54 results about "Bipartite graph matching" patented technology

The invention provides an all-weather autonomous inspection method and system based on cluster task dynamic load balancing, and relates to the technical field of inspection monitoring, and the method comprises the steps: collecting the remaining electric quantity, the positioning position, the task queue length and the sensor health state of an unmanned aerial vehicle cluster in real time through an airborne terminal; fusing the meteorological data and the multi-source environment sensing data, and constructing a dynamic obstacle map and a meteorological influence model; dividing an inspection area into a plurality of sub-areas based on an electronic fence, dynamically adjusting the inspection priority of each sub-area, setting task weights of a water taking head and a raw water pipeline facility, and distributing inspection tasks according to the priorities; based on the remaining power of the unmanned aerial vehicle, the task priority, the current load and the meteorological data, a bipartite graph matching model of the unmanned aerial vehicle and the task is constructed, the matching weight is dynamically calculated, the optimal matching distribution of the unmanned aerial vehicle and the inspection task is completed, and the efficient inspection operation of the unmanned aerial vehicle cluster in the complex environment is realized.

The invention discloses an intelligent port operation vehicle scheduling system and a scheduling robot, and relates to the technical field of intelligent port operation. The problems that in traditional port vehicle scheduling, the manual scheduling response is slow, the error rate is high, resource distribution is uneven, the labor cost is too high, and the efficient and intelligent requirements of modern ports are difficult to meet are solved. The vehicle no-load distance is reduced and the heavy load rate is improved by using a bipartite graph matching strategy, the prediction scheduling module plans in advance, task overstock and vehicle idleness are reduced, the operation efficiency is effectively improved, the constraint processing module optimizes task allocation according to task and vehicle conditions, and the multi-target optimization module gives consideration to the heavy load rate and order dispatching fairness. The scheduling robot integrates system functions, and can adapt to different working environments and realize intelligent scheduling through data acquisition and continuous optimization of an online learning technology, so that the scheduling accuracy and efficiency are improved, the labor cost is reduced, and the overall competitiveness of a port is improved.

The invention relates to a standardized project management and intelligent professional scheduling system, and belongs to the technical field of project management and human resource intelligent scheduling. The project standardization module is disassembled into standardization task steps through a business process association rule mining algorithm, and project complexity and resource tensity are adapted by using a task attribute dynamic weight algorithm; the archive matching module constructs professional archives, extracts feature vectors through a multi-criterion decision-weighted bipartite graph matching algorithm, and generates optimal matching pairs in combination with adaptive weight adjustment and an integer linear programming model; the dynamic scheduling module plans a task execution scheme according to a resource constraint scheduling mechanism, and realizes efficient scheduling in combination with a skill supply and demand prediction algorithm and calendar integration; and the quality optimization module adopts a deliverable anomaly detection algorithm to monitor compliance, feeds back an iterative matching and scheduling strategy through a time sequence prediction optimization algorithm, and perfects a skill map based on a map increment updating algorithm. The system realizes a project full-process closed loop.

The invention discloses a single tree trunk structure extraction method and system based on deep learning, and the method comprises the steps: obtaining two-dimensional image data of a single tree, marking the two-dimensional image data, and constructing a single tree trunk segmentation data set; constructing a spatial domain and frequency domain double-branch network based on the segmented data set, respectively extracting spatial domain features and frequency domain features, and fusing the double-branch features to generate a coding feature map; the coding feature map is decoded, in the decoding process, coordinate convolution CoordConv is adopted to enhance position perception, and mask segmentation and semantic label extraction are respectively carried out through a dynamic mask reconstruction branch DRMask Branch and an instance branch Inst Branch; based on a bipartite graph matching strategy, associating results of the mask segmentation and instance branches, and realizing segmentation of a single tree trunk and matching of instance-level labels; according to the method, the boundary precision and the detail reconstruction capability of trunk segmentation are remarkably improved, and the problem of feature loss of a traditional method in a complex under-forest environment is solved.

