1481 results about "Data aggregator" patented technology

An Al -based energy edge platform is provided herein with a wide range of features, components and capabilities for management and improvement of legacy infrastructure, coordination, and orchestration with distributed systems to support important use cases for a range of enterprises. An Al -based energy edge platform may include a graph neural network including a set of nodes respectively representing at least one distributed energy resource (DER) and a set of edges respectively interconnecting the set of nodes, wherein each edge represents at least one energy - related feature among at least two nodes of the set of nodes. The platform may incorporate emerging technologies to enable ecosystem and individual energy edge node efficiencies, agility, engagement, and profitability. Embodiments may forecast, plan for, and manage the demand and utilization of energy in greater distributed environments. Embodiments may employ intelligent provisioning, data aggregation, and analytics to leverage energy market connection, communication, and transaction enablement platforms.

The invention discloses a distributed slope monitoring system based on edge cloud collaborative intelligent adaptive decision. The distributed slope monitoring system comprises a plurality of intelligent sensing unit ISU nodes deployed at key positions of a slope and a data processing and intelligent analysis unit, each intelligent sensing unit ISU node is used for transmitting data to an edge gateway in an ad hoc network wireless mode or directly uploading the data to a cloud platform and carrying out slope monitoring based on a local adaptive monitoring strategy; the data processing and intelligent analysis unit comprises an edge intelligent module, a cloud gateway and an edge gateway; the edge gateway serves as a middle layer and is used for protocol conversion, data aggregation, temporary storage and preliminary analysis; the cloud gateway is used for providing calculation and storage resources, training a more complex AI model based on historical and real-time data, and performing pattern recognition, prediction analysis and anomaly detection tasks; the edge intelligent module comprises a plurality of edge computing nodes and is internally provided with a lightweight AI reasoning unit, and the edge intelligent module is arranged on the edge side and used for implementing edge intelligent processing.

The invention belongs to the field of intelligent edge systems and privacy enhancement computing, and particularly relates to a privacy protection-oriented robot large model cloud edge end collaborative reasoning and federated learning system, which comprises a cloud server layer used for deploying a large-scale pre-training model and executing complex reasoning and global federated learning coordination; the edge calculation layer is used for deploying an intermediate layer model and executing local data aggregation, privacy protection processing and intermediate feature calculation; the terminal equipment layer is used for deploying a lightweight model and executing data acquisition, primary processing and lightweight reasoning; the federated learning framework is used for optimizing the model; the privacy protection module is used for integrating data localization, differential privacy, homomorphic encryption, secure multi-party computing and a block chain verification mechanism; the adaptive allocation module is used for dynamically adjusting computing resources. According to the method, the problems of privacy leakage risk, computing resource limitation, network delay, insufficient data isolation and the like of the traditional AI service in a robot scene are solved, and efficient privacy protection and data security isolation are realized.

The invention discloses a high-efficiency computer software system resource scheduling method, system and device and a medium, and the method comprises the steps: deploying a hardware counter in an operating system, and monitoring the CPU utilization rate, memory occupation, disk I / O, network bandwidth, the use condition of data aggregation system resources and the resource demand of each process or thread in real time; based on monitoring data, analyzing a current resource supply and demand state by adopting an advanced algorithm, and predicting a future resource demand trend; according to an analysis result, a resource allocation strategy is dynamically adjusted, it is ensured that a high-priority task obtains needed resources in time, and meanwhile fairness of a low-priority task is considered; data feedback after scheduling execution is collected, a scheduling algorithm is continuously optimized, and the scheduling efficiency is improved; the system, the equipment and the medium are used for realizing the high-efficiency computer software system resource scheduling method. The problems that an existing computer system is low in resource utilization rate, poor in key task stability and high in operation and maintenance cost are solved.

