1247 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.

A web browsing application can implement a vector database to store session data. A web browsing application can automatically embed loaded web content into an embedded data store that maintains session data for a number of web sessions. A user can query the web browser using simple instructions. The web browser can interpret the instructions and use the embedded data store to quickly search across multiple modalities of embedded data to retrieve relevant results. The web browser can use machine-learned models to answer queries or perform other tasks by performing vector-based queries over the content of visited web data.

The invention discloses a quasi-brittle material damage field inversion method based on a physical information neural network. The method comprises the steps of data acquisition and processing; presetting initial damage field data, and taking the modulus of each unit in the initial damage field data as an independent to-be-inverted parameter; taking the position information of each node in the space as the input of a neural network, processing through a hidden layer of the neural network, and taking displacement data corresponding to the position information as network output data; constructing a loss function of a neural network according to the preprocessed displacement data, network output data and a mechanical law; and performing training optimization on the initial damage field data by using a loss function of a minimized neural network, optimizing hyper-parameters of the neural network by adjusting weights of control item loss, boundary item loss and data item loss until a preset convergence condition is met, and determining target damage field data. According to the method, the dependence of a neural network model on a data set is remarkably reduced, and the inversion result is ensured to have relatively high physical interpretability.

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.

Systems and methods are for mitigating hindsight bias related to designing and using artificial intelligence models for outlier events. More specifically, systems and methods for the use of synthetic data in the training and / or validation of model predictions in order to prevent overfitting and generate predictions that attempt to predict the unpredictable.

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.

Please delete the Abstract of the Disclosure, and replace it with the following: In the current 3GPP standard(s), there is no mechanism or procedure for Key issue. This disclosure provides a solution to this key issue. A method for a User Plane Function (UPF) includes receiving, from a core network node, at least one of Users Equipment ID (UE ID), Data Network Name (DNN), Single-Network Slice Selection Assistance Information (S-NSSAI) and information related to UE Route Selection Policy rule (URSP). The method includes checking whether user data for a User Equipment (UE) using a User Equipment (UE) session with the DNN and S-NSSAI matches the information related to UE Route Selection Policy rule (URSP) or not. The method includes sending to the core network node a result of the checking.

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, which relates to the technical field of network security protection, discloses a photovoltaic power station 5G network security protection device comprising the following components: a data acquisition module, an intelligent fusion analysis and verification system and a protection mechanism triggering module. According to the invention, by integrating an intelligent fusion analysis and verification system, multi-dimensional data such as photovoltaic power station operation data, 5G network characteristic parameters, external environment factors and personnel operation behaviors can be comprehensively collected and deeply analyzed, and a potential association relationship and a mutual influence mechanism among different factors can be accurately mined; meanwhile, a dynamic association rule self-adaptive updating mechanism can adjust the association rule in real time according to newly collected data and a mined new association relationship, the accuracy and timeliness of an analysis result are ensured, the credibility of the data is improved, and network security risks can be found and coped with in time.

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.

An edge enabler server of an edge data network is configured to receive a verification request comprising an edge enabler client identification (EEC ID), wherein the EEC ID uniquely identifies an edge enabler client (EEC), determine whether the EEC ID is an authorized EEC ID and provide a verification response based on whether the EEC ID is authorized.

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.

The invention provides an abnormal traffic management and control method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of traffic processing. The management and control method comprises the following steps: on the basis of traffic characteristic data of network traffic, identifying abnormal traffic, an abnormal type of the abnormal traffic and a service type of the network traffic, the network traffic comprising the abnormal traffic and normal traffic; predicting a self-adaptive bandwidth occupation demand of the abnormal traffic based on the traffic feature data of the network traffic, the service type of the network traffic and the abnormal type of the abnormal traffic; and adjusting the current bandwidth occupation of the abnormal traffic based on the self-adaptive bandwidth occupation demand of the abnormal traffic so as to control the abnormal traffic. The invention aims to at least solve the problems that in the prior art, high-volatility network requirements cannot be fully met, flexible bandwidth optimization capability is lacked, and the aspect of refining flow management is still insufficient. The method is suitable for abnormal traffic identification and control scenes.