The invention discloses a new energy power station inspection method based on a multi-modal large model small sample open set, and the method constructs a multivariate text learnable prompt, and guides a detection model to form a finer-grained category decision boundary through the text information related to an aggregation task. Due to the lack of real unknown class samples in the training process, the mining of unknown class pseudo samples is regarded as a bipartite graph matching task for the first time, and the model is optimized to form a compact unknown class decision boundary by adding unknown class virtual nodes, constructing a cost matrix and mining the unknown class pseudo samples. In order to solve the problem that known classes and unknown classes of small samples are prone to confusion, the method proposes unknown class optimization loss based on cost perception, considers the classification and positioning quality of the unknown classes, and improves the open set detection performance of the model. According to the method, transformation from a single-mode vision small model to a multi-mode vision large model is realized, and good generalized small sample open set target detection performance can be obtained only by a small amount of training data.

The invention discloses an end-to-end category level object pose estimation method based on space-time implicit anchor point query, which comprises the following steps of: inputting an RGB (Red, Green and Blue) image sequence into a visual encoder, extracting multi-scale semantic features and fusing depth and normal vector geometric features to generate 2.5-dimensional multi-scale features; a 3D implicit anchor queue is established based on the features, and queries in the queue are processed through confidence screening and camera pose offset transformation. Performing modeling on space-time correlation by using multi-head attention, outputting time sequence enhanced query features, and performing geometric perception feature sampling; and finally, mapping the implicit 3D query into 9-degree-of-freedom attitude parameters through bipartite graph matching to realize end-to-end training.

The invention relates to the field of relation triple extraction, in particular to a relation triple joint extraction method based on information enhancement and bidirectional modeling. According to the method, through entity-to-relation and relation-to-entity double-branch collaborative modeling, semantic information is enhanced through double-branch potential information complementation: a bipartite graph matching method is combined with subjects and objects extracted by an entity extraction module, and relation classification is optimized through an entity boundary mask attention enhancement method; a potential relation extraction module is used for guiding subject and object entity extraction, and entity boundary information is utilized to crosswise mask attention of a large related area; the two branches filter redundant triads through a relation triad cutting module; combining bidirectional results and outputting a complete triple set; according to the method, a bidirectional interaction method is introduced to realize mutual enhancement of entities and relationships, and attention distribution is optimized in combination with entity boundary masks, so that the problem of high dependence on an initial extraction result caused by unidirectional modeling in traditional joint extraction is effectively solved.

The invention relates to the technical field of internet data processing and resource scheduling, and discloses an intelligent order sending method and system for housekeeping service and a storage medium, and the method comprises the steps that a server side receives original order information containing house investigation data; calling a spatial granularity decomposition model to analyze the order into independent operation area sub-tasks, and calculating the lowest skill level demand in combination with the feature vector; attribute vectors such as comprehensive skill levels and real-time coordinates of service personnel are obtained; constructing a dynamic bipartite graph matching model by taking the subtask set and the attributes of the service personnel as input; an optimal mapping relation list is determined by solving the maximum value of an objective function, and a dispatching instruction is sent. The method further comprises an attendance checking judgment step based on a geofence and a closed-loop iteration step of dynamically updating skill levels according to attendance checking and evaluation data. According to the method, refined disassembly and global optimal scheduling of housekeeping tasks can be realized, and the resource allocation efficiency and the service quality are remarkably improved through a dynamic closed-loop mechanism.

The invention discloses a timing sequence statement positioning model training method and device based on proposal selection and anchor point distribution, and relates to the technical field of timing sequence statement positioning. The method comprises the following steps: initializing a fixed number of learnable queries according to a first static anchor point set; on the basis of the first learnable query, according to the unpruned long video and the natural language description text, performing proposal generation through a time sequence statement positioning model; based on the proposal selection module, redundant proposal filtering is carried out by using a non-maximum suppression algorithm; based on an anchor point distribution module, according to the untrimmed long video, the first static anchor point set and the candidate proposal set, performing bipartite graph matching by using a Hungary algorithm; and carrying out loss function weighting calculation according to the unpruned long video and the optimal proposal set, and carrying out parameter optimization on the time sequence statement positioning model to obtain an optimized time sequence statement positioning model. The method is an efficient and accurate time sequence statement positioning model training method combining proposal selection and anchor point distribution.

