51 results about "Adaptive services" patented technology

The invention provides an inter-micro-service dynamic secure communication method and device based on chaos cryptography. The method comprises the steps that a service provider instance registers a public key and chaos initial parameters to a service registration center; before calling, the service consumer instance acquires a public key and a chaos initial parameter of a target service provider instance from a service registration center; the service consumer instance performs key negotiation with the acquired public key by using a private key of the service consumer instance to determine a shared key; performing key derivation on the shared key and the chaotic initial parameter to generate an initial key; respectively using the initial keys to initialize the chaotic system so as to generate synchronous encryption key streams; and the two communication parties perform real-time stream encryption and decryption on the communication data between the micro-services by using the encryption key stream. The unpredictability of a chaotic system and modern cryptography can be combined, and a micro-service design mode is deeply integrated, so that a lightweight and high-security communication security mechanism which does not need to share a key in advance and can be adaptive to dynamic change of service is realized.

The invention discloses an information recommendation method and device, a storage medium and electronic equipment, and relates to the technical field of recommendation systems.The method comprises the steps that in response to terminal interaction operation, input data is obtained; filling the input data into a first preset prompt word template according to a preset format to form first fusion data containing prompt guide information; inputting the first fusion data into a prediction model, and outputting an intention label and an associated extended keyword set through intention analysis and keyword extension processing; performing semantic matching and preference matching based on the intention tag, the extended keyword set and locally stored chat robot Chatt related data to obtain a candidate Chatt set; filling a second preset cue word template with the first fusion data and the candidate Chatpot set according to a preset format to form second fusion data containing an adaptive service scene; and inputting the second fusion data into the prediction model, and determining Chatpot recommendation information.

The invention relates to the technical field of internet risk collaborative management, and discloses an internet data risk early warning and collaborative management system and method.The internet data risk early warning and collaborative management method comprises the steps that a service grid enhanced federal learning unified framework is constructed, and a tetrad structure is formed; a seamless integrated architecture of federal learning and a service grid is realized; developing a federal contribution evaluation mechanism based on a service level agreement; a self-adaptive service discovery and dynamic combination technology is realized, and an optimal service combination scheme is generated; constructing a federated aggregation algorithm of data quality perception, and introducing a quality weight factor optimization model for training; designing a multi-level cache strategy; according to the invention, through the fusion of federal learning and the micro-service architecture, the purpose of keeping efficient collaboration while protecting data privacy is realized, the original data of each participant is always kept locally, only necessary model parameters are shared, and the privacy leakage risk is reduced.

The invention relates to the technical field of information processing service, and discloses a multi-scene information intelligent processing and self-adaptive service construction method and system. The system comprises a scene information acquisition device, a feature evolution calculation engine, a core feature decision device, a self-adaptive regulation and control strategy generator, a scene feature reconstruction device and a service strategy mapping actuator. The scene information acquisition device acquires a multi-modal original scene data stream and analyzes internal attributes; the feature evolution calculation engine carries out dynamic mode decomposition on the data to obtain a multi-scale feature component and extract a non-linear measure to obtain a complexity evolution index; the core feature decision-making device screens scene essential core features; the self-adaptive regulation and control strategy generator integrates multiple factors to generate personalized optimal regulation and control frequency; the scene feature reconstruction device synthesizes a feature evolution sequence and decomposes a slowly-varying mode and a rapidly-varying mode; and the service strategy mapping executor judges the scene category and generates a self-adaptive service control instruction, so that efficient processing of multi-scene information and accurate service construction are realized.

A method may include, obtaining, by one or more processors, transaction logs of a database server captured in a predetermined time period, identifying, by the one or more processors, a rate of change of data within the transaction logs, classifying, by the one or more processors, portions of the time period according to the rate of change of data within the portions of the time period, and setting, by the one or more processors, a service level agreement (SLA) for each portion of the time period according to the classification of each portion.

