2003 results about "Graph model" patented technology

The invention provides a method and system for realizing electronic channel risk control processing based on a code insertion technology, and relates to the technical field of electronic channel risk control, and the method comprises the steps: encrypting user information and channel identification information through a dual Hash algorithm to generate a dynamic session identification code, and forming a target coding sequence through employing a self-adaptive segmentation algorithm and a business feature flag bit. And constructing a dynamic risk control rule model by using a graph neural network, extracting user multi-dimensional features, carrying out weighted fusion to generate a user risk score, comparing the user risk score with an adaptive risk threshold, analyzing abnormal behaviors based on a probability graph model, and generating an intelligent risk early warning report. And calling a reinforcement learning risk control strategy engine to determine an optimal disposal strategy, and issuing a multi-factor interaction verification instruction. And after verification, selectively activating a blocking mechanism according to a verification result, synchronizing interaction data and a result to a distributed risk control data center, and continuously optimizing the risk control model based on federal learning.

The invention provides a power plant intelligent early warning method and system based on big data, and relates to the technical field of power systems, and the method comprises the steps: generating a high-dimensional feature map, generating a multi-modal feature tensor through adaptive chaotic mapping, constructing a feature relation network, generating a time sequence multi-dimensional feature vector, and reconstructing a multi-dimensional state vector based on topological entropy. In combination with a topological data analysis method, carrying out high-dimensional topological space mapping on the multi-dimensional state vector, identifying an operation mode of the power plant equipment, calculating a fuzzy region and a mode abrupt change point of the operation state of the equipment, and obtaining a dynamic evaluation result of the health state of the equipment; a multi-level recurrent neural network is combined, a prediction result is obtained through multi-scale time sequence prediction, uncertainty corresponding to the prediction result is quantitatively solved based on a quantum probability graph model, quantum probability distribution of an early warning signal is generated, a real-time operation strategy of equipment is adjusted, and an intelligent early warning signal of the power plant equipment is generated.

The invention discloses a multi-modal-based power equipment health state monitoring method, and relates to the technical field of power equipment detection.The power equipment monitoring method is based on multi-modal data and knowledge graph fusion, cross check, expert rule cleaning and label correction are implemented by collecting sensing data such as chromatography, temperature, current and vibration in oil, and the detection result is obtained. Outputting high-credibility data; a graph model is constructed based on equipment topology by adopting self-encoder dimension reduction fusion, early anomaly detection is realized by utilizing a graph neural network, and an anomaly alarm is generated; mechanism matching and consistency evaluation are carried out based on the fault mechanism knowledge graph, and interpretable diagnosis is output; and when the diagnosis result is significantly deviated from the actual operation and maintenance conclusion, triggering an online increment and transfer learning updating model and expanding the knowledge graph to form a closed-loop self-learning mechanism. According to the method, the fault detection accuracy is remarkably improved, false alarms and missing alarms are reduced, the operation and maintenance decision-making efficiency is improved, and meanwhile operation and maintenance intelligence and real-time alarm are enhanced.

The invention discloses a multi-modal knowledge graph rule reasoning method and device based on a large model, and the method comprises the steps: carrying out the feature extraction and cross-modal alignment of input text data and image data, and generating a multi-modal feature vector of a unified semantic space; performing knowledge graph storage on the emotion entities and the relationships by adopting an attribute graph model to complete construction of an emotion knowledge graph; generating an interpretable inference rule from the emotion knowledge graph by using a large language model, and eliminating a conflict rule in combination with logic verification; calculating the confidence coefficient of a reasoning path based on an attention mechanism, and carrying out quantitative evaluation on a rule reasoning result; the knowledge graph and the rule base are updated online according to user feedback, and the real-time performance and accuracy of the inference system are optimized through weight adjustment and a forgetting mechanism. According to the method, through innovative technologies such as multi-modal data integration, dynamic knowledge evolution and interpretability reasoning, the limitation of a traditional sentiment analysis method in the aspects of evidence dimension, adaptive capacity, interpretability and the like is broken through.

