17 results about "Loop optimization" patented technology

This application relates to the field of fintech and discloses a dynamic hybrid parsing and deterministic verification system and method for fuzzy bond instructions. The system constructs structured data through an instruction preprocessing module; a routing decision module calculates complexity scores based on structure, semantics, and business characteristics, and generates path selection instructions using a resource-aware dual-threshold strategy; a hybrid parsing module responds to instructions, employing a rule engine to handle low-complexity tasks and a large-scale model with enhanced retrieval to handle highly ambiguous tasks; a fallback verification module performs confidence-weighted integration and logical consistency checks, and repairs anomalies through issuer set operations and edit distance matching; a mapping generation module constructs standard query statements using an abstract syntax tree; and a closed-loop optimization module dynamically updates parameters based on user interaction feedback. This invention effectively balances computational efficiency and parsing accuracy, eliminates model illusion risks, and achieves adaptive evolution of the system.

A method and system for adaptive adjustment of power grid operation modes involves acquiring multi-source heterogeneous power grid data and constructing a knowledge base based on this data. Based on the knowledge base, deterministic rule verification is performed on operation instructions to be verified, filtering out instructions that violate preset rules. A large-scale operation instruction model is constructed and trained using the knowledge base to obtain a trained model, outputting dispatch instructions that comply with safety constraints. For operation instructions that pass the deterministic rule verification, power flow calculation verification, N-1 security analysis verification, and a three-level verification for extreme power grid scenarios and major events are performed sequentially. If any level of verification fails, the dispatch instruction is reconstructed by the large-scale operation instruction model, and the three-level verification is re-executed until all verifications pass. This invention achieves automatic generation and closed-loop optimization of safe and compliant dispatch instructions, significantly improving the automation level and safety assurance capability of power grid operation mode adjustment.

The application discloses a generative AI industrial design topology closed-loop optimization method and system, and relates to the technical field of computer-aided design, comprising: obtaining an initial three-dimensional geometric representation of an industrial product and design constraints, and constructing a geometric parameter offset field; in iterative deformation, calculating discrete curvature, deformation energy, time sequence mutation and topology anomaly index, triggering and positioning a topology sensitive area; in the area, performing edge collapse, edge split, local re-meshing, Loop subdivision, constraint Laplace smoothing or SDF local reconstruction, and verifying and repairing the results by Euler characteristic number, genus, boundary ring, connected component and topology anomaly; calling a physically consistent proxy model to predict performance and evaluate confidence, triggering finite element sampling when confidence or safety margin is insufficient, error risk and topology sensitive area overlap, updating the proxy model by using finite element true value increment, and finally completing layered optimization and complete finite element verification.

A Petri net-based task scheduling method for heterogeneous environments relates to the field of collaborative decision-making and resource optimization in edge computing systems. During scheduling, the input tasks and their dependencies are first modeled as directed acyclic graphs (DAGs), and then converted into Petri nets to uniformly represent task states, resource constraints, and device collaboration relationships, in conjunction with device status information. Subsequently, graph attention networks are used to extract PN topological features, and a dynamic transition triggering strategy is learned using a dual-deep Q-network. The task unloading and resource allocation sequences are optimized through a reward function, and the sequence validity is further verified by a sequence detection module. During task execution, tasks are scheduled in real-time according to a reinforcement learning strategy, and the network state and decision model are dynamically updated based on environmental feedback, forming a closed-loop optimization. This invention significantly improves task throughput and resource utilization in heterogeneous edge environments, and is particularly suitable for device-to-device collaborative scenarios with complex task dependencies and dynamically changing resources.

The application discloses an edge video weighting cache and guided recommendation joint optimization method and system based on reinforcement learning, constructs a system model containing multi-rate video attributes, user dynamic preferences and edge computing resource constraints, accurately depicts four types of service states, i.e., accurate hit, edge transcoding, service degradation and backhaul acquisition, and QoE benefits, and converts them into a Markov decision process, and realizes high-level decision-making by using a state-meta-action-shaping reward framework. A PPO algorithm based on a meta-control architecture is proposed, a generalized weighting cache elimination strategy and a quality-aware hybrid recommendation strategy at the bottom are driven by outputting continuous control parameters, and a joint operation scheme of storage structure reconstruction and traffic soft guidance cooperation is efficiently generated under the condition of resource limitation. The method solves the competition contradiction between high hit rate and high quality in edge computing through deep integration of meta-reinforcement learning and heuristic logic, and has low computational complexity, strong environment adaptability and end-to-end closed-loop optimization capability.

