76 results about "Real time services" patented technology

The embodiment of the invention provides a local life service integration method and device and terminal equipment. The method comprises the following steps: integrating a plurality of heterogeneous local life service providers into a gas station service platform to obtain a local life service platform, and collecting real-time service data in real time; non-oil sales fluctuation of the gas station is predicted based on the real-time service data, and the order quantity and the delivery cycle of upstream and downstream suppliers are dynamically adjusted according to a prediction result; constructing a multi-dimensional data analysis model, predicting user traffic and various service demands in different time periods every day, and configuring a corresponding local life service time window based on a prediction result; dynamically updating callable local life service resources and dynamically deploying gas station human resources based on the order quantity, the delivery cycle and the local life service resource configuration; and generating a personalized service list of the user by using a collaborative filtering algorithm and a reinforcement learning algorithm, interacting with the user in an interaction interface, and providing corresponding local life service resources for the user.

The invention discloses a hotel check-in information management method and system based on a database, and belongs to the technical field of information management. Nodes and edges are defined based on the extracted data, node embedding is generated through a random walk method, a customer classification model is constructed, and classification labels of customers are generated; generating a room distribution result by constructing a linear programming model based on the classified customers in combination with the real-time room related information of the hotel; through short-time Fourier transform and BERT model extraction emotion features and text embedding, constructing an emotion analysis model and a demand recognition model, and generating an emotion score and a demand recognition result; and based on the customer classification, the room allocation result, the emotion analysis result and the demand identification result, using a logic rule to generate a personalized service recommendation list. An intelligent management mode from efficient data extraction, accurate customer classification and dynamic room allocation to real-time service response and recommendation is realized.

The invention discloses a renewable energy penetration power distribution system scheduling method and system based on a block chain, and relates to the technical field of power distribution system scheduling, and the method comprises the following steps: constructing a cross-hierarchy time sequence integrity observation layer, introducing a multi-source high-precision reference signal into a synchronous time service link, and carrying out the multi-source high-precision reference signal; the fingerprint acquisition module is used for acquiring external environment electromagnetic disturbance and atmospheric propagation delay data in real time and generating a phase offset fingerprint corresponding to a time service state; and performing causal decomposition operation based on the phase offset fingerprint, performing noise stripping processing on the acquired electromagnetic disturbance signal, and extracting a real time service error trajectory. Through combination of multi-source time service correction, phase fingerprints and virtual impedance time marks, time self-calibration and cross-node synchronization of the block chain power distribution system are realized, account book wrong sequence caused by time service drift is eliminated, power flow reversal is suppressed through time reversal traction and dynamic constraint, scheduling stability and energy flow consistency are ensured, and the power distribution efficiency is improved. The system safety and the clean energy utilization efficiency are improved.

The invention provides a multi-model intelligent routing method and system with memory and self-adaptive capabilities in the technical field of artificial intelligence and computer system resource scheduling. The method comprises the following steps: S1, executing an authentication operation based on a user request and extracting a user identifier; s2, performing intention recognition on the user request to extract multi-dimensional request features; s3, retrieving user preference information and a candidate model list related to the user request from a vector database based on the user identifier or the multi-dimensional request feature; s4, acquiring real-time service state information of each model in the candidate model list; s5, based on the multi-dimensional request features, the user preference information and the real-time service state information, a target model is selected from the candidate model list through a mixed decision strategy; and S6, the user request is routed to the target model, and a returned response result is received. The method has the advantages that the model scheduling accuracy, the resource utilization efficiency and the user experience are greatly improved.

