23 results about "Optimal methods" patented technology

The invention discloses an intelligent question answering system and method based on question decomposition and community semantic search. According to the method, firstly, a complex question is decomposed into a plurality of single-hop sub-questions through an original loop question decomposition technology, then information needed for answering each sub-question is obtained based on communities in a vector retrieval knowledge graph, and the sub-questions are answered by using a large language model. And finally, generating a final question and answer for the original complex question by combining the LLM with the answers of all the sub-questions. Compared with other methods in the industry, the method has the advantages that the information integration capability is remarkably improved, information fragmentation is avoided through community-level search and abstract generation, and high-quality and logically consistent answers are provided. When a complex query is processed, the effect of the method is superior to that of a general method in the industry and is improved by 29% at most; meanwhile, on the basis of ensuring a better question and answer effect, the efficiency is remarkably improved, and on the premise that the effect exceeds that of an existing optimal method GraphRAG, the token consumption is only 3.2% of that of the existing optimal method GraphRAG.

The invention relates to a cross-bearing single sample intelligent diagnosis method based on cognitive guidance and Riemannian manifold, and belongs to the technical field of rotating machinery fault diagnosis. Aiming at the problems of insufficient global task distribution learning ability, small sample over-fitting, Euclidean modeling limitation and the like of the existing meta learning method in a cross-domain single-sample scene, a cognitive guidance Riemannian meta learning framework is provided. According to the technical scheme, the method comprises the following steps: 1) constructing cognitive prototype learning global task distribution, and guiding a model to extract high-quality general meta-knowledge from multiple tasks; 2) designing a cognitive adaptive factor to dynamically adjust source domain memory, enhancing target domain adaptation and reducing single sample deviation; and 3) introducing a Riemann metric driving strategy, mapping the data to a Grassmann manifold space, and enhancing the non-linear feature discrimination ability by using geodesic distance. According to the method, the average diagnosis accuracy in a cross-bearing single sample task reaches 93.46% and is improved by 10.04% compared with an existing optimal method, and the accuracy and generalization ability under complex working conditions are improved.

In recent years, reinforcement learning has a significant effect in multiple fields, but a traditional method has limited adaptability in a dynamic environment or a multi-task situation. The invention provides a multi-task reinforcement learning method named as a priority weighted soft modularization (SM-PHT) method, which is used for enhancing the adaptability and the generalization ability of an intelligent agent, and relates to a multi-task reinforcement learning method named as SM-PHT (SM-PHT), which is used for enhancing the adaptability and the generalization ability of the intelligent agent. SM-PHT integrates three key mechanisms: priority weighted knowledge distillation, and student network robustness is improved through weighted fusion of multi-model knowledge; the hierarchical cache is used for respectively storing low-level experience and high-level parameter optimization efficiency; and task embedding: capturing environment characteristics to enrich task representation so as to promote knowledge transfer. In the Meta-World MT10 challenge, the success rate of SM-PHT is twice that of the existing optimal method, and the average reward is improved by 30%; in the MT50 challenge, the success rate and the average reward are both improved by about 10%. Results prove that the stability and generalization of the mechanical arm in complex tasks highlight the application potential in the field of actual multi-task mechanical arm operation.

The invention discloses a multi-energy complementary power grid combined dispatching optimization method considering wind-solar load uncertainty, and belongs to the technical field of power grid dispatching optimization. The uncertainty is quantized by introducing a nonlinear fuzzy opportunity constraint to represent the wind-light load uncertainty in a scheduling scene, a dynamic multi-target genetic algorithm is provided, and a complex multi-target optimization model is solved by introducing a dynamic control and disturbance correction mechanism so as to realize a day-ahead optimal plan of joint scheduling. Example results show that compared with a scheme without uncertainty representation, the method has the advantages that optimization target values of low carbon, economical efficiency and stability are improved by 7%, 21% and 25% respectively, reliable guarantee can be provided for power grid operation, diversified requirements of complex scenes are met, and the solving efficiency of multi-target optimization is improved.

