34 results about "Memory analysis" patented technology

The invention discloses a battery abnormal data prediction and state evaluation method, and relates to the technical field of battery state prediction, and the method mainly comprises the steps: carrying out the preprocessing of an experiment data set, and obtaining multi-dimensional time series data; a combined feature encoder, a pre-response encoder and a memory analysis module are constructed to realize a battery abnormal data fault prediction model; training the model by using the multi-dimensional time sequence data to obtain a trained model, and predicting the to-be-predicted data to obtain a prediction result; and calculating a reconstruction error between a prediction result and original data, constructing an AUROC evaluation model, and evaluating the battery abnormal data fault prediction model. By implementing the battery abnormal data prediction and state evaluation method provided by the invention, the feature extraction efficiency, the abnormal recognition precision, the detection stability and the generalization ability can be improved.

The invention relates to the technical field of memory training, in particular to a memory grading training algorithm and system, and the algorithm comprises the steps: carrying out the memory test of a to-be-trained person, and obtaining a first memory curve; matching the analysis information, the training target and a plurality of algorithm containers to obtain a first algorithm container; performing memory training on the to-be-trained person according to the first algorithm container; testing the trained to-be-trained person to obtain a second memory curve; and updating the first algorithm container according to a comparison result of the first memory curve and the second memory curve. According to the invention, the memory basis and characteristics of each to-be-trained person can be accurately grasped by performing memory analysis on the to-be-trained person. On the basis, the analysis information is matched with the training target and the algorithm container, the first algorithm container is selected for each to-be-trained person, personalized memory training is achieved, the training is more suitable for the personal actual situation, and the pertinence and effectiveness of the training are remarkably improved.

The invention discloses an industrial controller vulnerability hot repair method and system based on memory forensics, and the method comprises the steps: obtaining the memory and firmware information of an industrial controller in real time through a firmware extraction module, providing data support for the subsequent vulnerability analysis, and maintaining the stable operation of the system; vulnerabilities and abnormities in the system are accurately searched through memory analysis and behavior detection by a vulnerability positioning module, and rapid and real-time security response is provided; and the vulnerability repair module performs vulnerability repair on the basis of a vulnerability positioning result by utilizing a hot repair technology under the condition of not interrupting the operation of the control system, so that the security and high availability of the industrial controller are ensured. The firmware extraction module, the vulnerability positioning module and the vulnerability repair module cooperate with one another to form a closed-loop detection, analysis and repair system so as to ensure that the industrial controller can efficiently respond and maintain continuous and stable operation when facing security threats.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a memory analysis question and answer data construction method and device, equipment and a medium. The method comprises the steps of extracting a fact information set containing entities, relationships and values based on structured message representation, generating candidate question and answer pairs by utilizing a language model, executing multi-source verification to form a target question and answer set, performing semantic extension and question method transformation to generate a diversified question and answer set, and completing credibility screening based on a verification result to obtain a memory analysis question and answer set. And generating a memory analysis data set in combination with the user message sequence. According to the method, through multi-stage extraction, generation, verification and screening, reliability and diversity unification of question and answer content are achieved, the constructed memory analysis data set has the advantages of being consistent in context, accurate in fact, rich in expression and the like, and therefore the credibility of memory ability evaluation of the large language model is improved.

An IoT-based backup and recovery system that features: a system initialization to load policies and set baselines; a microcontroller connected to several modules, including: a multi-layered module that captures kernel events, network activity, user behavior, memory analysis, and container inspection; a module for time-bound differential micro-snapshots for signal processing to obtain spectral values; an AI-driven threat classification module for precise attack identification; an event tree-based forensic tagging module for creating maps of causal relationships between system events for a complete attack path; Policy-oriented recovery rules with context-aware policies that adapt depending on the type of attack and business impact; and a surgical rollback engine for component-specific recovery without service interruption.

The invention discloses a knowledge graph enhanced retrieval generation system and method based on a big language model and subsequent representation, and relates to the technical field of enhanced retrieval generation, the system comprises: a big language model module for completing knowledge graph construction and answer generation through a preset cue word instruction or template, integrating semantic vectorization and a reordering model to optimize a retrieval result; the knowledge base module comprises a vector library and a knowledge graph; the vector library performs semantic coding on graph nodes to form a vector set, and integrates a similarity index and a reordering strategy; the knowledge graph contains entity nodes and relation edges, provides a triple extraction object and a retrieval source for the large language model, and provides a node jump state space for the subsequent representation module; and the subsequent characterization module is used for bearing node retrieval jump tracks and preference memories through a matrix, analyzing node relevance, consolidating jump plots, characterizing retrieval strategy dynamic states and providing support for retrieval optimization and question and answer generation. According to the method, the behavior flexibility of large language model enhancement generation can be effectively improved.