The invention provides a health state assessment method and system for a rescue helicopter, and relates to the technical field of aviation engineering, and the method comprises the steps: 1, building an integrated data aggregation platform, synchronously collecting flight operation parameters, maintenance files, abnormal event records and surrounding environment monitoring indexes, and carrying out the maintenance of the maintenance files; comprehensively covering key dimensions of a rotor device, a power transmission unit, a propelling device and sea climate conditions to form a comprehensive original data set; and step 2, based on the comprehensive original data set, applying a self-adaptive parameter adjustment mechanism to perform comprehensive calculation on an accumulated operation time length index, a periodic component residual efficiency value, a core health score, an abnormal repeated occurrence probability, typical problem association strength, an emergency severity degree and a marine climate erosion factor, and generating a comprehensive state index. The method accurately evaluates the health state of the rescue helicopter, reduces the flight risk, and improves the rescue decision and execution efficiency.

The invention discloses a railway station facility full life cycle information query method based on digital twinning. The method comprises the steps that a unified facility object with a unique logic identifier is generated based on multi-source heterogeneous data aggregation; constructing a facility tense version model containing time sequence nodes by using an event-driven rule; in combination with an engineering constraint knowledge base and statistical characteristics, evaluating physical feasibility and statistical association strength between events, introducing life cycle stage risk factors, and constructing a facility full life cycle causal chain model; and based on the model, responding to the explanatory query, executing a constrained path search in the facility full life cycle causal chain model, and generating a life cycle query result set containing a causal path. According to the method, the pseudo-correlation problem generated by pure data mining can be effectively solved, the cumulative influence of early-stage risks in the design and construction stage on later-stage operation is quantified, and accurate tracing and explanation of cross-stage fault causes are realized.

The invention discloses a cross-regional vehicle-mounted network switching method and system based on digital twinning, and a medium. The method comprises the following steps: constructing a cloud side end digital twinning network architecture; acquiring vehicle state data and traffic environment sensing data through the terminal sensing layer; carrying out data aggregation on the vehicle state data and the traffic environment perception data through edge computing nodes to obtain a local digital twinborn model; meteorological data, operator base station data and a local digital twinborn model are integrated through a cloud digital twinborn center to obtain a global digital twinborn model, and the traffic congestion condition and the network congestion condition of each road network region in a future time period are predicted according to the global digital twinborn model. Determining a network switching strategy according to the traffic congestion condition and the network congestion condition; and performing a network switching decision on the target vehicle through the edge computing node. According to the method, the timeliness and accuracy of cross-regional vehicle-mounted network switching are improved, so that the stability and reliability of the vehicle-mounted network are improved, and the method can be applied to the technical field of the Internet of Vehicles.

The invention discloses a distributed time sequence library data management method supporting cold and hot data level-to-level management, and relates to the technical field of computer databases, comprising: receiving a write-in request of time sequence data, and executing preliminary write-in data aggregation and sorting; dynamically identifying data cold and hot attributes based on a multi-dimensional data cold and hot degree calculation model in combination with a write-in behavior and an access behavior of time series data; according to the cold and hot attribute recognition result, in combination with the hierarchy boundary of self-adaptive division, automatic hierarchical storage of the time series data is executed; based on a cold and hot data dynamic migration and scheduling mechanism, according to the access frequency and time decay characteristics of the time series data, dynamically adjusting the storage hierarchy of the time series data, and executing a data migration task; and constructing a hierarchical index system adaptive to different cold and hot attribute time series data, and combining query frequency dynamic identification and index structure automatic upgrading. By adopting a cold and hot data automatic identification and hierarchical storage mechanism, the overall performance and the resource utilization rate of the system are improved.

