3901 results about "Dynamic data" patented technology

The invention relates to the technical field of agricultural traceability, and discloses an agricultural non-point source pollution intelligent traceability method and system based on a knowledge graph, and the method comprises the steps: achieving the system integration of pollution source features through constructing the knowledge graph fusing multi-dimensional data, and building a sky-ground integrated monitoring network to obtain multi-scale dynamic data. An intelligent traceability mechanism is formed based on deep coupling of a knowledge graph and monitoring data, a high-precision pollution identification model is trained in combination with historical data, and real-time response and dynamic traceability of an over-standard pollution area are achieved. And verifying a traceability result through feature matching and semantic reasoning, quantitatively calculating a pollution contribution rate, and finally generating a reliable traceability conclusion containing the position, the type and the contribution rate. According to the scheme, the bottlenecks of data fragmentation, monitoring simplification, extensive analysis and the like of a traditional method are broken through, and the accuracy, timeliness and credibility of pollution traceability are remarkably improved through a series of full-chain technical systems of traceability identification, analysis and positioning.

The embodiment of the invention provides a power distribution network disaster risk assessment method and system based on multi-source big data, and the method comprises the steps: obtaining a multi-source dynamic data set associated with a power distribution network, carrying out the multi-source feature deep coupling of the multi-source dynamic data set, and generating a power distribution network risk coupling feature set; the power distribution network risk coupling feature set comprises an equipment state coupling feature, an environment interference coupling feature and a topological correlation coupling feature; inputting the power distribution network risk coupling feature set into a preset risk situation coupling deduction model to perform multi-dimensional risk situation coupling deduction, and outputting a power distribution network disaster risk situation map; key node risk traceability coupling analysis is carried out based on the power distribution network disaster risk situation map, and a power distribution network weak link set and a risk evolution dynamic parameter set are determined. According to the method, the weak link in the power distribution network can be accurately positioned, the change rule of the risk along with time can be captured, and the improvement from static recognition to dynamic traceability and evolution prediction is realized.

The invention discloses a fusion optimization method based on Kalman filtering and LSTM cascade. The fusion optimization method comprises three steps of dynamic state estimation, time sequence error modeling and closed-loop fusion optimization. IMU (Inertial Measurement Unit) data is used as input, dynamic state estimation is realized through Kalman filtering, modeling system errors are separated, an LSTM (Long Short Term Memory) network is used for carrying out time sequence modeling on a residual error sequence to capture nonlinear errors, and finally, a corrected state quantity is fed back to a Kalman filtering updating link through a closed-loop feedback mechanism. And collaborative optimization of error compensation and state estimation is realized. According to the method, IMU error accumulation is effectively inhibited through a dynamic-data dual-drive mechanism, the navigation precision and robustness in a complex scene are remarkably improved, and the requirements of high-dynamic applications such as intelligent driving and unmanned aerial vehicle navigation can be met. Finally, the optimized IMU data and the GNSS observation value are fused for integrated navigation, high-precision navigation solution is achieved through Kalman filtering, and indoor and outdoor seamless positioning and the all-attitude control requirement of a high-dynamic carrier are met.

The invention discloses a perception intelligent driving complex traffic scene dynamic risk prediction method, and relates to the technical field of risk prediction, and the method comprises the steps: obtaining the multi-source traffic dynamic data of a target region in real time; performing space-time alignment processing on the multi-source traffic dynamic data; inputting the space-time coupling feature matrix into a pre-trained depth space-time prediction model to generate a risk thermodynamic map; calculating a dynamic risk index of each traffic sub-region, and generating a risk level distribution sequence; and triggering a self-adaptive early warning response mechanism according to the risk level distribution sequence, and dynamically adjusting operation parameters of the variable information sign and the traffic signal controller. The technical problems of frequent traffic congestion and high accident risk caused by inaccurate traffic risk prediction and difficulty in dynamic adjustment according to the real-time traffic condition in the prior art are solved, and the technical effects of accurately predicting the traffic risk and improving the safety and traffic efficiency in a complex traffic scene are achieved.

