542 results about "Heterogeneous information" patented technology

The invention relates to the technical field of construction data modeling, in particular to a BIM (Building Information Modeling)-based construction progress dynamic optimization system, which comprises an abnormal event identification module used for detecting local abnormity based on multi-source data and marking an abnormal area; the local component reconstruction and dynamic fusion module is used for reconstructing an abnormal region component model and performing space mapping to obtain a global BIM model; the component-semantic double-layer graph modeling module is used for constructing a component topological dependency graph and a component semantic relation graph and jointly reasoning a state transition sequence of the component; the state recognition and self-adaptive classification module is used for fusing the geometric features of the component and the graph structure features and executing state recognition and judgment boundary adjustment; and the graph evolution calculation module is used for performing multi-round state propagation deduction to generate a component state evolution result. According to the method, in a heterogeneous information modeling environment, dynamic processing and state intelligent identification of component-level information are realized, and the data calculation precision and response capability under three-dimensional model driving are improved.

The invention discloses an intelligent data analysis method and system based on an industry large model, and relates to the technical field of data analysis, and the method comprises the steps: collecting business event data, carrying out the feature vector extraction according to the data type, calculating a zoom dot product attention score matrix through employing a self-attention mechanism, and carrying out the weighted output of a multi-modal fusion vector, constructing an industry knowledge database to determine an embedded vector; and calculating a relevancy value, screening a combination of the multi-modal fusion vector and the knowledge fragment, and forming a gating input vector to carry out fusion vector calculation. According to the method, unified expression and correlation mining of heterogeneous information such as structured data, text information and images can be realized through a multi-modal feature fusion and knowledge fragment retrieval scheme, the attention degree of each modal feature can be adaptively adjusted according to a service scene through a self-attention mechanism and a model, information complementation is realized, and the accuracy of information retrieval is improved. And automatically highlighting the multi-modal dimension most related to the current task.

The invention discloses a method for constructing a cross-modal knowledge graph, and relates to the technical field of knowledge graphs, and the method comprises the steps: obtaining multi-source heterogeneous data, and carrying out the collection, structural analysis, modal recognition and classification, cleaning and standardization processing, so as to form structured multi-modal data; extracting entities and relationships of the identified modals from the structured multi-modal data, and summarizing the entities and relationships to form a multi-modal knowledge element set; mapping different modal entities in the multi-modal knowledge element set to a unified semantic space, and generating a unified entity relationship set through semantic matching, alignment and fusion; and normalizing the data into knowledge triples, and storing and organizing the knowledge triples through a graph database to form a cross-modal knowledge graph. According to the method, the problems of difficulty in multi-modal heterogeneous information alignment and difficulty in entity relationship extraction can be relieved, semantic association is enhanced, and knowledge graph integrity and multi-scene adaptability are improved.

The invention discloses an intelligent agricultural condition monitoring system and method for grain and oil crops, and relates to the technical field of agricultural condition monitoring. Farmland multi-source data are acquired and preprocessed in real time through a multi-source data acquisition module; multi-factor step-by-step fusion of farmland multi-source data is realized by using a D-S evidence theory through a multi-source data fusion module, and a multi-factor fusion evaluation index is formed; constructing a grain and oil crop environment-growth state comprehensive model based on LSTM through an environment state fusion module, and fusing the grain and oil crop environment-growth state comprehensive model with farmland multi-source data and multi-factor fusion evaluation indexes to obtain a farmland digital twin system; and a growth deviation index is analyzed and generated through the decision feedback optimization module and is fed back to the optimization system. The problems that in the prior art, data acquisition hysteresis is remarkable, multi-source heterogeneous information is isolated and difficult to fuse, and early warning fails due to lack of correlation between environment mutation and crop response are solved, and accurate monitoring and risk active intervention of grain and oil crops in the whole growth period are achieved.

