6 results about "Memory behavior" patented technology

The invention discloses a self-adaptive memory allocation optimization method and device, terminal equipment and a storage medium, and belongs to the field of memory management. The method comprises the steps of monitoring thread data and system state data in real time, extracting thread memory behavior characteristics and resource state characteristics when one variable quantity exceeds a safety threshold value, and performing weighting calculation according to a preset weight coefficient to obtain a target strategy score; when the strategy score difference value of the target strategy score and the current strategy score is smaller than a preset difference value threshold value, the current memory allocation strategy is kept; when the strategy score difference value is not smaller than a difference value threshold value, a target memory allocation strategy is matched in a preset strategy library according to the thread memory behavior and the resource state characteristics, a current memory allocation strategy is updated, memory allocation parameters are adjusted, and a current strategy score is updated; when the current memory allocation strategy and the current strategy score are compared for the first time, the current memory allocation strategy and the current strategy score are the initial memory allocation strategy and the corresponding initial score, and the problem that efficiency and compatibility cannot be considered at the same time in the prior art is solved.

The application discloses a memory abnormality early warning method and device based on data mining and a medium, relates to the technical field of memory abnormality early warning, and comprises the following steps: performing semantic element mapping on memory running semantic data, and outputting a semantic state time sequence vector; performing cross-domain time sequence alignment and associated feature extraction on the semantic state time sequence vector and memory performance index data, and outputting memory associated rules; performing cause-effect analysis on the memory performance index data, constructing a graph structure on a cause-effect edge set through a PC algorithm, and outputting a memory index cause-effect graph; performing memory behavior deduction on real-time memory running semantic data according to the memory associated rules, outputting memory abnormality early warning data, performing cause-effect tracing and report integration on the memory abnormality early warning data through the memory index cause-effect graph, and outputting a memory abnormality early warning report. The application realizes interpretable early warning and root cause positioning of memory abnormality through the cooperation of semantic state time sequence vector construction and memory index cause-effect graph generation.

This invention discloses a memory-based behavioral adversarial interactive identity verification method and apparatus based on a metaverse. The method involves: acquiring first behavioral information from user feedback based on received interactive information during the interaction process; determining a difficulty weight index for each interactive message received by the user in historical interactions and its historical average response time; constructing a memory tree for each user based on the difficulty weight index; building an adversarial model; obtaining second behavioral information through the adversarial model's feedback of interactive information; performing conditional loop-based adversarial verification based on the first and second behavioral information to obtain a verification result; determining the order of interactive information by combining the memory tree and the previous verification result during the adversarial verification process; obtaining a comprehensive verification score based on all verification results and the difficulty weight index; and determining the user's identity verification result based on the comprehensive verification score, thus making the identity verification result more accurate and objective.

The invention discloses a memory abnormity early warning method and device based on data mining and a medium, and relates to the technical field of memory abnormity early warning, and the method comprises the following steps: carrying out semantic element mapping on memory operation semantic data, and outputting a semantic state time sequence vector; performing cross-domain time sequence alignment and associated feature extraction on the semantic state time sequence vector and the memory performance index data, and outputting a memory association rule; performing causal analysis on the memory performance index data, performing graph structure construction on the causal edge set through a PC algorithm, and outputting a memory index causal graph; and performing memory behavior deduction on the real-time memory operation semantic data according to a memory association rule, outputting memory abnormality early warning data, performing causal tracing and report integration on the memory abnormality early warning data through a memory index causal graph, and outputting a memory abnormality early warning report. According to the method, interpretable early warning and root cause positioning of memory anomalies are realized through dual-mechanism cooperation of semantic state time sequence vector construction and memory index cause and effect graph generation.

The invention relates to a generative dialogue recommendation system and method based on a large language model, the system comprises a three-tower neural network model formed by jointly training three independent encoders, and the three-tower neural network model comprises a user memory behavior tower, a current context tower, a user portrait tower and a feature fusion and decoder; the training module is configured to construct training data, calculate a loss function and complete training to obtain a generative dialogue recommendation model; and reasoning and recommendation, which is configured to output the generated Top-K most possible problem recommendation sequences based on the generative dialogue recommendation model and the input of the triple of the real user. According to the method, user intention mining, semantic matching recommendation and recommendation problem generation can be carried out.

The invention discloses a data processing method, device and equipment and a readable storage medium, and the method comprises the steps: obtaining game progress information corresponding to an object virtual character controlled by an object in a game world, and if the game progress information meets a state triggering condition, executing the state triggering; if yes, obtaining fact information associated with the game progress information in a fact knowledge base of the game world, and generating role state information according to the fact information and the game progress information; inputting the role state information into a retrieval model, and generating target role configuration data through the retrieval model; acquiring historical memory behavior data, and generating latest interaction data according to the target role configuration data and the historical memory behavior data; and generating a target fixed virtual character according to the target character configuration data and the latest interaction data, and putting the target fixed virtual character into a game environment associated with the game progress information. By adopting the method and the device, efficient updating and personalized interaction service for the interaction content of the fixed virtual character can be realized.