The invention discloses an intelligent resource scheduling method and system based on a dynamic data consanguinity atlas, and relates to the technical field of resource scheduling, the method comprises the following steps: collecting execution logs and flow metadata of tasks in a computing platform in real time, and constructing a dynamic directed weighted consanguinity atlas; calculating a blood relationship influence coefficient of each node in the dynamic directed weighted blood relationship map; obtaining a to-be-scheduled task, and calculating a comprehensive priority score based on the dependency weight of the dynamic blood relationship map of the to-be-scheduled task, the real-time load state of the target node and the scheduling execution time delay; performing priority ranking on the to-be-scheduled tasks based on the comprehensive priority score, and allocating cluster resources; and predicting the load trend of the target node, and triggering a migration decision when detecting that the predicted load of the target node exceeds a threshold value and the weight ratio of the key consanguinity tasks borne by the target node exceeds a preset threshold value. Through a dynamic consanguinity map and an intelligent scheduling algorithm, high efficiency and fairness of resource allocation are realized, the cluster utilization rate is improved, and task delay is reduced.

The invention discloses an artificial intelligence-based engineering resource allocation optimization method and system, and relates to the technical field of artificial intelligence and resource management crossing. The problems of resource configuration dynamic change and resource conflict coordination are solved. And processing the multi-modal heterogeneous data in the target engineering scene through space-time alignment and semantic coding to generate a dynamic data stream. A task, resource and environment node relationship is constructed based on a dynamic heterogeneous graph network, and a node dependency weight is updated through an event-driven mechanism. Two-stage collaborative optimization is executed, in the first stage, a baseline scheme is generated through Monte Carlo sampling, and in the second stage, resource conflicts are eliminated through back propagation negotiation. The resource dynamic entropy is monitored in real time, a rebalance algorithm is triggered during local overload, and a distribution scheme is adjusted in combination with security constraints. And finally, outputting the optimization scheme to an engineering management system for execution, dynamically returning updated graph network parameters, forming a data acquisition, optimization decision and feedback closed loop, and improving the intelligence and robustness of resource allocation.

The invention provides an energy-saving temperature control optimization method and system based on a central air-conditioning simulation platform, and relates to the field of central air-conditioning simulation platforms, and the method comprises the steps: collecting the operation parameters of a central air-conditioning system in real time through an Internet of Things sensor network, constructing a multi-dimensional dynamic data set in combination with historical data and outdoor meteorological data, and carrying out the calculation of the multi-dimensional dynamic data set; a physical model and machine learning hybrid driven simulation engine is adopted, a dynamic thermodynamic model of the central air-conditioning system is established, and a multi-objective optimization function of a simulation platform model is defined according to user comfort requirements, energy consumption cost constraints and environmental policy indexes; and adopting a hybrid optimization strategy of fusion of reinforcement learning and a genetic algorithm, iteratively optimizing control parameters in the simulation platform model, deploying a digital twin system according to the energy-saving strategy set, predicting potential faults through a virtual sensor, correcting the parameters of the simulation platform model, and realizing closed-loop control and continuous optimization. The method is used for overcoming the defect that a closed-loop feedback mechanism is lacked in the prior art.

The invention relates to the technical field of remote sensing monitoring, in particular to a remote sensing image ocean green tide monitoring method and system fusing physical prior and spatio-temporal evolution. The method comprises the following steps: acquiring a multi-modal remote sensing monitoring image; performing multi-modal feature extraction on the acquired image, wherein the multi-modal feature extraction comprises spectral reflectivity feature extraction, ocean dynamics feature extraction and feature alignment and unified representation; establishing a physical prior of a green tide characteristic wave band by using an ocean optical radiation transmission model; constructing a dynamic space-time diagram based on the extracted multi-modal features to obtain a node global feature vector and a dynamic adjacency matrix; carrying out adaptive graph convolution feature coding based on physical prior and a dynamic space-time diagram; through fusion of multi-spectral images of multiple platforms such as satellites and unmanned aerial vehicles and ocean dynamic data and combination of atmospheric correction and wave band resampling, consistency processing and high-precision extraction of multi-source features are realized, and comprehensiveness and reliability of green tide feature recognition are remarkably improved.