The invention discloses a data sharing system and method based on a smart contract, relates to the technical field of computer application, and realizes dynamic risk prevention and control and self-optimization closed loop of data sharing through deep integration of the smart contract and real-time monitoring. A three-dimensional feature vector is constructed by collecting the calling frequency, the response duration, the number of failures and the user address in real time, and the low / medium / high risk level is dynamically judged in combination with a preset rule base; user authorization parameters are automatically adjusted according to the risk level, a current limiting / fusing instruction is generated, and a data gateway limits a high-risk user request or switches a standby node in real time; historical data are periodically analyzed, a risk judgment reference value and a proportionality coefficient are dynamically updated, a rule base is optimized and is subjected to uplink storage, and a strategy is ensured to continuously adapt to system load changes. According to the scheme, the data sharing security, the system stability and the resource utilization rate are remarkably improved, and an efficient and intelligent risk prevention and control solution is provided for distributed data sharing.

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.

The current disclosure provides methods and systems that can manage the dynamic-uncertainty induced by resources of decentralized data networks, in order to ensure the stability and sustainability of task-oriented automated operations, such as operations conducted through intelligent agents. In contrast to the state-of-the-art object-based processing, the methods and systems enable object-aware processing, to ensure the establishment of stable and sustainable associations with physical and digital web-objects, while enabling those objects to be processed dynamically with full adaptability in response to contextual and structural alterations in real-time. Thus, the disclosure provides—both human and machine—users with the ability to develop and deploy modular systems capable of engaging any conceivable physical or digital interaction with the resources of a data network, such as dynamically linking and manipulating clusters of complex web-objects to execute complex tasks in complex and dynamic web environments—most importantly—stably and sustainably.

Various aspects of the present disclosure relate to predictive application context relocation. One apparatus is configured to receive a predicted route for a user equipment (“UE”), receive one or more data analytics parameters for at least one of a radio access network, a core network, or an edge data network associated with the predicted route of the UE, determine at least one predictive trigger action for remapping an application client from a first edge application server of a first edge data network to a second edge application server of a second edge data network based on the predicted route for the UE and the one or more data analytics parameters, and determine an application context relocation for the application client based on the at least one trigger action.

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 the technical field of network security analysis, in particular to a big data network signal security early warning system. The system comprises a network signal big data stream transmission module, a network signal ratio difference detection module, a network signal division result safety early warning module and a network signal abnormity safety response module. Data acquisition equipment can be deployed at an entrance and an exit of each network equipment, and network signal data real-time acquisition and big data stream type transmission conversion can be carried out, so that a network signal big data stream can be obtained; carrying out time sequence flow size and access frequency statistical analysis on the network signal big data flow, and carrying out network signal ratio difference detection division and abnormal safety early warning processing at the same time to generate a network signal abnormal safety early warning signal; and uploading the network signal abnormity safety early warning signal to a cloud platform to carry out abnormity safety response processing so as to generate a network signal abnormity safety response protection strategy. According to the invention, network signal abnormity can be efficiently monitored in real time and early warning can be provided.

The invention discloses a dynamic switching distributed file storage system based on an elastic data network, and the system comprises a multi-network architecture module which is used for deploying a plurality of independent network channels, and each network channel independently transmits data; the client elastic data network access module is used for accessing and storing data according to the data network and the priority, and automatically switching and recovering when the network fails; the intelligent data transmission module is used for selecting an optimal data transmission path; the data recovery module is used for calling the intelligent data transmission module and selecting an optimal path according to the network use condition; the network fault automatic detection and recovery module is used for detecting and processing network channel or path abnormity, detecting master and salve when a fault occurs, acquiring network health state data, and switching master and slave according to the network health state data; the data security processing module is used for encryption and verification; and the cache optimization module is used for collecting data access frequency data and heat dynamic data.

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.

The embodiment of the present application provides a method, device and system for obtaining an optical network performance optimization solution and diagnosing faults. The method includes: collecting operation logs, performance indicators and alarm information of various types of equipment in the optical network according to a pre-set data collection frequency, as well as collecting optical network topology data, network-level performance data, equipment configuration information and computer room environment data; cleaning and preprocessing all collected data; inputting all processed data into a pre-trained performance optimization model, a pre-built expert system and / or a pre-trained fault prediction model, generating an optical network performance optimization solution through the performance optimization model, and predicting the potential fault type, possible fault occurrence time and fault location of the optical network through the expert system and / or the fault prediction model. The embodiment of the present application can not only obtain a better optical network performance optimization solution, but also improve the fault location speed and accuracy.

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.