The invention belongs to the technical field of risk prediction, and discloses a landslide risk prediction method and system based on data intelligent analysis, and the method comprises the steps: obtaining corresponding multi-source heterogeneous information, carrying out the feature extraction and data fusion of the information, and obtaining a mountain monitoring basic data set; constructing a mountain twinborn feature model based on the mountain monitoring basic data set, and performing landslide scene simulation based on the mountain twinborn feature model to obtain corresponding risk feature information; constructing a trigger factor association network based on the risk feature information, and performing zoning evaluation and grading on the target landslide risk in combination with the mountain monitoring basic data set; constructing a corresponding grading risk zoning map; obtaining a landslide risk dynamic prediction result based on the grading risk zoning map; a corresponding grading early warning rule base is constructed; and the target mountain area is monitored in real time based on the grading early warning rule base, and the monitoring result is fed back. According to the invention, a comprehensive and efficient solution is provided for prevention and treatment of landslide disasters.

The invention discloses a multi-agent dynamic task allocation and collaborative path-finding system based on label-free distributed deep reinforcement learning. The system comprises the following steps: step 1, receiving state information and environment perception data of each agent in a multi-agent system based on distributed deep reinforcement learning; step 2, extracting feature representations of the environmental perception data and the intelligent agent state information, and performing multi-source heterogeneous information fusion through an attention mechanism to obtain state-task matching features; 3, transmitting the state-task matching characteristics to a multi-agent network in real time, realizing task allocation negotiation among agents by adopting a hierarchical scheduling and state exchange mechanism based on task priorities, dynamically detecting newly added task types, and performing incremental learning; the online updating iteration of the model is realized to assist the multi-agent optimization task allocation strategy and the path planning action; compared with the prior art, the method has the advantages that by applying the distributed deep reinforcement learning technology and a state exchange mechanism between intelligent agents, the system can quickly adapt to environment changes and task dynamics, and the resource utilization rate and the task completion efficiency are improved.

The invention belongs to the technical field of multi-source heterogeneous data fusion and intelligent decision, and discloses a multi-source heterogeneous information fusion and analysis method for an AI large model and data knitting, which comprises the following steps: accessing multi-source heterogeneous data through data knitting, calibrating time and space by taking a key event as an anchor point, generating time and space alignment data, and synchronously constructing a dynamic metadata network. Generating a metadata graph; constructing a dynamic hypergraph, and calling a modal exclusive coding cluster to generate a multi-modal fusion feature; edge-cloud collaborative reasoning is started, a collaborative reasoning intermediate result is generated, confidence data is output by the general large model to arbitrate cross-domain conflicts, and a final decision packet is generated; triggering two-way feedback closed-loop optimization, driving data source optimization and knowledge completion of a metadata graph by an AI large model, further dynamically scheduling the priority of a data stream, and encrypting and updating large model parameters; and iterating the metadata atlas, and feeding back to a link of constructing a dynamic metadata network and generating a multi-modal fusion feature.

The invention provides a chronic disease risk assessment and intervention strategy generation system based on data analysis. According to the system, multi-source heterogeneous information including clinical examination, behavior records, environment data and the like is collected, key features are extracted through a data fusion technology, and time and space features of data are enhanced through a space-time weighted tensor decomposition method. And in combination with a causal reasoning technology, the system can accurately evaluate the chronic disease risk of an individual, eliminate confounding factors and provide more reliable risk prediction. In addition, the system dynamically generates a personalized intervention strategy through a reinforcement learning algorithm, adjusts intervention measures according to real-time health data, and ensures accurate chronic disease management. The method has an efficient risk prediction capability and a personalized intervention scheme, and is helpful for improving the accuracy and effect of chronic disease management.

The invention provides a tunnel excavation ground surface settlement prediction method and system based on a machine learning hybrid model, and relates to the technical field of tunnel engineering and machine learning crossing, and the method comprises the steps: obtaining the multi-source heterogeneous information of a target tunnel, and constructing a ground surface settlement data set; a Transform-BiLSTM hybrid model is constructed, the robustness of the algorithm in a noise environment is enhanced based on a VMD (variational mode decomposition) algorithm, hyper-parameters are adaptively adjusted and optimized by using a PSO (particle swarm optimization) algorithm based on a ground surface settlement data set, the model prediction precision is maximized, and a ground surface settlement prediction model is obtained; and analyzing decision logic of the ground surface settlement prediction model through the SHAP value, and outputting interpretable engineering guidance suggestions. By constructing a machine learning hybrid model, high-precision and real-time prediction of ground surface settlement in the whole process of tunnel excavation is realized. The precision and generalization ability of the model are improved, the characterization ability of complex spatial-temporal characteristics is enhanced, and overfitting is avoided; and the interpretability is optimized, and the influence of key parameters on a prediction result is quantified, so that construction parameter adjustment is guided.

