8 results about "Tree representation" patented technology

A graphics processing system that is operable to perform ray tracing using micromaps is disclosed. A tree representation of a micromap is generated, and when it is desired to determine whether and / or how a ray interacts with a sub-region of a primitive, the tree representation of the micromap is traversed to determine a property value for the sub-region of the primitive.

A graphics processing system that provides a micromap defining property values for sub-regions of a primitive of a scene to be rendered; and generates a first tree representation of the micromap; and applies a first barycentric rotational transform to the micromap and for the first barycentric rotational transform generates a second tree representation of the micromap; and selects one of the first or second tree representations for processing as the selected tree representation. The selected tree representation of a micromap is used when it is desired to determine whether and / or how a ray interacts with a sub-region of a primitive, the tree representation of the micromap is traversed to determine a property value for the sub-region of the primitive.

A project success rate intelligent prediction method and device, comprising: acquiring multi-source data, and constructing a structured knowledge base containing project success cases and their characteristics; applying the merged tree representation technology and the extended transformation to the project data in the structured knowledge base to construct a data structure, realize feature extraction and substructure search; based on the project feature set, multi-dimensional feature analysis is carried out from the time dimension and the space dimension; based on the multi-dimensional feature vector, a project-platform allocation model with group fairness is constructed; for the allocation model, a polynomial time approximation algorithm based on linear programming and network flow is designed to solve the optimal allocation scheme; according to the optimal allocation scheme and the historical project success data, the improved Reed-Solomon code technology is used for feature coding to construct a project success rate prediction model; based on the prediction model, the success rate of the project to be evaluated is evaluated and the support intensity trend is predicted. The present application improves the accuracy and efficiency of project success rate prediction.

The invention discloses an intelligent project success rate prediction method and device, and the method comprises the steps: obtaining multi-source data, and constructing a structured knowledge base containing project success cases and features thereof; for project data in the structured knowledge base, constructing a data structure by applying a merge tree representation technology and expanded transformation, and realizing feature extraction and substructure search; performing multi-dimensional feature analysis from a time dimension and a space dimension based on the item feature set; based on the multi-dimensional feature vector, constructing a project-platform distribution model with group fairness; for the distribution model, designing a polynomial time approximation algorithm based on linear programming and network flow, and solving an optimal distribution scheme; according to the optimal allocation scheme and historical project success data, feature coding is carried out by using an improved folding Reed-Solomon code technology, and a project success rate prediction model is constructed; and based on the prediction model, performing success rate evaluation and support strength trend prediction on the to-be-evaluated project. The accuracy and efficiency of project success rate prediction are improved.

Systems and methods of graphics processing in which property values for sub-regions of primitives are defined by micromaps. A property value defined for a sub-region of a primitive by a micromap is used during ray tracing to determine whether and / or how a ray interacts with the sub-region of the primitive. A tree representation of a micromap is generated, and a property value for a primitive sub-region is determined by traversing the tree representation of the micromap.

This invention discloses a code incremental injection method, apparatus, electronic device, and storage medium, relating to the field of computer software development technology. The method includes: obtaining a syntax tree representation of target code; receiving a functional incremental description, wherein the functional incremental description defines the behavioral characteristics of the function to be injected; determining the injection position in the syntax tree representation based on the behavioral characteristics; generating a structured code fragment based on the code context corresponding to the injection position and the code template associated with the functional incremental description; integrating the structured code fragment into the injection position to obtain an updated syntax tree representation; and outputting the updated target code based on the updated syntax tree representation. This invention is applicable to any software development scenario requiring automated incremental expansion of existing source code, and can solve the problem that existing technologies cannot automatically determine appropriate positions in the source code and inject adaptable code according to functional requirements while maintaining the integrity of existing handwritten code.

The present application relates to the field of recommendation system, and discloses a point of interest recommendation method based on tree representation trajectory and multi-granularity contrast learning, comprising: obtaining user historical check-in data set and preprocessing; dividing user check-in into day level, time period level and original check-in level node according to check-in time, and constructing multi-time granularity trajectory tree structure; constructing a recommendation model integrating global enhancement and contrast learning, training and testing the model using the data set, and obtaining the prediction result. In feature extraction, a global graph is introduced for the trajectory tree structure, and graph convolution network is used to initialize the features of leaf nodes, so as to strengthen the model's ability to capture global dependency; contrast learning strategy is introduced at each granularity node level, and the model is guided to learn semantic consistent trajectory representation through positive and negative sample constraints. The present application balances local spatio-temporal constraints and global transfer rules while mining user multi-time granularity preferences, and significantly improves the accuracy and personalization of point of interest recommendation.

The invention discloses a vulnerability version verification method, system and equipment based on multi-source heterogeneous information, and the method comprises the steps: obtaining vulnerability information from a plurality of heterogeneous sources, extracting core elements, and carrying out formatting processing and verification to obtain structured vulnerability entries; collecting or generating a concept verification code, and positioning and generating through a vulnerability code if the concept verification code cannot be collected; performing automatic verification on the concept verification code, and recording a function call chain and a system behavior; and migrating the successfully verified concept verification code to other versions of the software, and determining the vulnerability influence range. The method further comprises the steps of selecting the core elements according to a preset priority rule, and processing the core elements by adopting a conflict resolution rule; vulnerability code positioning comprises similarity calculation, call graph analysis, symbolic execution and large language model generation; the automatic verification is executed and monitored in an isolated environment; code migration is realized by executing path tree representation, binary code comparison and path alignment. According to the method, the influence range of the vulnerability in different software versions can be accurately verified, and the vulnerability repairing efficiency is improved.