The invention discloses a traditional Chinese medicinal material defect detection method based on machine vision, relates to the technical field of intelligent detection, and solves the problems that in the prior art, a threshold cannot be migrated, so that an inter-batch judgment standard is unstable, and high-resolution block reasoning causes boundary pseudo defects and instance wrong affiliation. According to the method, a reference area is constructed in a batch domain, a statistical descriptor is extracted, and monotone mapping calibration is performed on defect scores so as to realize cross-batch unified judgment; meanwhile, a seam continuity field is introduced in the image block splicing process, defect connected domains are fused through a cross-window adjacency relation, and accurate attribution of defects and medicinal material pieces is achieved based on bipartite graph matching; according to the method, the judgment stability and the statistical reliability in the traditional Chinese medicinal material defect detection process are remarkably improved.

The invention discloses a multi-source geographic vector data matching method based on topological relevance, which comprises the following steps: collecting multi-source basic geographic vector data to form a preprocessed data set; constructing multi-index data quality evaluation based on local topological features; extracting and classifying spatial topological relation edges; constructing a weighted adjacent matrix of the target map; a neighborhood topology consistency measurement index is calculated, and neighborhood topology consistency quantization and adaptive weight fusion are carried out; establishing a global optimization objective function by taking the confidence coefficient matrix as input, solving a maximum weight bipartite graph matching problem with topology constraints by taking the topology retention rate as a hard constraint condition, and outputting a global optimal matching scheme; and constructing a quantitative evaluation index topology deviation measure based on topology invariant difference, judging whether a matching scheme has a significant topology conflict, and performing hierarchical repair according to the conflict condition. According to the method, the reliability and logic self-consistency of multi-source vector data fusion are improved, and the key problem of topological contradiction in the prior art is solved.

The invention provides a discrete manufacturing intelligent scheduling method based on a graph theory. The discrete manufacturing intelligent scheduling method comprises the steps of 1, constructing a heterogeneous graph model of scheduling elements; step 2, static scheduling optimization based on a critical path method; step 3, resource allocation optimization based on a multi-resource bipartite graph matching method; and 4, dynamic response and rescheduling are carried out. Based on a graph theory method, a complex'process-resource-constraint 'relationship is converted into a visual heterogeneous graph model, and a systematic scheduling solution based on the graph theory is constructed, so that the scheduling solution is suitable for a discrete manufacturing scene with multi-process, multi-equipment, multi-constraint and dynamic disturbance characteristics; the method is used for realizing static planning of production plan scheduling, resource optimization distribution and dynamic adjustment full-process optimization.

The invention belongs to the technical field of energy storage system operation and maintenance and data diagnosis, and particularly relates to a highway zero-carbon service area-oriented energy storage data diagnosis method and device, and the method comprises the steps: carrying out the real-time monitoring of an energy storage system asset SN set, channel-asset binding and channel physical feature data, recognizing an abnormality, and triggering self-verification; identifying a natural or artificial anchor point through a charging session and grid-connected power steady-state constraint, determining a time window, extracting channel fingerprint features, matching an asset SN historical feature template according to an operation mode, and calculating a normalized difference degree verification binding relation; when mismatching is bound, an optimal rematching scheme is solved through bipartite graph matching according to physical topology constraints, independent verification is carried out through a station-level power closing auditing model, a diagnosis baseline is reconstructed in a layered mode after verification is passed, and an alarm and diagnosis conclusion is output by adopting a hierarchical gating strategy in combination with multiple states. The alarm accuracy is improved, misdispatch work orders are reduced, the diagnosis conclusion can be traced and audited, and stable support is provided for zero-carbon operation accounting.

The invention discloses a monocular vector high-precision map data association method based on geometric context, and relates to the technical field of map data association. Extracting a map landmark set in a current frame view range from the map through the initial vehicle pose, and projecting a point set to a pixel plane; performing structured modeling on each point set in the detection road sign set and the projection road sign set; constructing a cost matrix for the current estimated vehicle pose; introducing a gating mechanism based on map distance and a self-adaptive gating range based on map depth, and eliminating matching pairs with inconsistent geometric structures; complete data association is obtained through cost matrix calculation, optimal bipartite graph matching is performed on the cost matrix by adopting a Hungary algorithm, and a data association relationship meeting global optimum is obtained. According to the method, feature information of road signs in a vector high-precision map is fully utilized, outlier matching relation pairs are eliminated through self-adaptive gating, finally, data association is conducted through a Hungary algorithm, and matching robustness and accuracy are improved.

