1627 results about "Graph model" patented technology

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 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.

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 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 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 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 a personalized commodity recommendation optimization method based on deep learning. The method comprises the following steps: S1, constructing a data set; s2, constructing a multi-dimensional knowledge graph, and embedding the multi-dimensional knowledge graph into a low-dimensional space by using a knowledge graph embedding algorithm; s3, constructing a user interest track graph by using the time sequence graph model, and generating a user interest vector based on capturing long-term and short-term interest changes; s4, obtaining an optimized user interest vector by using an improved moth fire suppression algorithm; s5, based on the optimized user interest vector, combining a graph neural network to learn relationships between users and commodities, between users and between commodities; and S6, generating a personalized commodity recommendation list according to a graph neural network learning result and real-time behavior feedback of the user. According to the method, a multi-dimensional knowledge graph, time sequence graph modeling, a moth fire suppression algorithm, a graph neural network and the like are fused, and optimization of personalized commodity recommendation is realized.

The invention relates to the technical field of polishing, and discloses an intelligent steel rail polishing control method which comprises the following steps: acquiring surface and internal defect data of a steel rail through laser and ultrasonic fusion detection, discretizing the data into multi-dimensional nodes, and constructing a dynamic graph model comprising a microscopic-macroscopic dual-cluster neural network to predict a defect evolution trend; based on fractal dimensions and information entropy, multi-source data are dynamically fused, abnormities are eliminated, chaotic optimization and wear prediction are adopted to generate global optimal polishing parameters, and finally parameter dynamic adjustment is achieved through closed-loop feedback of pulse frequency and control errors. Steel rail defect collaborative perception is achieved through a multi-scale dynamic graph model, fractal-entropy weight dynamic fusion is combined to enhance data reliability, global optimal parameters are generated through chaos optimization and abrasion prediction, accurate grinding is achieved through pulse-error closed-loop control, and the method is simple and convenient to implement. The problems of data fusion failure, defect response lag, parameter local optimization and insufficient control precision in the prior art are solved.

The invention relates to the field of manufacturing execution systems, intelligent production scheduling and industrial data integration, in particular to a clothing regulation and control production method and system based on MES, by integrating production state data and clothing order data which are collected in real time, a production graph model is constructed, Bayesian variational inference is adopted to carry out uncertainty modeling to generate graph representation data, and the production state data and the clothing order data are integrated. And constructing a hypergraph model based on graph representation data, generating a scheduling command by using non-Euclidean embedding and agent collaborative decision, issuing the scheduling command to each production unit for execution, collecting execution data and monitoring abnormality, triggering dynamic rescheduling according to abnormality feedback, and realizing continuous closed-loop optimization of the garment production process. The production efficiency and the resource utilization rate are remarkably improved, and the production risk is reduced.

The invention relates to the technical field of meteorological monitoring and climate prediction, in particular to a Qinghai-Tibet Plateau composite extreme climate event attribution evaluation method. The method comprises the steps that ground observation, remote sensing and reanalysis data are integrated through a multi-source data dynamic space-time weight fusion technology, and abnormal value correction and non-uniform interpolation are achieved; identifying a composite event by adopting a multivariable combined extreme index and a space-time coupling graph model and generating a structured label, wherein the structured label comprises strength, range, duration and evolution path; constructing a multi-scale causal network to analyze the contribution of the driving factor, and implementing physical constraint disturbance based on causal weight; recovering high-resolution response by using a Bayesian agent model and combining topographic constraint random downsampling, and deducing spatio-temporal evolution through an event propagation network; and a kernel polynomial hybrid uncertainty propagation framework is adopted to generate a probabilistic scene set, and multi-level risk early warning and dynamic knowledge base optimization are realized. According to the invention, the attribution precision and early warning efficiency of plateau composite extreme events are comprehensively improved.

