319 results about "Multiple domain" patented technology

A system and method for extending mobile-optimized multi-stage language model processing with federated persistent cognitive architecture. The system processes prompts through a first large language model to generate “thoughts,” which are cached and processed with the original prompt through a smaller language model. Building upon the three-tier thought caching, the system implements a federated multi-tier hierarchy with local device, domain-specific branch, and global collective caches. A federated cognitive orchestrator coordinates operations across multiple domain-specialized instances, managing thought routing, state synchronization, and cross-domain knowledge sharing while maintaining domain boundaries. During user inactivity, autonomous reasoning continues in cloud environments, generating insights from existing thoughts and interaction history. The system performs memory consolidation, thought cache optimization, and cross-domain pattern recognition without consuming mobile device resources, while maintaining privacy boundaries. This persistent cognitive architecture functions as an evolving reasoning partner rather than merely a responsive tool.

The invention provides an adaptive path selection system based on GNN and multi-agent DRL, which is based on a layered multi-domain SDN architecture and comprises a root server layer, a domain controller layer and a data layer. The root server layer comprises a root server and a root agent, and the root agent is used for calculating a global routing path strategy and optimizing and adjusting a routing path according to feedback of a local network domain. And the root server distributes the routing path to a domain controller layer according to a routing path strategy to carry out routing path forwarding and adjustment of a local network domain. The domain controller layer comprises a plurality of domain controllers and domain intelligent agents, the domain controllers construct a topological structure composed of boundary switches, and a routing decision is made in a local domain according to an optimal global routing path strategy. And the domain intelligent agent performs modeling analysis on the topological structure by using a deep reinforcement learning algorithm in combination with a graph neural network to realize intra-domain path selection. And the data plane layer feeds back the network topology information to the domain controller, and receives and executes a routing decision sent by the domain controller at the same time.

The invention relates to the technical field of fault prediction, in particular to a complex equipment fault prediction method and system based on multi-dimensional time sequence characteristic modulation. The method comprises the following steps: preprocessing acquired sensor data; performing initial feature extraction by using the preprocessed sensor data; performing multi-dimensional time sequence feature coding and multi-scale feature modulation based on the extracted initial features; performing feature refining and time sequence smoothing on the modulated features; and obtaining a fault classification result and a maintenance suggestion. The multi-dimensional data isomerism is eliminated through the data preprocessing module, and the DTFE module extracts global features from multiple domains, so that the diagnosis precision, robustness and real-time performance are remarkably improved.

Aspects of the invention provide a method, system, and computer program product for retrieval augmented generation. In one aspect, the method includes receiving a query. The method further includes classifying the query to a first domain within a plurality of domains. The method additionally includes determining an ambiguity associated with classifying the query. The method also includes retrieving an index of domain-specific vector embeddings corresponding to the domains when the ambiguity does not exceed a threshold for ambiguity. The method further includes prompting a large language model with the query and the domain-specific vector embeddings. The method also includes receiving a query response from the large language model as grounded with the most relevant index results. The method further includes forwarding the query response.

The invention discloses an underwater gliding robot anchoring process energy consumption optimization method and system based on a subdomain grey box model. The energy consumption optimization method comprises the steps that a white box energy consumption mechanism model in the underwater gliding robot anchoring process is established; the whole anchoring energy consumption stage of the underwater gliding robot is divided into a plurality of sub-domains, and a Latin hypercube design method is adopted to simulate and generate a plurality of sub-domain samples meeting variable range constraints by using a hardware-in-the-loop simulation platform; selecting a Kriging model as a sub-domain agent model, fusing the white-box energy consumption mechanism model and constraint conditions of an oil bag volume and a movable mass position to form a sub-domain grey-box model, and performing segmented energy consumption fitting by using the sub-domain grey-box model; and with minimization of energy consumption fitted by the sub-domain grey box model as a target, performing iterative optimization on the sub-domain proxy model by adopting a dynamic guidance self-adjusting particle swarm algorithm, and outputting optimal planning parameters. The method realizes anchoring full-process low-energy-consumption control, and is suitable for marine resource exploration, hydrological monitoring and other tasks.

Disclosed herein are techniques for managing search queries that require interactions with multiple domains. For example, a method implemented by a server computing device can include the steps of (1) receiving an unstructured query from a client computing device, (2) identifying one or more domains to which the unstructured query should be routed, (3) for each domain of the one or more domains: generating, based on (i) at least one respective model of the domain, and (ii) the unstructured query, a respective structured query, and providing the respective structured query to one or more knowledge sources to generate respective results, (4) aggregating the results to produce aggregated results, (5) filtering the aggregated results to produce filtered results, and (6) causing the client computing device to display at least a portion of the filtered results.

