a hardware link, data is directly connected and transmitted from a memory to the NVMe SSD storage array, data receiving and disk falling thread decoupling directly access the NVMe SSD storage array through asynchronous writing, network overload or storage blockage is avoided, and the data transmission efficiency is improved. The data transmission efficiency and reliability are high, and popularization and application in scenes needing large-scale image transmission processing such as semiconductor detection are facilitated.

The invention discloses a resource management method and device for an operating system kernel, equipment, a medium and a product, and relates to the technical field of resource management.The resource management method includes the steps that a computing power pool and a memory pool are integrated into a unified initialized resource pool, resource scheduling is achieved through resource allocation primitives, cross coordination among multiple modules is avoided, and coordination efficiency is improved; resource allocation, isolation and recovery are executed through independent primitives, so that the stability of resource adjustment is guaranteed; resource isolation primitives are executed through a hardware isolation mechanism, resource access between different tasks can be blocked from the physical level, and the security isolation effect is guaranteed; the whole resource management process is completed in the kernel mode of the operating system through the native primitive, and a user mode component does not need to be called to participate in scheduling or management, so that the switching overhead of the user mode and the kernel mode and the central processing unit resources occupied by the user mode component during operation are avoided, and the overall consumption is reduced.

The invention discloses an energy consumption risk closed-loop monitoring method, system, equipment and medium based on a brain-like inference model, and belongs to the technical field of energy digitization and intellectualization, and the method comprises the steps: collecting multi-source energy consumption data in real time, converting the multi-source energy consumption data into a pulse sequence through a pulse encoder, constructing a layered space-time memory pool to process the pulse sequence, and carrying out the real-time monitoring of the multi-source energy consumption data. And based on a neural plasticity rule, carrying out dynamic weight migration on a current risk mode and a historical risk disposal strategy, analyzing a risk conduction path, outputting risk inducements, calculating risk hormone factors, activating a dual-channel response mechanism and generating a disposal scheme by using a risk traceability tree diagram, and optimizing scheme weights through Q-learning to generate a virtual risk scene. And rehearsing the treatment scheme effect, and dynamically updating the model parameters according to the user response data and the treatment effect. According to the invention, through a multi-level cooperation and closed-loop feedback mechanism, full-process intelligentization of energy consumption risk monitoring is realized, the electric charge recovery rate is improved, and the system robustness is enhanced.

The invention discloses a memory pool device fusing an extended network and a management method, and the device comprises a super network card chip which is in communication connection with an intelligent computing cluster network through a network port, and provides a shared memory service for the intelligent computing cluster network based on a remote direct memory access protocol; the intelligent computing cluster network comprises a transverse expansion network and a longitudinal expansion network; the central controller is connected with the super network card chip; the CXL memory unit is connected with the super network card chip, and the FPGA expansion unit and / or the XPU expansion unit are / is connected with the super network card chip. According to the invention, through efficient communication between the super network card chip and the intelligent computing cluster networking, flexible scheduling and sharing of memory resources in the cluster are realized. Wherein the super network card chip is used as a core component, not only is responsible for communication of a transverse expansion network, but also realizes bridging access of a high-bandwidth domain of a longitudinal expansion network, so that the data transmission efficiency and the memory utilization rate can be improved, and high-bandwidth and low-delay data transmission and sharing are realized.

Datacenter workloads demand flexible memory architectures spanning from rack-level to pod-scale deployments. Embodiments herein disclose systems enabling CXL memory semantics over physical layers based on IEEE 802.3 PMA, facilitating memory disaggregation across datacenter fabric infrastructures. The embodiments comprise processing cores with coherent interconnects, MMUs for address translation, and memory channels supporting substantial memory capacities. Resource Provisioning Units (RPUs) translate between CXL data, optionally encapsulated, transmitted via physical layers based on IEEE 802.3 PMA, and CXL requests, enabling external entities to access memory across different physical address spaces. This architecture provides memory pooling using datacenter network infrastructure, supporting intra-rack memory sharing, inter-pod memory access, distributed AI training across datacenter resources, and elastic memory provisioning for cloud-native applications, overcoming physical layer limitations of traditional CXL implementations while maintaining protocol coherency suitable for GenAL, LLM inference, and HPC workloads.

The invention discloses a large-model multi-agent task scheduling method with memory and retrieval capabilities, and the method comprises the steps: carrying out the cognitive analysis of a complex task based on task scheduling agents, and carrying out the distribution of subtasks through combining the functional attributes of all task execution agents and an integrated tool; each task execution agent completes execution work of a specific task through an integration tool, a task result is returned to the task scheduling agent for integration, and execution of a next link subtask or completion of the task is determined according to a task condition; maintaining task context information and a task entity information base by adopting a dynamic memory pool; and cross-agent and cross-task node multi-dimensional information sharing is realized by using cross-agent search. According to the method, the problem of state loss of a multi-agent system is solved by introducing a memory enhancement mechanism, and the context sensing capability in the task execution process is remarkably improved; cross-task and cross-agent knowledge reuse is realized through a cross-agent search mechanism, and the decision quality of the system is improved; the method is compatible with multi-source heterogeneous task requirements, breaks through the limitation of a traditional multi-agent system on task complexity, cross-agent collaboration and knowledge reuse capability, and can be widely applied to the field of intelligent scheduling of complex scenes such as emergency rescue, industrial automation and urban public management.