The invention provides a financial statement generation method and system based on cloud computing, and the method comprises the steps: collecting multi-source data of an internal business system of an enterprise through a centralized data integration platform based on a cloud computing environment, carrying out the classified storage, and constructing a data set; verifying the data set, and obtaining key index fields related to financial statement generation in the verified data set; performing grouping calculation on the key index fields by adopting a data aggregation algorithm to form a financial data summarization result; dynamically mapping the financial data summarization result by adopting a report generation engine, and matching with a preset financial report template to obtain a report data framework; and performing secondary verification and encryption on the report data framework to obtain a final financial report. According to the technical scheme, automation, real-time performance and unmanned performance of financial statement generation are realized through full-link innovation of edge intelligent acquisition, streaming processing, incremental aggregation and zero-trust security.

The invention discloses a power equipment monitoring and early warning system based on intelligent patrol, and relates to the technical field of power system intelligent monitoring and safety early warning, the system fuses visible light, infrared, ultraviolet, acoustics, vibration and other multi-source signals, innovatively encodes multi-modal features in a complex manner, realizes high-speed and high-dimensional feature extraction through an optical convolution chip, and realizes high-precision and high-dimensional feature extraction. And the early perception capability of hidden dangers is greatly improved. The adaptive complex neural network is introduced, the model structure and parameter dynamic optimization, self-learning and self-evolution functions are achieved, the fault recognition accuracy is remarkably improved, and false alarm and missing alarm are reduced. The system supports cloud side end distributed collaboration, data real-time convergence and multi-scene visualization, has hierarchical deployment and online adaptive capabilities, and adapts to intelligent operation and maintenance requirements of multiple types of power stations. The system has the advantages of high performance, high intelligence, easiness in integration, expandability and the like, is prominent in innovativeness, and can be widely applied to intelligent monitoring and operation and maintenance in electric power and related fields.

The invention provides a method and system for achieving elevator energy feedback data communication. The method comprises the steps that multi-modal data are collected, and fault features are extracted, encrypted and packaged; and after decryption, phase deviation of elevator operation and power grid voltage is calculated by utilizing an algorithm, a current phase is synchronously fed back, and a feedback instruction is generated. Uploading the harmonic suppression parameter to an energy center, calculating a demand, generating a region-level feedback strategy, dynamically distributing a feedback power upper limit, synchronizing the harmonic suppression parameter, and obtaining region-level feedback data. And aggregating feedback data, and setting a global optimization target, including cost minimization, feedback maximization and carbon emission reduction. And calculating feedback energy and carbon integral according to a target adjustment algorithm, automatically settling to an operator account, and adjusting the target of the next period. Through an algorithm and an optimization mechanism, the problem of energy waste of a brake resistor in a traditional feedback technology is solved, the feedback efficiency is improved, and energy waste is reduced.

The invention discloses a multi-protocol channel data aggregation method based on 5G communication, and relates to the technical field of heterogeneous network data processing under the fusion of 5G communication and edge computing, and the method comprises the steps: constructing a protocol adaptive conversion engine, recognizing an input protocol type through learning, and automatically selecting a conversion strategy and a data packaging format. An edge-cloud collaborative intelligent scheduling architecture is introduced, a data aggregation task is dynamically determined, a dynamic channel aggregation algorithm of a 5G network state and protocol characteristics is designed, and a transmission path and an aggregation strategy are intelligently selected. By constructing a protocol adaptive conversion engine, an edge-cloud collaborative intelligent scheduling architecture and a dynamic channel aggregation algorithm fusing 5G network state and protocol characteristics, automatic identification, efficient aggregation and optimized transmission of multi-protocol data are realized, dynamic scheduling of tasks is realized in combination with an SDN / NFV technology, and the dynamic scheduling efficiency of the multi-protocol data is improved. The channel selection mechanism based on the utility function comprehensively considers the network state and protocol characteristics, and guarantees the transmission quality of high-priority and time delay sensitive data.