This invention discloses a personalized AI avatar construction and group digital experience asset inheritance system based on a private data domain, relating to the fields of artificial intelligence and digital asset management. This invention uses a user-isolated private data domain as its operating platform to construct a personalized AI avatar that highly matches the user's personality, thinking, and behavioral habits for an individual user or corresponding group. Through continuous collection and structured processing of user interaction and decision-making data, it accumulates storable, iterative, and transferable digital experience assets within the group. The system supports the authorized integration and scheduling of external compliant experience assets under secure isolation, and employs a dual-drive engine of "general knowledge + personalized experience" for intelligent decision-making and scenario-based responses. Based on anticipating potential user needs, it outputs decision references and adaptive services, while relying on the private data domain to achieve end-to-end data isolation, encrypted storage, and controllable permissions. This invention effectively solves the technical problems of insufficient personalization in existing AI avatars, the inability to scalably inherit digital experience, and the single, passive interaction mode. It can be widely applied to scenarios such as personal intelligent assistants, family management, team collaboration, experience inheritance, and digital experience circulation, demonstrating strong technological innovation and rich application scenarios.

The invention discloses an intelligent recognition system and method applied to legal instruments, and relates to the technical field of intelligent recognition of instruments, and the method comprises the steps: obtaining two-element-form filling data filled by a customer, carrying out the preprocessing and normalization of the filling data, obtaining the filling features of two-element-form tasks, calling the successful processing of the two-element-form tasks, and carrying out the recognition of the two-element-form tasks. Establishing a service personnel database according to the characteristics of successfully processing the element type two-state tasks, selecting adaptive service personnel, selecting the selected service personnel of the new element type two-state tasks according to the busy case number of the service personnel, and comprehensively considering the adaptation degree of the blank filling characteristics; and selecting the service personnel with the highest adaptation degree as the selected service personnel of the new element type two-state task, and if the client is not satisfied with the feedback of the new element type two-state task, analyzing the selected service personnel again to serve the client. And the subsequent matching better meets the core requirements of customers.

The application relates to the technical field of smart endowment, in particular to a smart endowment service automatic supervision system, which comprises classifying the activity items of the old people into timed activities and free activities, determining the mood states and activity characteristic states of the old people in different time periods based on heart rates, establishing the relevance of the mood and the activities and marking the relevant tags, and a service recommendation module combining the real-time mood, the relevant tags and the activity tags to push the optimal services; the application can push the adaptive services by combining the activity characteristics when the old people have negative emotions, timely relieve the emotions, break the vicious circle of "emotional problems leading to physical deterioration", reduce the health risks, optimize the resource allocation according to the correlation between the mood and the service effect, avoid the waste of inefficient services, provide more targeted personalized services, and comprehensively improve the endowment service quality and the life happiness of the old people.

The invention provides a robot control method and device, a robot and a storage medium, and the robot control method comprises the steps: receiving instruction information, and outputting a corresponding first action; receiving first input information and outputting corresponding first environment momentum, wherein the first input information comprises instruction information, a first action and a first evaluation result aiming at the first action; wherein the first evaluation result represents that the user is not satisfied when the robot executes the first action based on the instruction information; determining a second environment momentum under the condition that the set environment momentum having the similarity with the first environment momentum greater than or equal to a similarity threshold exists in a set database; adjusting the first action to obtain a second action; and executing the second action. According to the method and the device, the robot can adjust own behaviors according to user requirements and environment changes, so that the robot can provide more intelligent and adaptive services.

The invention relates to the technical field of public service platforms, and particularly provides a development method of a public service platform based on SaaS and cloud computing, which comprises the following steps: aggregating discrete demand units into a dynamic demand map with a topological structure through a demand association algorithm; screening corresponding components from the reusable service component library through a service component matching mechanism; combining the screened components into a preliminary service template by using a service flow arrangement logic; optimizing the service template according to specific demand attributes in the dynamic demand map through a parameter self-adaptive adjuster to form a self-adaptive service assembly engine; the method comprises the following steps: deploying an adaptive service assembly engine into a cloud environment, and collecting actual use data through a service runtime monitor; and the actual use data is processed by the feedback analyzer to generate an optimization instruction. According to the invention, the public service platform can flexibly respond to demand changes, rapid deployment and continuous optimization of services are realized, and the intelligent level and service quality of the system are enhanced.

Techniques related to providing adaptive services to users when user devices are connected to satellite networks are disclosed. In one example aspect, a device for wireless communication comprises at least one processor that is configured to receive information about a network condition associated with a satellite access network, adaptively enable or disable a service feature according to the network condition, and notify a user of the device that the service feature is adaptively enabled or disabled.

