2101 results about "Streaming data" patented technology

The invention discloses a computer big data information processing system, which comprises a data acquisition layer, a data processing layer and a data processing layer, wherein the data acquisition layer is used for accessing structured, unstructured and streaming data by using a multi-source adapter and Apache NiFi, executing format standardization, and extracting basic metadata and semantic tags through a rule engine and an NLP model; the metadata intelligent management layer integrates four modules, namely a federal learning framework for realizing cross-domain dynamic classification labels, an intelligent contract for real-time uplink storage evidence blood relationship change, a Neo4j combined graph neural network for constructing a knowledge graph for mining implicit association, and a reinforcement learning engine for optimizing a storage strategy based on frequency and risk indexes; the distributed storage calculation layer is used for processing batch and real-time metadata by adopting a Cassander + MinIO mixed framework and Spark / Flink, and dynamic partition balance performance is realized; and the application service layer is used for outputting functions of blood relationship query, classified browsing, compliance report and the like through a Vue.js portal and a Spring Cloud micro-service API (Application Program Interface) to form a full-link closed loop.

The invention discloses a financial risk assessment method based on big data, and relates to the technical field of finance, and the method comprises the following steps: S1, obtaining structured data, unstructured data and real-time streaming data of a target entity through a multi-source heterogeneous data collection module; s2, constructing an association relationship graph, and modeling risk propagation paths of a target entity and associated nodes thereof based on a graph neural network; s3, performing feature alignment and joint representation learning on the structured data, the unstructured text data and the time series data through a multi-modal data fusion module; and S4, based on the causal inference model, separating causal features and hybrid variables of the target entity risk event, generating causal risk factors, quantifying risk infection paths between nodes by setting an enterprise guarantee network and a supply chain relation graph dynamically constructed in a graph neural network, effectively identifying hidden risk nodes, and improving the risk assessment efficiency. And the chain reaction risk caused by the default of the associated enterprise is reduced.

The invention belongs to the technical field of intelligent traffic, and particularly discloses a traffic jam analysis method based on an intelligent traffic platform, which comprises the following steps: collecting multi-source traffic flow data of a target road section in real time, judging whether the target road section is jammed in combination with historical data, verifying the traffic flow in a continuous monitoring period to improve the accuracy, and if the target road section is jammed, judging whether the target road section is jammed or not. If yes, the congestion influence range is dynamically determined by identifying a core area and analyzing the upstream and downstream speed propagation trend, then frequent or accidental congestion types are accurately distinguished and the congestion level is evaluated by calculating the deviation degree of current data and a historical traffic mode in multiple dimensions, and finally the congestion influence range is determined according to the congestion types, the congestion level and the increase trend. A differentiated traffic dispersion scheme is generated and executed; according to the invention, through deep fusion of type identification, degree evaluation, range determination and scheme generation links, an integrated decision link is formed, and the intelligent level and response efficiency of the system for coping with a complex congestion scene are significantly improved.

The invention discloses a campus face recognition abnormal behavior monitoring method and system based on artificial intelligence, and the method comprises the steps: collecting the video stream data of a person in a campus in real time, and recognizing the behavior characteristics of the person in a video frame in real time through employing a lightweight target detection algorithm; extracting facial features of the personnel by using a pre-trained face recognition model, and recognizing identity information of the personnel; generating a personnel behavior semantic description vector with a spatio-temporal context based on the behavior characteristics and the identity information; inputting the personnel behavior semantic description vector into a dynamic early warning threshold engine, and outputting an abnormal behavior probability and risk level evaluation result; and triggering an early warning information pushing mechanism according to the abnormal behavior probability and the risk level evaluation result, and sending early warning information to a corresponding responsible person terminal. By using the embodiment of the invention, the accurate and automatic association of the abnormal behavior and the personnel identity can be realized, and the early warning accuracy, the handling response speed and the system adaptive ability of campus safety monitoring are improved.