The invention relates to the field of shale oil and gas, and discloses a method for identifying a shale oil and gas reservoir dessert, which comprises the following steps: acquiring a core CT image, three-dimensional seismic data and production dynamic data, and carrying out cross-scale preprocessing; constructing a fractional-order non-local seepage field model to represent nano-to-kilometer-level flow characteristics; predicting seepage field parameters through a Lie group symmetry constrained neural network; establishing a cross-scale coupling model of the quantum adsorption effect and the macroscopic seepage law; dynamically updating model parameters based on real-time monitoring data; and executing multi-target collaborative optimization to generate a sweet spot three-dimensional distribution and development scheme. According to the method, a fractal dimension dynamic constraint cross-scale data fusion technology is adopted, the effect of accurate mapping of nanopore and macroscopic fracture network parameters is achieved, and the problem of misalignment of CT scanning and seismic inversion data space registration is solved through pore communication fractal analysis.

The invention relates to the technical field of intelligent fault diagnosis, and discloses a fault diagnosis method for new energy power generation equipment, and the method comprises the steps: obtaining a multi-physical-quantity time dynamic data set; outlier elimination is carried out on the data set, and a deviation degree sequence of each physical quantity is extracted; based on the deviation degree sequence, normalization processing is completed, and a correlation matrix among multiple physical quantities is constructed; generating a preliminary network structure through correlation screening and symmetry completion; calculating a path weight based on the initial network structure and performing topology reconstruction to obtain a final network topology structure; performing deviation propagation analysis according to the final network topology structure, and determining potential fault position distribution; based on the fault position distribution, performing fault grade classification by adopting a support vector machine algorithm, and outputting a fault risk grade label; and in combination with the fault level label and the network topology structure, node risk scoring and area identification are executed, and finally fault positioning is completed. According to the method, accurate positioning of the fault position in a complex system can be realized.

The invention provides a tunnel fan group cooperative control dynamic optimization method and system. According to the method, dynamic data of vehicles in a tunnel are tracked through the Leiyu fusion technology, and a multi-dimensional game parameter set is constructed in combination with edge computing node analysis data and environment monitoring indexes. Based on a dynamic game theory model, fan cooperative response intensity is dynamically adjusted, smoke exhaust efficiency and evacuation channel wind speed constraint are balanced, fan control priority is calculated, differential rotating speed and deflection angle control is driven, and game strategy weight is iteratively optimized after environment feedback data is generated. And realizing rolling time domain control until the vehicle movement and the environment index reach a stable interval. According to the technical scheme provided by the invention, dynamic collaborative optimization of tunnel smoke exhaust and evacuation safety is realized, and the ventilation response efficiency and the evacuation channel safety in an emergency scene are improved.

The invention discloses a data-mechanism dual-drive-based rock burst early warning method and system, and the method comprises the following steps: deploying a multi-modal sensor network to collect coal and rock stratum data, building a rock burst disaster precursor information sample database, providing a rock burst disaster multi-modal data precursor feature recognition algorithm, and carrying out the recognition of rock burst disaster multi-modal data precursor features. Mining the relevance between the multi-modal data and disaster-causing key risk indexes, and establishing a rock burst disaster multi-modal data prediction model; establishing a three-dimensional geological geometric model, fusing a multi-field coupling dynamics constitutive model and a catastrophe criterion, constructing a PINN physical information neural network prediction model of the rock burst disaster, and obtaining a time-space evolution rule of an energy field of a target area; providing a loss function coupling calculation method of a multi-modal data driving sample error and a physical driving control equation residual error, dynamic data and mechanism prediction result weight, comprehensively calculating a risk score, and accurately judging a top disaster danger level.

