1029 results about "Data Web" patented technology

The invention discloses a supply chain warehouse management system and method based on AI digital intelligence, and relates to the technical field of intelligent warehousing, and the method comprises the steps that a data processing unit collects supply chain system data through a data processing module, and constructs an associated data network containing goods information, shipping space information and order information based on the supply chain system data; the goods allocation unit executes a goods allocation algorithm according to the associated data network, extracts an association score based on goods information, analyzes a collaborative ex-warehouse frequency, and calculates an affinity matrix; the storage scheme unit generates a storage position scheme based on the affinity matrix; the job scheduling unit constructs a scheduling model, performs task optimization and outputs a task allocation sequence; the order picking navigation unit is provided with an order picking path planning module, presents a path and guidance, collects execution data, and optimizes the system through analysis. The problems that in traditional warehouse management, the goods allocation efficiency is low, task scheduling is not optimized, and the goods picking path is unreasonable are solved.

The invention relates to the technical field of data processing, can be applied to business scenes such as financial science and technology and medical health, and discloses a self-adaptive data processing optimization method, device and equipment and a medium, and the method comprises the steps: collecting operation data, host performance data, network state data and historical task data of a target data source, and constructing an analysis model in combination with recovery parameters and strategy preference, predicting a task load state, resource consumption and execution duration, generating a task execution strategy, completing task scheduling and execution monitoring, and collecting execution feedback data for iterative optimization of the analysis model. According to the method, an analysis model is constructed by fusing multi-source system data and historical task information, a task execution strategy is generated in combination with a dynamic prediction result and a strategy weight, intelligent task scheduling and process monitoring are realized, and feedback data is used for model iterative optimization. The task execution efficiency is improved, the resource use rationalization is realized, and the model adaptive capability is enhanced.

The invention relates to the technical field of communication management, and provides a communication base station traffic prediction management system based on deep learning, which comprises a multi-source data acquisition module used for acquiring base station space-time traffic data, user behavior data, network state data, external influence factors and data set slice service parameters; the spatio-temporal feature processing module is used for performing spatio-temporal alignment, noise filtering and slice feature coding on the multi-source data; and the dynamic model prediction module is used for constructing a multi-modal prediction network of a space-time Transform, a graph neural network and a slice exclusive sub-model. A base station association graph based on geographical distance and service correlation is constructed, spatial features are extracted through a multilayer graph convolutional network, slice features and spatial-temporal features are fused by using a gating mechanism, a Pareto optimal strategy is generated by using a multi-objective optimization algorithm, and a strategy library is updated in combination with a forgetting factor, so that the probability of forgetting is reduced. The response time of the system in an abnormal scene is shortened, and the strategy optimization efficiency is improved.

The invention provides an infrastructure security analysis method and system based on a large model, and the method comprises the following steps: collecting multi-source heterogeneous data of an infrastructure, including physical equipment sensor data, network flow data, operating system logs and external threat intelligence; converting the multi-source data into standardized feature vectors of a unified time axis through a space-time alignment module; and inputting the standardized feature vector into the fine-tuned large language model for semantic understanding, and generating an equipment behavior semantic description and threat intention analysis result. According to the method provided by the invention, a large language model and reinforcement learning collaborative architecture is provided, and the large language model is improved, so that end-to-end mapping from multi-source data to threat semantics is realized, and the attack detection rate is improved.

The invention relates to the technical field of smart power grids, and discloses a source-grid load-storage cooperation method, which comprises the following steps: carrying out data fusion on real-time acquired source-side distributed power supply output fluctuation data, grid-side topology constraint parameters, load-side time-sharing load demand data and storage-side charge state data to obtain a multi-dimensional system state set; performing short-medium-long term multi-scale coupling prediction on the multi-dimensional system state set to obtain a source load dynamic balance prediction curve; constructing a multi-target collaborative optimization function according to the power fluctuation interval, and generating a target adjustment instruction set based on the multi-target collaborative optimization function; updating the target running state based on the adjustment instruction set; based on the deviation degree of the updated operation state and the prediction state, correcting a weight parameter of the multi-target collaborative optimization function, and based on the corrected multi-target collaborative optimization function, performing source-network-load-storage collaborative regulation and control on the target power platform; according to the invention, the efficiency of source-network load-storage collaboration can be improved.