A CIM-based distribution network power supply radius length optimization method uses CIM model data and the breadth-first search (BFS) algorithm to quickly calculate the power supply radius of all distribution transformers on the feeder. By screening, a local optimal method suitable for engineering applications is obtained, and a reasonable solution to the problem of excessive power supply radius is quickly formed.

The invention discloses a key point rotating target detection method and system based on fusion Gaussian measurement. The method comprises the following steps: firstly, outputting a multi-scale feature map through a feature extraction module and a feature fusion module; secondly, on the basis of the multi-scale feature map, a detection head module predicts the center point position of a target, and an initial key point set is regressed by using deformable convolution; gaussian modeling and quality evaluation are carried out on the initial key point set to obtain a final key point set, a loss function fused with Gaussian measurement is constructed in the training stage, key point distribution is fitted through maximum likelihood estimation, and KL divergence is used as distribution distance measurement to obtain a final key point set; evaluating the quality of the key points from five dimensions of classification, positioning, direction, correlation and Gaussian distribution; and finally, converting the final key point set into a rotation bounding box, carrying out post-processing by adopting a rotation box confidence coefficient propagation module, and outputting a final rotation target detection result. The method is superior to an existing optimal method in public data sets, and the detection precision is remarkably improved.

The present invention discloses a method for optimizing InSAR interferometer pairs based on coherence proxy for big data. The method calculates the coherence graph of sampled interferometer pairs, calculates the time baseline coherence proxy, the space baseline coherence proxy, the seasonal coherence proxy, and the comprehensive coherence proxy; selects a number of interferometer pairs with the highest coherence proxy as additional redundant interferometer pairs, takes the union of the redundant interferometer pairs and the interferometer pair network of the minimum spanning tree, and obtains a high coherence proxy interferometer pair network; detects whether all interferometer pairs in the high coherence proxy interferometer pair network can form a phase triangle closure, and completes the phase triangle closure for the interferometer pairs that have not formed a phase triangle closure to obtain the final InSAR optimized interferometer pair network. This optimization method not only improves the accuracy of phase unwrapping and deformation estimation, but also greatly reduces the number of interferometer pairs that need to be calculated in the case of big data InSAR, reduces the computational burden, and provides support for the realization of near-real-time processing of InSAR.

The invention discloses a mechanical arm scanning track planning method based on an improved ant colony algorithm, which comprises the following steps: step 1, dividing a to-be-measured workpiece into n primitives, setting n measuring points on the to-be-measured workpiece, and setting a complete set T of the measuring points as follows: T = {T1, T2, T3,..., Tm}; 2, solving a set coverage problem by using an improved greedy algorithm to obtain an optimal selection method of a scanning point position set; 3, solving a scanning track by using an improved ant colony algorithm and a combined algorithm based on a BFS strategy; step 4, optimizing the scanning track by using a particle swarm algorithm to obtain an overall scanning track with the shortest scanning time; and step 5, obtaining an overall scanning track with the shortest scanning time by using a particle swarm algorithm. According to the method, a scanning track is solved by using an improved ant colony algorithm and a BFS-based strategy, a larger area is selected to continue to carry out strategy path search, and a smaller area is solved by using the ant colony algorithm to obtain a loop. And opening the loop, combining the loop to an original path, and solving the whole path through the BFS to obtain an optimal path.

The application discloses an intelligent question and answer system and method based on problem decomposition and community semantic search. The method first decomposes a complex problem into multiple single-hop sub-problems through an original cyclic problem decomposition technology, then retrieves the community in the knowledge graph based on a vector to obtain the information required to answer each sub-problem and uses a large language model to answer the sub-problems, and finally uses an LLM to combine the answers to all sub-problems to generate the final answer to the original complex problem. Compared with other methods in the industry, the method significantly improves the information integration capability, avoids information fragmentation through community-level search and summary generation, and provides high-quality and logically consistent answers. In handling complex queries, the method outperforms the industry's general method, with a maximum improvement of 29%. At the same time, the method significantly improves the efficiency while ensuring better question and answer results. Under the premise of better results than the existing optimal method GraphRAG, the token consumption is only 3.2% of that of GraphRAG.