The invention relates to the technical field of server balancing, in particular to a server dynamic load balancing and resource management method which specifically comprises the following steps: acquiring CPU utilization rates of a plurality of servers in real time, grouping the plurality of servers according to the acquired CPU utilization rates of the plurality of servers, dividing the plurality of servers into priority queues and idle queues, the method comprises the following steps: acquiring a priority queue, arranging a plurality of servers in the priority queue from low to high along the CPU utilization rate, acquiring a real-time service request, executing the acquired service request according to the arrangement sequence of the priority queue, analyzing load fluctuation in a future time period, and performing elastic capacity expansion and contraction operation on the servers according to the acquired load fluctuation. According to the method, the CPU utilization rate of the server is monitored in real time, the server grouping and task allocation strategy is dynamically adjusted, and the future load fluctuation trend is analyzed in combination with historical data, so that intelligent management of server resources is realized, and the problem that part of servers are overloaded and part of servers are idle is effectively avoided.

A system for providing real-time services and functions in an Open Radio Access Network (O-RAN) architecture, includes: a first physical node comprising at least one first processor configured to execute instructions to implement an O-RAN centralized unit (O-CU); at least one second physical node comprising at least one second processor configured to execute instructions to implement: an O-RAN distributed unit (O-DU), and a real-time (RT) RAN Intelligent Controller (RIC) connected to the O-DU via an interface terminating at the RT RIC and having a latency of less than 10 ms; an O-RAN radio unit (O-RU); at least one third physical node comprising at least one third processor configured to execute instructions to implement a non-real-time (Non-RT) RIC operating at a time scale of greater than 1 second; and at least one fourth physical node including at least one fourth processor configured to execute instructions to implement a near-real-time (Near-RT) RIC operating at a time scale of 10 ms to 1 second, wherein the RT RIC is a software platform configured to host applications for controlling at least the O-DU over a real-time control loop with a latency of less than 10 ms.

Apparatuses for non real-time (Non-RT) radio access network intelligence controller (RIC) and Near-RT RIC services for machine learning (ML) model management in an open radio access network (O-RAN) are disclosed. The services include ML model monitoring, getting and putting ML models from and to an AI-ML producer and an AI-ML consumer, and terminating the use of an ML mode. The ML model monitoring includes the AI-ML consumer sending monitoring data to the AI-ML producer and the AI-ML producer processing the monitoring data and taking actions based on the monitoring data. The services may be performed over the AI interface using HTTP.

The invention provides an edge computing network-oriented energy efficiency service deployment and delivery method and system, and relates to the technical field of edge computing, and the method comprises the steps: extracting multi-dimensional service features, carrying out the coding fusion of the extracted multi-dimensional service features, and generating a global context feature representation; in combination with a system load dynamic prediction result, adaptively adjusting an upper layer decision time scale based on the global context feature representation; under the adjusted upper-layer time scale, executing a service deployment decision and a base station dormancy / activation switching decision; under the fine-grained lower-layer time scale, executing a service delivery decision and a resource allocation decision based on the real-time service request; and coordinating the decision process through a double-time-scale hierarchical learning framework. According to the method provided by the invention, the long-term network overhead can be effectively reduced, the dynamic service request can be effectively responded, the processing capability of the time coupling relationship between deployment and delivery weeks is improved, and the system cost is reduced.

Procedures, methods, architectures, apparatuses, systems, devices, and computer program products directed to real-time quality of service (QoS) monitoring and prediction are described. A first QoS monitoring request indicating one or more WTRUs may be received from a server. A second QoS monitoring request may be sent, the second QoS monitoring request indicating a session to be monitored by a WTRU of the one or more WTRUs. A QoS monitoring response may be received, the QoS monitoring response indicating (i) the session to be monitored and (ii) performance measurement information associated with the session to be monitored. First performance measurement assistance information may be sent, based on the performance measurement information associated with the session to be monitored, and QoS report information may be received, the QoS report information indicating one or more QoS metrics measured by the WTRU in the session.