The invention provides a super-large-scale MIMO system beam focusing method based on an adaptive time delay-phase structure, and relates to the technical field of radio communication, comprising the following steps: constructing an adaptive time delay-phase structure suitable for a super-large-scale MIMO system, and establishing a near-field beam focusing joint optimization mathematical model; solving an all-digital optimal beam focusing matrix through maximum ratio transmission; a switch matrix optimization problem is converted into a bipartite graph matching problem, and an optimal matching matrix is solved by using a Hungary algorithm; solving a phase shift matrix through a Riemann spectrum conjugate gradient method; optimizing the time delay matrix by using a gradient descent method; optimizing the digital precoding matrix by using a least square method; and giving a base station beam focusing matrix through alternate iteration, and completing beam focusing of the super-large-scale MIMO system. According to the structure provided by the invention, dynamic sharing with the antenna array elements can be realized by utilizing the switching network, so that the adaptive capacity of hybrid precoding to a channel environment is effectively enhanced.

The invention discloses a cutting track planning method and device and a storage medium, and the method comprises the steps: building a bipartite graph model of a workpiece graph set and a threading hole set, and building a weight matrix based on an edge set in the bipartite graph model; based on the weight matrix, performing optimal matching on a workpiece graph node set and a threading hole node set by adopting a bipartite graph matching algorithm, and determining a matching pair between the workpiece graph and the threading hole according to nodes which can be matched with each other; performing type division on the workpiece graph according to the matching pair to obtain a male die and female die workpiece graph set, and combining the cutting starting point position of the workpiece graph to obtain a first corresponding relation between the female die workpiece graph and the cutting starting point position and a second corresponding relation between the male die workpiece graph and the cutting starting point position; and according to the obtained first and second corresponding relations, carrying out cutting track planning on the workpiece graphs in the female die and male die workpiece graph set. Therefore, batch programming of a plurality of male dies and female dies is realized, and the efficiency of generating a machining program is improved.

The invention provides a resource target allocation method of a double-layer model based on bipartite graph matching. The method comprises the following steps: step 1, establishing a multi-target static resource allocation problem model; the multi-target static resource sub-problem allocation model comprises a target function and a constraint condition; 2, establishing a bipartite graph matching optimization model; and step 3, performing iterative solution on the multi-target static resource target allocation problem based on the bipartite graph matching double-layer model, and stopping iteration until a preset termination condition is met. According to the method, the joint damage efficiency of the plurality of resources to a certain target can be calculated, and the damage efficiency of the plurality of resources to a certain target is not simply accumulated, so that the solution accuracy is improved.

This invention relates to the fields of data processing and talent matching technology, and in particular to a two-way matching method for university-enterprise talent based on financial capability profiling. The method includes: constructing a corporate financial capability profiling indicator system, including indicators of corporate financial stability and job financial capacity; constructing a talent financial capability profiling indicator system, including indicators of financial knowledge reserves, financial practice skills, financial risk awareness, and financial digitalization capabilities; collecting and preprocessing data from both the corporate and talent sides; calculating a comprehensive evaluation value based on a combined weighting method using an improved analytic hierarchy process (AHP) and entropy weighting method; constructing a corporate job demand vector and a talent supply vector; solving for the optimal matching scheme based on a bipartite graph matching algorithm, with the objective function of maximizing two-way matching satisfaction; and outputting the matching results. By constructing a two-dimensional evaluation system of corporate and talent financial capabilities, and using an improved weighting method and matching algorithm, a precise two-way matching of university and enterprise talent is achieved, improving the scientific nature of the matching.

The application provides a kind of based on adaptive time delay-phase structure's super large scale MIMO system beam focusing method, it is related to radio communication technical field, including the following steps: constructing adaptive time delay-phase structure suitable for super large scale MIMO system, and establishing near-field beam focusing joint optimization mathematical model;Through maximum ratio transmission solving all digital optimal beam focusing matrix;Switch matrix optimization problem is converted into two-part graph matching problem, and optimal matching matrix is solved using hungarian algorithm;Through riemann spectrum conjugate gradient method solving phase shift matrix;Utilize gradient descent method to optimize time delay matrix;Least square method is used to optimize digital precoding matrix;Through alternate iteration, base station beam focusing matrix is given, and the beam focusing of super large scale MIMO system is completed.The structure provided in the application can be realized with switch network and the dynamic sharing of antenna array element, so as to effectively enhance the adaptive ability of hybrid precoding to channel environment.