The invention relates to the technical field of security and protection management, in particular to a campus security and protection management system based on deep learning, which improves the accuracy and robustness of identity recognition by acquiring access control card numbers, face images or fingerprint features and generating standardized identity authentication data. And on the basis of a comparison result of the identity authentication data and the campus database, a behavior chain initialization identifier is generated, and accurate identity binding of the school entering personnel is realized. Furthermore, by collecting multi-camera image stream data, pedestrian re-identification and similarity calculation are executed by using a deep feature matching network, and a cross-camera continuous trajectory data set is generated. And matching the behavior track data set with the conventional path template to generate a behavior offset feature vector. And carrying out joint modeling on the behavior offset characteristics and the identity information through a graph neural network model containing an attention mechanism, and outputting a behavior purpose label and a risk grade score. And a graded security response instruction is generated based on the risk score, so that the missing report rate and the false report rate are effectively reduced.

The invention discloses an intelligent data stream processing system based on Flink and an implementation method thereof, and belongs to the technical field of distributed streaming data processing, and the implementation method comprises the following steps: a data source access layer uniformly accesses multi-source data; the entity monitoring layer captures service entity change in a non-intrusive manner based on JPA, generates a standardized message body and asynchronously delivers the standardized message body to double channels; the unified event model layer maps the data into a standardized event and expands the standardized event; the Flink stream processing engine layer is used for complex event processing, state management and window calculation, and supports declarative pipeline definition and dynamic construction; the anomaly detection layer performs multi-dimensional anomaly detection; the distributed task scheduling layer schedules tasks according to a DAG model, and realizes load balancing through hotspot splitting and state transition; and the result storage and visualization layer adopts a cold and hot separation strategy to store data, and provides a dynamic visualization interface. The real-time performance, the consistency and the intelligent level of data processing are remarkably improved, and the development and maintenance cost is reduced.

The invention discloses a method and a system for supporting visual and AI bidirectional intercommunication editing views. The method comprises the following steps: establishing a bidirectional mapping rule between a rendering tree protocol and an intercommunication protocol; in response to an operation of a user on the visual editor, generating a first atomic operation instruction, applying the first atomic operation instruction to the current rendering tree and triggering local redrawing of a view; serializing the updated rendering tree into context segments in an intercommunication protocol format according to the mapping rule, and pushing the context segments to an AI model; responding to a natural language editing request initiated by a user to the AI model, receiving streaming data in an intercommunication protocol format returned by the user based on the request, analyzing the streaming data according to the mapping rule to obtain a second atomic operation instruction, applying the second atomic operation instruction to the rendering tree and triggering local redrawing of a view, and obtaining a second atomic operation instruction. The view is edited by the AI, so that real-time two-way intercommunication between visual editing and AI editing is realized, and the user experience is remarkably improved.

The invention belongs to the technical field of electric power automation, and discloses a power grid load flow calculation method based on a graph neural network and a related device. The method comprises the following steps: acquiring power grid data to generate a power flow data set; constructing a node feature vector and an edge feature vector based on the power flow data set; splicing the node feature vectors and the position codes of the nodes in the power grid to obtain splicing features; and inputting the splicing feature and the edge feature vector into a pre-trained graph neural network model to obtain a power grid load flow calculation result based on the graph neural network. Compared with a traditional Newton-Raphson method and a graph neural network method considering a local topological relation, the method has the advantages that the calculation speed can be remarkably increased, and meanwhile, the accuracy and reliability of a result can be kept; the method has the advantages of being high in adaptability, wide in application scene and the like, can adapt to power grids of different scales and topological structures, has good generalization ability, and can be applied to multiple application scenes such as real-time power flow calculation and future state power flow prediction.