The invention relates to the technical field of artificial intelligence, old-age service and quality monitoring, in particular to an artificial intelligence-driven precise old-age service matching and quality monitoring system, which comprises a service demand analysis module, a service resource matching module, a multi-modal data acquisition module, a topological data analysis module, a service quality monitoring module and a user interaction module, the physical health data and daily activity information of the old people are analyzed by adopting a deep learning technology, and personalized service demand evaluation is realized; intelligent scheduling of service resources is carried out based on a multi-agent system; the service quality manifold is constructed through the topological data analysis technology, the topological distance between the real-time service data and the standard service quality manifold is calculated, whole-process monitoring and early warning of the service quality are achieved, the problems that in existing old-age care services, demand evaluation is not accurate, the resource configuration efficiency is low, and service quality monitoring is not comprehensive are solved, and the service quality monitoring efficiency is improved. The service matching accuracy and the resource utilization efficiency are remarkably improved, and the service abnormity is found in advance and accurately positioned.

Systems and methods are disclosed for ensuring the integrity and security of data exchanges between mainframe systems and external channels. The invention includes a Data Distribution Service (DDS) that receives files from various channels and a Network Data Mover (NDM) that routes these files to a Gatekeeper Landing Zone (GK LZ) based on real-time server health and availability. A File Manager module detects new files, performs sanitization, converts files from EBCDIC to ASCII, and updates the Gatekeeper Control Database (GKC DB). The Validation module ensures data compliance, checks for duplicates, and logs exceptions. A Transaction Manager Resource Adapter (TMRA) splits data into transactions, identifies optimal processing paths, and converts data back to EBCDIC for transactions. The Reconciliation module verifies data accuracy and logs details, while the Backout module reverses transactions if discrepancies are found.

Technologies for providing large data throughput communication in a next generation cellular network (e.g., 5G wireless network, 6G wireless network) are described. The method includes receiving requests from producers and consumers for integration into a communication pathway. Based on producer data output, the producer can be connected to a message bus that can store data from the producer in short-term data store or long-term data store. A data consumer can request data of a data type or from a data producer be provided from the message bus to the data consumer. Based on the immediate usability of the data by the consumer, the consumer can direct to the message bus to store data in a short-term data store for quick transfer to the consumer for real-time data consumption, or to a long-term data store to be stored until the consumer is available for the data.

Systems, methods, and computer readable medium are disclosed for performance of network connection operations. Performance of network connection operations includes accessing a cloud service via a client device, the cloud service containing real-time network information including at least two of a real-time server load for a plurality of servers, real-time server capacity, a customer preference, or geolocation IP; receiving from the cloud service, identities of a plurality of candidate servers from the servers, wherein, based on the real time information, the candidate servers meet criteria for potential establishment of a connection with the client device; transmitting probe packets from the client device to at least some of the candidate servers; receiving responses to the probe packets; determining from the responses, communication characteristics for at least some of the probe packets; selecting a server based on the communication characteristics; and establishing a connection between the client device and the selected server.

The invention relates to an enterprise contact portrait construction and intelligent service system based on big data, constructs an enterprise-level contact resource management system supporting dynamic multi-dimensional portrait generation, privacy compliance calculation and intelligent scene service, and breaks through the technical barriers of data fusion, deep modeling and real-time service. Through the system structure, data collection-portrait-service full-link break-through is realized, and the UEID runs through all modules to ensure that accurate portrait construction and service generation are completed in a data desensitization state; flink stream processing and GNN are used to realize second-level updating of portraits, real-time performance of enterprise contacts is better, output structured results are more referenced, a visual reasoning path is set, and business credibility is enhanced; the user contact end is provided with a closed-loop feedback mechanism, and the setting quantity of the updating module is optimized through feedback of internal personnel of an enterprise.

Disclosed is a method for providing a service using code streaming, which is performed by an edge-device-based service providing system. The method may include: identifying a motion recognition code module for providing a first service to a user based on a user's selection for the first service; recognizing a motion of the user by executing the identified motion recognition code module based on a motion image of the user which is input to a camera of the service providing system; requesting a service code module which is computed in a code execution device of the service providing system to a service providing server to provide the first service; generating real-time service data for the first service based on the recognized user's motion as the service code module is streamed to be received; and outputting the generated service data through a service output device of the service providing system.