The invention discloses a multi-satellite pursuit game method based on a survival type differential game, and relates to a spaceflight pursuit satellite optimal method.The method comprises the steps that firstly, a particle swarm algorithm and a Newton iteration algorithm are combined, and a complex two-point boundary value problem is converted into a parameter optimization problem with constraints; and then solving a countermeasure saddle point corresponding to the game countermeasure for chasing the satellite. On the premise of considering the accuracy of the algorithm, shortening the calculation cost and ignoring the attitude problem of the satellite, the model is simplified, and the thrust direction angle of the satellite is used as the control quantity to realize the orbital maneuver of the satellite. Assuming that the satellites are all under the condition of continuous small thrust, taking terminal end time as a payment function by three game parties, taking a minimum function as a target by chasing the satellites, and contrarily taking the minimum function as the target by escaping the satellites. Simulation results show that the confrontation game of the pursuit satellites in the orbital plane is relatively intense, and the pursuit satellites with higher thrust acceleration have more advantages in confrontation.

This application relates to the field of electrical engineering technology, and proposes an optimization method and device for the expansion planning of a power grid framework considering extreme scenarios. The method includes: First, obtain historical data of extreme disaster scenarios of the power grid and perform preliminary processing to obtain a set of extreme scenarios; Then, construct an optimization objective function for the expansion of the power grid framework, and the optimization objective function for the expansion of the power grid framework includes economic indicators and stability indicators; Then, set constraint conditions based on the optimization objective function for the expansion of the power grid framework; Finally, use the TD3 algorithm improved based on the multi-task learning algorithm to solve the new energy multi-infeed power grid framework expansion planning model based on the set of extreme scenarios and the optimization objective function for the expansion of the power grid framework. It enhances the overall risk resistance ability of the power grid, helps to enhance the structural toughness of the power grid, comprehensively improves the safe operation level of the power grid under extreme weather conditions, and enhances the emergency response ability under extreme weather. It improves the stability, reliability and risk resistance ability of the power grid.

The application discloses a social network link prediction method based on a space-time feature perception time sequence diagram network, which divides key features in a time sequence diagram into two categories of time sequence features and structure features, for the time sequence features, through adaptive fusion of a continuous time method and a discrete method, long sequence dependence and recent dependence are effectively captured and weighed, for the graph structure features, through co-occurrence neighbor coding, graph structure information is effectively explicitly coded, and a hash-based method is used to improve co-occurrence neighbor retrieval efficiency. Such a method not only exceeds previous methods in prediction accuracy by fully capturing time sequence features and structure features, for example, the average accuracy is improved by 14.18%, 15.89% and 32.48% respectively on USLegis, UNtrade and Unvote data sets compared with previous optimal methods, and better trade-off is achieved in inference efficiency. And it is simple to realize and easy to reproduce.

The invention provides a cerebrovascular segmentation method based on a multi-scale tubular structure enhanced coding convolutional neural network. According to the method, four TOF-MRA public data sets including IXI-45, Brains, TubeTK-42 and EDEN are adopted for verification, firstly, data are randomly divided into a training set and a test set, and then a deep convolutional neural network model with an enhanced multi-scale tubular structure is constructed. The method has the advantages that the multi-scale tubular structure is designed to enhance the encoder, end-to-end optimization is achieved, and the feature extraction capacity of different-scale blood vessels is improved; and designing a self-adaptive blood vessel feature enhancement module to dynamically optimize the weight, suppress background noise and enhance the blood vessel contrast. Experimental results show that according to the method, the average Dice coefficients on four data sets respectively reach 88.81%, 82.22%, 79.54% and 92.45%, the topology maintenance Dice coefficients are 88.71%, 78.32%, 75.06% and 93.02%, the average surface distances are 0.1594 mm, 0.3119 mm, 0.6246 mm and 0.2222 mm, and all indexes are superior to those of an existing optimal method.