The invention relates to the technical field of intelligent control, in particular to a control method and system based on intelligent collaborative production. Comprising the following steps: aiming at a task cut-in influence range, traversing related nodes in a production network graph model, and detecting a device resource occupation conflict with an existing task to obtain a resource conflict detection result; recursively tracking the affected downstream process from the time delay predicted value along the dependence edge of the production network graph model to generate an influence propagation path; according to a weight dynamic quantification result, summarizing weight values of all affected nodes, generating a global view evaluation score, and determining an overall influence degree of new task cut-in; if the global view evaluation score exceeds a preset threshold, adjusting the cut-in node and time of the new task, and re-executing resource conflict detection to obtain an optimized task cut-in scheme; and extracting a process execution sequence and a resource allocation plan from the optimized task cut-in scheme, updating the production network diagram model, and generating a final collaborative production scheduling scheme.

The invention provides a method and a system for identifying and cleaning abnormal data, which relate to the technical field of data processing, and are used for identifying and cleaning abnormal data by acquiring real-time data flow, determining a self-adaptive threshold value by using a trained Gaussian mixture model and detecting abnormal data points. A deep belief network and long and short term memory network composite model is adopted to carry out deep feature learning and anomaly verification, and high-level feature representation and anomaly degree quantitative evaluation results are obtained; and evaluating a conditional dependency relationship among the abnormal data points by using the probability graph model, constructing a target probability dependency graph, and generating an abnormal data point recognition result. Based on an identification result, a reward mechanism excitation algorithm is applied to optimize a cleaning strategy; according to the method, the to-be-determined abnormal region is identified, the multi-modal fusion technology is introduced to perform correlation analysis, and the data in the to-be-determined abnormal region is judged and cleaned based on the analysis result to obtain the target cleaning strategy, so that the abnormal data identification and cleaning efficiency and accuracy are improved, and the method has relatively high adaptive capability and expansibility.

The invention relates to the technical field of gas station equipment monitoring, in particular to a gas station equipment monitoring system and method based on the Internet of Things. The method specifically comprises the steps of collecting multi-dimensional operation parameters of equipment and business process node dynamics in real time, constructing a bidirectional coupling graph model of the equipment and business, and extracting high-order features of physical association and business logic interaction between the equipment by using a graph convolutional network; predicting equipment fault risks by adopting a dynamic health scoring mechanism, backtracking a risk propagation chain in combination with an attention mechanism, and mapping multi-dimensional data into interpretable fault diagnosis information through an abnormal semantic generation network; a double-layer reinforcement learning game mechanism is introduced, an inner layer coordinates equipment maintenance and a risk suppression strategy, an outer layer overall plans service efficiency and a safety target, optimization actions such as resource scheduling and oil product allocation are generated, and a perception-diagnosis-decision closed loop is formed. According to the method, full-time perception of the equipment state, active fault early warning and cross-domain collaborative optimization are realized, and the operation and maintenance efficiency and safety of the gas station are remarkably improved.

The invention provides a multi-modal remote sensing and knowledge graph-based urban planning decision-making method and system, and the method comprises the steps: integrating multi-source heterogeneous data, achieving the feature alignment and fusion of an optical image and SAR data in a satellite remote sensing image through a deep learning technology, and generating an urban ground feature feature vector; associating the urban ground feature feature vector with an urban planning policy database, outputting a structured early warning report of an illegal construction early warning event set and a policy compliance label, and forming a dynamic policy constraint condition for subsequent multi-objective optimization; processing historical traffic flow data based on the dynamic graph model, and outputting a time-space distribution prediction result of future traffic conditions; and generating a Pareto optimal city planning scheme by combining multi-objective optimization with a spatial-temporal distribution prediction result of a future traffic condition. According to the method, high-precision urban surface feature classification, real-time violation extension early warning and traffic flow accurate prediction are realized through multi-modal remote sensing data fusion and a space-time knowledge graph technology, and multi-target optimization and digital twinborn verification are combined, so that the planning efficiency is improved, and extension applications such as carbon neutralization are supported.