The invention provides a processing process adaptive optimization and abnormal condition identification system and method, and the method employs a hierarchical decoupling architecture, a front-end display layer serves as an interaction interface, and the display content is obtained from a core algorithm layer through a data transfer and storage support layer; the core algorithm layer adopts a real-time and non-real-time service separation architecture, and the real-time and non-real-time service separation architecture is decoupled to ensure that real-time processing is not The hardware interaction layer realizes bidirectional data interaction with a machine tool numerical control system and a sensor through adaptive equipment and covers data acquisition and feedback control; the data transfer and storage support layer adopts a Redis publishing and subscribing mechanism, and a plurality of fixed channels are divided to realize data transmission decoupling; the system acquires processing full-dimension data through three-source data acquisition. Each layer is decoupled, data transmission is efficient, monitoring real-time performance, optimization accuracy and abnormity identification reliability in the machining process are effectively improved, and loss caused by abnormal working conditions is reduced.

In some aspects, a verification exchange system transforms consumer data (e.g., employment or income data) from different contributor computing systems to a standardized format and stores this standardized data in a consumer-status verification repository. The verification exchange system can selectively provide portions of the consumer data to authorized client system via a security portal to a public network. For example, the verification exchange system can use standardized consumer data to service verification queries requesting confirmation of employment or income level for consumers. The verification exchange system can do so by ensuring that a verifier system from which the query is received has provided a valid credential. In another example, the verification exchange system can restrict access to the standardized consumer data to accessors that are (i) an originating contributor computing system for the employment or income data or (ii) consumers described by the standardized employment or income data.

The invention relates to an Internet of Things application data processing method and system based on service architecture feedback, and belongs to the technical field of Internet of Things data processing. Equipment identity authentication and access control are realized by constructing a multi-protocol adaptive data access layer; generating a service chain optimization scheme based on reinforcement learning by using a dynamic service orchestration engine in combination with MDP and a real-time service quality index; a federated learning framework and a sliding window feature fusion technology are introduced to realize data privacy protection and feature extraction; and recording a decision basis through the block chain evidence storage module to form a traceable log. According to the invention, multi-protocol dynamic adaptation and equipment fingerprint authentication are innovatively realized, and the access efficiency and security are improved; adaptively reconstructing and optimizing resource allocation through a service chain and enhancing anomaly detection; federal learning and block chain technologies are combined, and privacy protection and decision interpretability are considered. According to the scheme, the dynamic adaptability, safety and interpretability of data processing of the Internet of Things are remarkably improved.

The disclosed techniques improve security testing of remote services. To improve coverage, a complete list of endpoints exposed by a service are identified. In some configurations, route objects that map request URLs to services are analyzed in-memory to identify endpoints. Test coverage is also improved at runtime by enabling a dynamic testing tool to access service endpoints without login credentials. In some configurations, code is injected into a live service process that bypasses an authorization component of a request handling pipeline. In some configurations, testing is improved by locating test clients on the servers being tested.

The invention discloses a service processing method and device, electronic equipment and a storage medium, which are used for solving the technical problem of low accuracy of an existing service fusing and degrading mode. The method comprises the following steps: receiving a service processing request sent by a user; analyzing an interface annotation corresponding to the service processing request, and obtaining a service priority and a service interface type; obtaining a user level of the user from a distributed cache; acquiring service context information from the service processing request according to the service interface type; acquiring a real-time service operation state; determining a service processing decision according to the service priority, the user level, the service context information and the real-time service operation state; the service processing decision is fusing or degrading; and executing the service processing decision.