The invention discloses a data security dynamic evaluation system and a protection method, belongs to the technical field of information security, and is used for solving the problem of terminal equipment access control and behavior risk dynamic collaborative protection. The method comprises the following steps: generating fingerprints through terminal equipment features, verifying authorization, inputting access feature slices into a time sequence model by authorized terminal equipment, generating a trust score based on cosine similarity of a behavior sequence and a prediction sequence, and constructing a Markov model to dynamically select an authentication mode according to the trust score; calculating a risk index by combining network and geographic information, and distributing the risk index to a real or virtual environment; and the unauthorized terminal equipment distributes the virtual environment after registration. Differentiated monitoring is carried out, a time sequence library is established in the virtual environment, and doubt scores are generated through sequence matching; the state deviation of the real environment is calculated through a hidden Markov model, and the risk score is generated by fusing the multi-dimensional features. And based on the result management authority, the suspicion terminal equipment isolates and shrinks the authority, the compliance terminal equipment authenticates and migrates, and the access is regulated and controlled according to the minimum privilege principle and the time window mechanism.

The invention relates to a network real-time synchronization communication system, and relates to the technical field of computer network communication. The system comprises four core modules: a hardware optical IO synchronization module which integrates a multi-wavelength optical transceiver array and an anti-jitter circuit and provides a nanosecond global clock signal, and the synchronization precision is less than or equal to 15ns; the time sequence arrangement module is used for dividing a fixed time window and a dynamic buffer window based on a time sensitive network, supporting a priority preemption mechanism of the SRIO bus and realizing deterministic data transmission; the distributed RTDATA nodes replace a traditional single-board computer, a heterogeneous computing unit and an intelligent storage controller are integrated, and protocol stack processing delay is reduced through the zero copy technology; according to the dynamic multicast routing system, a safely isolated multicast tree is constructed as required, and link load distribution is optimized in combination with machine learning. Through collaborative design of hardware optical synchronization and dynamic data arrangement, the problem of real-time performance reduction caused by multi-node communication concurrence is solved.

The invention relates to a multi-source sea area data intelligent supervision method, system and device and a storage medium, and the method comprises the steps: obtaining sea area geographic space data and historical sea area supervision data of a target sea area, and carrying out the monitoring construction, and obtaining an island monitoring grid; acquiring multi-source monitoring data of the target sea area according to the island monitoring grid, and performing grid data identification to obtain a global pattern spot dynamic data set; extracting a suspected specific target area coordinate of the global pattern spot dynamic data set and calculating a coordinate specific target confidence coefficient; and carrying out range comparison on the suspected specific target area coordinates, and carrying out behavior evaluation in combination with the coordinate specific target confidence to obtain a sea area specific target early warning report.

The invention discloses a dynamic weight correction and path deviation probability prediction method for a vehicle track, which comprises the following steps of: acquiring vehicle data and multi-source dynamic data of the vehicle track in real time through an optical sensor, a radio wave sensor and an inertial navigation sensor, carrying out space-time calibration, extracting obstacle characteristics and road structure characteristics, and predicting the path deviation probability of the vehicle track. The obstacle movement trend is quickly captured through a space-time diagram sequence and a diagram convolutional neural network, real-time obstacle avoidance is realized in combination with dynamic weight adjustment, a driving intention is predicted by using a Bayesian neural network, a trajectory planning strategy is adjusted through weight correction, and uncertainty is reduced by using multi-source data fusion and probabilistic prediction. A closed-loop feedback mechanism continuously optimizes the model, and efficient operation is kept in complex scenes such as intersections and roundabout through real-time weight adjustment and closed-loop feedback, so that the purpose of quickly responding to dynamic obstacles or driving behavior changes can be achieved, and the precision of the path deviation prediction probability is improved.

The invention relates to the field of distributed machine learning, provides a big data-oriented distributed model training optimization method, and solves the problems of load imbalance, low resource utilization rate, large communication overhead, insufficient fault-tolerant efficiency and the like caused by data fragmentation staticization in the prior art. Load balancing is realized through intelligent clustering and overlapping control; the multi-dimensional heterogeneous resource evaluation model monitors calculation / storage / network indexes in real time, and realizes adaptive scheduling in combination with a task prediction and optimization algorithm; the hierarchical gradient synchronization mechanism adopts a tree-shaped parameter server and a dynamic compression technology, so that the communication traffic is reduced by 50%, and the precision loss is less than 0.8%; the incremental checkpoint system uses erasure code coding and parallel recovery to shorten the fault recovery time from 15 minutes to within 2 minutes, the resource utilization rate reaches 85% or above, the convergence speed is improved by 30%-40% in ResNet, BERT and other model training, and the large-scale training efficiency and the system stability are remarkably optimized.