The invention discloses a black-box test zero-day vulnerability analysis method and system based on multi-dimensional data, and aims to solve the problems that a traditional black-box test means is weak in unknown vulnerability recognition capability, high in false alarm rate, lack of path modeling and verification mechanisms and the like. The method comprises the following steps: constructing a cross-time window behavior graph by acquiring multi-dimensional heterogeneous information such as network input, system call, log information and abnormal signals; on the basis, potential abnormal paths are identified through structure entropy change and graph structure mutation analysis, path vector representation is constructed by combining graph representation learning and a path embedding method, and path-level risk modeling and mode clustering analysis are achieved; furthermore, vulnerability confirmation is carried out on the suspicious path through multiple verification mechanisms such as attack replay, fuzzy testing and sensitive function combination identification. The system has a multi-module cooperation capability, can realize automatic mining, verification and visual tracing of zero-day vulnerabilities in a source-source-free environment, and has good universality and expansibility.

The invention relates to the technical field of deformation measurement, in particular to a building deformation monitoring system based on fusion and intersection of Beidou and machine vision technologies, and the system comprises a pose calculation module which is used for setting anchoring nodes, matching Beidou three-dimensional coordinates and inclination angle data of the anchoring nodes obtained in real time with two-dimensional pixel coordinates of the anchoring nodes in a visual image, and calculating the pose of the anchoring nodes; resolving a real-time pose, and converting the real-time pose into a deformation measurement result; the three-dimensional deformation field generation module is used for processing the topological graph of the building structure by adopting a graph calculation model, fusing multi-source heterogeneous information by aggregating and updating node features, and decoding to generate a three-dimensional deformation field of the whole building; and the measurement early warning module is used for constructing a normal behavior baseline model to predict a normal deformation state, carrying out deformation state evaluation and generating deformation early warning information by comparing deformation deviation degrees and combining context analysis and a deformation trend, so that the precision and comprehensiveness of building deformation monitoring are improved.

The invention discloses an intention recognition method based on cross attention and multi-scale uncertainty. The intention recognition method comprises the following steps: preprocessing multi-modal data; parallel multi-modal feature coding oriented to intention recognition; the invention relates to multi-scale uncertainty perception decoding. According to the method, a parallelized multi-modal feature extraction path is constructed, and a hierarchical fusion mechanism based on cross attention is designed, so that deep semantic alignment and complementary enhancement of four types of heterogeneous information including the posture, the motion track, the global scene and the local vision of a rider are realized; the problems of incomplete feature representation and insufficient cross-modal correlation modeling caused by dependence on a single information source or adoption of a shallow fusion strategy in a traditional method are solved, so that the accuracy and robustness of intention recognition in a complex traffic scene are remarkably improved. According to the method, a multi-scale uncertainty perception decoding framework is introduced, risk early warning or context auxiliary verification is carried out on a low-confidence identification result, and the reliability of an automatic driving system in a safety critical scene is improved.

The invention relates to the technical field of geographic space intelligence, and discloses a territorial space purpose control intelligent analysis system, which comprises the following modules: a multi-source data acquisition module, which is based on satellite remote sensing and an IoT sensor, plans vector data, adopts a spatio-temporal data fusion algorithm, and integrates territorial, ecological and economic field heterogeneous data through a distributed crawler technology; generating a territorial space total element data set; the multi-source data acquisition module comprises a remote sensing acquisition sub-module, an Internet of Things access sub-module and a planning data analysis sub-module. Through a distributed data crawling and real-time stream fusion technology and spatio-temporal data modeling, rapid integration of multi-source heterogeneous information is realized, low-efficiency delay of traditional manual acquisition is eliminated, high-precision deformation monitoring and land use change identification are synchronously completed, the violation behavior discovery timeliness is remarkably improved, and the method is suitable for large-scale popularization and application. The three-dimensional space analysis algorithm accurately quantifies the above-ground and underground space element interaction relation, and the engineering conflict risk is effectively avoided.