The invention discloses a social network link prediction method based on a spatial-temporal feature perception time sequence diagram network, and the method comprises the steps: dividing key features in a time sequence diagram into time sequence features and structural features, and carrying out the adaptive fusion of a continuous time method and an internal discrete method for the time sequence features, long-sequence dependence and recent dependence are effectively captured and balanced; for graph structure features, explicit coding is effectively carried out on graph structure information through co-occurrence neighbor coding, and meanwhile the co-occurrence neighbor retrieval efficiency is improved through a hash-based method. According to the method, the time sequence features and the structural features are fully captured, the prediction precision exceeds that of a preorder method, for example, the average precision of a USLegis data set, a UNtrade data set and a Unvote data set is improved by 14.18%, 15.89% and 32.48% compared with that of a preorder optimal method, and better balance is achieved in reasoning efficiency. The method is easy to implement and easy to reproduce.

The invention discloses a polynomial kernel-based support vector regression model reverse inversion simulation parameter optimal value method, and relates to the technical field of numerical simulation parameter calibration. According to the method, a data set is constructed by designing a simulation parameter value combination, a prediction model is constructed by adopting polynomial kernel function support vector regression (Poly-SVR), hyper-parameter optimization is realized by combining 5-fold cross validation and grid search, and finally, reverse mapping of a response quantity and a simulation parameter is completed by utilizing an L-BFGS-B algorithm. The method solves the problems that a traditional response surface method is low in calibration precision and high in overfitting risk in a small sample scene, achieves efficient and accurate inversion of simulation parameters, and is suitable for various engineering application scenes where the simulation parameters need to be reversely determined through the actual measurement response quantity.

The embodiment of the invention provides an optimization selection method and device for waste slag field design, electronic equipment and a storage medium, and relates to the technical field of geotechnical engineering.The method comprises the steps that the attribute value of a target analysis factor in each to-be-evaluated waste slag field design is obtained, the target analysis factors comprise one or more of an ecological land occupation rate, a maximum slag stacking height, a slag stacking coefficient and a stacking height coefficient; for each to-be-evaluated abandoned slag yard design, according to a pre-configured scoring rule, respectively mapping an attribute value of each target analysis factor in the abandoned slag yard design into a single factor score for representing the value rationality of the attribute value, determining a comprehensive score of the abandoned slag yard design according to the abandoned slag yard design and the single factor score corresponding to each target analysis factor; and determining an optimal abandoned slag yard design according to the comprehensive score of each abandoned slag yard design to be evaluated. By applying the embodiment of the invention, the optimal waste slag field design can be determined from the plurality of to-be-evaluated waste slag field designs.

The invention discloses a container platform fault disposal scheme preferential method and device and electronic equipment. The method comprises the following steps: obtaining current index values of a plurality of fault disposal schemes on a plurality of evaluation indexes respectively; the fault processing scheme is used for processing a fault generated when a container platform realizes business service; the container platform is built based on a plurality of running nodes, and the running nodes realize corresponding business services through running containers; according to the current index value, a first disposal scheme and a second disposal scheme are obtained, and the index value of the first disposal scheme on each evaluation index is optimal in all the fault disposal schemes; the index value of the second disposal scheme on each evaluation index is the worst in all the fault disposal schemes; and according to the first disposal scheme and the second disposal scheme, screening out a target disposal scheme meeting a preferred condition from the plurality of fault disposal schemes.

The application provides a sandstone and conglomerate OVT domain partitioned gather optimization stacking offset optimization method, which comprises the following steps: step 1, determining the fan body burial depth H and the fan body plan distribution map of the target layer in a research area; step 2, reasonably optimizing a real drilling well reconstruction time-depth relationship function formula A in the research area; step 3, calculating a fan body dip angle alpha; step 4, determining the maximum effective offset l2 of the reflection flat section of the target layer in the research area; step 5, calculating the minimum effective offset l1 of the target layer fan body in the research area; and step 6, obtaining the optimal gather stacking offset range l of the target layer fan body in the research area by comprehensively combining the step 4 and the step 5. The sandstone and conglomerate OVT domain partitioned gather optimization stacking offset optimization method can calculate the optimal offset range of the OVT domain partitioned gather optimization stacking of the target layer, and the obtained seismic data volume can better reflect the sandstone and conglomerate fan body distribution range and the effective reservoir development area.