The invention provides a fatigue life prediction and residual life evaluation method for a high-power heavy-duty gearbox, and belongs to the technical field of intelligent operation and maintenance based on computer data processing. Comprising the following steps: acquiring dynamic data in an operation process, and performing multi-scale decomposition to form multi-source multi-scale data; inputting the multi-source multi-scale data into a designed multi-scale fatigue feature extraction module and a health state prediction module to obtain a multi-scale health index sequence and a health state label; establishing a fatigue damage evolution model, introducing the generated health index sequence for self-adaptive updating, outputting a comprehensive damage value, performing staged evaluation of fatigue degradation to obtain a damage label set, and performing multi-scale health index sequence and health state labels as well as the comprehensive damage value and the damage label set to obtain a multi-scale health index sequence and health state labels; inputting into a designed double-source fusion fatigue life prediction model, and outputting residual life prediction quantity; according to the invention, high-precision prediction and residual life evaluation of the fatigue life of the high-power heavy-duty gearbox are realized.

The invention provides an intelligent water conservancy digital twinborn simulation system based on multi-source data, and belongs to the technical field of digital monitoring. Minute-level data acquisition and transmission are realized by constructing a space-air-ground three-dimensional sensing network and combining edge intelligent preprocessing, data acquisition, noise reduction and abnormity identification are realized by utilizing sensors such as millimeter wave radar and laser radar, and the system is used for realizing data acquisition and transmission. Dynamic data fusion and intelligent calibration are carried out, and second-level alignment and credible verification of data are realized by means of a space-time calibration algorithm, Kalman filtering and a block chain evidence storage technology; a hybrid simulation and intelligent decision model is established, a physical model, a machine learning architecture and a dynamic threshold decision tree are adopted, flood routing minute-level prediction and emergency response are realized, and the system improves water conservancy monitoring prediction precision and emergency response efficiency.

The invention relates to the technical field of sensing data security, and discloses a sensing data chip-level dynamic key negotiation method, which comprises the following steps of: acquiring a unique hardware identifier and key parameters of a sensor node, and generating a dynamic key seed matrix; after acquisition is completed, randomly intercepting data segments, performing median filtering and normalization preprocessing, extracting local statistical features and global features to generate a data feature sequence, and splicing the data feature sequence to a seed matrix to obtain a dynamic key generation matrix; a dynamic negotiation key is generated through standardization and SM3 Hash algorithm encryption, and is stored in a cloud and node security unit; during verification, dual verification is realized through hash comparison and plaintext bit-by-bit matching; and setting an environment parameter exception triggering mechanism, and updating the key if accumulative exception exceeds the limit. According to the method, hardware and dynamic data features are fused, and the key security and adaptability are improved.

The invention discloses a traffic large model construction and decision-making method and device based on multi-modal two-way map reasoning, and the method comprises the steps: constructing a multi-modal data set of a text, an image and a track, generating fusion features through spatial-temporal clustering and cross-modal Transform coding, carrying out the two-way map reasoning in combination with a traffic knowledge map, and carrying out the decision-making of the traffic large model. The method comprises the following steps: generating an embedded representation through a forward graph neural network, reversely mapping a decision scheme generated by a language model to a graph to verify consistency, outputting knowledge to enhance embedding, fusing multi-modal features and knowledge embedding by adopting an LoRA multi-task joint fine tuning technology, adapting to traffic field tasks, deploying a real-time inference engine, and carrying out real-time inference on the traffic field. And processing the dynamic data flow through an aging perception attention mechanism, and outputting traffic event identification, path planning and scene question and answer results in parallel. Compared with the prior art, the method has the advantages that the problems of insufficient multi-source heterogeneous data fusion, low knowledge utilization efficiency and poor real-time decision consistency can be solved, and the semantic understanding and decision accuracy of the traffic large model is effectively improved.

