31 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 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 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.

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.

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.

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.

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.

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.

A method for agentless adaptive service mapping for enhanced observability in information systems is described herein. The method includes executing various commands to access, from computers in a computer network, information regarding selected services executing thereon. The selected services are mapped to their respective applications and versions numbers. This mapping information is stored in a return dataset, and may be accessed through a dashboard display to provide IT personnel information at a glance. The accessing of service information and mappings is carried out without the use of agents that would otherwise be installed on each of the computer systems of the network.

The application discloses an AI service deployment system of an embedded platform, an AI service loading method, a storage medium, an electronic device and an embedded device. The system comprises a cloud deployment device and an AI service loading device. The AI service loading device comprises an interface unit and a scheduling component. The cloud deployment device provides the AI service loading device with adaptive service components based on information of the embedded device and information of a target AI service to be deployed provided by the AI service loading device. The interface unit realizes the interaction between the AI service loading device and the user and the cloud deployment device. The scheduling component obtains the service components from the cloud deployment device based on a deployment instruction of the target AI service issued by the user, and loads and triggers the running of the service components. The application can effectively improve the deployment efficiency of the AI service.

The invention discloses a supermarket multi-mode self-adaptive service method and system based on digital twinning. The method comprises the following steps: collecting physiological state data through a multi-mode non-sensing network; using the dynamic differential equation model to construct and update the state vector of the user digital twin; inputting the state vector into a dynamic knowledge graph decision maker to generate a service strategy; and controlling a flexible actuator network execution strategy based on a calculation result of the multi-parameter intention recognition model. According to the method, a dynamic differential equation model is creatively adopted to realize continuous simulation and prediction of a user state, a phase field kinetic equation is utilized to drive adaptive evolution of a knowledge graph relation weight, and accurate intention recognition and flexible triggering are realized through a multi-parameter probability function; the problems of perception fragmentation, understanding superficial layer, decision islanding and interaction mechanization are effectively solved, and the service normal form change from passive response to active foreseeing and natural adaptation is realized.

The invention discloses a network performance optimization system and method based on data analysis, and the system comprises the steps: integrating multi-modal network data collection, multi-modal network knowledge graph construction, adaptive business intention analysis, an intention-driven graph attention time sequence prediction model (IGATP) and a multi-target intelligent arrangement and feedback module; the deep understanding of the network service intention, the accurate prediction of the network performance, the anticipation of the intention violation risk and the intelligent active arrangement of the network resources are realized. According to the system, the business intention, the knowledge graph, deep learning and reinforcement learning are creatively combined, the problems of insufficient intention perception, lack of association analysis, prediction lag and passive optimization decision in an existing network optimization scheme are solved, the reliability, efficiency and user experience of network services are remarkably improved, and the network is promoted to advance towards high-level autonomy.

The invention discloses a server design method and electronic equipment, and relates to the technical field of computers. Structured parameters and component constraint conditions are used as design bases, excessive dependence on subjective judgment of engineers is eliminated, supplementary requirements are automatically deduced through a preset design dimension association relation, requirement coverage is perfected to avoid design omissions, meanwhile, a target component combination is screened and determined from a preset component library, and the design efficiency is improved. According to the method, the design consistency is guaranteed, the component matching accuracy and the design feasibility are improved, the tedious steps of manually carding requirements and screening components from zero are omitted, and finally the design efficiency of the server is greatly improved, and the requirement for rapid iteration of adaptive services is greatly met.

A method, an apparatus, and a computer program product are provided. In the context of a method, the method receives a quality of service profile that includes a range of quality of service attribute values or a first limit value, where the range of quality of service attribute values describes a range of authorized quality of service configurations. The method determines a second limit value when the first limit value is received, where the first limit value and the second limit value define a range of quality of service attribute values. The method determines a first quality of service configuration based on the range of quality of service attributes, wherein the first quality of service configuration describes a set of quality of service attribute values within the range of quality of service attribute values. The method applies the first quality of service configuration to communication with the user equipment.