The invention belongs to the field of industrial quality management, and provides a turbine anti-mixing inspection method which comprises the following steps: acquiring a turbine image to be detected, extracting a two-dimensional comparison value and a three-dimensional analysis value, judging whether data are matched or not through a comprehensive matching degree formula, and binding characteristic parameters with a two-dimensional code; if not, judging whether the materials are mixed or not by using a two-dimensional deviation analysis model; if yes, starting a reverse tracking mechanism, determining an actual model by combining cosine similarity, and checking the cause of material mixing; mixing cases of the same type are obtained, type difference degree groups are calculated, distinguishing methods are formulated according to frequency sorting, and the qualified cases are optimal methods and are summarized into a mixing distinguishing library; constructing a feature digital library containing multiple sub-libraries, generating a mixing risk optimization model by using federal learning training, and dynamically updating to iteratively optimize an optimization distinguishing method; according to the scheme, the turbine detection accuracy and the mixing risk prevention and control capability are improved.

To address the problem of providing a system for proposing the way to spend, a route and the like matched to a user's needs.SOLUTION: Provided is a system including: means for proposing the way to spend, a route and the like matched to a user's needs; means for linking the data to an SCPF and converting it into an algorithm; and means for calculating similar answers.SELECTED DRAWING: Figure 1

The invention discloses a small sample increment training method and device for PCB defect detection and a medium. The consideration of increment expansibility is embedded in the initial optimization stage of basic characterization. Comprising the steps of setting a small sample class incremental learning task and constructing a standard small sample class incremental learning normal form; based on a random scene selection strategy, a dynamic relation projection module constructs a self-propelled prototype refining scheme, the classification model training effect of PCB defect small sample increment is optimized, and the continuous learning ability of the model is improved; and training the model, and evaluating the performance according to a result obtained by training. The invention strictly follows small sample increment (FSCIL) settings for evaluation. Experimental results on a standard data set show that a subsequent continuous learning process can be effectively supported through basic characterization enhanced by the core technology points, and the model is remarkably superior to an existing optimal method in recognition performance and anti-forgetting ability.

The invention provides a target point error sensitivity index-based attitude adjustment mechanism position control axis optimization method, which specifically comprises the following steps of: firstly, deducing an attitude deviation equation of an aircraft large component according to a coordinate system conversion principle; thirdly, constructing a mapping relation between the motion error of the positioner and the pose deviation of the large component, so as to determine Jacobian vectors corresponding to all axes of the positioner, and obtaining a transfer matrix between the motion error of the positioner and the positioning error of the large component of the aircraft in combination with the spatial position deviation of the target point; and finally, optimizing the combination of the position control shafts according to the maximum singular value of the positioning error transfer matrix of the large part of the aircraft. According to the method, the weighted deviation of the target point is taken as an optimization target, and the position control axis combination is optimized in real time based on the positioning error transfer matrix of the large aircraft component, so that the positioning precision of the large aircraft component is improved.

The present invention belongs to the technical field of power system equipment reliability assessment and provides a reliability assessment method, device, and medium for key components of a turbine governor. The method comprises: applying a hierarchical analysis method to screen key components of the turbine governor; processing original fault operation and maintenance records of the key components to obtain a data set containing censored data; calculating the censoring rate based on the data set; and dynamically selecting the optimal method based on different censoring rates to complete the reliability assessment of the key components. The present invention achieves adaptive selection of parameter estimation methods by partitioning censoring rates into intervals, improving adaptability to data with varying censoring levels while also balancing accuracy and robustness under different censoring rates.