The present application relates to the technical field of automatic logic operation connection simulation, in particular to a table-based automatic logic operation connection simulation method, comprising the following steps: a user fills in a structured table containing storage type, logic operation type, and space mapping rule core parameters, the system automatically generates a global configuration ID and initializes default values, and completes template standardization and dependency pre-checking.The present application drives full-process automation through a structured table, realizes seamless connection from requirement definition to signature verification, and only needs to fill in a standardized parameter table, so that the system automatically completes logic model construction, mixed storage configuration, interrupt awareness code generation, and closed-loop optimization, completely eliminates manual coding errors, ensures data consistency through versioned objects and global configuration, reduces resource consumption of traditional general logic operation through lightweight flexible logic units, and improves energy efficiency ratio through dynamic power consumption regulation and key path optimization.

The present application relates to the technical field of medical information parallel computing, in particular to a multi-thread parallel computing method and system for brucella spondylitis diagnosis parameters, the method comprising: a diagnosis task dependency graph construction step, a directed acyclic graph is constructed based on the calculation dependency relationship of the diagnosis parameters and the task priority is determined; an adaptive thread scheduling step, a dynamic load balancing is realized by using a work stealing strategy and a lock-free concurrent queue; a vectorized parallel computing step, a single instruction multiple data stream instruction is used to perform batch calculation on the diagnosis parameters; a feedback-driven optimization step, a closed-loop optimization is formed by dynamically adjusting the scheduling strategy according to the performance index, the four core modules are deeply coupled to realize synergistic effect.

This invention discloses a multi-level mapping method for characterizing machine tool accuracy degradation and optimizing its key parameters, belonging to the field of machine tool accuracy retention technology. This method constructs a complete technical framework of forward modeling and prediction followed by reverse parameter optimization, achieving quantitative prediction and proactive control of machine tool accuracy retention throughout its entire lifecycle through four-level mapping. First, based on the small displacement screw method and multibody system theory, a whole-machine spatial accuracy model considering geometric errors at large assembly interfaces is constructed. Second, combining numerical simulation, the Arcard wear model, and the superposition method, a time-varying degradation model of guideway straightness under multi-physics coupling is established. Third, by integrating the straightness and perpendicularity degradation laws, a time-varying equation for whole-machine spatial accuracy is derived, clarifying the mapping relationship of multi-level time-varying factors. Finally, using the accuracy degradation rate as an evaluation index, the machining and assembly process parameters are optimized through reverse derivation, forming a closed-loop optimization system. This invention is of great significance for improving the accuracy retention of high-end CNC machine tools.

This invention relates to the field of energy management technology and discloses an energy optimization decision-making method, system, device, and electronic equipment. The method includes: acquiring multi-source heterogeneous data of the current production environment; performing reverse calculations using a pre-constructed digital twin model based on preset product quality qualification standards and multi-source heterogeneous data to generate time-varying lower limit constraints on process parameters for product quality in future optimization cycles; constructing a multi-constraint rolling optimization model based on preset constraints and time-varying lower limit constraints on process parameters, with the goal of minimizing comprehensive energy costs; and solving the multi-constraint rolling optimization model at the beginning of each optimization cycle to generate a sequence of optimization control instructions for future optimization cycles, which are then issued and executed. This invention solves the problem of energy optimization decision-making being disconnected from the quality control requirements of production processes, leading to optimization results violating key process constraints and thus affecting the final product quality. It achieves deep collaboration and closed-loop optimization between energy flow and production quality flow.

This invention belongs to the field of quantum computing and relates to a quantum computing method, device, and computer equipment based on quantum state collaborative manipulation. The method includes: constructing and initializing a data foundation; decomposing an external computing task into multiple sub-tasks using a classical intelligent decision-making model, and dynamically allocating the number of qubits, computing power weights, and multi-dimensional encoding schemes to each sub-task; building a hierarchical asymmetric entangled topology and constructing an entangled resource pool to achieve dynamic scheduling of idle resources; performing dynamic dimensional basis vector transformation through multi-dimensional quantum state combination encoding and composite quantum pulse synchronous manipulation; performing real-time monitoring and hybrid verification and error correction using a classical dual-model collaboration; outputting the computation results encoded in the quantum state and completing the sedimentation of the entire process of computation data, forming a closed loop of computation-data-optimization-re-computation. It enables end-to-end integrated collaboration, dynamic and efficient utilization of resources, and full-process data closed-loop optimization.

The application relates to a NumPy numerical calculation optimization method, system and application based on a RISC-V vector extension, and belongs to the technical field of computer science and high-performance computing application, wherein a core calculation part not calling a BLAS library is subjected to high-performance optimization, such as vector element-by-element operation and matrix element-by-element calculation. On the basis of analyzing a calculation step, the performance of NumPy in a broadcast operation and an element-by-element operation scene is improved through technologies such as bottom code replacement, loop optimization, data loading and memory alignment optimization, so as to provide support for scientific calculation and high-performance calculation based on a RISC-V architecture.

