103 results about "Hierarchical decomposition" patented technology

The invention provides a novel power system multi-scale network construction energy storage planning optimization method based on source network load clustering. The method comprises the following steps: firstly, processing source network load data by adopting a multi-dimensional adaptive feature extraction and fusion method, and realizing accurate processing of the data through interval adaptive probability density estimation, adaptive kernel tensor decomposition and marginal information entropy maximization; then, a multi-level data clustering method is adopted, and accurate clustering is achieved through hierarchical distance measurement and boundary self-adaptive adjustment; then, a multi-scale energy storage modeling method is adopted, and accurate modeling of the energy storage system is achieved through the dynamic characteristic matrix and self-adaptive topology embedding; and finally, a multi-objective optimization solution method is adopted, and efficient solution of an optimization problem is realized through adaptive hierarchical decomposition and hybrid evolutionary optimization. According to the invention, the accuracy and reliability of energy storage planning can be obviously improved, and the system operation efficiency is improved.

The invention provides a network security policy automatic execution method and system based on an artificial intelligence large model, and relates to the technical field of network security, and the method comprises the steps: firstly obtaining a network threat chain data set comprising a threat initiating node, an intermediate propagation node, a target attacked node and propagation path description; calling a pre-trained threat chain analysis large model to carry out hierarchical disassembly and variation trend prediction, outputting a threat chain disassembly map, then extracting a matching strategy gene segment from a preset security strategy gene pool, generating candidate security strategies through model gene recombination and cross-strategy collaborative adaptation, and carrying out threat chain analysis on the candidate security strategies; inputting the candidate strategies, the current network topology and the equipment resource load data into a strategy execution deduction system, outputting an executable security strategy set adaptive to the current network state, finally issuing the executable security strategy set to network security equipment, and collecting execution feedback data for updating the threat chain analysis large model. And automatic, efficient and accurate generation and execution of the network security policy are realized.

The invention discloses a task allocation and optimization method, system and device based on a hierarchical task network, and the method comprises the steps: generating an unmanned plane task set through expert knowledge, and initializing task allocation parameters; then task level decomposition is carried out, a task allocation initial solution is established, and a to-be-executed sub-task set is formulated for each unmanned aerial vehicle; establishing a task constraint and an optimization target, and solving a task planning problem by adopting an improved genetic algorithm; and finally, a conflict-free task sequence is obtained through a conflict resolution and replanning algorithm. The system comprises an unmanned aerial vehicle cluster system, a ground station communication module, a central processing unit and a task planning solving module. An expert knowledge base for realizing task execution of the unmanned aerial vehicle cluster and a pre-execution instruction are stored in the device. By using the invention, the unmanned aerial vehicle cluster can efficiently execute the specified task set as required. The method can be widely applied to disaster rescue fields such as fields and urban areas.

Methods, non-transitory computer readable media, apparatuses, and systems for training a machine learning model include creating, using the machine learning model, a first training set by applying a first window size to a time series and a second training set by applying a second window size to the time series, where the second window size is less than the first window size. A training component trains a first layer of the machine learning model using the first training set and a second layer of the machine learning model using the second training set.

The invention discloses a hunting camera imaging quality optimization method and system based on multimode data fusion, and relates to the related field of image processing, and the method comprises the steps: obtaining the data source features of multimode data, and building a multimode data feature description library; analyzing the imaging composition factors, and performing hierarchical decomposition on the imaging composition factors to obtain imaging hierarchies; performing imaging quality dimension correlation analysis on the imaging hierarchy to obtain a forward quality factor and a reverse quality factor of the imaging hierarchy; establishing a compensation effect relationship between each data feature in the multimode data feature description library and the forward quality factor and the reverse quality factor; and based on the compensation effect relationship, performing imaging compensation optimization on each data feature in the multimode data feature description library. The technical problems that the optimization effect of the imaging quality of an existing hunting camera is limited, and the robustness and accuracy of the imaging quality are insufficient are solved, and the technical effects of improving the optimization effect of the imaging quality and improving the robustness and accuracy of the imaging quality are achieved.