The invention provides a multi-AGV distributed path planning method based on graph reinforcement learning, which is used for solving the technical problem of low path planning efficiency of a multi-AGV system in an information limited dynamic environment. The method comprises the following steps: establishing a dynamic heterogeneous graph model in multi-AGV collaborative path planning; constructing a distributed partially observable Markov decision model based on a graph structure in combination with path planning and collaborative decision; on the basis of a multi-agent deep reinforcement learning framework of a graph neural network and a multi-head attention mechanism, a multi-AGV cooperative path planning algorithm for coping with an information-limited dynamic environment is designed, information aggregation is realized on the basis of local perception and neighborhood interaction by adjusting weights of nodes and edges in a dynamic heterogeneous graph, and a multi-AGV cooperative path planning algorithm for coping with an information-limited dynamic environment is realized. And the environment change is updated and adapted in real time. According to the method, the path planning efficiency can be improved through an information aggregation mechanism in an information-limited dynamic environment, multi-AGV cooperative path planning is optimized through the graph neural network, and efficient and stable operation of the system is ensured.

The invention discloses a hyper-converged server multi-resource integration system and a scheduling method, belongs to the technical field of computer resource management, and aims at solving the problems that a traditional hyper-converged server is dispersed in resource scheduling, low in storage efficiency, difficult to identify abnormities and the like. Multi-source data is obtained by means of distributed acquisition nodes, a distributed time sequence database architecture is adopted for storage, a standard data set containing a multi-dimensional index is constructed, and efficient storage and rapid retrieval of the data are achieved. Positioning target data to construct a graph model, building a layered and partitioned distributed graph database, and capturing change events in real time to realize dynamic updating. And mining a resource causal relationship by using a graph neural network, and screening an effective causal chain. Meanwhile, according to a graph model, monitoring weights of nodes and connecting edges are calculated, differential monitoring is implemented, potential abnormal points are accurately identified, and a resource scheduling strategy is generated in combination with a causal relationship. Real-time feedback adjustment and database updating are performed during execution, multi-resource deep integration and intelligent scheduling are achieved, and the resource utilization rate and stability of the system are remarkably improved.

The invention discloses a modeling method based on shield tunneling data feature analysis and parameter relevance, and relates to the field of tunnel engineering data processing. The method comprises the steps that shield tunneling time sequence parameters are obtained, and a non-uniform time sequence is resampled into a space-aligned standardized footage domain sequence through state cleaning and coordinate domain transformation; by means of mixed variable rejection and lagging correlation analysis, environment common cause interference is stripped, physical response delay among parameters is recognized, and a time-delay directed correlation graph model is constructed; and inputting the footage domain sequence and the graph model into a graph neural network, performing feature learning by using a time delay compensation aggregation mechanism, and outputting a key parameter influence degree set with symbols based on a prediction gradient. According to the method, the problem of data space-time dislocation caused by propelling speed fluctuation and the problem of parameter relevance misjudgment caused by physical response lag are solved, and accurate identification and explanation of shield tunneling key parameters are achieved.

The invention relates to the technical field of virtual power plant operation monitoring, and discloses a virtual power plant distributed resource cluster anomaly monitoring system, method and equipment, and the system comprises a resource node data collection module, an anomaly trend score calculation module, an association graph model construction module, a single node influence calculation module and a comprehensive anomaly early warning output module. In the prior art, a single-point alarm mode depends on a fixed threshold value or a rule base, and especially under the conditions that distributed resources in a virtual power plant are complex in type and space coupling and controller sharing exist between nodes, accurate identification and dynamic early warning of trend anomalies, cooperative faults and potential propagation paths are difficult to realize. According to the method and the device, the multi-dimensional parameter modeling and the graph neural network anomaly propagation analysis are fused, so that the anomaly in the distributed resource cluster is identified and dynamically evaluated, and the early warning accuracy and the intelligent operation and maintenance efficiency of the virtual power plant are improved.