The application relates to the technical field of internet risk cooperative management, and discloses an internet data risk early warning and cooperative management system and method, wherein the internet data risk early warning and cooperative management method comprises the following steps: a service grid enhanced federal learning unified framework is constructed to form a four-tuple structure; a seamless integration architecture of federal learning and a service grid is realized; a federal contribution evaluation mechanism based on a service level agreement is developed; adaptive service discovery and dynamic combination technology is realized to generate an optimal service combination scheme; a data quality-aware federal aggregation algorithm is constructed to introduce a quality weight factor optimization model for training; and a multistage cache strategy is designed; through the fusion of federal learning and a micro-service architecture, the application realizes the target of protecting data privacy while maintaining efficient cooperation, the original data of each participant is always kept locally, only necessary model parameters are shared, and the risk of privacy leakage is reduced.

In one embodiment, an apparatus comprising networking circuitry adapted to receive, at a mobile network operated by an SP, flows associated with enterprise applications executing on device(s) in an enterprise network, the device(s) including a CPE and / or one or more devices behind the CPE, and processing circuitry adapted to vary, during a duration in which there is IP connectivity between the CPE and the mobile network, a pool of dedicated bearers for binding the flows, in response to at least part of changes occurring during the duration with respect to the flows, wherein the changes include variations during the duration in number of the flows and / or differences during the duration in detected characteristics of the flows, the detected characteristics of the flows having been detected based on inspection of respective packet(s) of the flows, and bind at least part of the flows to respective dedicated bearers included in the pool.

The present invention claims protection for a service migration method based on reinforcement learning in mobile edge computing, including the following main steps: S1, constructing a reward function based on the server location where the user task is located, the area location where the user is currently located, and the load of the server processing the task; S2, constructing a state transition matrix based on the current location of the user, the previous moving direction, and the migration decision; S3, making a migration decision using the value iteration algorithm based on the reward function and the state transition matrix; S4, performing normalization processing on the delay consumption and network consumption between routes to assign link consumption; S5, based on the normalized link consumption, using the reinforcement learning algorithm to select a path and adaptively update the link selection to adapt to the link changes in the dynamic network. The present invention introduces mobile prediction to make the model more in line with the actual scenario; uses reinforcement learning to solve the adaptive service migration path in a dynamic network environment.

The application discloses a service quality prediction method, device and equipment, the method comprises the following steps: acquiring at least one index parameter related to service response success rate; inputting the at least one index parameter into a service quality evaluation model for processing to obtain a target service quality level corresponding to the service response success rate, the target service quality level belongs to one of a plurality of service quality levels, the plurality of service quality levels are obtained by self-adaptive service quality level division according to the service response success rate through a preset algorithm; acquiring a time sequence sample set composed of a plurality of target service quality levels contained in a sliding time window; inputting the time sequence sample set into a service quality prediction model for processing to obtain a service quality level at a next moment. Through the above manner, the application can perform self-adaptive division of service quality levels, and through the service quality evaluation model and the service quality prediction model, the accuracy of service quality prediction is improved.

Managing Quality of Service (QoS) policies using real-time traffic analysis, cross-layer feedback, an adaptive policy engine, and security measures may be provided. Managing QoS policies can comprise receiving network data comprising one or more Physical (PHY) layer metrics and one or more Media Access Control (MAC) layer metrics, and determining one or more network conditions based on the network data. An application type of an application is determined by evaluating a packet associated with the application. Then, a QoS mark is set for traffic of the application based on the network conditions and the application type.

The present invention belongs to the field of service matching and deep learning technology, and discloses a cloud-edge collaborative service matching method based on multimodal user preferences. In the cloud-edge scenario, the present invention uses machine learning and deep learning models to perform service matching based on user short-term preference information and long-term service usage record data under the condition that the perception amount can be controlled. Among them, the user's multimodal information data is used to identify preference information using a recurrent neural network model and an attention mechanism as classifiers, and the K-nearest neighbor algorithm is used for the user's long-term access sequence to obtain the user's long-term preference. The present invention utilizes the characteristics of multimodal data and numerous method models of machine learning and deep learning to fully extract the large amount of multimodal data generated by users due to service calls, providing strong support for adaptive service matching in cloud-edge scenarios.

Method for application control and Quality of Service (QoS) handling may be provided. A request may be received for scheduling a communication between an Access Point (AP) and a user device for sending data of an application. It may be determined that the request does not comprise network characteristics of the application. In response to determining that the request does not comprise the network characteristics of the application, the network characteristics may be requested from a Wireless Local Area Network (WLAN) controller. The network characteristics may be received from the WLAN controller. Schedules for the application may be determined based on the network characteristics. The schedules may be enabled.