The invention discloses a multi-scale linear array camera splicing method and system based on point cloud. The method comprises the following steps: completing acquisition and preprocessing of point cloud data and image data of a target area; determining an overlapping region range between adjacent images; extracting spatial structure characteristics in the point cloud data, and performing multi-scale hierarchical decomposition on the point cloud through a multi-scale segmentation method; meanwhile, multi-scale image feature extraction is carried out on the images of the linear array camera; solving gradients in X and Y directions by adopting an optical flow method aiming at any pixel in the overlapping region, and calculating a motion vector between the pixels; fusing the optical flow information obtained under each scale, and constructing a globally consistent optical flow vector field; according to the fused optical flow vector, calculating to obtain a geometric transformation matrix of the whole overlapping region; and after image transformation and alignment are completed through the transformation matrix, fusion processing is carried out on overlapped areas. And the unification of the visual effect and the spatial integrity of the spliced image is ensured.

The invention discloses a sewage treatment process effluent total phosphorus prediction method based on a hierarchical decomposition-integrated neural network, relates to the field of artificial intelligence, and solves the problems that the effluent total phosphorus concentration trend in the sewage treatment process is difficult to master, the prediction cost is high and the like. Aiming at the problems that the sewage treatment process is influenced by various environmental factors, the effluent total phosphorus embodies various characteristics such as multi-periodicity and nonlinearity, and the prediction precision is influenced, a hierarchical decomposition-integrated prediction model framework is provided, a time sequence is decomposed into a multi-periodicity component, a trend component and a residual error, and the prediction precision is influenced by the multi-periodicity component, the trend component and the residual error. Therefore, each component is accurately modeled, and the prediction performance of the prediction model is improved. Meanwhile, aiming at the problem that a common attention mechanism only pays attention to the internal importance of input variables, an improved attention mechanism is provided, the training efficiency is improved by removing redundant input components, the prediction precision of the model is improved by weighting important components, and the management operation of the sewage treatment plant is promoted.

The invention relates to the technical field of environmental noise monitoring, and discloses a road traffic noise intelligent monitoring and three-dimensional sound field reconstruction system, which comprises an acoustic sensor module, a data collection and storage module, a data analysis and evaluation module, a three-dimensional sound field construction and display module and a traffic flow feature library. According to the system, a differential geometry principle is adopted, a sound field is regarded as a Riemannian manifold with a local microstructure, and accurate description of an irregular sound field is realized through a curvature self-adaptive sound field manifold construction technology; introducing a covariant derivative in Riemannian geometry, and constructing a sound propagation model adapted to a complex road environment; and realizing hierarchical decomposition and reconstruction of the sound field by using a multi-scale analysis theory. According to the method, the sound source positioning precision and the calculation efficiency are improved, seamless analysis from microcosmic to macroscopic is realized, an innovative solution is provided for traffic and noise collaborative management, and intelligent traffic and environmental noise management are effectively supported.

The invention relates to the technical field of hydropower dispatching, and discloses a multi-agent water-wind-light short-term power generation optimization dispatching method considering space-time coupling constraints. Firstly, on the basis of drainage basin characteristics, a hierarchical decomposition strategy is adopted, and overall centralized optimization solution of the water-wind-light complementary power supply system is converted into partial multi-agent collaborative optimization scheduling, so that the solution efficiency is improved; secondly, in combination with a multi-agent and unified optimization operation theory, constructing a double-layer nested short-term water consumption minimum model considering wind power and photovoltaic power prediction output, so as to realize intelligent modeling; and finally, an improved alternating direction multiplier method is adopted, and interaction among multiple agents is coordinated based on a modification strategy of a flow balance and electric quantity matching principle, so that the solving quality is improved. According to the method, the solving efficiency and precision can be effectively improved, and a solid theoretical basis and a feasible thought are provided for short-term power generation dispatching of a large-scale water-wind-light complementary power supply system.

The invention relates to the technical field of embedded data storage, and discloses a data storage method and system for an embedded storage chip. The method comprises the following steps: acquiring a to-be-stored original data stream through a data acquisition interface and performing segmentation marking; performing hierarchical decomposition on the data segments by adopting a dynamic decomposition algorithm to generate a plurality of data components; calculating an information density index of each data component, and screening key data components and non-key data components according to the information density index; key data components are stored in a high-speed cache region, and non-key data components are stored in a conventional storage region; performing redundancy check on the key data components in the cache region and generating a check result; after correcting the key data component based on the verification result, recombining the key data component with the non-key data component to generate an optimized data stream; writing the optimized data stream into a physical storage medium and recording a storage position; and monitoring the storage load state, and dynamically adjusting the distribution proportion of the two storage areas.