There is provided a method for providing energy data analytics for a data network associated with a wireless communication system, the method comprising receiving at least one input parameter associated with energy usage corresponding to the data network, deriving energy data analytics for the data network based on the received at least one input parameter, and sending an energy data analytics output parameter based on the derived energy data analytics, wherein the energy data analytics output parameter comprises an energy usage statistic or energy usage prediction for the data network.

The invention belongs to the technical field of logistics information, and discloses a multimodal transport trusted data space construction method based on privacy calculation and a block chain. The method comprises the following steps: deploying a modular intelligent data gateway at a core node of a multimodal transport line, unifying the format of multi-source heterogeneous original data, and chaining a hash value; after the multi-source heterogeneous data is subjected to customization processing, key metadata attributes of all the data are registered and linked in real time; constructing a core ontology model, and establishing a cross-domain semantic interoperation capability; establishing a privacy calculation and block chain collaborative dual-channel architecture, and cooperatively training an ETA prediction model in a local data isolation environment by adopting transverse federal learning to ensure that original trajectory data is not out of a domain; constructing a dispute arbitration and credible verification system, and realizing an arbitration process of judicial verification when a transportation dispute occurs; data minimization disclosure and compliance sharing are achieved through dynamic access control. According to the method, multimodal transport data can be available and invisible, and the whole operation process can be audited.

Embodiments of the present disclosure includes methods for a client in an edge data network. Such methods include obtaining an initial access credential for a server in the edge data network, before accessing the server; providing the initial access credential to the server for authentication of the client; and obtaining an updated access credential for the server based on expiration of one of the following: the initial access credential, or a further access credential provided by the server; and providing the updated access credential to the server for authentication of the client. Other embodiments include complementary methods for a server and for a credential provider, as well as UEs, network nodes, and / or computing systems configured to perform such methods.

The invention relates to the technical field of garbage collection and transportation full scenes, and particularly discloses a garbage collection and transportation full scene data monitoring system which comprises a full scene data acquisition module, a dynamic collection and transportation queue generation module, a collection and transportation vehicle distribution module, a transfer station distribution feedback module and a database. Garbage collection full scene data is collected in real time through sensor groups of garbage cans, garbage collection vehicles and transfer stations, traffic road condition information is fused to construct a data network, regional demands are predicted based on historical data, a dynamic priority queue is generated in combination with a garbage amount exceeding threshold proportion, and a path is optimized through capacity matching and real-time road conditions, so that the real-time garbage collection is realized. And the congestion compensation duration is calculated, it is ensured that classified garbage is precisely matched with vehicle partitions, the transfer station capacity is dynamically calculated, and overload tasks are automatically distributed to adjacent stations. According to the method, the problems of data islands, static scheduling and mixed loading of a traditional system are solved, the resource efficiency and environmental protection benefits are remarkably improved, and the clearance efficiency and the service quality are improved.

The invention discloses a substation dispatching data network security communication method based on quantum tunnel encryption, relates to the technical field of large-scale power grid security assurance, and aims to solve the problems of endpoint security loss, boundary solidification, weak quantum threat resistance and inflexible deployment of a dispatching data network. According to the method, the establishment of a secure session and the encapsulation, encryption and restoration of data are realized by integrating quantum key distribution and a post quantum cryptographic operation module through quantum secure communication terminals deployed in a dispatching master station and a transformer substation. According to the method, application layer encryption and signature are carried out on telecontrol protocol data, a double-layer encryption tunnel is constructed, and transmission of various physical network media is supported. By means of the scheme, end-to-end protection is achieved, quantum attacks are effectively resisted, double encryption is provided, and networking flexibility and smooth evolution are improved.