The invention discloses a sub-domain self-adaption-based cross-subject and cross-period electroencephalogram emotion recognition model, and belongs to the technical field of emotion recognition of electroencephalogram signals. The method comprises the following steps: firstly, acquiring domain-invariant shallow feature data through a shared feature extraction module, and inputting the acquired shallow features into a domain specific feature extraction module to extract a plurality of high-level feature representations; then, through an emotion classifier, on one hand, performing emotion prediction on the source domain data, and on the other hand, predicting a pseudo label of a target domain sample for a sub-domain adaptive process; then, a sub-domain adaptation loss PLMMD is adopted to optimize the model, multiple domain invariant representations between a source domain and a target domain are learned, and a hierarchical pseudo-label weight generation module is adopted to relieve pseudo-label noise in the sub-domain adaptation process so as to improve the sub-domain adaptation effect; according to the method, the effect of the electroencephalogram emotion recognition model is obviously improved, and better practicability is achieved.

Multiple agent root cause analysis techniques are described. In an implementation, a processor performs operations to troubleshoot performance of a system operation by a plurality of domains (e.g., of a distributed system). The processor executes a system artificial intelligence (AI) agent to determine which domains of the plurality of domains include domain functions in support of the system operation, and to generate, using machine learning, queries to the determined domains based on the system operation. Domain responses are received from domain AI agents associated with the determined domains responsive to the queries and generated based on domain data associated with respective domains. A system response is generated by the system AI agent using machine learning based on the domain responses.

Disclosed embodiments provide computer-implemented techniques for aggregating of data involving multiple domains in the format of a news bulletin or “newsletter” format. For various situations, this format can provide a quicker and more comprehensive understanding than is achievable with a visual dashboard. In disclosed embodiments, multiple domains are specified, where each domain can include a subject or category. One category is selected as a major domain. Other categories, referred to as satellite domains are also specified. A user provides scores that indicate the semantic importance of each satellite domain. A mathematical distance between each satellite domain and the major domain is computed based on the scores. Based on the mathematical distances, percentages of articles pertaining to each domain are computed, and used in assembling customized data that is output in a news bulletin format.

The invention belongs to the technical field of block chains and information security, and particularly relates to a cross-domain access method for enhancing security privacy protection based on a block chain. The architecture comprises a global coordination chain and a plurality of domain alliance chains; the global coordination chain deploys a strategy Merkel tree root, a cross-domain authentication log and an SGX security area verification record to form a global credible verification anchor point; the domain alliance chain adopts a distributed autonomous mode, equipment registration information is maintained in each independent domain, a policy tree and an anonymous certificate library are dynamically accessed, and data sovereignty isolation is realized. The invention aims to solve the problems of identity authentication centralization, easy leakage of privacy data, low cross-domain trust transmission efficiency and the like in the traditional cross-domain access technology.

The invention provides a rotary machine fault diagnosis method based on transfer learning, and the method comprises the steps: carrying out the multi-mode noise suppression processing of an obtained original vibration signal, and generating a noise reduction data set; inputting the noise reduction data set into a multi-layer deep residual network for feature extraction to obtain a time-space synchronization feature set; wherein the space-time synchronization feature set comprises source domain data and target domain data; inputting the time-space synchronization feature set into a multi-core domain alignment model based on physical constraints to obtain domain invariant features; and performing fault diagnosis on the domain invariant features through an adversarial migration fault classifier to obtain a target domain fault type. By adopting the method, high-robustness diagnosis of cross-working-condition, cross-sensor and other multi-domain difference scenes in a strong noise environment can be realized.

The invention discloses an efficient method for unsupervised domain adaptive target detection, and relates to the technical field of image processing. According to the invention, a field query module is designed, and the characteristics output by an encoder are subjected to adversarial alignment, so that a detector can extract more domain invariant characteristics, in addition, a category prototype alignment module is provided, the category prototype characteristics of each category can be extracted from a decoder, category-known cross-domain characteristic alignment is realized, and the accuracy of the category-known cross-domain characteristic alignment is improved. In each training iteration, a local category prototype gradually generates a global category prototype, a detector is guided to realize global feature alignment through comparative learning and confrontation loss, and a large number of experimental results show that the proposed model realizes excellent detection performance on a multi-domain adaptive reference data set.