The invention relates to the technical field of electrochemical energy storage systems, and discloses an electrochemical energy storage system battery charging effect evaluation method, which comprises the following steps: acquiring energy storage system operation data and battery charging records, extracting time sequence characteristics, equipment state characteristics and charging parameter characteristics, and calculating the charging effect of an electrochemical energy storage system battery. Constructing a multi-dimensional feature vector and generating an initial charging effect feature library; the evaluation model is dynamically adjusted based on feature library feedback, potential effect features are identified through mode clustering analysis, feature priorities are optimized, and model parameters are updated; and in combination with system configuration information and target model parameters, determining an optimal power compensation effect evaluation result by adopting a hierarchical disassembly and classification statistical method. According to the method, through multi-dimensional feature fusion, dynamic model optimization and refined statistical analysis, the accuracy and adaptability of electricity supplement effect evaluation are remarkably improved, and a scientific basis is provided for efficient operation and maintenance of the electrochemical energy storage system.

The invention provides an intelligent customer service query system and method based on natural language understanding, and relates to the technical field of natural language processing. A query intention and a core entity of a natural language query request input by a user side are extracted; performing hierarchical deconstruction on the query intention, and constructing a query semantic tree according to a hierarchical deconstruction result and a semantic association relationship between core entities; generating a decision tensor representing a query path priority according to a node semantic weight of the query semantic tree and an access delay feature of the multi-source database; further adjusting an access sequence and a connection strategy of the multi-source data through a decision tensor and a dynamically updated query cost model to obtain a query execution link adaptive to the current query request; and calling a corresponding data source interface according to the query execution link to execute data retrieval and integration operation to obtain a target result set responding to the query demand of the user. By the adoption of the scheme, multi-source data optimal cost retrieval based on the natural language can be achieved.

The invention provides a decentration convex optimization method and system for enhancing AGV edge computing energy efficiency, and the method comprises the steps: building a task unloading model based on an edge computing network; based on a preset base station index set, an AGV index set and the calculation task of the AGV, obtaining a state space and an action space of the task unloading model; establishing a flow model, a calculation model and a communication model; integrating the models to obtain a joint optimization problem of task unloading and resource allocation; decomposing the joint optimization problem into a task unloading problem and a resource allocation problem by adopting a hierarchical decomposition method; a probability task unloading strategy is adopted, load prediction is carried out on a task unloading problem, a resource allocation problem is decomposed and convexly optimized, and a joint optimization problem is solved in combination with a load prediction result. The invention aims to realize calculation unloading satisfying energy constraint in the AGV edge calculation network so as to reduce the time delay of the edge calculation network and promote the development of the edge calculation technology.

The invention provides a processing chatter signal feature sample extraction method and a chatter detection method and device, and belongs to the technical field of industrial signal processing, and the method comprises the steps: carrying out the optimization variational mode decomposition of an original processing chatter signal, and obtaining a plurality of IMF signal components; performing fusion index evaluation on the IMF signal components to obtain a quality score of each IMF signal component, and performing screening reconstruction on the IMF signal components according to the quality scores to obtain reconstructed signals; recursive hierarchical decomposition is carried out on the reconstructed signal based on a preset matrix operator to obtain a plurality of hierarchical node components, and singular spectrum entropy feature sample extraction is carried out on the hierarchical node components to obtain a feature sample data set; according to the method, weakening of short-time abrupt change features in signal segments is avoided through recursive hierarchical decomposition, local abrupt change features in signals are captured through singular spectrum entropy, sensitivity to short-time abrupt change or transient events is improved through combination of hierarchical decomposition and hierarchical singular spectrum entropy of singular spectrum entropy, and accurate feature samples are obtained.