A method of processing a protocol data unit (PDU) set for a data network includes steps of (a) receiving a first PDU packet of the PDU set, (b) receiving, subsequent to reception of the first PDU packet, a second PDU packet of the PDU set, (c) determining, after receiving the first and second PDU packets, a congestion status of the data network for the PDU set, (d) controlling, based on the determined congestion status, a handling state of the second PDU packet to match a handling state of the first PDU packet, and (e) transmitting the first PDU packet and the controlled second PDU packet to a destination receiver of the data network.

System and methods are provided for achieving data standardization and normalization through an Agnostic Data Format (ADF) architecture. ADFs systems and processes provide a transformative bridge, enabling disparate data sources to converge into a unified and standardized format within the Real-Time Data Mesh (RTDM) framework. This dynamic process utilizes Artificial Intelligence (AI) and Machine Learning (ML) algorithms to interpret and align diverse data attributes. The ADF management system, integrated into a dynamic event-driven architecture, allows vendors to interact with RTDM by translating and standardizing their data. The synchronized data integrates canonically, incorporating real-time updates and collaborative decision-making across the distribution platform. This innovative approach enhances operational efficiency, enables data-driven decision-making, and provides users improved ability to use data within the distribution ecosystem.

A service provider may deploy a security threat detection and mitigation platform in a multi-tenant virtualization environment that includes pluggable data collection, data analysis, and response components. The data analysis components may apply machine learning techniques to generate (based on training data sets) and refine (based on subsequently received data sets and feedback about the resulting classifications) predictors configured to detect particular types of security threats, such as denial of service attacks, botnets, scans, or remote desktop attacks. A data collection layer may collect, filter, organize, and curate network packet traffic data, network packet header data, or other information emitted by computing instances or applications executing on them, and provide the curated data as streams to the analysis layer. A response layer may automatically take action in response to threat detections (which may be overridden by an administrator) and may store classification data for subsequent analysis, feedback, and predictor refinement.

The invention relates to an AI-based service anomaly detection method, and belongs to the technical field of data processing. Comprising the following steps: acquiring runtime data of a production environment, including service layer data, hardware layer data, network layer data and link layer data; supplementing the collected data, carrying out classified marking on data information, and defining a hierarchical relationship among the data; sending the data to an AI model, carrying out correlation analysis through a time dimension and a space dimension, and carrying out link analysis; the AI large model updates the knowledge graph and generates the model according to each operation and maintenance; and displaying the fault knowledge graph by using a graphical front end. According to the AI-based service anomaly detection method provided by the invention, multi-dimensional data are comprehensively collected and correlation analysis is carried out, so that rapid positioning of a fault is realized. The fault discovery time is greatly shortened through the real-time detection capability, and the fault processing efficiency is further improved through incremental updating and intelligent optimization of the knowledge graph.

The invention provides a printing color consistency automatic calibration and management system and method, and the method comprises the steps: collecting a current environment temperature and humidity ink matching parameter density curve data dot increase compensation parameter in real time through a state detection device, and forming a current state data set; performing similarity analysis on the reference data set and the current state data set by adopting a matching degree evaluation algorithm, and judging a relationship between a matching degree numerical value and a preset threshold value; adjusting and correcting the calibration reference value and the color control parameter in the reference data set according to the offset compensation set to obtain a compensated and adjusted parameter set; on the basis of the compensated and adjusted parameter set, generating update code data by combining a current time identifier and an operator identifier, and forming new two-dimensional code information; and outputting the new two-dimensional code information to a subsequent work order carrier by adopting a code generation module, and establishing a parameter transmission tracing chain.