The present invention discloses a method for secure processing of bidding data based on blockchain, belonging to the field of data encryption technology. Specifically, it includes: dividing the original data into logical blocks along the time axis, and generating a dynamic feature code with a hierarchical relationship by non-linearly fusing the current data hash value with the last feature segment of the previous block. Constructing a three-dimensional verification topology network including a time-stamped vertical chain, a content-associated horizontal tree, and an environmental parameter space matrix. Establishing a dynamic verification channel, activating the cross-verification of the three-anchor point association path when retrieving data, and generating an integrity proof. A self-destruction mechanism is implanted during verification, and the data block is gradually erased in case of anomalies. Finally, a traceability map is generated, the key parameters are encrypted and stored dispersedly in multiple domains, and the hash digest of the storage domain coordinates is recorded through the blockchain; the present invention is applicable to high-security electronic bidding scenarios.

The invention provides a three-dimensional target object identification method, which can be used in the technical field of security check, and comprises the following steps: processing acquired target object data based on a processing model, and acquiring three-dimensional identification information of a target object; the recognition result of the target object is obtained based on the three-dimensional recognition information, the processing model is a model obtained by conducting fine tuning processing on a multi-task processing model trained based on source domain data, and the fine tuning processing comprises the steps that the source domain data and the target domain data are processed based on the multi-task processing model, and candidate region information is obtained; obtaining a plurality of domain migration sub-modules based on a multi-task structure in the multi-task processing model; obtaining updated candidate region information based on the plurality of domain migration sub-modules; and the multi-task processing model is finely adjusted based on the updated candidate region information. According to the method, the recognition precision and the model adaptability are improved through multi-task decoupling, multi-path training and a domain migration mechanism. The invention further provides a three-dimensional target object recognition device, equipment, a medium and a program product.

Methods for providing persistent, continuous, and / or customized expert interactions with a user, the method performed by a computer system (100) implementing a dedicated expert avatar (DEA), and corresponding systems and computer-readable mediums. A method includes receiving (702) a prompt (330) from a user and receiving (704) user history (306) and user preferences (308) corresponding to the user. The method includes applying (706) the user history (306) and the user preferences (308) to the prompt (330) to produce a plurality of queries (334), where each of the plurality of queries (334) corresponds to a respective expert avatar (XAv) system (320). The method includes transmitting (708) the plurality of queries (334) to the corresponding XAv systems (320) and receiving (710) an avatar response (336) from each of the XAv systems (320). The method includes validating and customizing (712) the avatar responses (336) by applying the user history (306) and the user preferences (308) to the avatar responses (336) to produce a user-customized response (332), and transmitting (714) the user-customized response (332) to the user as a response to the prompt (330).

The application relates to a centralized electronic and electrical architecture applied to an electric vehicle and the vehicle, wherein the electronic and electrical architecture comprises multiple domains, each domain having a domain controller and multiple sub-domain controllers connected to the domain controller in the domain; at least part of the functions of the domain controller are redundantly realized in the remaining capacity of the sub-domain controllers in the domain through fallback software or in the remaining capacity of the sub-domain controllers belonging to other domain controllers or other domain controllers across domains; each domain has a domain gateway, which is used for coordinately distributing the functions borne by the domain controller of the domain to the sub-domain controllers in the domain and / or other domain controllers across domains and / or the sub-domain controllers belonging to other domain controllers; and the key domain controller functions are redundantly realized in the other controllers in the sub-domain or the other controllers across domains, so that the situation that the domain controller is irreversibly faulty is solved.

The invention relates to the field of computer vision, in particular to a transformer-based domain adaptive target detection method and electronic equipment, and the method comprises the following steps: S1, obtaining a source domain and a target domain; s2, establishing a model; s3, establishing a domain confrontation feature alignment module; s4, an adaptive domain discriminator; according to the method, a domain confrontation feature alignment module is designed in an encoder part, a learnable domain confrontation token is used as a query, features generated by an encoder layer are used as key value pairs, and the features generated by the encoder are aligned through adversarial learning; a self-adaptive domain discriminator is designed, so that the model can better process hard cases, and the domain self-adaptive capability of the model is further enhanced; the method provided by the invention shows good detection effect improvement on a plurality of domain adaptive public data sets, and compared with the prior art, the method provided by the invention is effectively improved in the aspect of feature alignment of the features output by the encoder, has obvious advantages in domain adaptive target detection, and has excellent domain generalization ability.