The invention discloses an interactive task generation method and device and a storage medium, and relates to the field of man-machine interaction, and the method comprises the steps: obtaining context information, task knowledge and a task target; constructing a long-term preference map and a heterogeneous task knowledge base; finely adjusting the large language model; obtaining interaction state information; short-term emotional state memory is constructed, and a preference result is analyzed; generating a query vector; generating a retrieval result; generating a memory enhanced template; generating a current interaction task; collecting behavior data when the current interaction task is executed; analyzing the behavior data, and constructing a multi-dimensional reward; and updating the long-term preference map and the short-term emotional state memory. By constructing a long-term preference map and a short-term emotion recognition mechanism, the modeling ability for the individual state of a user and the environment context is improved, and the space consistency, semantic rationality and personalized adaptive recommendation of a task generation process are realized; and the comprehensive requirements on cognition authenticity, execution accessibility and user experience continuity in a multi-modal interaction scene are met.

The invention discloses a memory data analysis method and device, electronic equipment and a storage medium, and relates to the technical field of computers.The method comprises the steps that original memory data is sorted, and target memory data based on a reference relation is obtained; wherein the original memory data comprises a memory use condition and object reference relation data; and performing visual analysis on the target memory data. According to the method, the target memory data based on the reference relationship is organized and generated, so that the actual reference chain between the objects can be revealed, a developer is helped to accurately grasp the object life cycle and the memory occupation condition, and the memory analysis accuracy is improved; the target memory data is visualized, and the hierarchical structure and association between the objects can be displayed, so that optimization of performance analysis is facilitated.

The invention provides a memory leak detection and analysis method, system and device and a medium, and belongs to the technical field of memory detection. Memory use data based on an application program is obtained and stored; based on a memory analysis module, analyzing the memory use data, and identifying a memory leak position and memory leak data; key information of the memory leak data is extracted, and a memory leak data report is generated; and displaying the memory usage amount and the leakage mode through a graphical interface. According to the method, the existing memory leak detection technology is highly fused, and the advanced visual analysis technology is adopted, so that the detection accuracy and efficiency are improved, the security of the system is enhanced, and an efficient, convenient and reliable memory leak solution is provided for a user.

The embodiment of the invention provides a vehicle-mounted application memory occupation detection method and device, computer equipment and a storage medium. The method comprises the steps that a to-be-detected vehicle-mounted application is split into a plurality of assemblies; for each component, recording a test script of the current component by using a preset script recording tool; based on a preset memory analysis tool and a memory index script, performing pressure test on the current component according to the test script of the current component; and based on a preset memory occupation sample and a memory occupation standard, determining whether the memory occupation of the to-be-detected vehicle-mounted application is abnormal according to the memory snapshot file and the memory index obtained by the pressure test, and detecting whether the memory occupation of the to-be-detected vehicle-mounted application meets the standard of the vehicle-mounted equipment in the high-frequency and high-strength long-time use process of the vehicle-mounted application. Whether abnormal conditions of memory leak and memory jitter exist or not is judged, and it is ensured that the vehicle-mounted application is kept in a running state in a complex environment or is paused and recovered at a proper time so as to meet user requirements.

Systems, methods, and processing devices for aiding with cyber intrusion investigations that includes capabilities for extracting data from a specified range of a volatile memory of a target processing device, reconstructing data structures and artifacts from the extracted data; and generating and presenting a visualization of the reconstructed data structures and the reconstructed artifacts.

The application discloses a memory detection method and system of a virtual machine. The method is applied to a memory detection system, and the memory detection system is used for running a main process to execute the following method: in response to an inter-process communication request of at least one detection process, determining to-be-detected memory of a virtual machine; performing memory analysis on the to-be-detected memory according to a detection item, and obtaining an analysis result, wherein the analysis result comprises memory information of the to-be-detected memory under an operating system where the virtual machine is located; transmitting the analysis result to the detection process through inter-process communication between the main process and the detection process, wherein the analysis result is used for detection according to the detection item in the detection process, and a memory detection result is obtained; and obtaining the memory detection result corresponding to the analysis result through inter-process communication. The application solves the technical problem that the stability of a detection system for detecting memory of a virtual machine by relying on proxy software is poor.