Techniques are disclosed for sending downloadable software packages that may be executed to perform tests of a network device without requiring the network device to update firmware. In one example, the network device obtains a software package comprising instructions for simulating a network instance and stores the software package in a volatile memory of the network device. The network device simulates, based on the instructions, the network instance to obtain data indicative of a performance of the network instance. The network device sends, via a communication channel established with a network management system (NMS) based on the software package, the data indicative of the performance of the network instance. In some examples, the NMS uses the data indicative of the performance of the network instance to identify a root cause of a failure of the network device or a remedial action to be performed upon the network device.

Aspects of the disclosure relate to providing a secure large language model data platform. The secure large language model uses a machine-learning large language model and gateway to prevent attacks and unauthorized access to enterprise-managed information and resources. The secure large language model may utilize pre-enrollment at a secure gateway providing a unique identification to each client. A private / public key pair may be generated and stored in the secure gateway database and large language model respectively. In some embodiments, a unique anonymization rule set may be generated and used for each client. Threat actors cannot query the large language model directly based on the pre-enrollment process. Unauthorized requests cannot be decrypted by the large language model due to missing paired keys.

The invention relates to the technical field of telecommunication. By providing the multi-domain strategy collaborative dynamic decision optimization method, system, equipment and medium, the method comprises the following steps: extracting cross-domain common characteristics of network layer topology, equipment state and alarm information, retaining differential characteristics of a transmission network and a data network, and generating a uniform resource data set; on the basis of the unified data set, a global optimization strategy is generated through linkage decision making of a three-level collaboration module; matching the strategy into an atomic instruction set executable by a heterogeneous network manager by utilizing semantic ontology mapping and syntax tree matching; and according to the instruction execution feedback data, dynamically adjusting the strategy weight and carrying out iterative optimization, and generating an updated global strategy, so as to achieve the technical effects of improving the cross-domain resource perception precision, enhancing the hierarchical decision collaborative efficiency and reducing the multi-manufacturer instruction conversion error rate.

A method for monitoring a data network in a motor vehicle is provided, wherein a switch device is operated in the data network, in which physical ports for receiving and for transmitting data packets are interconnected via a switch circuit. A network processor reads out detection data in monitoring positions of the data packets and forms pseudo target determination data for an associative memory therefrom, which actually is to process target determination data for a target port determination, and it is examined if certain action data results, which signalizes a necessity of a detection action, by inputting the pseudo target determination data into the associative memory.

The invention provides a network card vibration detection method, device and equipment and a storage medium, and the method comprises the steps: deploying a plurality of groups of three-axis acceleration sensor arrays in different function regions of a network card, and collecting vibration data when the network card is not powered on; after the network card is powered on, the substrate management controller reads vibration data, vibration spectrum characteristics are extracted by using a time-frequency analysis method, and dangerous frequency components are identified in combination with server heat dissipation system characteristics and network card physical parameters; based on the vibration data and the danger frequency component, vibration source positioning and classification are carried out by combining electromagnetic characteristic changes before and after the network card is powered on, and a vibration threat level is determined; and selecting and executing corresponding protection measures according to the vibration source information and the real-time communication state of the network card. According to the method, the problems of a spectrum analysis blind area and vibration source recognition fuzziness are solved, and network card data and communication stability are effectively protected through targeted protection measures.

The invention relates to the crossing field of supply chain management and block chain technologies, discloses a block chain-driven circulating packaging ownership tracing and reuse optimization system, and solves the problems that existing circulating packaging ownership tracing is not credible, the reuse rate is low, data sharing is barrier and settlement is low in efficiency. The system comprises a sensing layer, a network layer, a block chain layer and an application layer, wherein the sensing layer collects and packages full life cycle data; the network layer carries out preprocessing and 5G data transmission; the block chain layer adopts an alliance chain to deploy ownership change, full life cycle records and privacy protection contracts; the application layer is internally provided with a core unit for XGBoost demand prediction and genetic algorithm scheduling, and functions of ownership tracing, multiplexing scheduling and the like are realized. Implementation shows that the ownership tracing credibility is 100%, the reuse rate is improved to 90% or above, the settlement time is shortened from 3 days to 1.5 hours, and the method is suitable for circulating packaging scenes such as cold chains.