A method and apparatus for training a text-graph model, a device, and a storage medium, relating to the artificial intelligence. The method comprises: on the basis of a training set of graph-text sample pairs, performing iterative training on a model to be trained, to obtain a text-graph model; in a training process: selecting a graph-text sample pair from the training set of graph-text sample pairs, the graph-text sample pair comprising a sample image and a description text, and the sample image comprising at least two objects; obtaining mask images corresponding to the at least two objects and associated object class names, the mask images being used for distinguishing position regions of the objects in the sample image; inputting the sample image and the description text to a model to be trained, to obtain an image prediction noise of the sample image, and inputting at least two mask images and the associated object class names to the model to be trained to obtain object prediction noises respectively associated with the at least two mask images; and constructing a loss function on the basis of the image prediction noise and the object prediction noises, and by using the loss function, adjusting a parameter for the model to be trained.

The invention discloses a grape disease recognition and early warning method based on the Internet of Things, and relates to the technical field of plant disease recognition, image acquisition equipment and environment sensing nodes are arranged in a vineyard, and leaf images and corresponding temperature and humidity, illumination and soil moisture parameters are obtained; inputting the image into a neural network fusing dilated convolution and a residual attention mechanism, realizing extraction of a disease spot region and a disease spot variation feature, and generating a preliminary recognition result; constructing a multi-factor evolution sample set in combination with the recognition result and the environment state of the time node; constructing a space-time correlation graph model based on a graph neural network, estimating a disease propagation risk path and a diffusion probability, and performing early warning judgment at a gateway end through a multi-factor gating discrimination algorithm; the method disclosed by the invention is high in recognition precision and strong in response timeliness, has adaptive prediction and targeted treatment capabilities, and remarkably improves the intelligence and precision level of grape disease management.

The invention relates to the technical field of data analysis, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a data consanguinity map processing method, device, equipment and medium, and the method comprises the steps: collecting metadata change information and data operation event information, and building a mixed consanguinity map model; monitoring a metadata change event and a data operation event, and triggering incremental updating based on an event type; and performing data change influence analysis based on the updated blood relationship map model, generating an analysis result, and outputting the mixed blood relationship map model and the data change influence analysis result. According to the invention, by collecting metadata, incrementally updating the blood relationship map and carrying out data change influence analysis, full-link data tracing and influence analysis are realized, the problems of blood relationship breakage and insufficient real-time data tracing in traditional data blood relationship analysis are solved, and the accuracy of data tracing and the timeliness of business decision are improved.

The invention discloses a load transfer path regulation and control method and system based on a maintenance topology rule base, and relates to the technical field, and the method comprises the following steps: constructing a graph model, obtaining a load node of a shutdown device in the graph model, and obtaining a load path switching node based on a connected component analysis method; correcting the graph model to obtain a power grid topological graph of load path switching nodes, and generating a load path switching scheme based on a shortest path algorithm; a pre-switching adjustment strategy is set, and a load transfer action instruction is generated and executed step by step based on a load path switching scheme; carrying out local control on load transfer path adjustment based on a load transfer action instruction and a distributed control technology; the optimal load switching path is identified through connected component analysis and the shortest path algorithm, and the load transfer path of the maintenance equipment is controlled and switched according to the load transfer action instruction in combination with the distributed control technology, so that the stability, the flexibility, the response speed and the regulation and control precision of load path regulation and control are improved.

The invention, which belongs to the technical field of wireless communication, provides a position-guided channel knowledge graph construction method comprising the following steps: constructing an unmanned aerial vehicle auxiliary communication scene comprising a ground base station, and constructing a channel knowledge graph model; designing a back propagation neural network, training the back propagation neural network by using enough training samples corresponding to a ground base station j so as to learn a recessive nonlinear relationship between the position of the unmanned aerial vehicle and a channel gain, and representing a channel knowledge graph model as a dominant derivable function of the position of the unmanned aerial vehicle and the channel gain; and introducing a transfer learning algorithm, and performing fine tuning on the trained back propagation neural network of the ground base station j by using a limited sample corresponding to the target ground base station j 'to obtain a back propagation neural network of the target ground base station j', thereby obtaining a channel knowledge graph of the target ground base station j '. The method is a classical optimization method suitable for unmanned aerial vehicle trajectory optimization and deployment problems.