Systems and methods for favicon comparison-based similar domain detection include receiving a base domain, the base domain being associated with an enterprise; receiving a domain list comprising a plurality of domains; performing a favicon comparison between the base domain and each of the plurality of domains within the domain list; and classifying each of the plurality of domains within the domain list as one of being associated with the enterprise or not being associated with the enterprise based on the favicon comparison.

The invention discloses a computing power network resource scheduling method and device, a storage medium and electronic equipment. The method comprises the following steps: acquiring the total task time delay of a plurality of calculation tasks; the total computing cost of computing power service nodes included in multiple domains is obtained, the multiple domains are obtained by dividing the computing power network, and the computing power service nodes are used for indicating devices providing computing resources; constructing an optimization target based on the total calculation cost of the computing power service nodes included in the plurality of domains and the total task delay of the plurality of calculation tasks; and based on the optimization target, performing cross-domain resource scheduling optimization on the plurality of computing tasks to obtain a resource scheduling result, the resource scheduling result being at least used for indicating computing resources respectively allocated to the plurality of computing tasks. The technical problem of low computing power resource utilization rate caused by low scheduling accuracy of a computing power resource scheduling method in related technologies is solved.

The invention discloses a domain name resolution method based on a cloud computing technology and a cloud management platform, and belongs to the technical field of cloud computing. The method is applied to a cloud management platform, and comprises the following steps: grouping a plurality of DNS servers to obtain a plurality of DNS server groups, the plurality of DNS servers being used for analyzing domain names in a target name area in a DNS name space; obtaining a plurality of domain name suffixes, wherein the plurality of domain name suffixes are suffixes of domain names in the target name area; generating a target mapping relation based on the plurality of domain name suffixes and the plurality of DNS server groups; and distributing a plurality of domain name suffixes to the corresponding DNS server group based on the target mapping relationship, so that the DNS server in the corresponding DNS server group analyzes the domain name resolution request when receiving the domain name resolution request. According to the method and the device, the DNS servers are flexibly grouped, and the DNS servers are scheduled by the groups to perform domain name resolution, so that the high availability of the domain name resolution service is ensured.

The application discloses a kind of plankton detection method, system and electronic equipment, it is related to computer vision technical field.The present application can be based on the image data of different regions, different river reaches of the river to be detected by using river plankton detection model, accurately, quickly get the plankton detection result of each pixel.And, multiple domain self-adaptive feature extraction modules are arranged in the river plankton detection model of the application, the distinguishing ability of target and background difference in image data can be improved, multiple boundary alignment modules can enhance the target feature extraction capability, the accuracy of plankton detection can be significantly improved, and the economic and ecological cost of plankton management can be effectively reduced.

A method to manage domain-based security profiles in a content delivery network (CDN) is disclosed. The method includes receiving security events detected by one or more security solutions implemented by one or more CDN instances of the CDN, determining, for each of a plurality of domains, a risk score for the domain based on the security events, determining possible next level domains for a CDN instance of the CDN, determining an updated order of an auto-adjusting list maintained by the CDN instance based on risk scores for the domains included in the auto-adjusting list and the possible next level domains for the CDN instance, and sending an update to the CDN instance to cause the CDN instance to update the order of the auto-adjusting list to reflect the updated order, wherein the order of the auto-adjusting list indicates an eviction priority for the domains included in the auto-adjusting list.

The invention discloses a cluster fault management method and apparatus, an electronic device and a medium. The method comprises the steps of obtaining performance index data of a multi-field unit in a cluster; constructing a dynamic fault propagation model of the cluster through the performance index data based on the graph model; according to the dynamic fault propagation model, constructing four-dimensional fault visualization based on a time sequence; and cluster fault analysis is carried out through four-dimensional fault visualization. Therefore, on the basis of the time sequence, the performance index data of the different field units are constructed into the dynamic fault propagation model, cross-domain fault positioning, analysis, monitoring and deduction based on the time axis are realized, cross-domain fault space-time correlation splitting caused by independent fault monitoring of each field unit is avoided, and the fault positioning efficiency and accuracy are improved. In addition, based on the four-dimensional fault visualization expression of the time sequence, the multi-screen switching monitoring complexity of operation and maintenance personnel is reduced, and the cluster fault management efficiency is improved.

Apparatuses and methods for a CSI report configuration for CSI predictions in one or more domains. A method performed by a user equipment (UE) includes transmitting capability information indicating capability of the UE to support machine learning (ML) based channel state information (CSI) prediction in one or more domains, receiving configuration information that indicates parameters for ML based CSI prediction in the one or more domains; receiving CSI reference signals (RSs), and measuring the CSI-RSs. The method further includes determining, based on the configuration information and the measured CSI-RSs, a plurality of CSI predictions in the one or more domains; determining a CSI report including one or more of the plurality of CSI predictions and dependency information indicating dependencies between CSI predictions in the plurality of CSI predictions; and transmitting the CSI report.