The present invention provides a data network, a data ingestion system and a method of data ingestion in the data network for a supply chain management enterprise application. The data network includes one or more data objects of different data types received from different data sources structured on multiple distinct architecture, connected to each other for executing multiple functions in the enterprise application.

The invention belongs to the technical field of data fusion and intelligent decision-making, and particularly discloses an enterprise operation risk intelligent decision-making method and system based on multi-source heterogeneous data fusion, and the method comprises the steps: crawling multi-source heterogeneous data of each department of an enterprise, constructing a unified data network, comprehensively reflecting the operation condition of the enterprise, and carrying out the intelligent decision-making of the enterprise operation risk. Potential risks in enterprise operation can be identified more accurately, and management comprehensiveness of enterprise operation data is realized. A dynamic resource allocation mechanism is constructed by taking continuous operation risk factors of all departments of an enterprise as key references, and multi-source heterogeneous data are automatically processed and analyzed, so that real-time monitoring and early warning of the operation risk of the enterprise are realized, resource allocation of the enterprise is optimized, and meanwhile, the enterprise can be ensured to quickly adapt to market changes; by establishing a plurality of operation progress systems of each business optimization department and analyzing the optimization expectation, targeted business optimization suggestions can be provided for each department of an enterprise.

The invention relates to the technical field of chemical production monitoring, and discloses a cloud computing-based chemical production monitoring system, which is characterized in that an on-site data acquisition module is used for acquiring chemical production data in real time and carrying out filtering and denoising processing to obtain preprocessed data; the network transmission module is used for encrypting the transmitted data and reducing the data transmission quantity by adopting a data compression technology; the cloud computing platform module is used for dispersedly storing the data on a plurality of storage nodes by utilizing a distributed storage technology, classifying the data, analyzing the data in real time by adopting a streaming computing technology, detecting the abnormal condition of the data, and immediately triggering an alarm mechanism when the abnormal condition exceeds a preset safety threshold value; the monitoring application module is used for displaying various parameters and equipment operation states, and providing decision suggestions for production optimization in combination with a chemical production process and equipment; the production efficiency can be improved, the production cost can be reduced, the product quality can be improved, and the market competitiveness of enterprises can be enhanced.

The invention relates to the field of data encryption transmission, in particular to a big data network security data transmission method. Setting a plurality of encryption algorithms and corresponding decryption algorithms associated with the data volume interval, the sequence sequence, the time interval and the number sequence, selecting the encryption algorithm to encrypt the plaintext based on the data volume of the plaintext to be sent to obtain an encrypted ciphertext, and splitting the encrypted ciphertext into a plurality of ciphertext segments based on the number sequence associated with the encryption algorithm, and encrypting each ciphertext segment through a public key, sending the encrypted ciphertext segments to a disguise receiving end according to a sequence and a time interval, selecting a decryption algorithm associated with the matched data volume interval, calling the ciphertext segments based on the sequence and the time interval associated with the decryption algorithm, decrypting through a private key, and combining to obtain an encrypted ciphertext, and the plaintext is obtained after the encrypted text is decrypted through the decryption algorithm, so that the security of data transmission in the network is improved.

The invention discloses an urban rainstorm disaster dynamic risk assessment method and system based on multi-source data fusion, and relates to the technical field of industrial data processing and big data, and the method comprises the steps: deploying an edge data network at a distributed source end, and determining urban dynamic data; constructing a simplified feature space, adding a time axial direction, and updating the simplified feature space; a multi-dimensional index system is introduced, a dynamic evaluation module is constructed, rainstorm disaster risk evaluation is executed, and a risk evaluation result is determined; executing interface display of the dynamic risk; the multi-dimensional index system comprises disaster-inducing factor danger, disaster-pregnancy environment exposure, disaster-bearing body vulnerability and disaster prevention and reduction capability. The technical problems that traditional urban rainstorm disaster risk assessment mainly depends on a single data source or a simple assessment model, multi-aspect factors are difficult to comprehensively consider and a multi-source data fusion effective mechanism is lacked are solved, accurate and comprehensive assessment of urban rainstorm disaster risks is achieved, and the urban rainstorm disaster risk assessment efficiency is improved. And scientific decision support is provided for urban emergency management.

