13 results about "Logical process" patented technology

Embodiments of the present application provide a kind of graph scheduling method, device, electronic equipment and computer program product, the method includes: the each service processing stage of target service is as layer node, and each layer node is connected based on the execution order of each service processing stage direction, each logic process in each service processing stage is as the operator in the corresponding layer node, and each operator in the corresponding layer node is connected based on the dependency relationship of each logic process in service processing stage direction, obtain the directed acyclic graph DAG corresponding to target service;When receiving the service request for target service, each layer node in DAG is scheduled according to the execution order of each processing stage, and each operator in layer node is scheduled according to the dependency relationship between each logic process in each layer node, until all operators in DAG are scheduled.The scheme in this paper, since the expression ability of layer node and operator is stronger, the readability of the obtained DAG is higher, and then the convenience of graph scheduling is improved.

This invention relates to the field of artificial intelligence interaction, and discloses an AI-based interactive system and method. The system includes: a data acquisition module configured to acquire user task requests and generate corresponding task execution plans; a compilation module configured to compile the task execution plans in real time and render them into an interactive execution flow graph; a data processing module configured to update the execution status of each node in the interactive execution flow graph in real time when generating the corresponding task execution plan; the data processing module is further configured to acquire user graphical operations on specific nodes or connections in the interactive execution flow graph and compile these graphical operations into intervention instructions for the task execution logic; and an execution module configured to adjust the task execution plan in real time based on the intervention instructions and update the structure and status of the interactive execution flow graph. This application presents the logical and analytical processes, enhancing the user's sense of control.

The invention provides an equipment diagnosis and maintenance method and system based on a knowledge graph and inference chain distillation, and belongs to the technical field of artificial intelligence and industrial operation and maintenance. The method comprises the following steps: firstly, constructing a knowledge graph representing equipment maintenance field knowledge in detail; then, through a controlled path sampling strategy, extracting a multi-hop knowledge path from the map, and automatically generating a corresponding question and answer pair and a reasoning chain; thirdly, constructing a distillation data set by using the data containing the logic process; and finally, distilling structured knowledge and logical reasoning ability in the knowledge graph to a lightweight student model through a staged training strategy of introducing reasoning chain alignment loss. According to the scheme, the defects of a traditional knowledge distillation method in the aspect of transmitting complex logic knowledge are overcome, the obtained small model is high in diagnosis accuracy, the decision-making process is clear and explainable, safety specifications are met, and the method is very suitable for being deployed in an industrial field environment with limited computing resources.

The application provides an automatic test method for pre-setting mode processing of upgrade and downgrade of a switch, relates to the technical field of communication equipment maintenance, and comprises the following steps: a test environment is built, an upgrade image is prepared, and a configuration file of a pre-setting page element positioning mode is prepared; an automatic script is checked, the number of times of upgrade and downgrade is set and saved, and the automatic script is manually started to be executed; the automatic script simulates mouse clicking of a web interface to execute an upgrade operation, judges whether the execution result is successful when each type of page element is operated, then judges whether a search result is matched, and finally judges whether pictures or text attributes are matched with the page elements; after all version tests of each type of page element are completed, a function test script logic process is run, an assertion result is obtained, and a test report is output. Through the pre-setting of the positioning mode of the page elements in different versions, the code structure redundancy is optimized, the fault tolerance of the automatic process is enhanced, and it is ensured that the upgrade and downgrade processes can be continuously performed without manual intervention.

The technologies described herein are generally directed to modeling network systems. For example, a method described herein can include receiving request data representative of a planning request for a network equipment project applicable to network equipment that is part of a network. Further, based on the request data and a logical inventory model of resources, the method can include identifying a logical process model for the network equipment project, corresponding to characteristics of service equipment applicable to the network equipment project. The method further includes transforming the logical process model into a physical process model that references the service equipment applicable to the network equipment project, and based on the request data and the physical process model, facilitating, for the network equipment project, the service equipment.

This invention discloses a trustworthy large-scale model enterprise scenario contract review system and method based on link backtracking, including: a document editor module, a backend engineering module, a vectorization tool module, a vector library module, a review checklist management system module, and a workflow module. This invention establishes a precise link between contract document fragments and review results, enabling direct traceability of review results to the original clauses. By assigning unique identifiers (such as slice IDs) to contract document segments, it achieves an explicit association between the segments and the representations in the vector library module, ensuring that segments retrieved based on vector retrieval can be accurately traced back to their original positions. A full-process link backtracking mechanism for large-scale model review is constructed, making the derivation basis and logical process of review conclusions transparent, allowing users to trace the derivation process of review results, significantly improving the system's credibility and acceptability in enterprise compliance scenarios.