The invention relates to the field of power system planning, in particular to a micro-grid boundary quantitative evaluation method and system for multi-dimensional analysis and dynamic verification. The method comprises the steps that power distribution network and micro-grid scheme data are acquired, and scene recognition and decoupling modeling are carried out after standardization processing; the method comprises the following steps: extracting end supply-preserving scene data, analyzing cost elements, and generating a critical cost threshold table through normalization processing and a threshold approximation algorithm; performing multi-dimensional parameter correlation analysis and integrated learning training based on the table, and constructing a multi-dimensional boundary index model; performing benefit matching calculation and green value accounting according to the result to generate an economic benefit decomposition structure; real-time streaming data processing and stability verification are combined to generate an economical efficiency boundary index set; and finally, generating a standardized evaluation file through matrix mapping and weight dynamic adjustment. According to the method, dynamic quantitative evaluation of the economy boundary of the micro-grid is realized, the capacity substitution benefit and the green value are effectively integrated, and an accurate basis is provided for planning decision.

The invention relates to the technical field of data governance, and discloses a data governance method based on an intelligent decision engine, which comprises the following steps: carrying out business semantic classification and marking on preliminarily processed real-time streaming data, and constructing a data portrait library; constructing a dynamic topological graph, learning an abnormal propagation rule based on a graph neural network, analyzing an influence range and establishing an influence grading mechanism; performing multi-dimensional quality evaluation on the data, and generating a dynamic data quality score and a grading strategy; constructing a data quality historical problem and reason case library, and generating a quality anomaly root cause judgment and influence quantification report by using a large language model agent; generating a candidate strategy set, and selecting an optimal governance strategy from the candidate strategy set by establishing a multi-objective optimization model; and performing compliance test and conflict identification on the optimal governance strategy by using a large language model agent, and dynamically adjusting the decision weight of a rule engine by using a reinforcement learning algorithm to realize a closed loop of data governance and dynamic learning.

A method for testing sheath impedance in a high-voltage cable cross-bonding system. Three-phase current data and three-phase grounding circulating current data from a first cable body segment and a last cable body segment in cross-bonding units, and three-phase current data, three-phase grounding circulating current data, and three-phase induced voltage data from a cable body at a cross-bonding box are detected and collected, thereby directly calculating three-phase sheath impedance of the three cable segments and determining whether the three-phase sheath impedance is abnormal (step 5), thus determining whether the tested cross-bonding units have a defect. The present invention involves a simple operation, achieves an accurate testing result, and can achieve energized testing without an external excitation source. By performing real-time testing on the cross-bonding units and calculating the sheath impedance of the three cable segments in the cross-bonding units in real time, the states of the cross-bonding units can be determined in real time, which makes it possible to detect a cable fault immediately, thereby preventing a loss caused due to the cable fault.

The invention discloses an unmanned aerial vehicle intelligent inspection system based on AI vision and a detection switch cabinet, and relates to the technical field of unmanned aerial vehicle intelligent inspection, the unmanned aerial vehicle intelligent inspection system comprises an unmanned aerial vehicle inspection platform, and the unmanned aerial vehicle inspection platform is in communication connection with the following modules: an unmanned aerial vehicle end, which is used for collecting and preprocessing video stream data of an inspection area; extracting a key frame from the preprocessed video stream data; and the AI visual analysis module is used for analyzing the extracted key frame by using an AI visual algorithm and identifying key information and abnormal fragments in the key frame. According to the invention, through the AI vision algorithm based on the convolutional neural network model, abnormal features can be automatically learned and identified, the abnormal types and specific conditions can be rapidly determined through deep analysis of the key frames, accurate positioning of abnormal segments and comparison with the preset abnormal feature database, compared with manual detection, the accuracy is greatly improved, and the detection efficiency is improved. Tiny abnormal changes can be found in time, and potential faults can be warned in advance.