A system and method of encoding and decoding using generative AI for compression of video stream uses the system for encoding and decoding using generative AI for compression of video stream that comprises a camera, an encoder comprising generative artificial intelligence (AI) software operative in the encoder to encode video data, a transmitter, a receiver, a processor, a decoder, and a visual display operatively in communication with the decoder. Using the system, video data obtained from the camera are provided to the encoder; the generative artificial intelligence (AI) software encodes the video data at encoder to produce a compressed data set; the compressed data set are transmitted in a kilobit per second range (kbps-range) bandwidth at a low bandwidth using transmitter and receiver and, subsequently, from the receiver to a distant site via a further data network; and the compressed data set decoded at the distant site.

A system for event-driven distributed data network analysis, the system includes: a multitude of domain data nodes, each comprising a stream storage, a multitude of transformation pods, and a product API, with each domain data node ingesting heterogeneous event streams, validating schema compliance, performing enrichments, and making data products available under versioned schema contracts; each domain data node further comprises a hardware-based edge intelligence appliance comprising an enclosure, a compute module with heterogeneous processing units selected from CPUs, GPUs, and TPUs, a secure enclave module for confidential execution of transformation code on encrypted data, a protocol-structured stream storage medium supporting multi-level retention, and industrial I / O interfaces configured for direct interaction with sensors, programmable logic controllers, and cloud-native services; an event mesh backbone processor configured to connect the multitude of domain data nodes via a publish-subscribe architecture, with the backbone guaranteeing exactly one-time delivery, partition-level sorting, and geo-replicated durability; a Contextual Intelligence Engine coupled with the Event Mesh Backbone processor, the engine comprising a semantic graph memory and a Streaming Graph Reasoner configured to map raw events into an ontology, perform graph joins in real time, and recognize composite causal patterns across multiple domain data nodes; a governance and policy control unit integrated into both the data and control layers. This layer enforces zero-trust security, role-based access control, real-time provenance tracking, and regulatory compliance; and An actuation interface coupled with the contextual intelligence engine and the governance layer. The actuation interface is configured to generate control commands for operational technology devices and digital workflows, thus closing the loop from data acquisition to autonomous decision-making.

A method of managing capabilities of a media streaming network using at least one processor, including receiving a capability request for media streaming capabilities of an edge data network (EDN); determining the media streaming capabilities of the EDN; transmitting a capability response based on the determined media streaming capabilities; receiving a media processing workflow request based on the capability response; establishing a media streaming session according to the media processing workflow request; and streaming media content based on the media streaming session.

The invention discloses an intelligent network technology service management system and method based on big data, and particularly relates to the technical field of intelligent network technology services, and the system comprises a data collection module which is used for configuring a distributed crawler, an API interface and a sensor to obtain multi-source heterogeneous data from a user terminal, network equipment and a third-party platform, comprising user behavior data, network flow data, equipment state data and service quality logs; the data storage module is used for storing data by adopting a fusion framework of a column database and a time sequence database; a column database and a time sequence database are fused, a partition storage strategy is matched, unified storage of structured / unstructured data is achieved, cross-database ETL batch processing bottlenecks are eliminated, a data processing module integrates a streaming computing engine and a batch processing framework, high-concurrency real-time analysis is supported, query delay is reduced from the hour level to the second level, and the query efficiency is improved. And automatically triggering computing node / bandwidth elastic expansion when responding to burst traffic.