The application discloses a buddy memory management-based simulation platform running method and device, equipment and a storage medium, which are applied to a parallel discrete event simulation platform, relate to the technical field of system simulation, and include the following steps: obtaining running data of a target logical process corresponding to a current simulation period, and respectively predicting first running overhead of the target logical process in a first running mode and second running overhead of the target logical process in a second running mode in a next simulation period based on the running data and a preset event overhead prediction model; judging whether the target logical process meets a preset mode switching condition; if the preset mode switching condition is met, switching the running mode of the target logical process to the second running mode, and after a corresponding buddy state pool is allocated for the target logical process, performing corresponding simulation operations with the target logical process based on the second running mode. In this way, the event processing rate and the memory utilization efficiency of large-scale simulation can be improved.

The invention relates to the technical field of electric digital processing, in particular to an automatic analysis method and device for a process of an automobile standard. The method comprises the following steps: acquiring a standard text of an automobile, and preprocessing the text; analyzing by adopting a pre-trained language analysis model to obtain an entity in the text and a relationship between the entities; based on domain knowledge and inference rules, performing association inference on the relationship between the entities to obtain comprehensive information; organizing the comprehensive information into a logic process, and representing the logic process in a directed graph form; wherein the requirements and steps in the standard are mapped into nodes of the graph, and the causal relationship between the steps is mapped into edges of the graph; and carrying out visual display on the logic process, and carrying out detailed annotation on each node. According to the method, technologies such as a language analysis model are utilized, key content is extracted from a standard file, the logic relation and operation steps of the key content are recognized, and a text is converted into a structured flow chart.

The application provides a method and system for supporting parallel discrete event simulation of low-orbit satellite constellation, which comprises: allocating all simulation nodes in simulation node configuration data to a plurality of logical processes for management, and determining a plurality of pairs of associated logical processes, wherein the simulation nodes comprise low-orbit simulation nodes corresponding to each low-orbit satellite in the low-orbit satellite constellation, and there is a need for cross-logical-process communication between the simulation nodes managed by each pair of associated logical processes; simulating the simulation motion of each simulation node in the simulation node configuration data in a motion simulation model, and dynamically determining the communication delay of the simulation between the simulation nodes according to the simulation motion result; determining the look-ahead amount of each pair of associated logical processes according to the minimum cross-logical-process communication delay between the simulation nodes in the pair of associated logical processes; and performing parallel discrete event simulation of the low-orbit satellite constellation by using the plurality of logical processes, and representing the communication behavior of all simulation nodes as events at discrete time points.

The present invention discloses a method for automatically identifying and repairing geometric defects in the process of building BIM data exchange based on AI technology, which includes two stages: model training and model use; the logical process of the model training stage includes data collection, data analysis, data conversion, data labeling, data set division, model selection, model architecture design, training model, model evaluation, model deployment and model use; the process of the model use stage includes data input, geometric defect identification, intelligent feedback, geometric repair and data output. The present invention belongs to the field of BIM data conversion technology, and specifically provides a method for automatically identifying and repairing geometric defects in the process of building BIM data exchange based on AI technology. The method can realize intelligent geometric defect detection and identification, and intelligent geometric repair, so as to solve the problems of time-consuming manual repair, low repair efficiency, insufficient precision and low degree of automation existing in the prior art.

An intelligent data analysis system according to an embodiment of the present invention may comprise the operations of: acquiring data to be analyzed and a user query in a natural language form; analyzing the user query so as to derive an analysis objective, and establishing an analysis plan defining a logical procedure to be performed in order to achieve the analysis objective; generating, on the basis of the established analysis plan and structural attribute information of the data to be analyzed, a program code mechanically executable in a computing environment; and driving the program code in an execution environment so as to calculate and output an analysis result corresponding to the user query.

The technologies described herein are generally directed to modeling network systems. For example, a method described herein can include receiving request data representative of a planning request for a network equipment project applicable to network equipment that is part of a network. Further, based on the request data and a logical inventory model of resources, the method can include identifying a logical process model for the network equipment project, corresponding to characteristics of service equipment applicable to the network equipment project. The method further includes transforming the logical process model into a physical process model that references the service equipment applicable to the network equipment project, and based on the request data and the physical process model, facilitating, for the network equipment project, the service equipment.

This application discloses a natural language reasoning method, apparatus, and device based on reasoning paths, relating to the fields of internet technology and language models. By adding reasoning path annotations to a subset of target samples in the model training set to simulate human thought processes, the model can mimic the entire reasoning and computation process for new questions, thereby obtaining the correct answer and improving the effectiveness and accuracy of the model's reasoning. The method includes: extracting a predetermined number of target samples from the model training set corresponding to the target task; annotating the target samples with reasoning paths and adding the annotated target samples to the model training set; using the sample data in the model training set to train a first language model to obtain a second language model, where the first language model is the base language model; acquiring target task data and inputting the target task data into the second language model for language recognition to obtain the reasoning result output by the second language model.