The invention relates to the technical field of intelligent traffic and energy-saving illumination, in particular to an intelligent management and control method and system for energy-saving illumination of a highway tunnel and a computer program, and is suitable for an intelligent illumination control system for improving tunnel driving safety and energy efficiency of an illumination system. Dynamic response and fine energy consumption adjustment of tunnel lighting are realized through out-of-tunnel brightness multi-source fusion prediction, entrance section feedforward, closed-loop dimming control, a segmented intelligent lighting strategy based on vehicle detection and an in-tunnel brightness closed-loop and stepless dimming mechanism. The system has a multi-sensor redundancy mechanism and can automatically return to a safety mode when equipment fails, so that the continuity and the safety of illumination are guaranteed; and meanwhile, data summarization and energy-saving statistics are combined, control parameters are optimized in cooperation with a digital twinning or self-learning algorithm, the energy-saving effect and the operation and maintenance efficiency are further improved, and the purposes of traffic safety and low-carbon operation are considered.

The invention provides a ship comprehensive energy efficiency improvement platform and a data management and application method thereof, and belongs to the technical field of ship comprehensive energy efficiency optimization and big data. The ship comprehensive energy efficiency improvement platform comprises a ship end, a cloud end and a shore end, wherein the ship end realizes equipment data acquisition, edge processing and scheduling through a data acquisition module, an edge calculation module and a data scheduling module; the cloud end completes data aggregation, isolation and distributed storage through the data aggregation module, the data isolation module and the distributed storage module; and the shore end realizes data standard management, algorithm version control and micro-service business application through the data center layer, the algorithm center layer and the business center layer. The ship energy efficiency comprehensive improvement platform provides comprehensive management and optimization for various energy-saving devices and various ship energy efficiency improvement software, and optimization and improvement of ship comprehensive energy efficiency are achieved.

The invention relates to the technical field of intelligent traffic, and discloses a vehicle and road cloud data processing method and system based on an edge computing center, and the method comprises the steps: deploying a sensing module, collecting vehicle, road and environment data, and carrying out the standardization and alignment; constructing a three-dimensional tensor, and calculating a local gradient to reflect data aggregation; evaluating a node collaboration demand, and constructing a graph structure and a collaboration weight based on a gradient; constructing an optimization objective function, and balancing delay, resources and task importance scheduling; when the gradient exceeds a threshold value, nodes are selected, and state saving and task migration are executed; when communication is abnormal, autonomous reasoning processing is completed based on a local model and features; the system comprises a sensing module, a tensor modeling module, a resource collaborative evaluation module, a task scheduling module, a task migration module and an autonomous reasoning module. According to the method, the three-dimensional tensor structure is introduced to express the multi-dimensional information of the vehicle, the roadside equipment and the environment sensing terminal, the multi-dimensional information is fused and modeled, and the purpose of automatically extracting the area aggregation situation is achieved.

The invention provides a geographic information data dynamic encryption method and system based on spatial-temporal characteristics. The method comprises the following steps: dynamically identifying a hot spot region by analyzing a timestamp, geographic coordinates and frequency data of a user access request, and generating a multi-dimensional access weight matrix fusing a time attenuation factor and a spatial distance factor; and dividing data block granularity according to the access popularity value in the matrix, and performing spatial segmentation on the geographic information data. A dynamic key seed is generated in combination with a space-time factor, a temporary encryption key is obtained through discrete logarithm operation, homomorphic encryption is implemented, and a space-time feature identifier is embedded. And during cross-mechanism aggregation, the authority is verified through the identifier, ciphertext superposition is completed, and secure data aggregation is realized. According to the method, through spatio-temporal dynamic encryption and authority verification, secure sharing and efficient aggregation of multi-mechanism geographic information data are realized.

The invention discloses a federal learning data aggregation method for predicting traffic flow, a medium and equipment, and the method comprises the steps: setting a relation between a cloud server end and a vehicle end, and building a corresponding system; collecting vehicle data through the system for local training and data preprocessing; the vehicle side encrypts the preprocessed data through a homomorphic encryption technology and uploads the data to the cloud server side; carrying out weight distribution on the encrypted and uploaded data based on a kernel ridge regression technology; the encrypted update quantity is aggregated based on the adjusted weight, so that the update contribution to the global model is more similar, and the more reliable vehicle update quantity plays a more important role in the prediction process; and the aggregated encryption update quantity is decrypted, pre-training model updating is completed, finally, the server in the training wheel screens the weights of all vehicles, a part of vehicles with too low weights are eliminated, certain residual data similarity is ensured, and data aggregation of residual data is easier.