The invention provides an intelligent emergency decision support method and device based on multi-Agent cooperation. The method comprises a task planning module, an information acquisition module, a data fusion module and an execution monitoring module. The task planning module adopts a hierarchical decision-making mechanism, performs task decomposition in a plan making stage, generates a plurality of candidate execution paths by using thinking tree reasoning in a plan execution stage, and selects an optimal scheme. The information acquisition module acquires multi-source information such as network search, knowledge graph and geographic data through a plurality of professional Agents. And the data fusion module adopts a blackboard mode to manage heterogeneous information, and realizes intelligent abstract and correlation analysis through a large language model. And the execution monitoring module realizes dynamic optimization and fault self-recovery of the system through a multi-dimensional progress evaluation and cooperative monitoring mechanism. According to the invention, the problems of insufficient information processing capability, low decision-making efficiency and poor system stability of a traditional emergency decision-making system are solved. The information collection and processing efficiency is improved through large language model multi-Agent cooperation, the decision quality and accuracy are improved through a hierarchical decision mechanism, and long-term stable operation of the system is guaranteed through self-adaptive monitoring. The method is suitable for complex emergency decision-making scenes such as natural disasters, safety accidents and public health events.

The invention discloses an automatic conference summary generation method based on multi-source heterogeneous information fusion. The method comprises the following steps: extracting facial features of a speaker by using a face recognition technology and recognizing a face identity of the speaker; acquiring an audio signal through a microphone array, and recognizing a voiceprint identity of a speaker by using a voiceprint recognition technology; in combination with video and audio information, through a multi-source heterogeneous information fusion technology, alignment of video and audio data is carried out in time; through a sound source positioning technology, a voiceprint identity and a face identity are matched and aligned in space, and the identity of a speaker is accurately positioned and recognized; the spokesman and the speaking content are calibrated and separated, and it is ensured that the identity of the spokesman is accurately matched with the speaking content; and generating a spokesman abstract and a conference summary according to the calibrated speech content by using natural language processing and a deep learning model. The method is suitable for various conference scenes, the speaking content of different spokesmen can be recognized, the spokesman abstract is generated, and the efficiency and accuracy of conference summary generation are improved.

The invention discloses an industrial robot predictive maintenance method and system based on multi-source data fusion, and the method comprises the steps: synchronously collecting vibration, current, temperature, acoustic and visual signals through multiple types of sensors, carrying out the filtering, correction and normalization processing, and constructing a multi-modal feature set; cross-modal alignment is realized through time compensation, after dimensionality reduction, a mechanical vibration group, an electrical performance group, a thermodynamic group and a motion precision group are divided, mahalanobis distances of the groups are calculated based on a historical health reference to serve as local anomaly degree scores, weights are dynamically adjusted according to the change rate, and the weights are combined into a preliminary health index. And introducing a nonlinear amplification mechanism to enhance high-value response, adaptively switching smooth intensity according to a degradation trend, and outputting a comprehensive health index. According to the method, comprehensive perception and dynamic evaluation of the operation state of the practical training platform are realized, multi-source heterogeneous information is effectively fused, the limitation of single signal monitoring is overcome, and the anomaly recognition accuracy is remarkably improved.

The invention discloses an entity digitization and link framework algorithm based on a heterogeneous graph attention network, and the algorithm comprises the following steps: S1, heterogeneous information network construction: carrying out the unified modeling of all multi-source heterogeneous data into a heterogeneous information network containing various types of nodes and various types of edges, s2, meta-path definition and guidance: defining "meta-paths" connecting different types of nodes to capture a complex deep semantic relationship, S3, heterogeneous graph attention network embedding: adopting an attention mechanism to enable a model to automatically learn importance of different neighbor nodes and different meta-paths, generating a final embedding vector of each entity, and establishing a heterogeneous graph attention network model; according to the method, the information fidelity is higher, modeling is directly conducted on different types of nodes and relations on a heterogeneous graph, more abundant and heterogeneous semantic information in data can be reserved compared with a multi-view method, and the end-to-end learning ability is higher; and the complexity of manually designing a fusion strategy is reduced.