The invention provides a highway situation awareness method and system, and the method comprises the following steps: collecting camera video stream data to extract traffic flow data and parking data, and inputting the traffic flow data into a graph neural network to construct a traffic flow propagation model; constructing an abnormal event influence evaluation model based on the recurrent neural network and the long-short-term memory network; and through an abnormal event influence evaluation model, outputting influence range data including an affected road segment set and predicted abnormal recovery time, integrating the data and outputting the data to a visual interface. According to the method, the traffic flow state of the expressway is accurately evaluated by collecting, processing and analyzing the video stream data of the roadside camera, and a reliable situation awareness model is constructed in combination with toll station entrance and exit data, portal snapshot data and the like, so that real-time and accurate monitoring and prediction of the traffic condition of the expressway are realized, powerful decision support is provided for traffic management, and the traffic flow state of the expressway is accurately evaluated. And the operation efficiency of the expressway is improved.

The invention relates to the technical field of rail transit intelligence, and discloses a rail transit intelligent scheduling management method and system, and the method comprises the steps: collecting the entering and exiting data of passengers, the number of people in a waiting area, the train load factor and the platform congestion degree through an automatic fare collection system, a video monitor and a sensor of each station of rail transit; the collected passenger flow data are preprocessed, a box plot about passenger flow distribution is constructed, and sudden passenger flow fluctuation areas are identified in different time windows; a K-means clustering algorithm is adopted to classify passenger flow modes in peak periods, and the distribution type of passenger flow fluctuation is analyzed; a time sequence prediction model is constructed by adopting a Transform model in combination with weather, holidays and festivals and emergencies, the future short-term and medium-and-long-term passenger flow trend is predicted, and the train departure interval is optimized; and based on the predicted passenger flow distribution, a scheduling optimization objective function is constructed, and a reinforcement learning algorithm is combined. The method has the advantage of improving the passenger flow prediction precision in the peak period.

The invention relates to the field of financial science and technology, and particularly discloses a cross-platform financial data integration and real-time analysis system and method. The system is characterized in that a distributed heterogeneous data acquisition module accesses multi-source data in parallel through a pluggable protocol adapter; the streaming data cleaning engine realizes field-level normalization based on a dynamic template and semantic mapping; the hybrid storage architecture forms a three-level system by a memory database, a time sequence database and distributed file storage; the stream processing core engine adopts an elastic sliding window to execute CEP complex event processing; and the dynamic analysis decision module integrates online machine learning to generate a real-time transaction signal. The method comprises the steps of multi-source concurrent acquisition and microsecond-level timestamp injection, context sensing data standardization, dual-channel data storage, event time window aggregation, dynamic risk calculation and block chain audit evidence storage. The defects of low data fusion efficiency, high real-time analysis delay and the like in the prior art are overcome, and cross-platform data processing timeliness and decision accuracy are remarkably improved.

The invention relates to the technical field of urban rail transit station-city collaborative planning, in particular to an ontology-based station-city collaborative data integration and planning prediction method, which comprises the following steps of: obtaining rail transit station passenger flow data, resident travel behavior data and station periphery built environment index data; forming a space-time sample sequence according to the unified space-time granularity of the site walking service area; constructing an urban rail transit station-city cooperation ontology, and carrying out semantic annotation and semantic fusion on the space-time sample sequence to generate a feature sequence; inputting the feature sequence into a multi-task space-time diagram convolutional neural network prediction model to output a passenger flow prediction result and establish an environment index prediction result; and calculating a feature contribution degree based on a Shapley additive interpretation value, optimizing a background sample set by using a genetic algorithm to determine a key action element set, outputting a planning index threshold and an intervention measure parameter, and realizing an interpretable station-city collaborative prediction and planning decision closed loop.

The invention relates to the technical field of industrial internet of things operation and maintenance, and particularly provides a remote maintenance auxiliary method integrating video monitoring and three-dimensional modeling. The method comprises the following steps: acquiring engineering graphic data and point cloud scanning data of maintenance equipment, and collecting video stream data of a maintenance equipment site; the video stream data is used for describing the operation state of maintenance equipment; the video stream data comprises a plurality of video frames; matching the point cloud scanning data with the engineering graphic data, and constructing a watertight three-dimensional grid model according to a matching result; mapping texture features of the maintenance equipment in a target video frame to the surface of the watertight three-dimensional grid model to obtain a target three-dimensional model; and receiving a first maintenance instruction marked in the target three-dimensional model by a remote expert, and sending the first maintenance instruction to a video picture of a client of an on-site maintainer. According to the technical scheme provided by the invention, the time consumption for positioning the overhaul part can be reduced.

