12 results about "Relationship Identifier" patented technology

The embodiment of the invention discloses a causal relationship tracking method, device and system and electronic equipment. The causal relationship tracking method comprises the steps that an eBPF probe mounted on a specified path of a kernel space detects a first event triggered by a first process, wherein the first event is used for creating a second process; determining whether the first process is a parent process of the second process; under the condition that the first process is not the parent process of the second process, generating a causal relationship identifier of the second process, and establishing a corresponding relationship between the process identifier of the second process and the causal relationship identifier; and under the condition that the first process is the parent process of the second process, obtaining a causal relationship identifier of the first process, and establishing a corresponding relationship between the process identifier of the second process and the causal relationship identifier.

The invention discloses an attitude control method and system for continuously locking a ground target by an unmanned aerial vehicle, and the method comprises the steps: obtaining a target position and an unmanned aerial vehicle attitude data stream, carrying out the coordinate transformation to generate a target attitude relation identifier, and carrying out the deviation vector decomposition to form a visual axis deviation feature set; disassembling the image into a fast and slow response chain through attitude mapping processing, repairing a fracture node, constructing an attitude correction track, extracting a control feature mode, generating a response feature factor, and constructing an attitude compensation constraint matrix; frequency domain decomposition and cross coupling calculation are carried out on the optical axis deviation feature set to determine the coupling correlation degree, and a superposition disturbance sequence is identified and dynamically reconstructed to generate a compensation gain sequence; executing stability evaluation to determine time sequence distribution, identifying abnormal disturbance characteristics and extracting key moment control characteristics; the constraint matrix and the gain sequence are fused to generate a fusion control law, a dynamic priority control coefficient is determined, a target continuous locking attitude control instruction is output, and continuous and stable locking of the moving target is achieved.

The invention provides a multi-party cooperative progress visualization monitoring method for intelligent building facilities, and relates to the technical field of intelligent building construction monitoring, and the method comprises the steps: collecting data, and building a three-dimensional model; establishing a causal relationship identifier based on the time sequence and the associated features to obtain a causal map, calculating an abnormal propagation probability and labeling a risk region; performing information mapping to generate a monitoring data stream and extracting features; calculating feature correlation, and mapping the feature correlation into a scene semantic segmentation map; and rendering in combination with the risk area and the progress information to obtain a visual three-dimensional dynamic construction progress. According to the invention, risk early warning and progress visual monitoring of the construction process are realized.

This application discloses a user profiling analysis method and server based on online advertising. The method includes: first, acquiring the target user's long-tail search keyword sequence and community interaction record sequence; merging the two sequences into a user behavior action unit chain and generating a set of behavior evolution relationship edges to form a user behavior semantic script; then, performing dependency parsing to generate an intent discourse structure containing a set of subordinate relationship identifiers and a set of causal modification relationship identifiers; performing hierarchical attention propagation operations to generate a hierarchical attention distribution map; and finally, extracting key plot nodes and semantic transition relationship nodes, mapping them to a set of advertising placement semantic anchor points, and generating a user profile feature cluster. By constructing a user behavior semantic script and parsing its intent discourse structure, this application can generate an accurate profile feature cluster that reflects the hierarchical organization and evolution of user interests.

Embodiments of this application provide a task processing method and a chip, and relate to the field of computer technologies. The task processing method includes: separately placing, in a plurality of instruction queues that are in a one-to-one correspondence with a plurality of execution units, all instructions in an instruction sequence used to implement a task; determining, based on a relationship identifier corresponding to a restriction instruction at a head of a first instruction queue in the plurality of instruction queues, a processing status of a second instruction queue on which the first instruction queue depends in a data dependency relationship indicated by the relationship identifier; when the processing status is processing completed, extracting the restriction instruction corresponding to the relationship identifier from the first instruction queue, to execute an instruction after the restriction instruction, where the processing status is obtained by updating based on the relationship identifier; and completing processing of the plurality of instruction queues to obtain a processing result of the task. In embodiments of this application, load of a compiler during asynchronous execution of different tasks can be reduced, and task processing efficiency can be improved.

Systems and methods are disclosed for providing secure digital communications using a relationship based trust framework. A relationship object associated with a relationship identifier and relationship scoped validation material governs communication acceptance or rejection based on a structured relationship state model. Communications referencing the relationship identifier are evaluated deterministically and accepted or rejected prior to delivery. Continuity and trust events are recorded in a relationship scoped ledger. The framework optionally avoids reliance on identity authentication, shared secrets, or probabilistic risk scoring and enables delegation, suspension, and revocation without affecting unrelated relationships. Optionally, delegated authority binding enables workflow integration without converting the relationship into an identity account.