The invention relates to the technical field of resource scheduling, in particular to a smart park resource scheduling method and system, and the method comprises the steps: constructing a heterogeneous information network representing the current state of a park; performing feature extraction on the heterogeneous information network by adopting a graph attention convolutional network, and generating a state embedding vector fused with high-order neighborhood information; inputting the state embedding vector into a scheduling strategy model, and predicting the expected variation of the candidate scheduling action to future park energy consumption, security index and traffic efficiency; according to the state embedding vector and an external management instruction, determining an optimization target of a current scheduling period from a preset operation normal form, and allocating a dynamic weight; and calculating a comprehensive utility score of each candidate scheduling action, and issuing and executing the candidate scheduling action with the highest score as a final scheduling instruction. According to the invention, the refinement level and the overall operation benefit of park management are improved.

The invention belongs to the technical field of marine environment monitoring, and discloses a multi-mode deep learning fused marine remote sensing sea wave parameter inversion method and system, and the method comprises the steps: obtaining three-degree-of-freedom motion time calendar data of a ship in a target region and spaceborne synthetic aperture radar (SAR) image data; preprocessing ship motion time calendar data and the SAR image data, and constructing time-space aligned multi-modal input data; and inputting the preprocessed multi-modal input data into a pre-trained sea wave parameter inversion model, and calculating to obtain an inversion value of the sea wave parameter. According to the method, an attention mechanism is innovatively used to realize adaptive alignment and complementation of heterogeneous information in two dimensions of space and time, and an attention module automatically allocates weights according to task correlation; through time-space collaborative feature enhancement, the model can stably extract characterization strongly related to target parameters even under complex sea conditions, so that the inversion precision and robustness of sea wave parameters are remarkably improved.

The invention relates to an intelligent traffic flow prediction analysis method based on artificial intelligence, and the method comprises the steps: collecting and fusing traffic flow, environmental factors and event information according to traffic levels, and achieving the standardization and automatic clustering preprocessing of multi-level space-time attributes through regional factor labels; and then, expressing a multi-dimensional structure and a dynamic attribute of each node by using regional factor vectorization, dynamically modeling a spatial node heterogeneous adjacency relationship in combination with a self-organizing graph neural network, introducing a cross-level dynamic attention mechanism to perform weighted fusion on multiple spatial and temporal features, and outputting multi-granularity traffic prediction through a hierarchical fusion decoding network. And the model is combined with actual feedback to realize self-adaptive optimization of the area factors and model parameters. The method has the advantages that high-precision prediction of the traffic flow under multiple scales of roads, blocks, cities and the like is achieved, the self-learning and self-adaptive capacity for heterogeneous information, emergencies and spatial dynamic changes is improved, and hierarchical decision making and flow management are supported.

The invention provides a multi-source heterogeneous data fusion analysis method and system, and relates to the technical field of data processing. The method comprises the following steps: acquiring multi-source heterogeneous event knowledge, and extracting entity information and attribute information of multi-source heterogeneous data; fusing the attribute information, constructing a multi-label classification model, and extracting a sequential relationship of the multi-source heterogeneous data through the multi-label classification model; a time sequence label is added to the time sequence relation, and then an initial knowledge graph of the multi-source heterogeneous data is constructed; obtaining an entity time sequence state sequence of the multi-source heterogeneous data through the initial knowledge graph and the entity information; extracting features of the entity time sequence state sequence; and processing the characteristics of the entity time sequence state sequence to update the initial knowledge graph to obtain the time sequence knowledge graph of the multi-source heterogeneous data. Massive and diversified knowledge is organized and expressed orderly, uniformly and associatively through an entity and attribute extraction technology of multi-source heterogeneous information, a time sequence multi-label relation extraction technology and a time-space big data standardization expression technology.