This invention relates to a pre-caching method based on vehicle location. The method includes: constructing a highly dynamic scenario with different cached content requirements based on heterogeneous network units, and obtaining parameters such as vehicle location; modeling base stations, roadside units, and vehicles as leaders and followers in a Stackelberg game; designing a utility function based on data caching rate and data caching cost calculated from vehicle location and other parameters; obtaining the optimal pre-caching alliance members by solving the established game model; calculating caching efficiency and node utility values ​​under this game model by jointly considering the timeliness, popularity, and data volume of cached content; constructing a multi-dimensional knapsack objective function constrained by the alliance members; and then using dynamic programming to backtrack and solve for the optimal pre-cached content combination and its deployment scheme. This invention can effectively reduce redundant transmission and energy consumption under complex business loads while compressing transmission latency to the theoretically optimal range, providing crucial underlying support for real-time vehicle services.

A method and apparatus are provided for dynamic resource allocation, scheduling and signaling for variable data real time services (RTS) in long term evolution (LTE) systems. Preferably, changes in data rate for uplink RTS traffic are reported to an evolved Node B (eNB) by a UE using layer 1, 2 or 3 signaling. The eNB dynamically allocates physical resources in response to a change in data rate by adding or removing radio blocks currently assigned to the data flow, and the eNB signals the new resource assignment to the UE. In an alternate embodiment, tables stored at the eNB and the UE describe mappings of RTS data rates to physical resources under certain channel conditions, such that the UE uses the table to locally assign physical resources according to changes in UL data rates. Additionally, a method and apparatus for high level configuration of RTS data flows is also presented.

The invention provides a method and device for establishing a space grid system and a remote sensing image processing system. The method belongs to the technical field of remote sensing images, and carries out unified structured management on remote sensing image data by constructing a three-dimensional holographic management framework composed of a time grid, a space grid and a spectrum grid. The space grids adopt global subdivision grids and are endowed with unique codes, the time grids establish indexes according to years, seasons and months, and the spectrum grids are divided according to professional spectrum segments. The corresponding device comprises a data receiving module, a grid coding module, a storage module, a scheduling module and a processing module. The system supports micro-service cluster deployment, visual low-loss compression and open API integration. According to the method, pixel-level scheduling, dynamic processing and real-time service release of the remote sensing data are realized, the bottlenecks that the traditional file-level management efficiency is low, the preprocessing is tedious, and the instant service demand of massive high-resolution images is difficult to meet are overcome, and the data management, processing and service efficiency is remarkably improved.

The application relates to the technical field of information recognition, and discloses a car owner information intelligent recognition system for car washing services; the system comprises a data query module, a first portrait fusion module, a second portrait construction module and a package recommendation module; the data query module extracts comprehensive car owner information from available data; the first portrait fusion module fuses user labels into a user car washing portrait; the second portrait construction module constructs a real-time service portrait with annotations; and the package recommendation module sequentially recommends service packages to a user end; the car owner information intelligent recognition system can integrate historical car washing service information and current individual demand service information of a user into one, realizes double-portrait construction of the user in a past time period and a current time period, avoids the problem that the car owner information is limited in comprehensiveness when a portrait is constructed once, and ensures that the screened service package can be highly matched with the car owner information.

The invention relates to the technical field of data processing and management, and discloses a department data resource scheduling management method and system.The method comprises the steps that a data node actively broadcasts data reputation beacons containing endogenous reputation points and authoritative update timestamps of the data node, and a data demander terminal preliminarily sorts the received beacons and sends the data reputation beacons to the data demander terminal; and finally determining a unique authoritative data source and initiating a data request by combining with a result of performing real-time service health degree detection on the authoritative candidate data source. An administrative approval mode in traditional data scheduling is converted into a cooperation mode that data departments actively maintain data quality and service stability in order to preferentially adopt data of the data departments, and therefore internal power of data sharing is stimulated.