The invention discloses a self-adaptive service navigation method, device and system and a computer readable storage medium, and belongs to the technical field of artificial intelligence. The self-adaptive service navigation method comprises the following steps: acquiring to-be-processed text data input by a user; performing semantic analysis on the to-be-processed text data by adopting a field classification model based on an attention mechanism, and determining an input type corresponding to the to-be-processed text data: analyzing the to-be-processed text data based on a pre-constructed hierarchical intention classification model under the condition of determining that the input type represents a business requirement type, positioning a target service node in the intention classification structure of the full-service menu; and based on the target service node and the menu mapping rule of the target navigation scene, determining a target service and performing navigation. Therefore, classification accuracy and positioning precision are improved, business menu logics of different scenes can be quickly adapted, the difficulty of cross-mechanism deployment is reduced, flexible popularization and efficient configuration are realized, and scene adaptation flexibility is enhanced.

The invention discloses an intelligent seat system and method with an interaction function, and relates to the field of intelligent seat systems, and the intelligent seat system comprises a core control module, a biological rhythm sensing module, an environment state sensing module, a multi-mode non-inductive interaction module, a self-adaptive execution module, an environment linkage module and a learning optimization module. According to the intelligent seat system and method with the interaction function, non-inductive interaction is achieved through micro-action triggering and physiological rhythm linkage, a user does not need to actively send voice or touch instructions, and the system and method completely accord with the natural behavior habits of the human body. The user can enjoy the self-adaptive service of the system without distracting operation in the working, resting or driving process, the interference of interactive operation on the concentration degree of the user in reality is avoided, the method is particularly suitable for driving and other scenes where attention needs to be highly focused, the use convenience is remarkably improved, the problems of module disjunction, adjustment delay and the like cannot occur, and the user experience is improved. And the stability and continuity of system operation are ensured.

The application provides an adaptive service packaging method and device and electronic equipment, and belongs to the technical field of distribution. The adaptive service packaging method comprises the following steps: determining at least one service type according to preset business requirements of a target system, and packaging business logic corresponding to the at least one service type in at least one service module respectively; determining a service packaging strategy corresponding to a preset business scale of the target system according to the preset business scale; and packaging the at least one service module in at least one service process according to the service packaging strategy. The application realizes the unbundling between a service process and business logic, and then can flexibly configure the number of service modules and corresponding service types packaged in one service process according to different service scale requirements of a target system, so as to flexibly adapt to systems of different sizes and effectively improve the universality of a service system architecture.

The technology described herein is directed towards resource consumption forecasting, e.g., with KUBERNETES (K8S). Artificial intelligence / machine learning model-based forecasting of future resource needs can operate with respect to multi-cloud and hybrid cloud environments. Future resource consumption needs such as CPU / memory / disk / network usage is based on a weighted combination of historical resource consumption records, platform (e.g., K8S) configuration changes, target business demands and different software versions of the platform. Based on the forecasted resource consumption needs, IT departments can perform budget planning following the company's budget cycles. Different strategy level considerations such as minimizing total cost or maximizing delivery speed can be designed in resource plans. The system adapts to dynamic environment changes. Because budgeting in hybrid cloud and multi-cloud environments each have a different cost structure, different optimization policies can be applied, and resource needs can be divided into business cycles.

The invention provides a multi-period memory cooperation method and system. The method comprises the following steps: S1, collecting behavior data and learning data of a user in an education scene; s2, based on the time characteristics and the importance degree, carrying out screening and respectively storing in a short-term memory database, a middle-term memory database and a long-term memory database; s3, cooperatively calling high-frequency interaction data, staged summary data and long-term value data according to the service type of the current education service request, and dynamically adjusting a calling weight based on at least one factor of the service type, a user feedback effect and an education task priority; and S4, based on the calling weight, weighting and fusing the high-frequency interaction data, the staged summary data and the long-term value data to obtain multi-cycle memory data, generating education service content and pushing the education service content to the user. The beneficial effects of the invention are that the system can achieve the full-period and hierarchical management of the education data, and can intelligently cooperate with the data in different periods to provide dynamic adaptive services.