The embodiment of the invention provides a network security validity verification and quantitative evaluation method and system, and relates to the technical field of network security, and the method comprises the steps: obtaining global dynamic threat intelligence and a multi-dimensional global network security risk data source, and carrying out the preprocessing; constructing a global feature engineering system based on heterogeneous information network atlas and sequence analysis, forming a feature vector matrix, and mapping the feature vector matrix into an index state vector; inputting the feature vector matrix, the index state vector and the external environment information vector into an evaluation model, dynamically adjusting the weight of the feature vector matrix of each dimension, and outputting the validity score of each safety control point; based on the score, calculating a safety effectiveness index based on a time decay factor; identifying a weak link based on the index, and performing simulation verification to obtain a simulation attack actual measurement result; and an error vector is constructed based on the result and the validity score, and parameter adjustment and weight calibration are carried out. According to the scheme, the accuracy and the real-time performance of network security evaluation are improved.

The invention discloses a ship anti-collision early warning method based on multi-source heterogeneous information fusion, and the method comprises the steps: S1, obtaining the multi-source target information of a ship navigation radar, an AIS, and an infrared camera, and unifying the targets of all sensors to a same coordinate system; s2, preprocessing the radar and the AIS target; s3, performing information fusion on the preprocessed radar and AIS target; s4, performing information fusion on the target after radar and AIS fusion and the infrared image recognition target; and S5, based on the dynamic information of the final fusion target and the state of the ship, calculating the relative distance and orientation with the ship, and when the target is located in a preset fan-shaped area right in front of the ship and the relative distance is smaller than a dynamic danger threshold value, triggering a multi-stage acousto-optic character alarm. According to the invention, various sensor information can be integrated to detect and track the water surface target, and the robustness and the detection rate are improved, so that a more accurate early warning effect can be achieved on the water surface obstacle when the ship sails.

The invention relates to the technical field of enterprise data management, in particular to a lightweight-based enterprise data dynamic integrated management system, which is characterized in that an acquisition module is used for deploying edge computing nodes, receiving multi-source heterogeneous information streams from manufacturing execution systems and equipment logs, dynamically analyzing and standardizing the information streams, and adding metadata tags; uploading is carried out in a batch processing mode; the map construction module is used for constructing a semiconductor blood relationship map by taking the standardized key information as a blood relationship clue; a graph database is used for efficient storage, and a RESTful API interface is configured to support batch import, so that data storage and relevance expression are more flexible and efficient; the prediction module performs reasoning on the atlas by adopting a graph neural network to generate predictive risk distribution, and a correlation analysis set generated by the prediction module is stored back to the atlas in a structured manner; by introducing a multi-thread concurrent write-in and lock mechanism, the atlas supports complex combination query based on a Cypher query language, and supports multi-level and traceability query.

The invention discloses an intelligent fusion analysis and decision support system and method for multi-source heterogeneous data of a spacecraft. The intelligent fusion analysis and decision support system for the multi-source heterogeneous data of the spacecraft and the intelligent fusion analysis and decision support method for the multi-source heterogeneous data of the spacecraft are included. The system comprises a dynamic adaptation access layer, a unified semantic modeling layer, a space-time semantic fusion engine, a digital twin collaborative decision-making center, a knowledge enhancement decision-making auxiliary subsystem, a cross-department collaborative interaction platform and a side cloud collaborative intelligent processing framework. The method comprises the steps of multi-source heterogeneous information dynamic access and semantic modeling, space-time and semantic deep fusion, digital twin driven decision deduction, knowledge enhanced decision assistance, cross-department collaborative decision and interaction, edge cloud collaborative intelligent processing and the like. According to the method, the safety, reliability and decision-making efficiency of spacecraft tasks can be remarkably improved, and the method is suitable for development, testing and on-orbit task stages of various spacecrafts.