The invention relates to the field of image comparison, and discloses a multi-layer PCB alignment deviation detection system and method based on image comparison, and the method comprises the steps: obtaining high-resolution image data before and after lamination of a multi-layer PCB, carrying out the unified registration preprocessing of an original image through the combination of a multi-mode image fusion algorithm and a geometric distortion correction technology, and obtaining a multi-layer PCB alignment deviation detection result; constructing a standardized image registration input set; key alignment feature extraction is carried out on the image registration input set, and a multilayer structure graph model is constructed based on a graph neural network; in combination with the alignment reference map, dynamically adjusting an image comparison window and a search region by adopting a region attention mechanism and a local adaptive matching algorithm, and constructing an alignment deviation mapping map; constructing a deviation evolution model by using a time sequence behavior recognition network according to the constructed alignment deviation mapping graph and historical process deviation data; and performing comprehensive evaluation on the image registration input set based on the deviation evolution model and the generated early warning information. The method has the advantage of improving the accurate detection level.

The invention provides an expectoration auxiliary control method and device. The method comprises the steps that audio signals of breathing sounds and cough sounds at multiple positions of the chest of a patient are collected through an audio sensor array, and a standardized audio feature data set is obtained through noise reduction, segmentation and feature extraction; a dynamic weighted graph model is constructed, and a random edge sampling algorithm and a parallel batch processing dynamic algorithm are combined to analyze and obtain a sputum viscosity index and a sputum distribution position map; based on the result, mapping the vibration parameter space into an unweighted disk diagram, and obtaining a personalized vibration treatment scheme by using a shortest path algorithm; in combination with the real-time breathing cycle of the patient, working parameters of the sound wave vibrator and the negative pressure suction device are synchronously controlled, and a coordinated and consistent multi-mode treatment execution instruction is obtained; and collecting real-time feedback data in treatment, and dynamically adjusting working parameters through reinforcement learning to obtain an optimized expectoration adjuvant therapy scheme. Intelligent adjustment can be achieved based on the real-time breathing state and sputum characteristics of the patient, the expectoration efficiency is improved, and discomfort of the patient is reduced.

The invention relates to the technical field of data processing, in particular to an earthwork balancing method based on discrete elevation points and a free-form surface creation technology, and the method comprises the steps: generating topographic feature partition tags through an improved DBSCAN density clustering algorithm, and constructing a regularized elevation field in combination with octree and R * tree mixed indexes; generating an elevation standard deviation thermodynamic diagram based on Gaussian process regression, and outputting a multi-target digging and filling scheme set in combination with an FEM-DEM coupling model; a multi-layer graph model fused with soil bearing capacity is constructed, a collision-free construction path is optimized through an improved D * Lite algorithm, a positioning track and a planning deviation are fused by using Kalman filtering, and an online sequence extreme learning machine is triggered to realize dynamic updating of the model. According to the method, the complex terrain modeling efficiency and the earthwork volume calculation precision are improved, the resource allocation path is optimized, the construction cost is reduced, and the problem of excavation and filling imbalance caused by data discreteness and modeling staticization in a traditional method is solved.

The invention relates to the technical field of medical related information and communication, and discloses a hospital logistics one-stop service inspection and repair platform. The hospital logistics one-stop inspection and repair method comprises the following steps: processing equipment operation data through a multi-modal data fusion network, and generating a state feature vector set; constructing an equipment association graph model by using the space-time diagram attention network in combination with the topological information; detecting the abnormity of a single device and predicting a propagation path through a hierarchical time sequence abnormity detection network, and generating a minimum intervention strategy; an abnormal root cause analysis report is generated by means of a multi-stage interpretability engine, and decision making is supported; routing inspection routes and repair task allocation are optimized based on reports and strategies; according to the invention, integrated management of the whole process from abnormity prediction, inspection planning, fault diagnosis to maintenance dispatching can be realized, the hospital logistics pain point is solved, and the system has significant technical innovation and practical value.