A method may include, obtaining, by one or more processors, transaction logs of a database server captured in a predetermined time period, identifying, by the one or more processors, a rate of change of data within the transaction logs, classifying, by the one or more processors, portions of the time period according to the rate of change of data within the portions of the time period, and setting, by the one or more processors, a service level agreement (SLA) for each portion of the time period according to the classification of each portion.

The application discloses an intelligent identification system and method applied to legal documents, relates to the technical field of document intelligent identification, acquires fill-in data of a customer filling in a two-form element type, carries out pretreatment and normalization on the fill-in data, obtains fill-in characteristics of the two-form element type task, successfully processes the two-form element type task, establishes a service personnel database according to the characteristics of the successfully processed two-form element type task, selects adaptive service personnel, selects selected service personnel of a new two-form element type task according to the busy case number of the service personnel, comprehensively considers the adaptive degree of the fill-in characteristics, selects service personnel with the highest adaptive degree as the selected service personnel of the new two-form element type task, and if the customer is not satisfied with the new two-form element type task, the selected service personnel serves the customer again. The application has the capability of continuously optimizing the service system by means of a continuous change model, so that subsequent matching is more in line with the core needs of the customer.

Managing Quality of Service (QoS) policies using real-time traffic analysis, cross-layer feedback, an adaptive policy engine, and security measures may be provided. Managing QoS policies can comprise receiving network data comprising one or more Physical (PHY) layer metrics and one or more Media Access Control (MAC) layer metrics, and determining one or more network conditions based on the network data. An application type of an application is determined by evaluating a packet associated with the application. Then, a QoS mark is set for traffic of the application based on the network conditions and the application type.

The invention discloses a reconfigurable artificial intelligence (AI) ecological system. The ecological system comprises an edge device based on machine learning, a cloud operating system and a modular service platform. The cloud operating system communicates with the edge device and the modular service platform to manage service delivery. After a task request from user equipment is received, the ecological system selects a proper edge model, generates a corresponding machine learning edge module and deploys the machine learning edge module to edge equipment. These edge devices then collect task-specific data, trim deployed modules locally, and use improved models to provide responsive and adaptive services. The ecosystem employs an open architecture, supporting cross-distributed environments to generate and deploy dynamic AI services on demand.

A machine (1) for preparing a beverage has: a body (1′) comprising a seat (4) and a beverage processing line (1″, 1″′) extending from a liquid inlet (1″) to a beverage dispenser (1″′); and a service unit (10) that is removably received in or on the seat (4) and that comprises a foot part (11) and a recipient part (12) removably received in or on the foot part (11) so as to be supported thereby. The foot part (11) has a bottom face (11′) configured to rest on an external horizontal support surface (100) when the service unit (10) is removed from the seat (4). When the recipient part (12) is received in or on the foot part (11), the recipient part (12) is movable relative to the foot part (11) from a first stable position when the service unit (10) rests on the external horizontal support surface (100) to a second stable position when the service unit (10) is received in or on the seat (4), and vice versa.

This disclosure describes a set of techniques that include establishing and managing quality of service standards across storage cluster resources in a data center. In one example, this disclosure describes a method that includes establishing a quality of service standard for a tenant sharing a storage resource with a plurality of tenants, wherein the storage resource is provided by the plurality of storage nodes in the storage cluster; allocating a volume of storage within the storage cluster, wherein allocating the volume of storage includes identifying a set of storage nodes to provide the storage resource for the volume of storage, and wherein the set of storage nodes are a subset of the plurality of storage nodes; and scheduling operations to be performed by the set of storage nodes for the volume of storage.

The present application relates to a kind of learning style tendency adaptive diagnostic system and method based on online behavior, including data storage module, data acquisition module, learning style tendency diagnostic module, adaptive service module and user interface module;Data storage module supports the storage of learning material, interactive activity, process behavior, learning style index, user information and related additional information;Data acquisition module carries out acquisition and record to the behavior generated by learner on learning content object in learning process;Learning style tendency diagnostic module is based on the classification index of learning style and the behavior of learner accessing different types of learning content object, and the calculation of learning style is carried out;Adaptive service module provides learning material, learning activity and learning mode suitable for the style of learner based on diagnosed learning style;User interface layer realizes the support of adaptive learning based on multi-terminal to user.The present application promotes the promotion of online learning adaptability and learning effectiveness.