The invention discloses a manufacturing capacity trend prediction method based on AI visual analysis, and relates to the technical field of industrial big data analysis, and the method comprises the following steps: building a cross-time scale alignment baseline, generating a phase consistency evidence set of a continuous time structure through timestamp reconstruction and sampling calibration, and obtaining a phase consistency evidence set; extracting a high-confidence time anchor point as a unified time reference; and constructing a nonlinear disturbance spectrum diagram based on the time anchor points, and carrying out hierarchical decomposition on energy mutation, slow drift and pseudo-stable fragments in a continuous time period to generate a traceable disturbance fingerprint set. Through time sequence alignment, disturbance modeling, weight repair and dynamic regulation and control mechanisms, stable convergence and trend consistency prediction of the artificial intelligence model under nonlinear disturbance are realized, the accuracy of productivity prediction and the reliability of decision making are improved, and a manufacturing enterprise is assisted to realize intelligent productivity management.

The invention discloses a distribution cable multi-source data fusion method based on spectrogram reconstruction. The distribution cable multi-source data fusion method comprises the following steps: S1, constructing a distribution cable operation state multi-source database; s2, using contourlet transform to extract features from the cable partial discharge signal diagrams in the database, and performing hierarchical decomposition of multi-source data spectrograms on the extracted features; s3, graying processing is carried out on the multi-source data spectrogram obtained after grading decomposition, grayed images are decomposed through NSCT, and multi-layer high-frequency sub-band coefficients of all the images and discharge spectrogram contour feature information are obtained; and S4, adopting a low-frequency sub-image fusion rule of local entropy, selecting a fusion coefficient of a high-frequency sub-image based on an NSCT decomposition structure, and reconstructing an optimal fusion image through NSCT inverse transformation. According to the method, multi-scale decomposition and multi-directional analysis can be realized, the feature extraction capability of cable multi-source data is enhanced, the comprehensiveness and accuracy of data fusion are improved, and a distribution cable state spectrogram with heat, electricity and vibration fused is realized.

The invention provides a script modification iterative optimization method and system and electronic equipment. The method comprises the following steps: analyzing an original script into a structured script data object; performing hierarchical decomposition and feature dimension distribution on the script optimization task; performing macroscopic analysis on the script data object based on a task decomposition scheme, generating a global evaluation result containing an improved strategy, and converting the global evaluation result into scene-level operation guidance; analyzing each scene based on operation guidance to generate modification suggestions, and integrating the modification suggestions to form a unified modification scheme; performing routing classification on the modification suggestions according to content types based on the scheme; executing modification operation for different elements of the script according to the suggestion distribution instruction; and evaluating the quality of the optimized script, and iteratively optimizing the script until a quality evaluation result meets a convergence condition. The problem of low generation quality caused by insufficient complex task processing capability, incoherent narration, difficulty in context consistency maintenance and the like in a single generation process and a normal form of an existing long script generation method is solved.

The invention relates to the technical field of bridge construction supervision, and discloses a bridge construction supervision method based on part management, and the method comprises the following steps: S1, carrying out the hierarchical decomposition of a construction project, and guaranteeing the clear task, standard and acceptance content of each level from the project type to the process; s2, generating a three-dimensional section graphic model of each part; the invention also discloses a bridge construction supervision system based on part management, and the system comprises a part decomposition and three-dimensional graph generation module which is used for generating the decomposition model and the three-dimensional section graph of the construction project; and the data acquisition and synchronous updating module is used for acquiring construction data in real time and synchronizing the construction data to the three-dimensional section graphic model. By combining a new thought of'part 'and'process' management and utilizing a three-dimensional section graphic model, a dynamic adjustment mechanism and an intelligent supervision system, problems in traditional construction management are solved, the management precision and efficiency are improved, and high efficiency, precision and intelligence of construction management are realized.

The invention provides a double-head ultraviolet laser cutting control method and system, and relates to the technical field of laser cutting, and the method comprises the following steps: obtaining geometrical characteristics of a workpiece, constructing a three-dimensional constraint model, and forming a multi-dimensional hierarchical decomposition tree; generating an initial cutting path and evaluating the priorities of the subtasks; in the execution process, a dynamic interference trend is pre-judged, and an obstacle avoidance rule set is established for path fine adjustment and working area reconstruction; and according to the machining difficulty, partition planning is conducted, partition coupling relation parameters are derived, and cutting parameters and movement speed are configured. Intelligent cooperative control of the double-end ultraviolet laser is achieved, the cutting efficiency and quality are improved, and interference and collision are avoided.