Computerized systems and methods are disclosed for automating Configure to Order (CTO) and Quote to Order (QTO) processes. Methods include receiving user inputs for desired product configurations, retrieving corresponding data from a bill of materials database, and calculating optimized pricing through intelligent rules based on real-time market data. Automated quotes are generated and transferred to orders in a vendor system, selected based on pre-set criteria like vendor reputation and delivery time. Validation steps reduce errors, and real-time reports are generated. The system integrates a Real-Time Data Mesh for data aggregation, a Single Pane of Glass User Interface for user interactions, and Advanced Analytics and Machine Learning Modules for implementing rule-based and learning algorithms. The system is accessible across various devices and standardizes data for uniform consumption, while also employing machine learning models to continually optimize processes. Notifications are sent to users upon successful execution of orders or completion of quotes.

The invention discloses a three-dimensional digital twin modeling method, system, equipment and medium taking a reservoir as an object, and relates to the technical field of reservoir monitoring, and the method comprises the steps: constructing a three-dimensional space model facing a reservoir entity, and forming a visual simulation environment; accessing multi-source data related to reservoir operation, and establishing a unified data aggregation and management mechanism; integrating a real-time operation state in the three-dimensional space model, and dynamically mapping perception information and business activities of the reservoir; and based on a set analysis model and a determination rule, carrying out intelligent processing on the reservoir operation data, identifying potential risks and generating an early warning result. According to the invention, by establishing a multi-type analysis model and a judgment rule system, real-time calculation and risk identification are carried out on key indexes, intelligent early warning of risks such as emergencies, trend anomalies and historical extreme value overrun is realized, and the risk prevention and control capability of the reservoir is effectively improved.

The invention discloses an automatic driving control method and system based on multi-source data aggregation, and particularly relates to the technical field of automatic driving. The method comprises the following steps: acquiring sensor data, extracting an instantaneous relative frequency drift feature and a sensor data fusion drift error feature, evaluating the accuracy of sensor data time synchronization based on an adaptive weighted fusion algorithm, and dynamically adjusting the confidence coefficient weight of each piece of sensor data. The recurrent neural network is used for predicting the future movement track of the obstacle on the data with high time synchronization accuracy, in addition, through a confidence feedback mechanism, the sensor time synchronization accuracy and the environment perception data quality are combined, the data fusion error is dynamically corrected, the control precision and the driving stability are improved, and the driving stability is improved. According to the method, the problem of data mismatching caused by time synchronization errors can be effectively reduced, the sensing accuracy, trajectory prediction capability and safety of an automatic driving system are improved, and the vehicle has more stable and intelligent decision-making capability in a complex traffic environment.

The invention relates to the technical field of data transmission of the Internet of Things, in particular to a self-adaptive data compression and transmission method of a low-power-consumption wide-area Internet of Things. According to the method, a three-layer collaborative architecture comprising an equipment layer, a fog computing layer and a cloud computing layer is constructed, data preprocessing and feature analysis are performed on the equipment layer, and data types, entropy values and repeatability information are extracted; the fog calculation layer selects an optimal compression algorithm based on a multi-dimensional decision engine, and reduces redundancy through spatial-temporal correlation analysis and data aggregation; the cloud computing layer collects compression performance data, adopts reinforcement learning and federal learning to train a global optimization model, and dynamically issues strategy parameters; and the fog node adaptively adjusts a compression strategy in combination with the system state to realize the optimal balance between the compression ratio and the reconstruction precision. The method is suitable for field deployment environments with limited electric quantity and network, has the advantages of low power consumption, high efficiency, strong adaptability and the like, and can be widely applied to Internet of Things scenes such as remote monitoring, smart energy and the like.