The application belongs to but is not limited to the technical field of automatic student behavior analysis, and discloses a classroom student behavior recognition method and system based on a Transformer detection and hierarchical soft routing decision. Frame images are intercepted from classroom monitoring videos and input into a pre-trained RT-DETR model; multi-scale features of the images are extracted by a hybrid encoder of the RT-DETR and input into a Transformer decoder, global context information of the images is captured by using a self-attention mechanism, and a set of boundary boxes of N student targets is predicted by using a bipartite graph matching mechanism; each boundary box detected is dynamically expanded and cropped; a pre-trained visual-language large model CLIP is used to construct a hierarchical decision system with a logical tree structure to perform hierarchical feature extraction; and an output result is obtained by reasoning based on a low-threshold parallel activation soft routing decision mechanism; and the application significantly improves the recognition accuracy of fine-grained actions.

The invention relates to the technical field of vehicle task scheduling, in particular to a dynamic vehicle task scheduling method and system based on two-stage differentiation and multiple targets. The method comprises the following steps: acquiring vehicle data and task data; performing time slice static modeling based on the task data; carrying out vehicle classification and attribute modeling based on the vehicle data; according to a modeling result, performing two-stage differentiation and multi-target dynamic vehicle task allocation, including a first stage of sparse bipartite graph matching allocation and a second stage of CMOEA-EG multi-target optimization; performing fairness evaluation and feedback based on a distribution result; and updating and optimizing based on a feedback result. The task execution result is fed back in real time through the dynamic updating module, and multi-round iterative optimization is supported.

The application discloses a speaker recognition method based on bipartite graph matching and an electronic device, and belongs to the technical field of audio recognition. The method comprises the following steps: acquiring an audio stream, and splitting the audio stream into continuous target time length audio segments in real time; determining a voiceprint embedding vector corresponding to each target time length audio segment; determining an embedding matrix based on the voiceprint embedding vector; determining a target voiceprint feature corresponding to each target time length audio segment according to a bipartite graph method; calculating the similarity between each voiceprint embedding vector; grouping each voiceprint embedding vector based on the similarity between each voiceprint embedding vector, clustering the voiceprint embedding vectors corresponding to the same speaker, obtaining a clustering result, and generating a target recognition report. The application can reduce the calculation complexity while improving the recognition accuracy.

The application discloses a photovoltaic array fault detection and positioning method based on bipartite graph matching, comprising the following steps: 1) based on the connection structure of a photovoltaic array, a photovoltaic array bipartite graph is established, and the connection points of components on the same photovoltaic component string constitute an independent node set in a sub-bipartite graph; 2) under the complete matching rule of the bipartite graph, the minimum edge covering rule and the constraint that equal-weight points in the bipartite graph are not connected, edge matching of the bipartite graph is derived, the edge is converted into a voltage sensor, and optimal configuration of the voltage sensor is obtained; 3) the fault voltage threshold of the photovoltaic component under different faults is calculated, the actual measured value of the photovoltaic component voltage collected by the voltage sensor is compared with the fault voltage threshold, and photovoltaic array fault detection and positioning are realized. The application realizes accurate detection and positioning of open-circuit, short-circuit, aging and shading faults of each photovoltaic component by using fewer voltage sensors, and the voltage sensor only needs to be connected between adjacent photovoltaic component strings, and is easy to operate.

This invention discloses a client selection method and system for multi-task federated learning. Addressing the shortcomings of client selection and insufficient consideration of task urgency in multi-task dynamic federated learning scenarios, this invention first constructs a multi-task federated learning system model, defining a utility function that includes learning quality and penalty terms. Second, it establishes fairness constraints and introduces a fairness queue to transform the problem into a queue stability problem. Then, based on Lyapunov optimization theory, it constructs a drift-plus-utility function, transforming a long-term stochastic optimization problem into a deterministic optimization problem for each round of communication by minimizing its upper bound. Finally, it constructs an auxiliary bipartite graph to transform client selection into a minimum-weight bipartite graph matching problem. This invention, by jointly optimizing fairness, learning quality, and task urgency through the Lyapunov framework, transforms long-term constraints into a solvable problem for each round, reducing computational complexity and achieving efficient and fair dynamic client selection.