The application relates to a relationship graph construction method and device, computer equipment and a storage medium. The method comprises the following steps: determining the types of nodes, dividing a plurality of nodes into management nodes and instance nodes subordinate to the management nodes according to the types; associating first attribute information with each management node and associating second attribute information with each instance node; wherein the first attribute information is information representing node management rules, and the second attribute information is information representing entity business attributes; configuring a first relationship identifier between each management node and the instance nodes subordinate thereto, and configuring a second relationship identifier between each management node and the adjacent layer instance nodes dependent thereon; and constructing a relationship graph model according to the management nodes, the instance nodes, the first attribute information, the second attribute information, the first relationship identifier and the second relationship identifier. The method can improve the accuracy and efficiency of data management based on a relationship graph.

This invention relates to the field of traditional Chinese medicine (TCM) model technology, and discloses a multi-dimensional TCM large-scale model collaborative intelligent agent system, comprising: a data segmentation unit that performs semantic segmentation on TCM data and omics data, and uniformly encodes TCM nodes and their relationships; a first collaborative unit that, based on relationship identifiers, inputs prescription nodes and their associated component nodes into the large model, and generates paths for target nodes and pathway nodes based on the relationship identifiers; a second collaborative unit that inputs the component-target correspondence in the candidate action path dataset as a constraint into the small model, and solves in parallel the affinity results and molecular property results between components and targets; and a collaborative update unit that determines the decision output based on the screened candidate action path dataset, and updates the relationship identifiers, the large model, and the small model. This invention ensures the reliability of multi-dimensional TCM large-scale model collaboration.

The invention discloses a teacher and bearing relationship prediction method for science and technology data fusion with a new subject atlas, and the method comprises the steps: carrying out the analysis and statistics of paper and patent data in a plurality of fields, constructing a network representation learning device through employing a network representation learning algorithm, and carrying out the prediction of the teacher and bearing relationship based on a deep neural network-mixed principal component analysis (PCA) algorithm and a pooling layer technology means. The method comprises the following steps: establishing a teacher-bearing relationship identifier, adding attribute network information, establishing a new subject map identifier based on subjects, performing classification optimization on different subjects, forming a data set of advisor-advisor advisor pairs and advised pairs by using the teacher-bearing relationship identifier, and combining the teacher-bearing relationship identifier and the new subject map identifier to obtain an advised-advisor advisor-advisor advisor-advisor advisor-advisor advisor map. A reliable machine learning prediction model is established to predict a tutor-student relationship, and statistics and modeling are carried out to calculate a cooperation network of the technology in different periods of talents in papers and patents, so that the teacher-bearing relationship of the talents is judged, for example, who is the tutor of the talents and which students are carried by the tutor of the talents, and other teacher-bearing relationships are judged.

The application relates to a data migration method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: writing data in a first variable table into a second variable table; the data in the first variable table and the data in the second variable table are respectively tree structure data; modifying the parent-child relationship identifier in the second variable table according to the self-increment identifier in the second variable table to obtain a modified second variable table; inserting the modified second variable table into a target data table and performing self-increment processing on the self-increment identifier in the inserted second variable table according to a preset self-increment strategy to obtain a processed variable table; repairing the parent-child relationship identifier in the processed variable table according to the processed self-increment identifier to obtain a repaired target data table; and storing the repaired target data table. The method can realize fast migration of data without changing the structure of the target data table.

The application discloses a method and system for continuously locking the posture of an unmanned aerial vehicle (UAV) on a target, which comprises the following steps: obtaining the target position and the UAV posture data stream, performing coordinate conversion to generate a target posture relationship identifier, and performing deviation vector decomposition to form a visual axis deviation feature set; performing posture mapping processing to disassemble the visual axis deviation feature set into fast and slow response chains, repairing broken nodes to construct a posture correction trajectory, extracting a control feature mode to generate a response feature factor, and constructing a posture compensation constraint matrix; performing frequency domain decomposition and cross-coupling calculation on the visual axis deviation feature set to determine a coupling correlation degree, identifying a superimposed disturbance sequence, and dynamically reconstructing to generate a compensation gain sequence; performing stability evaluation to determine a time sequence distribution, identifying an abnormal disturbance feature, and extracting a key time control feature; fusing the constraint matrix and the gain sequence to generate a fusion control law, determining a dynamic priority control coefficient, and outputting a target continuous locking posture control instruction, so that the moving target is continuously and stably locked.

The application provides a multi-party collaborative progress visualization monitoring method for intelligent building facilities, relates to the technical field of intelligent building construction monitoring, and comprises the following steps: collecting data to establish a three-dimensional model; establishing a causal relationship identifier based on time sequence and correlation characteristics, obtaining a causal graph, calculating an abnormal propagation probability, and marking a risk area; performing information mapping to generate monitoring data flow and extract features; calculating feature correlation and mapping into a scene semantic segmentation graph; and rendering to obtain a visual three-dimensional dynamic construction progress in combination with risk area and progress information. The application realizes risk early warning and progress visualization monitoring of the construction process.