The invention relates to the technical field of spacecraft assembly, and provides a spacecraft assembly process template generation method and device. The method comprises the following steps: carrying out hierarchical disassembly based on spacecraft assembly process elements, and packaging each hierarchical element into a corresponding process structure body; defining a filling function corresponding to each process structure body to serve as a template function; user parameter input is received, the spacecraft assembly process template is generated based on the configured multi-class code building blocks according to the template function, and in the process of generating the spacecraft assembly process template, displayable debugging is conducted on the spacecraft assembly process template based on the building block identification. According to the method, the generation process of the spacecraft assembly process template is optimized, and the reuse efficiency of the template is improved.

The application discloses a hierarchical simulation geological modeling method under anisotropic tensor and uncertainty constraint, relates to the technical field of geological modeling, and comprises the following steps: inputting selected profile sample grade data, fitting a variation function and defining simulation parameters; performing conditional simulation according to the simulation parameters to generate a plurality of simulation implementations reflecting spatial anisotropy; controlling a hierarchical decomposition process through checking a termination condition, performing threshold smoothing processing on a sub-region after termination, saving an optimal threshold value and a class probability field of the sub-region, updating a global probability field by using a back transmission mechanism, outputting a grid of a final class probability field, and forming a rasterized ore body shape. The application obtains local threshold optimization through continuous hierarchical simulation and iteration, replaces unoptimized results, and forms optimal simulation results of the ore body, so that the uncertainty transmission of a traditional method is effectively reduced, and the accuracy and rationality of geological domain division are improved.

The embodiment of the invention relates to the technical field of vehicles, and discloses a dynamic total path sound insulation performance uncertainty optimization method and device, equipment and a medium. The method comprises the following steps: constructing a hierarchical decomposition framework of acoustic package sound absorption and insulation performance based on a power assembly; based on the power assembly, constructing a hierarchical decomposition framework of acoustic package sound absorption and insulation performance; constructing an ear noise prediction model based on a preset learning algorithm, the hierarchical decomposition architecture and the test parameter set; and on the basis of the basic parameter set, the ear noise prediction model, a Monte Carlo strategy and a preset optimization algorithm, completing global uncertainty optimization in a forward design stage. The problem of low accuracy in the acoustic packet performance prediction and optimization process is solved, the prediction accuracy is improved, and the engineering adaptability is improved.

The embodiment of the invention provides a power grid fault identification method, a model training method, devices and equipment. The method comprises the following steps: acquiring real-time fault data of a target power grid; performing hierarchical decomposition processing on the real-time fault data to obtain at least one piece of fault feature data corresponding to the real-time fault data; and based on a triple weighted extreme learning machine, processing the fault feature data to obtain a fault identification result of the target power grid. The method can improve the accuracy and efficiency of power grid fault identification.

The invention discloses a layered simulation geologic modeling method under anisotropic tensor and uncertainty constraints, and relates to the technical field of geologic modeling, and the method comprises the steps: inputting selected profile sample grade data, fitting a variation function, and defining simulation parameters; performing condition simulation according to the simulation parameters to generate a plurality of simulation implementations reflecting spatial anisotropy; and controlling a hierarchical decomposition process by checking a termination condition, performing threshold smoothing processing on the terminated sub-region, storing the optimal threshold and the class probability field of the sub-region, updating the global probability field by using a return mechanism, outputting a grid of the final class probability field, and forming a rasterized ore body form. According to the method, local threshold optimization is obtained through continuous layered simulation and iteration, an unoptimized result is replaced, and an optimal simulation result of the ore body is formed, so that uncertainty conduction of a traditional method is effectively reduced, and the accuracy and rationality of geological domain division are improved.

The present invention discloses a method, a system and a device for extracting vibration signal characteristics of a hydropower unit, relating to the field of feature extraction, including: obtaining the vibration signal of the hydropower unit; hierarchically decomposing the time series of the vibration signal by using moving average and moving difference methods based on conditional bubble entropy to obtain the matrix form of the corresponding operator for each layer; constructing a vector of non-negative integers e and obtaining the matrix form of the corresponding operator for the vector at each layer, and obtaining the hierarchical component at each layer node e by multiplying the matrix form of the corresponding operator for each layer by the time series; calculating the CBE value of each layer, and defining the set of CBE values of all components as the improved hierarchical conditional bubble entropy value HCBE; finding the optimal parameter of HCBE, and when the non-negative integer e is less than the threshold, outputting IHCBE as the feature characterizing the fault. The present invention proposes an improved hierarchical conditional bubble entropy, which makes the obtained information more detailed and improves the accuracy of hierarchical bubble entropy calculation.