In some examples, there may be provided systems, devices, and methods for using data from disparate databases to determine characteristics of a set of users within an organizational unit and generate customized user interface elements for display within a user interface.

The invention discloses a vehicle-mounted electrical control unit (ECU) debugging simulation system and method based on digital twinning. The system is composed of a data aggregation and time reference module, a consistency evaluation module, a synchronization and correction module, a semantic communication and scheduling module, an arbitration and security debugging module, and a cross-layer adaptive degradation and execution and iteration module. Channel-level time delay is estimated through a unified time reference, a consistency difference spectrum containing amplitude, time sequence, event and derivative components is constructed, confidence is given, time delay compensation and event-level alignment are calculated, and deterministic and priority scheduling is implemented according to semantic benefits; actions such as parameter recharge, message injection, task rhythm perturbation and environmental stimulation are generated and limited under the constraint of a security invariant set, control right soft handover and gating are realized based on confidence, and grading and degrading are performed when the confidence is low or the constraint is triggered; millisecond-level bidirectional collaborative debugging of twin and physical ECUs is realized, and consistency convergence, safety and bandwidth time arrival are improved.

The invention relates to the technical field of capacity evaluation and redistribution, in particular to a 4G baseband processing capacity dynamic expansion method and system, which comprises the following steps: acquiring a scheduling cycle connection request and rate, generating a load identifier in combination with a processing utilization rate trend, cross-positioning a high-density target block matching activation path, and carrying out dynamic expansion on the 4G baseband processing capacity. Extracting a frequency band request frequency and a flow analysis bearing level, processing interference intensity sorting frequency resources, screening frequency points, and splicing to generate a path configuration result. According to the invention, the connection request number and the data transmission rate in the 4G baseband scheduling period are collected to accurately identify the increased load section, the potential load rising pressure can be perceived in advance, the target block is crossly positioned according to the connection request position distribution and the data aggregation degree, and the processing resources are accurately bound to the high-demand path. The precision of spectrum resource scheduling is enhanced through priority ordering, and communication quality guarantee and continuous supply of processing resources in a high-concurrency access scene are supported.

The invention discloses a data aggregation method based on multi-modal features, which comprises the following steps: collecting multi-modal data, preprocessing the multi-modal data, extracting features according to modals, dividing the features into high-dimensional data, medium-dimensional data and low-dimensional data, and positioning neighbor points by using a ball tree algorithm for the high-dimensional data; the neighborhood range of the medium-dimensional data is dynamically adjusted based on the distribution density; a neighborhood of low-dimensional data is calculated through Euclidean distance, then the low-dimensional data is mapped to a low-dimensional space by means of local linear embedding, then the low-dimensional data is traversed, discrete data value frequency and continuous data probability density are counted, marginal probability is calculated in combination with information entropy, data aggregation weight is determined accordingly, and finally features after dimension reduction are spliced according to a high-level sequence, a middle-level sequence and a low-level sequence. And probability normalization is carried out in each hierarchical block to generate aggregated comprehensive features. According to the method, through multi-dimensional differentiation processing and weight calculation based on data distribution, aggregation of multi-modal features is realized, and feature complementarity and accuracy are improved.

The invention relates to a water-light storage area power grid optimization method based on federated and layered multi-agent reinforcement learning. The method comprises the steps of constructing a federated and layered multi-agent model, defining states, actions and reward spaces of layered agents, implementing federated reinforcement learning offline training with privacy enhancement, deploying the model and executing online distributed optimization. Through the federal learning framework, it is ensured that private data of all the energy main bodies are locally calculated, the privacy leakage risk caused by data collection in a traditional centralized method is avoided, and a technical basis is provided for building collaborative trust among multiple main bodies. Meanwhile, the designed hierarchical intelligent agent architecture decouples a complex power grid system into subproblems with clear hierarchies, and when the scale of the system is expanded, new energy units can be accessed in a modular mode, so that the overall maintenance and upgrading difficulty of the system is reduced, and the expandability of the system is improved.