The invention relates to the technical field of safety monitoring, and discloses a multi-source data fusion-based personnel behavior safety early warning system, which comprises a data acquisition module, a fusion decision module, a dynamic adjustment module, a cross validation and correction module, an accumulated score management module, an early warning comparison module, an early warning response module and an equipment linkage module, the data acquisition module acquires identity permission, dynamic position, environmental perception and video stream data in real time, and encrypts and transmits the data to the fusion decision module, the fusion decision module performs modeling by using an improved D-S evidence theory, identifies illegal behaviors and gives initial scores, the dynamic adjustment module performs weighting according to time and position coefficients to obtain dynamic scores, and the dynamic adjustment module performs decision making according to the dynamic scores. The cross validation module calculates correction scores such as conflict coefficients, the score accumulation module performs rolling accumulation according to 24 hours, attenuation and reset rules exist, the early warning comparison module marks three-level threshold values to determine risk levels, the early warning response module triggers corresponding strategies according to the levels, and the equipment linkage module controls hardware to realize closed-loop control so as to guarantee regional safety.

The invention provides an electromechanical equipment fault prediction method and system based on multi-source information fusion, and the method comprises the steps: collecting operation data, including vibration data, temperature data and current data, during the operation of electromechanical equipment; performing feature extraction on the operation data based on a principal component analysis algorithm to obtain a fusion feature vector; inputting the fusion feature vector into a fault prediction model based on a deep belief network, and outputting a prediction result; wherein the deep belief network adopts a small-batch stochastic gradient descent algorithm combined with an adaptive learning rate adjustment strategy during training; and judging whether the electromechanical equipment has a fault hidden danger or not according to the prediction result. According to the method, the relevance between different types of data is mined, and the defect of low prediction precision is overcome.

A technique for resource allocation estimation for media content items is described. In accordance with the described techniques engagement by a set of user accounts with respective media content items of at least one media content service provider system is obtained. The media content service provider system and / or a payment service system generates historical streaming data for the respective media content items based on the engagement of the set of user accounts. An estimated streaming count of a media content item over a time period based on the historical streaming data for the respective media content items is determined. An estimated resource allocation for the artist is determined based on the estimated streaming count and an advance of funds is facilitated based on the estimated resource allocation to an account of the artist during the time period.

The invention provides a data co-processing method and system for a linear servo actuator. The method comprises the following steps: firstly, acquiring a multi-level temperature data set of a key heat source area in the linear servo actuator; performing dynamic coupling analysis processing on the multi-level temperature data set based on a thermal field prediction model to generate a temperature change trend prediction result; a dynamic heat dissipation strategy grade is matched according to the temperature change trend prediction result and real-time load current data, a heat dissipation execution control instruction is generated, and temperature feedback data in the heat dissipation process is monitored in real time; and based on a comparison result of the temperature feedback data and a preset temperature threshold, triggering a thermal failure protection mechanism to adjust the execution priority of the composite heat dissipation operation. According to the invention, power output can be reduced, external active heat dissipation control is triggered, equipment shutdown and other related instructions which are in effective contact with an abnormal working state can be triggered, so that the risk of equipment damage caused by abnormal operation of the actuator is effectively reduced, and the reliability and stability of the system are improved.

The invention provides a method, a system and equipment for improving the computing performance stability of a hardware platform and a medium, and belongs to the technical field of computer system performance optimization. The method comprises the following steps: acquiring architecture characteristic data of a bottom hardware platform through a hardware detection tool, and generating and loading a first-stage system kernel parameter configuration based on the architecture characteristic data; reading the architecture characteristic data, carrying out behavior analysis and type identification on the running process based on the architecture characteristic data, and generating a second-level process scheduling strategy according to the behavior analysis and type identification; reading cache structure information in the architecture characteristic data, and driving a file system to intelligently prefetch and reconstruct a storage layout of memory data based on the cache structure information; and continuously collecting performance index flow data when the system runs, performing real-time analysis on the performance index flow data by using a performance degradation model, generating a feedback control instruction according to an analysis result, and dynamically correcting the kernel parameter configuration of the first-stage system and the scheduling strategy of the second-stage process.