The invention relates to a multi-vehicle automatic driving simulation system and method supporting DORA middleware, and belongs to the technical field of automatic driving. The system comprises a simulation platform and a domain controller group. The simulation platform comprises a multi-vehicle Dora-bridge, traffic scene simulation software and vehicle dynamics simulation software, the multi-vehicle Dora-bridge receives multi-vehicle state data, sensor data and vehicle cooperative control information generated by the traffic scene simulation software, and the multi-vehicle Dora-bridge publishes the multi-vehicle state data, the sensor data and the vehicle cooperative control information in the form of a DORA topic for each domain controller to subscribe; meanwhile, control instructions issued by the domain controllers in the form of DORA topics are received and issued to the vehicle dynamics simulation software. The domain controller group comprises a plurality of domain controllers, each domain controller corresponds to one automatic driving vehicle, and software of the domain controller group consists of a plurality of DORA function nodes to realize an automatic driving function. According to the invention, an effective solution is provided for simulation of the multi-vehicle automatic driving system based on the DORA middleware, the development efficiency is improved, and the development cost is reduced.

A process that includes receiving a session identity protection query that includes a target domain. The process further includes accessing a security database of compromised cookie data associated with a plurality of domains and determining the target domain is associated with first compromised cookie data of the compromised cookie data included in the security database. The process includes providing the first compromised cookie data in response the session identity protection query.

The invention belongs to the technical field of software engineering, and particularly relates to a field-oriented environment association type scene software development method and system. The software development process is divided into three stages of field customization, scene configuration and application development, and a corresponding development system is provided and comprises a meta-tool environment, a field development platform and a scene application development platform. In the field customization stage, the meta-tool environment is used, and configuration is customized for a specific field to obtain a field development platform; in the scene configuration stage, environment-related scene configuration is carried out on the field development platform, intermediate representation of physical equipment is established, configuration access of various Internet of Things platforms is achieved through description, and the field development platform is configured as a scene application development platform; and in the application development stage, development of software in multiple fields is realized by multiplexing the environment-related scene. According to the method, the environment elements in the scene can be quickly configured, and the efficiency of developing and deploying customized scene software is improved.

For future generation networks, it has been proposed to establish an autonomous networking framework where the customer-provider interaction can happen autonomously. In the multi-domain interactions paradigm, it is important that the resource usage be minimized when providing a service or service slice to a customer while ensuring the quality of service. By dividing a geographic area into separate grids and combining grids requiring a similar resource allocation, historical analysis can allow an estimation of future resource requirements. During service provision or during run-time, the customer requirement can be obtained as a statistical distribution of the expected demand over the given geographic area. The problem of resource allocation is thus addressed by leveraging the concepts of a gridded map, domain abstraction by learning the resource usage of communication services, and conversion of a customer requirement to grid-based requirements

A model pipeline has been created that allows an organization to gain a clear understanding of contents of a collection of documents despite varying lengths and multiple domain content. A collection of documents with content of different domains are normalized by summarizing the documents. For summarizing, the pipeline uses a language model that has been trained for text summarization across domains to a constrained summary length or length limit. The pipeline extracts embeddings of the summaries and clusters the summary embeddings. The pipeline uses the summaries to label the clusters using another language model that has been trained to generate a label or name for a cluster based on a set of summaries corresponding to a sample of cluster members. The labeled clusters can then be used to generate an organized presentation of the content of the documents in the document collection.

The invention discloses a cross-domain access control method based on a cloud management platform and the cloud management platform, which can reduce the operation of a tenant when making an access control strategy and provide convenience for the tenant so as to improve the experience of the tenant. The method comprises the following steps: for a plurality of domains contained in an organization of a tenant, when the tenant needs to perform access control on a first cloud service contained in a first domain in the plurality of domains, the tenant can input an access control strategy formulated by the tenant for the first cloud service of the first domain to an access control interface provided by a cloud management platform, therefore, the cloud management platform can receive the access control strategy through the access control interface. And if the access control strategy is used for indicating that the access authorization range of the first cloud service contained in the first domain is the second cloud service contained in the organization of the tenant, the cloud management platform allows the second cloud service contained in any domain in the organization of the tenant to access the first cloud service contained in the first domain.