The invention discloses an automatic river network grading and river topological relation identification method based on a graph theory, and relates to the technical field of hydrological geographic information. The method comprises the following steps: acquiring and cleaning a vector river network, a key point location and DEM data of a target drainage basin; constructing an initial river network graph model based on the line element connection relationship; integrating DEM topographic evidence and graph theory connection features, constructing and solving a global potential energy field equation containing topographic driving and boundary constraint, and calculating flow potential energy attributes of nodes of the whole network to determine a flow relationship; based on the flow direction relation, identifying topology abnormal structures such as strong connectivity components in the network, and performing ring breaking processing by using direction confidence to generate a ring-free directed network structure; and performing river grade division based on a topology transfer rule, and associating the key point location to a river network skeleton. According to the method, through global potential energy field solving and topological optimization, the problems that the flow direction of the plain micro-geomorphic area is difficult to recognize and complex loops cannot be graded are solved, and automatic construction of the river network topology is achieved.

The invention relates to the technical field of power grid operation and maintenance, and discloses an AI-based reactive compensation optimization method and system, and the method comprises the following steps: integrating the four-dimensional characteristics of traffic flow, power grid load, user preference and equipment health degree, constructing a space-time correlation graph model, achieving the adaptive evolution of a topological structure through dynamic graph convolution and an attention mechanism, and obtaining a reactive compensation optimization model. The method comprises the following steps: fusing spatial-temporal features of a graph neural network with a BiGRU time sequence for modeling, injecting an equipment health degree attenuation factor, generating a charging demand prediction result with uncertainty quantification, expanding an intelligent agent state space, improving a reward function, and embedding equipment health degree constraints in a strategy network and near-end optimization. By sensing the degradation state of the equipment in real time and intelligently adjusting the reactive compensation strategy, the service life of the old equipment is prolonged while the voltage stability is guaranteed, the deviation accumulation effect is effectively inhibited through feedforward-feedback composite control and dynamic rescheduling, and accurate matching of a scheduling instruction and actual output is ensured.

The invention relates to the technical field of script execution, and provides a multi-script execution method for a virtual controller, and the method comprises the steps: setting a script structure, a graph model structure and a graph node structure; the method comprises the following steps: performing grammar and semantic check on script data to be executed, converting the script data into a script object according to a script structure, converting the script data into a directed acyclic graph according to a graph model structure, and traversing the directed acyclic graph to obtain an execution sequence of graphs in each level of the directed acyclic graph; and scheduling the script object according to an FIFO algorithm, traversing the directed acyclic graph during scheduling to obtain graph nodes in each execution sequence, and performing interpretive execution on the script object according to an ignition rule and the graph nodes in each execution sequence. The method aims at analyzing and executing various scripts, the reliability of the virtual production line is further improved, and the use cost of the platform is reduced.

The invention belongs to the technical field of power distribution network fault recovery, and provides a power distribution network fault recovery method based on graph reinforcement learning and a related device, and the method comprises the steps: constructing a power distribution network fault recovery problem graph model, and carrying out the embedding of a power distribution network model graph based on a graph attention neural network; based on the power distribution network fault recovery problem graph model, an agent set of multi-agent reinforcement learning is constructed, and the power distribution network fault recovery problem is modeled as a Markov decision process; and solving the Markov decision process, carrying out multi-agent training, and generating a fault recovery strategy by the trained agents according to the current state of the power distribution network. According to the method, the technologies of graph theory, deep learning and reinforcement learning are deeply fused, and efficient and accurate power distribution network fault recovery strategy generation is realized.