The invention discloses a source-grid-load-storage integrated coordinated management and control system for an intelligent micro-grid, and relates to the technical field of intelligent micro-grids and comprehensive energy regulation and control. The system comprises the following components: a multi-energy-flow data acquisition unit, a multi-energy-flow coupling modeling and optimizing unit, a multi-energy-flow gradient utilization execution unit, a mode self-adaptive switching unit and a main control unit, according to the invention, through a multi-energy flow coupling modeling and optimization unit, an electric-thermal-gas multi-energy flow coupling model comprising a heat supply network transmission loss calculation sub-model is constructed, a renewable energy consumption constraint sub-model is additionally arranged, and an improved hybrid particle swarm optimization algorithm is adopted to carry out dynamic optimization solution, so that an optimal scheduling strategy is generated; according to the strategy, the consumption rate of renewable energy sources is increased, electricity-heat gradient utilization and efficient configuration are achieved through a heat energy distribution priority regulation and control mechanism and efficiency optimization control of the waste heat recovery module, and dependence of heat loads on electric energy is remarkably reduced.

The invention discloses a hydrological flow monitoring method and a hydrological flow monitoring system based on a dynamic topology adaptive technology. The hydrological flow monitoring method comprises the following steps: performing hydrological flow monitoring in real time by adopting a trained online flow measurement model; the method comprises the following steps: firstly, acquiring actually measured hydrological element data in the same period of an online flow measurement data sequence, and establishing a river section hydrological element information base; then based on edge computing gateway equipment, flow velocity analysis is carried out by adopting a dynamic topology adaptive technology, parameter calibration is carried out on an online flow measurement model, and the average flow of a test section is calculated; performing comparative measurement analysis and precision evaluation on the flow online monitoring result based on the edge computing gateway equipment; and finally, outputting the finally trained online flow measurement model. According to the method, the applicability of the online flow measurement equipment under different hydraulic conditions and water flow characteristics can be further explored, and a key technical support is provided for application of flood control early warning and other aging sensitive scenes and construction of a cloud-side-end collaborative distributed hydrological monitoring network.

The invention discloses a power grid intelligent inspection method and system based on an unmanned aerial vehicle, and the method comprises the following steps: collecting image flow data of a target region, constructing a three-dimensional point cloud model, and generating an initial inspection path based on a fast marching tree method in combination with spatial position information; controlling the unmanned aerial vehicle to fly according to a path and collect image frames in real time, and executing an optical flow estimation algorithm through an edge calculation chip to obtain a pixel motion vector; associating the motion vector with a space coordinate corresponding to each inspection point in the inspection path, dividing an optical flow detection area and distributing an initial detection weight; recognizing a dynamic abnormal area according to the motion features, extracting image data and space coordinates, and driving an acousto-optic load assembly carried by the unmanned aerial vehicle to respond; and based on the space coordinates of the abnormal region, re-executing the fast marching tree method to generate a local update path, and adjusting the detection weight of the related region. According to the invention, dynamic sensing and path updating linkage in the unmanned aerial vehicle inspection process can be realized.

The invention discloses an urban traffic signal real-time collaborative optimization system and method based on a space-time diagram convolutional network and reinforcement learning, and relates to the technical field of intelligent traffic control. In order to overcome the defects of traffic signal fixed period control, the technical scheme adopted by the invention comprises edge computing equipment which is deployed beside an intersection camera and is used for acquiring video stream data in real time through a built-in local model, extracting traffic flow state characteristics and realizing dynamic phase timing optimization through cross-intersection collaborative decision, meanwhile, local model parameters are generated and uploaded to the cloud federated learning platform; the cloud federated learning platform is used for aggregating and optimizing the local model parameters of the edge computing devices, and regularly issuing global update parameters to the edge computing devices; and the traffic signal control equipment is deployed at the intersection and is used for adjusting the display state of the traffic signal lamp in real time according to the dynamic phase timing instruction. The traffic efficiency of the urban road network can be obviously improved, and the traffic control cost is reduced.