The invention relates to the technical field of geological disaster monitoring, and discloses an intelligent early warning method and system for geological disasters in geotechnical engineering, and the system comprises a data collection module, a data processing module, a feature extraction module, an early warning model module, a response execution module and an optimization feedback module. Static geological parameters, dynamic environment parameters and historical disaster data are integrated, a standardized space-time correlation data set is constructed, the limitation of a single data source is broken through, multi-dimensional dynamic response characteristics of a rock-soil body are captured, a reliable data basis is provided for accurate early warning, the rigidity defect of a traditional fixed threshold value is avoided, and the early warning accuracy is improved. The method achieves the self-adaption of the risk early warning sensitivity, reduces the misjudgment and missing judgment caused by environment interference, intercepts a dynamic data stream in real time through a sliding window, calculates the risk mean value and variance, quickly responds to sudden environmental changes such as rainfall sudden change and vibration abnormality, and generates a graded early warning signal.

The invention provides a dynamic self-optimization and adaptive learning method for a large model of a water conservancy system, and the method comprises the steps: data collection and preprocessing, model construction and initialization, real-time monitoring and model evaluation, difference analysis and adaptive adjustment strategy generation, model updating and optimization, continuous learning and knowledge accumulation, etc. According to the method, through dynamic data driving, intelligent difference analysis and self-adaptive optimization, conversion of a water conservancy system model from static passive to dynamic active is realized. Compared with the prior art, the method has accuracy, real-time performance, economical efficiency and sustainability, provides a landing technical path for intelligent upgrading of a water conservancy system, and has wide application potential in the fields of flood control and disaster reduction, agricultural irrigation, clean energy production and the like.

The invention relates to the technical field of oil field databases, and discloses a dynamic data management method, device, equipment and medium for an oil and gas field database, and the method comprises the steps: docking multi-source data of the oil and gas field database, the multi-source data comprising a shaft sensor real-time monitoring data flow, geologic modeling gridding structure data and an equipment management tree coding table; the method comprises the following steps: performing abnormal value cleaning and timestamp alignment on real-time monitoring data flow of a shaft sensor, converting geologic modeling gridding structure data into a reservoir unit set with a coordinate system label, extracting a set membership chain of well IDs and equipment IDs in an equipment management tree-shaped coding table, generating an identifier system with a unified coding format, and storing the identifier system in a database; the identifier system comprises a well identifier, a reservoir unit identifier and a device identifier; generating a structured data pool based on the identifier system; and when the data pool is updated, sending a change instruction to a plurality of subsystems of the oil and gas field database, and updating each subsystem based on the change instruction.

The invention discloses a supply chain intelligent management system based on dynamic collaborative optimization, and relates to the technical field of hotel supply chain management, and the system comprises a supply chain intelligent management platform which is in communication connection with the following modules: a multi-modal data sensing module, a multi-modal data processing module and a multi-modal data processing module. The multi-modal data acquisition module is used for acquiring multi-modal data in a hotel through a LoRaWAN + BLE hybrid sensor network, accessing an external data source and acquiring real-time information through an API (Application Program Interface); and the dynamic demand prediction module captures a time sequence trend through bidirectional LSTM based on an ASTGNN model. According to the method, external dynamic data such as social media public opinions and weather are fused, the multi-modal data analysis technology is combined, the accuracy of demand prediction is remarkably improved, the ASTGNN model is utilized, the time sequence trend and the demand association between the branches are analyzed in combination with bidirectional LSTM and GCN, and the feature weight is dynamically adjusted, so that the demand prediction error rate is greatly reduced, and the demand prediction efficiency is improved. A more reliable demand prediction basis is provided for enterprises, and optimization of inventory management and production plans is facilitated.