The invention relates to the technical field of electric power system operation control, in particular to a point-to-point energy market clearing method and device for motivating a producer and a consumer to form a federated power plant, and the method comprises the steps: obtaining market participant demand data, real-time electricity price and the like, and inputting the data, the real-time electricity price and the like as parameters into an optimization model; based on the model, establishing a coordination mechanism of a power distribution system operator, a federated power plant manager and an energy producer, and proposing a dynamic price incentive mechanism; establishing a three-layer model, and converting the three-layer model into a mathematical programming problem with equilibrium constraint; applying an incentive mechanism in mathematical planning, encouraging production and disagger to form a federated power plant and relax complementary constraints, introducing a distributed bipartite graph matching algorithm to calculate an optimal transaction result, and solving the problem to obtain a market clearing result under the condition of satisfying power network physical constraints; a pruning distributed Hungary algorithm is utilized, a decentralized market settlement method is designed, bilateral negotiation of production and disagger is simulated, the transaction cost is clearly reflected, the communication complexity is reduced, and meanwhile privacy is protected.

The invention provides a multi-robot autonomous navigation mapping method and equipment, and the method comprises the steps: employing a global planner to learn a neural distance between a robot and a leading edge point through a multi-graph neural network, and carrying out the building of an affinity matrix for graph matching; based on an affinity matrix of graph matching, through a differentiable linear distribution layer, a bipartite graph matching problem is solved, and a unique leading edge point is distributed to each robot as a target point pose. And adopting a local planner and a motion controller, planning a barrier-free moving track according to the current pose of the robot, the estimated target point pose and the constructed 2D grid map, and executing specific actions. And repeating the process until the mapping is completed. According to the invention, the limitation of the prior art can be effectively overcome, and the complete construction of the scene map can be completed in the shortest time.

The invention discloses a bipartite graph matching-based photovoltaic array fault detection and positioning method, which comprises the following steps of: 1) establishing a photovoltaic array bipartite graph based on a photovoltaic array connection structure, and forming an independent node set in a sub bipartite graph by component connection points on the same photovoltaic component string; 2) exporting edge matching of the bipartite graph under the constraints of a complete matching rule and a least edge coverage rule of the bipartite graph and unconnected medium weight points in the bipartite graph, converting edges into voltage sensors, and obtaining optimal configuration of the voltage sensors; and (3) calculating fault voltage thresholds of the photovoltaic module under different faults, collecting voltage measured values of the photovoltaic module through a voltage sensor, and comparing the voltage measured values with the fault voltage thresholds to realize fault detection and positioning of the photovoltaic array. According to the method, fewer voltage sensors are adopted, accurate detection and positioning of open circuit, short circuit, aging and shielding faults of each photovoltaic module are achieved, the voltage sensors only need to be connected between the adjacent photovoltaic module strings, and operation is easy.

The invention discloses a coal mine area personnel limit monitoring method, device and equipment, a medium and a product. The method comprises the following steps: acquiring image data of a coal mine area access channel at the current moment in real time, and preprocessing to obtain processed image data; performing background detection and updating processing on the processed image data according to a background model and an image segmentation threshold value at the previous moment to obtain a foreground model and a background model; performing personnel fitting modeling on the foreground model to obtain a spatial position set of all personnel in the foreground model; and according to the spatial position set, a time sequence bipartite graph matching method is adopted to generate a personnel movement track of the coal mine area, and the real-time number of people in the coal mine area is determined so as to carry out area personnel limiting monitoring. The method can accurately position the positions of people in the foreground, avoids repeated counting or missed counting, guarantees the accurate real-time counting of people in the region, and provides reliable data support for personnel limit monitoring.

The present invention discloses a method for matching passengers getting on and off the bus and estimating OD demand based on bus door videos, comprising: S1, identifying and tracking bus passengers to obtain the getting on and off information of each passenger; S2, extracting passenger features, which include passenger appearance features and passenger getting on and off the bus features; S3, calculating passenger appearance feature distances, and obtaining the distribution of passenger appearance feature distances through the calculation of passenger appearance feature distances; S4, matching passengers getting on and off the bus, and then performing bipartite graph matching based on the passenger getting on and off matching; S5, allocating OD amounts of unsuccessfully matched passengers; and S6, estimating bus OD demand.