The present invention discloses a memory analysis method, device and system. The method comprises: obtaining a memory snapshot file when the system is running, the memory snapshot file comprises at least one type of memory object, each type of memory object comprises attribute information organized according to a predetermined data specification; creating at least one table structure, each table structure corresponds to a type of memory object; parsing the memory snapshot file, and parsing the attribute information of each memory object into a corresponding table structure according to the corresponding data specification; searching at least one table structure, so as to perform memory analysis according to the search result. The method collects the attribute information of the memory objects in the memory snapshot file into multiple table structures, so as to analyze and locate the memory problem by searching the data in the table structure.

The invention discloses an information management method and system based on a government affair comprehensive management platform, and relates to the technical field of data processing, and the method comprises the steps: obtaining to-be-managed government affair information, carrying out the classification processing of the to-be-managed government affair information based on an intelligent analysis terminal, and determining the authority government affair information and the non-authority government affair information. According to the method, authority analysis, content analysis, information sorting, format analysis and memory analysis are carried out on the to-be-managed government affair information, the authorized government affair information and the unauthorized government affair information are determined, then similar information in the authorized government affair information and the unauthorized government affair information is merged, and when corresponding keywords are subsequently input, the corresponding keywords are input, so that the management efficiency is improved. In addition, storage information of the government affair information to be managed is analyzed, whether the government affair integrated management platform can complete storage of the government affair information to be managed or not is determined, and orderly management of the information is achieved.

Methods and apparatus consistent with the present disclosure may use instrumentation code that remains transparent to an application program that the instrumentation code has been injected into. In certain instances, data sets that include executable code may be received via packetized communications or be received via other means, such as, receiving a file from a data store. The present technique allows a processor executing instrumentation code to monitor actions performed by the program code included in a received data set. Malware may be detected by scanning suspect program code with a malware scanner, malware may be detected by identifying suspicious actions performed by a set of program code, or malware may be detected by a combination of such techniques.

The invention provides an AI platform data recommendation method and device and computer equipment, and relates to the technical field of data processing, and the method comprises the steps: obtaining a historical consultation dialogue record set of a target user, carrying out progressive multi-round dialogue iterative memory analysis, and determining the understanding category of the target user; obtaining a real-time active reasoning dialogue sequence; when a backtracking detection module is called to carry out recommended data completeness detection on the real-time active reasoning dialogue sequence, a recommended data completeness detection result is obtained; and triggering a recommendation output instruction according to the recommendation data completeness coefficient, and pushing recommendation data based on the recommendation output instruction. The data recommendation method and device have the advantages that the technical problems that in the data recommendation process in the prior art, in-depth interaction with the user cannot be dynamically conducted, initiative questioning in accordance with the actual situation of the user cannot be achieved, the recommendation quality is low, and the real-time requirement of the user cannot be met easily are solved, and the technical effect of improving the data recommendation quality is achieved.

The application discloses a memory management method and terminal, when a process is started, existing data in the memory is divided according to memory attributes of memory data, attribute identifiers are added, and associated memory structure tables are respectively established for different memory attributes according to forms and states of the memory data; the memory data is monitored in real time, when new data is found, attribute identifiers are added to the new data according to memory attributes of the new data, and the memory structure table associated with the memory attributes is updated with data according to the forms and states of the new data; when memory data analysis is needed, the corresponding memory structure table of the memory attribute can be directly called, and data can be further filtered according to the forms and states of the memory data, so that all memory attributes do not need to be traversed, and the efficiency of memory analysis and management is effectively improved.

The application relates to a heap memory analysis method and device, computer equipment, a chip and a chip module. The method comprises the following steps: in response to a heap memory processing instruction for a to-be-analyzed heap memory, determining the array identifier of a preset array corresponding to the to-be-analyzed heap memory according to the heap memory address corresponding to the heap memory processing instruction; updating the array information of the preset array according to the heap memory processing instruction and the array identifier, to obtain the updated array information of the preset array; and generating a memory analysis report corresponding to the to-be-analyzed heap memory based on the updated array information. The method can improve the analysis efficiency of the heap memory.