The invention discloses a vertical take-off and landing airport air traffic collaborative management method and system. The method comprises the following steps: constructing an airport air model integrating a physical environment, an airspace structure, a natural environment, an aircraft state and observation information; receiving observation information in real time to drive model synchronization; performing prospective multi-dimensional conflict prediction based on the synchronous model and eVTOL dynamics; on the basis of the prediction result, generating a cooperative regulation strategy when multiple constraint conditions are met; converting the strategy into an instruction, issuing the instruction through a security data link, and verifying an execution effect based on observation information to realize closed-loop control; according to the method, the air traffic environment of the vertical take-off and landing airport is systematically and digitally reconstructed through the quintuple model, unified representation of the operating environment and the traffic subject is achieved, the problem of heterogeneous information fusion is solved, the airspace utilization efficiency and safety under high-density operation can be remarkably improved, and the method is suitable for large-scale popularization and application. And the method has strong adaptive capacity to dynamic changes such as wind power disturbance and emergency conditions.

The invention discloses a spatio-temporal crime prediction method and system fusing spatio-temporal heterogeneous information. The method comprises the following steps: constructing a spatio-temporal data set; constructing a crime time sequence signal data set; periodically decomposing the time sequence signal into a plurality of intrinsic mode functions (IMF); the sample entropy is used as a fitness function to evaluate the advantages and disadvantages of decomposition results under different parameter combinations so as to determine the optimal input time window length; performing clustering processing on the crime data to identify urban crime hotspot areas, and generating crime spatial distribution data; performing feature analysis in time and space, and further completing contribution evaluation of key features to the crime situation; constructing a space-time prediction model, and segmenting a three-dimensional space-time data set into samples in a sliding window mode according to a time window determined in a self-adaptive mode to serve as input and output of the model; and completing training and tuning of the model. And a more accurate and efficient solution is provided for urban crime situation analysis.

The invention provides a high-robustness data real-time synchronization method and system, a storage medium and electronic equipment, relates to the technical field of data synchronization, and can significantly reduce the data synchronization delay between different systems by introducing mixed timestamps and fragment increment verification and calculation and adopting a reliable transmission mechanism and idempotent processing protocols based on message queues, thereby improving the data synchronization performance. Almost real-time information sharing is realized; meanwhile, the data consistency of the cross-heterogeneous information system can be effectively improved, and the accuracy and integrity of the data among multi-source systems are ensured; in addition, the reliability and robustness of data synchronization in unstable environments such as limited network bandwidth, easy jitter or interruption and the like can be greatly enhanced.

The invention provides an imaging method and system based on a polarization imaging lens and visual collaborative optimization, and a medium, and the method comprises the steps: integrating a four-way polarization filter array on the surface of an imaging sensor, and synchronously collecting a four-channel polarized light intensity image through single-frame exposure based on the imaging sensor; obtaining a three-channel RGB image, and inputting the four-channel polarized light intensity image and the three-channel RGB image into a polarization-RGB fusion neural network; polarization features and RGB features are extracted, heterogeneous information coupling optimization is carried out, and fusion features are obtained; constructing a polarization-color joint decoding network, carrying out polarization and RGB information collaborative decoding on the fusion features, training a polarization decoding process and a material classification task together based on an end-to-end joint optimization strategy, and outputting a material classification result; high-efficiency acquisition of original polarization information on a hardware level is guaranteed through the polarization filtering array; and an end-to-end joint training strategy is adopted to realize optimal decoupling and fusion of perception information.

The invention relates to the technical field of fuel cells, in particular to a method and system for accurately controlling the hydrogen stoichiometric ratio of a fuel cell, and the method comprises the steps: firstly, obtaining a quantitative anode health state value through a fusion calculation model by collecting load current, anode inlet and outlet pressure, temperature, high-frequency resistance and other multi-source signals in real time; a feed-forward model based on a load current change rate predicts sudden change of hydrogen demand, and an optimal hydrogen flow instruction is jointly generated in combination with output of a PID controller which adaptively adjusts parameters according to a health state value. According to the method, the integrated diagnosis parameter, namely the anode health state value, is created, so that the internal state of the fuel cell can be accurately perceived and prospectively judged. Through deep fusion of multi-source heterogeneous information such as high-frequency resistance, anode pressure difference, current and temperature, a comprehensive index capable of comprehensively and quantitatively reflecting anode water content, gas concentration and runner smoothness is generated.