The invention belongs to the technical field of system operation and maintenance monitoring, and particularly discloses a system health degree assessment method and system, a terminal and a medium. Comprising the following steps: collecting log stream data of a target system and a heterogeneous subsystem to obtain log feature data; inputting the log feature data into an exception detection engine of a preset training model, and performing exception judgment on the log feature data based on the exception detection engine to obtain an exception detection result; according to the anomaly detection result and the operation index of the target system, performing multi-dimensional comprehensive calculation on the operation index to obtain a system health degree score; and performing processing calculation according to the log feature data, the anomaly detection result and the system health degree score, and outputting a system operation state analysis result. By introducing the anomaly detection model based on the support vector data description and the improved grey wolf optimization algorithm, efficient analysis and anomaly recognition of complex heterogeneous system logs are realized, and the system stability and the operation and maintenance efficiency can be improved.

The invention discloses a collected image recognition method and system for a surveying and mapping unmanned aerial vehicle, surveying and mapping image data and surveying and mapping point cloud data of a target surveying and mapping area are obtained through a multi-mode sensor carried by the unmanned aerial vehicle, and meanwhile, a time camera is used for capturing a motion signal of a dynamic target to generate event stream data; histogram equalization and multi-scale enhancement are carried out on the surveying and mapping image data, and voxel filtering and curved surface reconstruction are carried out on the surveying and mapping point cloud data; wavelet packet decomposition is utilized to extract frequency domain features of an image in the preprocessed acquisition data, and the frequency domain features are fused with motion trail features in event stream data; and inputting the mixed feature vector into a lightweight adaptive model, dynamically adjusting weights of different modes through an attention mechanism, and outputting a surveying and mapping result of the target surveying and mapping area. The information of the image in different frequencies and directions can be described more comprehensively and meticulously, and the surveying and mapping efficiency is effectively improved.

The invention provides a water body water level monitoring method of an image segmentation model, and the method comprises the steps: obtaining multi-modal water flow data of a target water area, carrying out the feature extraction of the multi-modal water flow data, and obtaining a turbulence feature map, the multi-modal water flow data comprising water flow motion data and water body image data; performing physical constraint segmentation processing on the turbulence feature map and the water body image data to obtain a water body region segmentation mask and a corresponding pixel-level confidence map; carrying out water gauge geometric correction processing on the water body region segmentation mask to obtain a corrected water line; and performing water level value conversion processing on the corrected water level line and the pixel-level confidence map to obtain a water level measurement value and uncertainty data. By adopting the method, the real water line and the dynamic water flow artifact can be effectively distinguished, and the monitoring precision under the complex hydrological condition is ensured.

The invention relates to the technical field of building engineering construction management, and discloses a house steel building construction prediction progress and deployment system based on deep learning. According to the system, periodic stable paragraphs and non-periodic fluctuation paragraphs are recognized by analyzing the time sequence form of construction flow data, and construction links are stripped accordingly. And performing cross mapping on the hoisting event sequence in the link and the environmental monitoring reading of the associated link to generate a link toughness spectrogram representing the construction robustness. And determining a resource demand based on the spectrum graph, and forming a multi-dimensional resource demand vector, so as to drive a graph convolutional network to construct a dynamic construction deduction graph. And carrying out message passing and neighborhood aggregation on the graph, analyzing a key bottleneck path, and iteratively generating a progress prediction and resource allocation scheme. According to the method, the construction dynamic toughness can be evaluated, and accurate positioning and dynamic optimization of resource bottlenecks are realized.