Methods and apparatus consistent with the present disclosure may use instrumentation code that remains transparent to an application program that the instrumentation code has been injected into. In certain instances, data sets that include executable code may be received via packetized communications or be received via other means, such as, receiving a file from a data store. The present technique allows a processor executing instrumentation code to monitor actions performed by the program code included in a received data set. Malware may be detected by scanning suspect program code with a malware scanner, malware may be detected by identifying suspicious actions performed by a set of program code, or malware may be detected by a combination of such techniques.

This disclosure presents systems, methods, and apparatus for determining cluster efficiency during hydraulic fracturing, the method comprising: measuring acoustic vibrations in fracking fluid in a fracking wellhead, circulating fluid line, or standpipe of a well; converting the acoustic vibrations into an electrical signal in a time domain; recording the electrical signal to memory; analyzing the electrical signal in the time domain for a window of time and identifying two amplitude peaks corresponding to a fracture initiation; measuring a time between the two amplitude peaks; dividing the time by two to give a result; multiplying the result by a speed of sound in the fracking fluid to give a distance between the fracture initiation and a plug at an end of a current fracking stage of the well; and returning a location of the fracture initiation to an operator based on the distance between the fracture initiation and the plug.

The invention provides a computer memory release system based on data analysis, and relates to the technical field of computer memory management. The system comprises a memory data acquisition module, a multi-dimensional memory analysis module, an intelligent release decision module, a hierarchical release execution module and a memory fragment optimization module. The memory data acquisition module acquires and preprocesses data by adopting kernel mode and user mode dual channels; the multi-dimensional memory analysis module realizes process memory consumption mode recognition and release potential calculation through feature matrix construction, improved LSTM network prediction and entropy weight method evaluation; the intelligent release decision-making module realizes dynamic decision-making based on an AHP model in combination with scene adaptation; the hierarchical release execution module adopts a three-stage mechanism of pre-cleaning, verification and release to guarantee safe release; and the memory fragment optimization module realizes free block integration and closed-loop feedback. The system solves the problems that a traditional memory is slow in release response, blind in release and poor in stability.

The embodiment of the invention discloses a memory analysis method and a related device. The method comprises the following steps: controlling a target game application to run in target equipment; in the running process of the target game application, first memory occupation information of the target game application on an operating system layer and second memory occupation information of the target game application on a game engine layer are obtained through a low-level memory tracker; acquiring local heap memory occupation information of the target game application, and acquiring video memory occupation information of the target game application; and according to the first memory occupation information, the second memory occupation information, the local heap memory occupation information and the video memory occupation information, determining a comprehensive memory occupation condition of the target game application. According to the method, the memory occupancy condition when the game application runs can be determined in a fine-grained mode, it is guaranteed that information reflected by the determined memory occupancy condition is rich, and the game application can be optimized and adjusted in a targeted mode according to the information.

The invention relates to the technical field of distributed databases, and discloses a distributed database memory analysis method, which comprises the following steps of: presetting the state and starting duration of a memory diagnosis switch of a target computing node; configuring an acquisition mode according to the acquired target memory statistical task; according to the acquisition mode, the memory statistical command is issued to a proxy node, and the proxy node executes the memory statistical command at a corresponding target computing node and / or a target data node to obtain memory statistical data; the memory statistical data is transmitted back to the operation and maintenance management platform through the agent node, and the operation and maintenance management platform stores the memory statistical data into system tenants; and extracting the memory statistical data of the corresponding time period and the corresponding node from the system tenant, and generating a memory trend chart according to the extracted memory statistical data. According to the method, the content statistical brush data is collected through the configured target calculation node and target statistical node, and the memory use trend chart is generated, so that the analysis efficiency of the memory use condition is improved.

The invention provides a memory analysis method and device for a virtual scene, electronic equipment, a computer program product and a computer readable storage medium. The method comprises the steps of obtaining a software package of a virtual scene; allocating a memory for the software package, and running the software package based on the memory; acquiring performance data from the memory; determining the distribution condition of the performance data in the memory based on the performance data; determining a memory hotspot corresponding to the performance data in the memory based on the distribution condition; acquiring an acquisition label configuration, and acquiring an acquisition engine configuration, in which the acquisition label configuration records a label of the memory data to be acquired, and the acquisition engine configuration records an acquisition engine for acquiring the memory data to be acquired; acquiring memory data with a label from the memory hotspot through an acquisition engine to serve as to-be-analyzed memory data; and analyzing the to-be-analyzed memory data to obtain an analysis result of the to-be-analyzed memory data. The memory data acquisition efficiency can be improved.