The invention relates to the technical field of power distribution network evaluation, and discloses a power distribution network bearing capacity evaluation system based on dynamic correction. The system comprises a dynamic data acquisition module, a multi-dimensional state space construction module, a security domain analysis module, a partition coupling degree calculation module and a bearing capacity evaluation engine module. The dynamic data acquisition module acquires a power injection quantity sequence, a voltage deviation ratio sequence and uncontrollable parameter fluctuation data of each partition node of the power distribution network; a multi-dimensional state space construction module performs dimension raising mapping on the sequence to generate a linearized power flow state space model containing a power-voltage Jacobian matrix; the security domain analysis module corrects the boundary of the model according to uncontrollable parameter fluctuation and generates a dynamic security operation constraint set; the partition coupling degree calculation module quantifies an electrical independence index by means of a spectrum radius; and the bearing capacity evaluation engine constructs a chance constraint optimization model, outputs the photovoltaic maximum accessible capacity of each partition and a safety guarantee supply control strategy set, and improves the evaluation accuracy and practicability.

The invention relates to the technical field of e-commerce data processing, in particular to an e-commerce sales platform background data management method and system. The method comprises the following steps: S1, collecting a commodity dynamic data stream, a user behavior event stream and a promotion strategy stream in real time, and generating a standardized data stream through time sequence alignment; s2, constructing a dynamic coupling data cube; s3, executing a real-time decision: in response to the payment request, selecting an inventory distribution or replacement commodity pushing strategy based on a commodity-user coupling matrix value; in response to the resource overload state, triggering a resource scheduling strategy; and S4, dynamically adjusting calculation parameters of the coupling matrix according to decision execution feedback. By solving the problems of real-time standardization processing of multi-source heterogeneous data and real-time coupling of dynamic inventory and user behaviors, the data processing capacity, inventory distribution efficiency and recommendation accuracy of the platform are remarkably improved.

The invention discloses an intelligent fusion analysis system for multi-source heterogeneous data, and the system comprises a dynamic data collection module which is used for carrying out the data collection, and carrying out the processing of a collected mixed data flow; the semantic alignment module is used for constructing a domain ontology knowledge graph according to a preset scene target and carrying out semantic alignment and coordinate alignment on the collected data; the self-adaptive fusion engine module is used for fusing the collected multi-source heterogeneous data; the trusted computing module integrates a secure multi-party computing protocol and a homomorphic encryption algorithm to realize that data is available and invisible; and the intelligent decision-making module constructs a state action reward model based on reinforcement learning according to a preset scene target, and performs analysis and decision-making by using historical data and data acquired in real time. According to the invention, the capability and effect of data processing and decision support are improved.

The invention provides a method and a system for monitoring and predicting the health state of a storage battery based on multi-modal feature fusion, and relates to the technical field of storage battery monitoring, and the method comprises the steps: obtaining multi-modal data through periodically collecting static data of the storage battery and collecting dynamic data of the storage battery in real time; and then the method is realized through the steps of data monitoring, shared bottom layer feature extraction, SOH prediction, RUL prediction, multi-target joint optimization and the like. According to the method, multi-modal feature data generated by the storage battery can be monitored and analyzed on line, a multi-modal feature fusion and multi-target joint prediction and optimization mechanism is introduced, and SOH and RUL are predicted by adopting independent branches based on an SOH prediction model and an RUL prediction model, so that the health state of the storage battery is predicted; according to the method, automatic analysis on multi-modal mass data of the battery can be realized, the workload is reduced, the analysis efficiency is improved, the accuracy of a prediction result can be improved, and meanwhile, a constructed feature sharing mechanism can reduce the calculation complexity and improve the prediction efficiency.

The invention belongs to the technical field of transportation, and particularly relates to a block chain-based transportation box full-process traceability management method and system, and the method comprises the steps of transportation box identifier generation, initial data chaining, transportation data collection, node verification recording, automatic abnormality response, and full-process traceability. According to the method, the unique block chain encryption address and node signature verification of the transport case are adopted, and the data are linked with a physical lock through Hash chain binding and encryption chips after being linked, so that the whole-process data cannot be tampered; an Internet of Things sensor is adopted to collect temperature and humidity / position data in real time, edge calculation is adopted to filter abnormal values, and intelligent contract threshold alarm and authority freezing are carried out, so that automatic collection and response of dynamic data are realized; a block chain timestamp and a chain structure are adopted to generate a space-time atlas, binding of an operation record and a responsible person signature and a fragmented block chain support cross-regional collaboration, and multi-agent collaboration tracing and accurate positioning are realized.