A computing system and method can be used to implement a version of federated learning (FL) that incorporates adaptivity (e.g., leverages an adaptive learning rate). In particular, the present disclosure provides a general optimization framework in which (1) clients perform multiple epochs of training using a client optimizer to minimize loss on their local data and (2) a server system updates its global model by applying a gradient-based server optimizer to the average of the clients' model updates. This framework can seamlessly incorporate adaptivity by using adaptive optimizers as client and / or server optimizers. Building upon this general framework, the present disclosure also provides example specific adaptive optimization techniques for FL which use per-coordinate methods as server optimizers. By focusing on adaptive server optimization, the use of adaptive learning rates is enabled without increase in client storage or communication costs and compatibility with cross-device FL can be ensured.

The application provides a bank-enterprise interconnection method and device, a proxy server, a medium and a product, which can be applied to the field of financial technology. The method is applied to the proxy server, the proxy server is loaded with a management service software and at least one adaptive service software, and the method comprises the following steps: receiving a first service request for a target service sent by a user terminal through a management port corresponding to the management service software; determining a target adaptive service software corresponding to a target bank identifier from the at least one adaptive service software through the management service software; calling an interface of a corresponding bank server through the target adaptive service software to send a target service request to the bank server, so that the bank server returns response information; and returning the response information to the user terminal through the management port to make the user terminal display a service execution result based on the response information. The bank-enterprise interconnection method can meet the individual needs of enterprises and reduce functional redundancy.

The invention relates to the technical field of server cases, and discloses an air cooling and liquid cooling switching method and a self-adaptive server case, a case main body comprises an air cooling assembly, a liquid cooling assembly, a heating unit and a control unit; the liquid cooling assembly comprises a cold conduction structure arranged on each heating unit, and the liquid cooling assembly is used for conveying a cooling medium to the cold conduction structures when being started; the air cooling assembly comprises a fan module; a fin structure is formed on the side, deviating from the heating unit, of the cold conduction structure, so that a plurality of air channels are formed in the cold conduction structure through the fin structure, and the fan module is used for conveying air cooling airflow into the air channels when started; temperature detection units in signal connection with the control unit are respectively arranged on the cold conduction structures; and a switching control signal of the liquid cooling assembly and the air cooling assembly is generated according to temperature data detected by the temperature detection unit. The problem that when a cooling system of an existing server case breaks down, the whole system cannot work normally is solved.

The invention relates to the technical field of smart old-age service, in particular to a smart old-age service platform operation method based on a self-adaptive algorithm, and the method comprises the steps: 1, collecting data in real time; 2, dynamically analyzing a service demand level; 3, quantifying the environment constraint, generating an environment influence factor, and calculating an indoor safety coefficient according to the indoor environment parameters; 4, executing a self-adaptive service decision; when the service demand level is emergency medical treatment and the environmental influence factor does not exceed the limit, starting on-site medical rescue; when the service demand level is emergency medical treatment and the environmental impact factor exceeds the limit, remote medical treatment and unmanned aerial vehicle distribution are activated; when the service demand level is daily nursing enhancement and the indoor safety coefficient reaches the standard, an enhanced indoor rehabilitation scheme is executed. The intelligent pension service platform not only can respond to the health requirements of the elderly in real time, but also can continuously optimize the service content, improve the comprehensiveness and adaptability of the pension service, and further promote the intelligent development of the pension industry.

Aspects of the subject disclosure may include, for example, obtaining first network performance data characterizing a wireless network, wherein the wireless network provides one or more users of a plurality of users access to a collection of servers that enable a plurality of services; obtaining second network performance data characterizing a wired network, wherein the wired network provides the one or more users access to the collection of servers that enable the plurality of services; obtaining first service performance data characterizing a first service of the plurality of services that are enabled by the collection of servers; obtaining first service requirements of the first service; and responsive to a first user of the one or more users indicating a preference to utilize the first service, selecting one of the wireless network or the wired network as a selected network for the first user, wherein the selecting of the selected network is based upon the first network performance data, the second network performance data, the first service performance data, the first service requirements, or any combination thereof. Other embodiments are disclosed.