The invention relates to the technical field of medical artificial intelligence, in particular to a drug discovery task processing method, device and equipment based on large model reasoning, and the method comprises the steps: obtaining a drug discovery task instruction of a user; the drug discovery task instruction is input to the target large model, the target large model outputs an execution result of a drug discovery task, and the reasoning process of the target large model comprises the steps that the drug discovery task instruction is analyzed to obtain an instruction intention and instruction parameters, a target key matched with the instruction intention is selected from a parameterized memory pool based on the instruction intention, and the target key is input to the target large model; the instruction parameter is mapped into a target key value, a target calling tool format is generated based on the target key and the target key value, and a corresponding target discovery tool is called based on the target calling tool format to execute a drug discovery task; and sending an execution result of the drug discovery task to the user. According to the method, the target large model automatically executes the reasoning process, various tools are automatically called, and intelligent drug discovery is achieved.

The invention discloses a multi-target tracking method and system for unmanned aerial vehicle small targets, and belongs to the technical field of computer vision. The method comprises the steps of obtaining multi-level feature maps based on a video frame image, and then unifying the multi-level feature maps into an initial fusion feature map; calculating to obtain a two-dimensional enhanced feature map based on the initial fusion feature map and the original image; calculating to obtain a spatial enhanced feature map based on the two-dimensional enhanced feature maps of the front frame and the rear frame; after splicing the two-dimensional enhanced feature maps of the front frame and the rear frame, sequentially performing feature alignment processing and memory pool interaction processing to obtain a time enhanced feature map of the current frame, and updating the memory pool; and fusing the spatial enhanced feature map and the time enhanced feature map, performing down-sampling and splicing, and further combining an output result with a ByteTrack tracking strategy to realize multi-target tracking. According to the method, the accuracy and identity consistency are remarkably improved, and meanwhile, efficient and real-time reasoning performance is kept.

The application is suitable for the technical field of electric power, and provides a memory pool configuration method, system, electronic device and computer program product, wherein the method comprises the following steps: a first virtual relay protection device of a relay protection device is constructed; in a virtual communication environment, the number of memory pool nodes of the first virtual relay protection device is expanded from a first number to a second number based on the required storage space capacity size of power interaction data in communication; based on the second number of memory pool nodes, the capacity size types of memory blocks contained in all memory pool nodes in the virtual memory pool of the first virtual relay protection device are obtained, and the number of memory blocks of each capacity size type is obtained; based on the obtained information, the number of memory pool nodes of the relay protection device is adjusted to a third number, and a target number of memory blocks of the same capacity size type are configured for each memory pool node. The scheme can realize active optimization of memory pool configuration, reduce personnel cost, and improve memory pool configuration efficiency.

The present disclosure provides a description of exemplary systems and methods for superseding pending transactions in a distributed ledger. The methods and systems may include a processor which may receive a first transaction associated with a digital asset in a memory pool of a distributed ledger network, the first transaction having a first priority. The processor may receive a second transaction associated with the digital asset in the memory pool of the distributed ledger network, the second transaction having a second priority. The processor may analyze the first priority of the first transaction and the second priority of the second transaction to determine a higher priority. In response to determining that the first priority of the first transaction is less than the second priority of the second transaction, the processor may validate the second transaction having the second priority.

The invention discloses an open set anomaly detection method (MP-DRA) combining a multi-scale memory pool and adaptive pseudo anomaly enhancement, and aims to solve the challenges of unstable detection performance, insufficient generalization ability and the like caused by scarcity and uncertain distribution of real abnormal samples in an open set task. According to the method, a multi-scale feature memory pool which can be dynamically updated is constructed and is used for storing representative normal sample features; during detection, the optimal reference benchmark is dynamically selected for each sample to be detected from the memory pool through a self-adaptive strategy combining the historical detection success rate and the feature uncertainty of the samples to be detected, so that the robustness and accuracy of detection are improved. Meanwhile, the invention designs a staged self-adaptive pseudo-anomaly generation mechanism which is linked with the real-time performance of the model, and by dynamically adjusting the training difficulty, the model training stagnation is effectively avoided, and the generalization ability of the model to unseen anomaly is enhanced. Experimental results on a plurality of real world anomaly detection data sets show that the method provided by the invention obtains excellent and stable performance under various scenes.