Provided herein is an AI-based energy edge platform with a wide range of features, components, and capabilities for managing and improving traditional infrastructure and coordinating with a distributed system to support important use cases for a series of enterprises. The platform may employ emerging technologies to improve the efficiency, flexibility, participation and profitability of an ecosystem and a single energy edge node. Embodiments may predict, plan, and manage energy demand and utilization in a larger distributed environment. Embodiments may use AI, IoT, and techniques to more efficiently filter, process, and move data across communication networks. Embodiments of the platform may utilize an energy market connection, communication, and transaction support platform. Embodiments may employ intelligent provisioning, data summarization, and analysis.

The invention relates to a multi-stage extension method and device for an IO-Link slave station, computer equipment and a storage medium. The method comprises the following steps: constructing a chained encapsulation path for process data transmission in an IO-Link first-stage slave station; the first-stage slave station receives the output process data sent by the master station and transmits data segments, which do not belong to the ontology, in the output process data to the lower-stage slave station in an unvarnished transmission manner; the last-stage slave station collects input process data and uploads the input process data to the upper-stage slave station, and each middle slave station receives the input process data sent by the lower-stage slave station, splices the input process data of the stage to the input process data and uploads the input process data to the first-stage slave station stage by stage; the first-level slave station uploads a complete data frame to the master station when completing input data aggregation; and when any slave station detects that the lower-level module is offline, initiating re-scanning according to pre-stored structure mapping information, and reconstructing a data encapsulation path and recovering a forwarding process after identifying the corresponding module. The method and the device have the effect of improving the inter-site communication efficiency.

The invention relates to the technical field of zero-trust and security collaborative optimization, in particular to a security collaborative optimization method for data aggregation of an intelligent network connection vehicle group under a zero-trust architecture, which comprises the following steps: S1, acquiring original data and generating a data packet through each vehicle in the intelligent network connection vehicle group; s2, carrying out data anomaly detection on the vehicle through a road side unit; s3, a credible evaluation model is obtained from the cloud through the road side unit, and the real-time credibility of each vehicle is evaluated through the credible evaluation model based on the data anomaly detection result of the vehicle and the historical credibility; s4, generating a data communication strategy of each vehicle according to the real-time credibility and the global communication resource use condition, and realizing data communication of each vehicle through the data communication strategy; and S5, storing the original data and the real-time credibility of each vehicle through the cloud, and regularly training the credibility evaluation model. According to the method, the communication efficiency, the collaboration and the resource utilization efficiency of the intelligent network connection vehicle group under the zero-trust architecture can be improved.

The invention discloses a method for aggregating ocean buoy data, which comprises the following steps of: establishing a space-time correlation model according to the spatial distribution density of buoy clusters and the dynamic characteristics of an ocean environment, and adaptively dividing buoy groups with a data compression cooperative relationship; performing a data-related compression operation within the buoy group; constructing a transmission priority dynamic sorting engine, and calculating a dynamic weight coefficient; performing hierarchical packaging processing on the differential coding data, distributing enhanced forward error correction coding resources for high-priority data, and reserving redundant transmission time slots; the method comprises the following steps: receiving a hierarchical data packet through a low-orbit satellite and executing on-satellite preprocessing, reconstructing an original data stream by using a distributed decoder, and meanwhile, fusing multi-dimensional space-time related data to generate an aggregated data product; and finally, the aggregated data product is transmitted to a ground data center through a satellite downlink. According to the invention, uplink data volume is reduced, disaster monitoring timeliness is improved, ground station processing load is reduced, and system environment adaptability is enhanced.