The invention provides a calculation core code generation method and device, equipment, a storage medium and a program product. The method comprises the steps of generating an initial code population according to a calculation task and hardware environment parameters; the code optimization step is executed in a loop mode till the loop stopping condition is met, and an optimal calculation core code is obtained; the code optimization step comprises the following steps: evaluating a calculation core code to obtain a fitness index; the fitness index comprises a fitness score and an evaluation log; based on the evaluation log, an optimization suggestion is generated through the reflection agent; determining a parent computing core code based on the fitness score; and according to the optimization suggestion, performing variation and crossover operation on the parent calculation core code by using the evolutionary agent to generate a next-generation code population. According to the method, a closed-loop optimization mechanism is constructed through the reflection agent and the optimization agent, attribution analysis is carried out by utilizing the reflection agent, a better code variant is generated by utilizing the optimization agent, and a calculation core code with the highest fitness is obtained.

The application provides a code coverage closed-loop test optimization method. The application focuses on changed code through an incremental coverage calculation engine, solves the resource waste problem of full statistical calculation, a closed-loop optimization engine drives test generation and verification with coverage gaps, forms a self-evolution feedback loop, an intelligent scene recommendation engine quantifies priority based on multi-dimensional scoring, and converts subjective decision-making into objective standards. The three engines work together to realize accurate placement of test resources to changed areas and core scenes, reduce runtime overhead, improve defect detection rate, and establish an efficient and controllable automated test paradigm.

This invention discloses an artificial intelligence-based knowledge graph question-answering interaction method and system, belonging to the field of knowledge graph technology. It includes four steps: temporal knowledge validity screening, reasoning path quality assessment, causal attribute consistency verification, and reliable result output and closed-loop optimization. After receiving a user's natural language query, the system first extracts the query time interval, filters valid triples matching the time dimension in the knowledge graph, calculates the path time matching degree and candidate answer attribute matching degree based on the valid triples, and then calculates the credibility after fusion before outputting the qualified answer. This invention integrates multiple technologies, using dual verification to ensure the accuracy of the answer in terms of temporality and logic, reducing errors. The system continuously learns and improves based on application feedback, enhancing the accuracy of the knowledge graph and the quality of question answering. Furthermore, the data interaction between modules is standardized and logically clear, enabling flexible responses to various queries and providing users with traceable, high-quality answers to meet diverse needs.

The application provides a pipeline support and hanger integrated optimization method and system based on a process engine, and the method comprises the following steps: S1, creating or opening at least one optimization project; S2, constructing an optimization process; S3, setting optimization targets and constraint conditions, selecting target parameters to be optimized, and setting hyperparameters of an optimization algorithm; S4, starting automatic execution of the optimization process; S5, in the optimization process, asynchronous signal slot mechanism is used to realize decoupled communication between the optimization algorithm and the interface, and a multi-target dynamic curve diagram is used to draw the convergence trend of each optimization target in real time; S6, after the optimization is completed, multiple candidate schemes are selected, compared with the initial arrangement, the technical rationality of each scheme is comprehensively evaluated, and the optimal scheme is selected. Through the construction of a visual modeling and automatic execution mechanism based on the process engine, the traditional support and hanger optimization process which depends on manual repeated iteration is converted into automatic execution, and full-process closed-loop optimization of analysis, optimization and feedback is realized.

The application provides a kind of intelligent course arrangement system architecture and implementation method based on multidimensional constraint and data snapshot, applied to data processing technical field.The application obtains basic business data and multidimensional constraint parameters, and completes entity time sequence association, constraint structured modeling and resource overall planning through modular decoupling design, to generate standardized input data set.Through creating course arrangement task exclusive data snapshot to lock data state, combined with core algorithm and conflict detection mechanism to generate initial course arrangement scheme, to support manual fine-tuning and real-time verification optimization.Relying on snapshot traceability mechanism to associate historical data, to build closed-loop optimization system, finally through multidimensional timetable display model and snapshot archiving retrieval function, to output evaluation report and visual timetable containing course arrangement efficiency, constraint satisfaction rate and other indicators, to realize efficient, compliant and traceable intelligent course arrangement.

This invention discloses a genetic scheduling optimization method and system based on a large language model to repair infeasible solutions. The method includes formal definition of the scheduling problem and semantic constraint modeling, chromosome encoding and initial population generation, genetic operations to generate a set of offspring individuals, constraint violation detection and infeasible individual identification, targeted repair based on a large language model, fitness evaluation and population update, iteration termination and optimal solution output, and human-machine collaborative constraint incremental update and model closed-loop optimization. The system includes a human-machine collaborative interaction module, a constraint semantic modeling module, a genetic algorithm engine module, a constraint violation detection module, a repair prompt construction module, a large language model invocation module, a repair instruction parsing and execution module, a repair experience management module, and a scheduling solution decoding and visualization module. This invention transforms a large number of infeasible solutions into high-quality feasible solutions, significantly improving search efficiency and solution quality, while simultaneously making the scheduling process interpretable.