The invention relates to the technical field of active testing of circuit breakers, discloses an active testing method and system for a circuit breaker, and is used for solving the problem that an existing active testing method is difficult to realize self-adaptive adjustment of a threshold value. Time-frequency analysis is carried out on the contact speed signal to extract time domain and frequency domain features, and heat flow analysis of the thermopile array is combined to obtain a heat engine coupling feature vector; a thermo-electro-mechanical coupling dynamic graph with the sensors and the components as nodes and the reciprocal of thermal conductance as the edge weight is constructed, the structure is updated in real time, and node drift is predicted through a graph convolutional network; fusing the environment derating and the predicted drift amount to adaptively adjust a threshold value; extracting a thermoelectric coupling component of a contact speed fast change component and a heat flow refraction angle change rate, and generating a reversed polarity pulse compensation signal to suppress interference; time synchronization, closed-loop feedback of data and threshold adjustment results are realized through secondary alignment of IEEE1588 and the cloud, graph model node weight and threshold rules are dynamically updated, synchronization with equipment health degree is ensured, and accurate early warning and protection are realized.

The invention relates to the technical field of data management, and provides a cross-enterprise closed-loop order transfer and data cooperative processing method, which comprises the steps of collecting multi-modal data, generating a unique distributed hash identifier for the multi-modal data based on a block chain technology, and performing intelligent association on the multi-modal data in combination with transfer node information; constructing a cross-enterprise order flow graph model by using the multi-modal data fusion; detecting an abnormal mode in real time by combining a graph embedding algorithm with a deep neural network; on the basis of the order flow graph model, a dynamic responsibility allocation algorithm is utilized, and in combination with real-time environmental factors when an anomaly occurs, a specific responsibility node where the anomaly occurs is positioned, and a corresponding responsibility weight is allocated; and generating an adaptive repair strategy based on a multi-objective optimization algorithm according to the responsibility weight. According to the invention, the transparency and cooperation efficiency of cross-enterprise order transfer are significantly improved, and the stability and efficiency of a supply chain system are ensured.

The invention relates to the technical field of circuit board line planning, in particular to an automatic design arrangement and path planning method and system for a double-sided board and multi-layer board printed circuit. The method comprises the following steps: acquiring input parameters of a circuit board, and analyzing to generate an initial structure of a wiring grid diagram; abstracting a wiring problem into a graph model, and extracting a wiring topological structure and density distribution characteristics; a deep reinforcement learning algorithm is adopted to optimize and generate a wiring path, and factors such as a wiring direction, layer selection and path length are considered at the same time; detecting, adjusting and optimizing wiring design rule conflicts on the basis of generating the wiring path; a comprehensive evaluation system of a wiring path planning scheme is established, indexes such as wiring efficiency, density and interlayer balance are considered, preset quality requirements are met through iterative optimization, and an optimized wiring path is output. According to the method, the wiring strategy can be adaptively learned and improved, and the reliability, efficiency and quality of wiring planning are improved.

The invention discloses a video abstraction method based on a graph model and a multi-scale attention mechanism, and relates to the technical field of computer vision. According to the video abstraction method based on the graph model and the multi-scale attention mechanism, the multi-scale attention mechanism is introduced, modeling of a local inter-frame relation and modeling of a global long-term dependency relation are carried out separately, the deviation of attention weight in the calculation process is effectively reduced, meanwhile, the GATv2 graph attention mechanism is adopted to extract the relevance between local frames, and the accuracy of video abstraction is improved. According to the method, global feature information is aggregated by using a CLS identifier, efficient fusion of global and local information is ensured, redundant frames are removed by using non-maximum suppression, the quality of video abstracts is further improved, the redundancy problem caused by position information injection is avoided, and more accurate inter-frame relation modeling and video content abstracts are realized.

The invention discloses an energy system fault prediction method based on a knowledge graph, and relates to the technical field of graph neural networks, and the method comprises the steps: collecting new energy real-time data, and carrying out the preprocessing of the real-time data; constructing a dynamic graph model based on the preprocessed data; outputting a result based on the dynamic graph model, and identifying abnormal data by using a graph neural network; the dynamic graph model is used again for analysis and fault prediction through the identified abnormal data; according to the historical data and the real-time data, a fault judgment result after dynamic optimization is obtained through an adaptive fault threshold adjustment algorithm; according to the method, the action value is estimated through the two independent Q networks, the over-estimation problem in traditional Q learning is effectively relieved, and therefore the stability and accuracy of decision making are improved.