The invention discloses an image-based three-dimensional reconstruction method and device, a medium and a product, and relates to the technical field of three-dimensional reconstruction, and the method comprises the steps: obtaining one of m original images as a reference image, and N-1 original images as source images; the reference image and the N-1 source images are sent to a pre-trained segmentation model and a pre-trained feature pyramid network, so that a reference feature images with different scales and a * (N-1) source feature images with different scales are obtained; sending the image to a pre-trained cost body-depth map model so as to enable the pre-trained cost body-depth map model to output an ultimate depth image; taking each image in the m original images as a reference image in turn, and executing the steps to obtain m ultimate depth images; and sending the m ultimate depth images to a point cloud fusion algorithm to output a three-dimensional point cloud model of the target object. According to the invention, the perception of the weak texture region is improved, so that the precision of three-dimensional reconstruction is improved.

The invention relates to the technical field of robots, and discloses a task planning method and device for a robot, equipment and a medium, and the method comprises the steps: building a scene relation graph model containing environment space topology and object attributes; inputting the received natural language instruction into the first language model, and outputting a structured task target; on the basis of the structured task target and the real-time environment perception data, auxiliary information is generated through a second language model, and the scene relation graph model is updated; inputting the updated scene relation graph model into a reinforcement learning model, and outputting an action decision sequence; and the target robot is controlled to execute the action decision sequence, and reinforcement learning strategies of the scene relation graph model and the reinforcement learning model are adjusted according to the environment feedback information. According to the invention, efficient, flexible and accurate task planning of the robot in a complex dynamic scene can be realized, and the efficiency and adaptability of task planning of the robot are improved.

The invention provides an intelligent logistics distribution path optimization method under multiple constraint conditions, and relates to the technical field of logistics distribution, and the method comprises the steps: constructing a constrained graph model according to a time window, vehicle information and real-time traffic API data; performing initial path planning by adopting an improved genetic algorithm; performing dynamic path adjustment according to the initial path set and the real-time traffic API data; and in combination with the optimized path set and customer preference information in the order information, screening an optimal solution through a plant rhizome growth optimization algorithm, and then outputting a final path optimization strategy according to a preset rule engine. According to the method, a plant rhizome optimization algorithm is adopted, a biological growth rule is simulated to realize multi-target co-evolution, and customer preference and task migration flexibility are considered while premature convergence is avoided; through flexible adjustment of constraint conditions and algorithm parameters, the method can adapt to more scenes, and can still maintain stable solving capability under the condition of continuous congestion or sharp increase of orders, thereby remarkably improving the anti-risk capability of a logistics system.

The invention relates to a power supply quantity self-repairing method based on deep learning, and the method comprises the following steps: S1, obtaining power multi-dimensional data through an intelligent sensor and terminal equipment, and carrying out the preprocessing of the data; s2, constructing a power network graph model based on the power multi-dimensional data, and optimizing the power network graph model by introducing a graph attention mechanism and a time dynamic model to obtain power grid topology dynamic dependency information; s3, based on the processed electric power multi-dimensional data and power grid topology dynamic dependency information, using deep learning to fuse multi-modal information, detecting abnormal behaviors of power supply quantity, and determining abnormal types and grades; s4, according to key nodes identified in the power network graph model and an abnormal grading result, dynamically distributing a repair priority, and outputting a repair value; and S5, performing restoration based on the restoration value and a predetermined execution sequence, and returning the power supply curve to the power dispatching center after restoration is completed. According to the invention, rapid positioning and grading of power supply abnormity and accurate generation of a restoration scheme are realized.

The invention discloses a big data-based natural gas pipeline maintenance optimization method and system, relates to the technical field of natural gas pipeline maintenance, and aims to construct a structured monitoring data set, construct a graph model of pipeline states and risk factors, establish a prediction model, introduce a memory mechanism to process low-frequency anomalies, and generate and optimize a maintenance scheduling scheme. According to the natural gas pipeline maintenance optimization method provided by the invention, the map model and the prediction model are constructed based on the multi-source monitoring data, the causal path between the pipeline operation state and the external risk factor can be identified, and the maintenance efficiency is improved. Maintenance task generation, early warning triggering and terminal linkage operation are completed through risk level evaluation and scheduling optimization, collected monitoring data are used for model training and map updating after being processed, a closed-loop management process is formed, and the method is suitable for natural gas pipeline state recognition and maintenance scheduling in various operation environments.