The invention discloses a campus teacher-student intelligent agent development and service platform, and the platform comprises an access layer which is used for accessing the platform through a plurality of terminal devices, and supporting an MQTT protocol and an HTTP protocol for communication; the interface layer is used for providing a standardized API interface and realizing front and rear end separation and data exchange service layer, is realized based on a. NET6 micro-service architecture, and comprises an agent management module, an API gateway and a data aggregation module; the data layer is used for storing and managing business data, statistical data and log data; the storage layer comprises a relational database, a cache system and a file storage and is used for persisting business data and course data; the platform integrates campus multi-system data through a middle platform architecture, and supports low-code agent development, containerized deployment and real-time service collaboration. Different using requirements of teachers and students can be better met.

The application discloses a kind of double strategy adaptive cache replacement method based on popularity prediction, comprising: collecting equipment request data;With time slot as unit statistics content popularity and constructs model dataset;Build content popularity prediction model based on sequence model Seq2Seq, carry out model training and the structure and weight of the trained popularity prediction model are saved to file respectively.The double strategy adaptive cache replacement method based on popularity prediction provided by the application is for the scene of industrial edge node cache, for the characteristics that data popularity changes with time, excavates the heat trend and tidal nature law of data, and proposes double strategy adaptive cache replacement algorithm and method based on Seq2Seq in combination with traditional cache replacement algorithm and deep learning method.When the prediction model encounters the request sequence of unvisited access popularity, the adaptability of the method successfully improves the cache hit rate of system, reduces network delay, and guarantees the real-time service performance of industrial edge network.

1. Name of the Design Product: Graphical User Interface for Intelligent Operation and Maintenance of Electromechanical Equipment for Electronic Devices. 2. Use of the Design Product: An electronic device. 3. Design Key Points of the Design Product: Lies in the content of the graphical user interface. 4. Picture or Photo that Best Illustrates the Design Key Points: Front View. 5. Use of the Graphical User Interface: For visual display of cabinets in the subway station machine room, and can visually monitor the overall operation of the 3D visual machine room, as well as various data and alarm information of the cabinets. 6. Description of the Variation Status of the Graphical User Interface: The front view shows the lines, names of each station, and alarm information. Click on the "Buzheng" station icon in the front view to jump to the interface variation status diagram 1; click on the right arrow icon in the upper right corner of the interface variation status diagram 1 and select "Cabinet Information" to jump to the interface variation status diagram 2; click on the picture of the real-time server 1 shown inside the cabinet in the interface variation status diagram 2 to jump to the interface variation status diagram 3.

A person (the user) interested in determining the authenticity of another person (the subject) enters the subject's name and other information into a mobile app or associated website. The subject information is then sent to a server program to automatically perform iterative searches for correlated online information. The server program also executes an analysis engine to perform unique heuristic and pattern matching analysis on the subject data and search results. The analysis engine is composed of a rules-based component, a machine learning pattern discovery component, and a machine learning pattern detection component. Search and analytics results are aggregated for presentation to the user in the app and website UI, organized in a categorical manner based on topic area. The result set consists of identified subject attributes that are potential areas of concern, subject attributes aligning to certain predictive trends, and subject information found on the internet.

The application belongs to the technical field of wireless communication, and discloses a self-adaptive switching and fusion method based on Bluetooth intercom and cloud intercom, which comprises the following steps: creating a Mesh voice network; constructing a shadow chat network in a cloud network; detecting the online state of all Mesh nodes in real time, and updating the online state of the Mesh nodes in a server in real time; when a Mesh node is out of connection, other Mesh nodes not out of connection report the out-of-connection information of the Mesh node to the server, and simultaneously upload voice data of the node to the server; the server mixes the received voice data, and distributes the mixed voice data to the out-of-connection Mesh node; and the voice data between other Mesh nodes not out of connection in the Mesh network is still transmitted through the Mesh voice network. The method can smoothly switch the voice transmission channel, and keep the real-time performance and the integrity of voice data transmission.