This invention discloses a vulnerability exploitation agent system, method, and apparatus based on the driving engineering framework, relating to the field of artificial intelligence, to address the vulnerability problem in the autonomous execution of long-chain tasks by vulnerability exploitation agents. It constructs a vulnerability exploitation agent system comprising an environment perception and object representation layer, an execution layer, an orchestration layer, a memory layer, a semantic management layer, and a security layer. Based on the logic of global constraint pre-positioning – progressive information disclosure – full-link behavior control – autonomous closed-loop execution – standardized output of results, it leverages the core mechanisms of the driving engineering framework, such as event-driven interception and verification bus, dynamic loading of prompts, memory compression, autonomous task execution loop, and tool permission gating, to form a fully traceable vulnerability exploitation scheme. This application can solve the problems of difficult binary semantic analysis and memory analysis in long-chain vulnerability exploitation tasks, improving task execution efficiency and accuracy.

The present invention provides an industrial data monitoring system based on a streaming computing engine, including a status monitoring module that uses Prometheus, which is natively supported by the streaming computing engine, to collect and store real-time indicators, periodically captures all sub-node indicators through a meta node for aggregation, provides a unified data source to Grafana for visualization, and configures alarms through a dashboard; a resource allocation module that analyzes real-time tasks, provides sufficient memory for the job, and processes task messages in real time. It combines relevant indicators obtained from the real-time task memory analysis and the rationality of the real-time task concurrency to derive a preset value for real-time task resources, adjusts real-time task resources, and achieves real-time task resource configuration; an abnormal status module that uses SideOutput to collect data on errors in each link of the streaming job, aggregates them into a unified error stream, and issues real-time alarms by discovering patterns that meet set characteristics in the streaming data. The present invention combines complex event processing with a streaming computing engine to enhance the real-time nature and applicability of alarms.

The invention belongs to the technical field of data analysis and artificial intelligence, and particularly relates to a multi-scene switching audio loudspeaker tone quality adaptive adjustment system. Comprising a four-dimensional perception layer, a mixed scene-content-user-copyright fusion analysis module, an auditory memory analysis module, a multi-user preference arbitration module, an extreme environment compensation unit, a copyright-tone quality cooperative adjustment module, an equipment aging adaptation module, a dynamic decision-making layer, a feedback execution layer, a hearing quantitative evaluation engine and a cloud cooperative engine. The dynamic decision-making layer fuses multi-module output according to the constraint priority to generate a compliance adjustment instruction; the feedback execution layer executes adjustment and realizes partitioning; the hearing quantitative evaluation engine optimizes the adjustment effect through objective indexes and user feedback; the cloud collaboration engine synchronizes global data, provides support for decision making, and forms full-closed-loop self-adaptive adjustment of perception-processing-decision making-execution-evaluation-collaboration.

The application discloses an interactive task generation method and device and a storage medium, relates to the field of human-computer interaction, and comprises the following steps: acquiring context information, task knowledge and a task target; constructing a long-term preference graph and a heterogeneous task knowledge base; fine-tuning a large language model; acquiring interactive state information; constructing a short-term emotional state memory, analyzing preference results; generating a query vector; generating a retrieval result; generating a memory-enhanced template; generating a current interactive task; collecting behavior data during execution of the current interactive task; analyzing the behavior data, constructing a multi-dimensional reward; and updating the long-term preference graph and the short-term emotional state memory. By constructing a long-term preference graph and a short-term emotion recognition mechanism, the modeling capability for individual state and environmental context of a user is improved, spatial consistency, semantic rationality and personalized adaptive recommendation in the task generation process are realized, and the comprehensive needs for cognitive authenticity, execution accessibility and user experience continuity in a multi-modal interactive scene are met.

The application discloses an implementation method of a market electric refrigerator compatible with different power supply stability, comprising the following steps: formulating a refrigeration operation rule one of a power stability area and a refrigeration operation rule two of a frequent power failure area; the refrigerator records the total power-on time length and the power-off times in the total power-on time length through a memory chip; the memory chip analyzes and judges the total power-on time length and the power-off times in the total power-on time length; the refrigerator control center gives different judgment result instructions according to the comparison results of the total power-on time length and the preset judgment time length and the comparison results of the power-off times and the preset judgment times; and the refrigerator executes corresponding control operation rules based on the different judgment result instructions. The method is convenient to operate, does not need additional investment, meets the requirements of generalization, modularization and standardization, reduces the consumption of manpower and material resources, reduces the risk of wrong installation, and improves work efficiency.