The invention belongs to the technical field of robot task planning and execution, and particularly relates to a two-arm robot long-time sequence planning method based on tree-shaped hierarchical decomposition. The method specifically comprises the following steps of: initializing an input task instruction into a root node of a sub-target tree, decomposing the root node by utilizing a sub-target decomposition model, and constructing a first layer of sub-targets; further decomposing the current sub-target based on the current sub-target, the father node information and the current environment to generate a new sub-target; taking the sub-target with the finest granularity as a leaf node, evaluating each leaf node, judging whether the leaf node meets a termination condition or not, and performing further decomposition if the leaf node does not meet the termination condition; according to the primitive action sequence generated by the leaf nodes, the robot executes specific actions, environment feedback is obtained in the execution process, and dynamic adjustment planning of the sub-target trees is carried out; and when the primitive actions corresponding to all leaf nodes are executed and the task target is reached, completing the task.

This invention discloses a language-conditional generalization learning method for deformable robot operations. Addressing the challenges of high-dimensional state space, dynamic complexity, and difficulties in cross-task transfer in deformable robot operations, it constructs a generalization framework integrating language understanding, skill decomposition, and cross-modal learning. The method collects multimodal demonstration data such as image sequences and natural language commands. It utilizes a large language model to perform semantic parsing and hierarchical decomposition of tasks, forming reusable basic skill units. Then, through a Transformer-based cross-modal skill learning model, it combines images and skill descriptions to predict operation target points and execution parameters, establishing a general skill library. When faced with a new task, the large language model generates skill sequences and calls upon the skill library for step-by-step execution, thereby achieving generalized operation of unseen tasks in both simulation and real robots, significantly improving the system's generalization, stability, and scalability.

The invention provides a complexity determination method and device for a flight operation program and a man-machine interaction system, and is applied to the technical field of aviation equipment evaluation. Comprising the steps of performing hierarchical decomposition on a flight operation program corresponding to a flight task to generate a solution sequence of the flight operation program; drawing an action control chart according to the operation action information in the solution sequence; drawing an information interaction diagram according to the attention distribution condition in the solution sequence; drawing a control mode pattern according to the use condition of the control device in the solution sequence; determining operation logic complexity and operation quantity complexity according to a first node in the action control chart; determining information interaction complexity according to a second node in the information interaction graph; the control mode complexity is determined according to a third node in the control mode graph; and determining the first complexity of the flight operation program according to the operation logic complexity, the operation number complexity, the information interaction complexity and the control mode complexity. And the accuracy and continuity of the first complexity are improved.

An example computer-implemented method for temporal data analysis and forecasting utilizes topological hierarchical decompositions to process historical and future time windows. The method receives temporal data and generates multiple sets of historical time subsets with varying lengths, where information in shorter subsets is duplicated in longer ones. Future time windows are also generated in a similar manner. Future time windows are chronologically after a given initial time. The method creates past and future topological hierarchical decompositions and directed graph adjacency arrays. Customer attention matrices are generated for past and future windows, and matrix multiplications are performed to create self-attention arrays. These arrays are then multiplied together. The method culminates in providing a dashboard for forecasting demand after an initial time point, enabling comprehensive temporal data analysis and prediction.

The application belongs to the technical field of embodied intelligent robots, and particularly relates to an embodied intelligent robot long-term behavior generation method based on thought chain strategy decomposition; the method comprises the following steps: constructing a primitive action unit library of a robot operation scene; establishing a coordinate system model based on three-dimensional atomic action semantics; performing hierarchical task planning based on a thought chain for a target task; generating a subtask sequence with a unified interface; converting action instructions according to a corresponding strategy generation module according to a subtask type; performing real-time state feedback and dynamic adjustment in an execution process; and continuously optimizing the model by using online reinforcement learning. The application realizes hierarchical decomposition of long-term tasks through thought chain reasoning, decouples high-level strategy planning and low-level action execution, retains the advantages of large model reasoning while avoiding the uncertainty of directly intervening in low-level control, and realizes adaptive, safe and reliable robot operation in a complex scene.