382 results about "Multiple domain" patented technology

An example operation may include one or more of receiving a plurality of architecture documents of a plurality of different domains of a software architecture, identifying a missing component that is missing from within the software architecture between a first domain and a second domain among the plurality of domains based on execution of a generative artificial intelligence (GenAI) model based on the plurality of architecture documents of the software architecture, generating a recommended modification to the software architecture based on the identified missing component, and displaying the recommended modification via a user interface.

An example operation may include one or more of training a generative artificial intelligence (GenAI) model via execution of the GenAI model on descriptions and diagrams of a software architecture, receiving a plurality of different architecture views from a plurality of different domains of the software architecture, generating an architecture diagram of the plurality of domains of the software architecture in combination based on execution of the GenAI model on the plurality of different architecture views from the plurality of different domains, and displaying the architecture diagram of the plurality of domains of the software architecture via a user interface.

This disclosure includes a process for merging legacy databases having different data schema into a principal data store. A computing device retrieves a legacy database from each of a plurality of domains of an organization to form a plurality of legacy databases, where at least two legacy databases in the plurality of legacy databases have a different data schema. For each of the plurality of legacy databases, the computing device retrieves data from the respective legacy database, places the data from the respective legacy database into a respective unstructured database, and merges the respective unstructured database into a principal data store designed to hold data for every domain of the plurality of domains.

Domain specialty instructions may be generated for performing text analysis tasks. An input text may be received for performing a text analysis task. One or more domain entities may be extracted from the input text using a machine learning model trained to recognize entities of a domain in a given text. The one or more domain entities may be inserted as part of generating instructions to perform the text analysis task using a pre-trained machine learning model fine-tuned to the domain. The pre-trained machine learning model may be caused to perform the text analysis task using the generated instructions and a result of the text analysis task may be provided.

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.

Domain specialty instructions may be generated for performing text analysis tasks. An input text may be received for performing a text analysis task. A domain specialty may be identified for the input text. Specialty domain identifiers may be inserted as part of generating instructions to perform the text analysis task using a pre-trained large language model fine-tuned to a domain that includes multiple domain specialties. The pre-trained large language model may perform the text analysis task on the input text using the generated instructions. A result of the text analysis tsk performed on the input text may be provided.

The present disclosure describes an electronic device. The electronic device includes a memory and a processor. The processor is configured to provide an utterance to an NLU model, which selects an intent from the list of intents based on the utterance. The processor is also configured to cause a response to the utterance to be provided to a user based on the selected intent. The NLU may be trained by obtaining a dataset comprising one or more words, generating a list of intents based at least in part on the dataset, grouping the list of intents into one or more domains, generating a list of training utterances for each intent in the list of intents, and modifying one or more parameters of the NLU model based on the list of intents, the list of training utterances, and / or the one or more domains.

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.

Establishing and advertising co-routed bidirectional paths across multiple domains includes determining a path from the router to a BGP peer, in a Segment Routing (SR) network, as a reverse path SR policy; appending a Binding Segment Identifier (BSID) of the reverse path SR policy in a BGP update message; and sending the BGP update message to the BGP peer. This can be used to ensure an Operations, Administration, and Maintenance (OAM) packet is guaranteed to traverse the same forward and reverse path for fault detection thereon.

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.

Embodiments of this application provide an information verification method, apparatus, and device. The method includes: sending a first message to a server that includes a target domain name and first indication information, which indicates a status of first verification information stored in a client, the first verification information is verification information generated based on multiple domain names and owner information of the multiple domain names, the first verification information verifies the owner information of the multiple domain names, and the multiple domain names include a target domain name; receiving a second message sent by the server based on the first message, where the second message indicates target first verification information, which is used by the client to verify owner information of the target domain name; and verifying the obtained owner information of the target domain name based on the second message.

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.

Systems and methods for data hydration are provided. In some examples, a method includes accessing a compiler. In some examples, the compiler is associated with a source graph, a domain graph, and a mapping profile. In some examples, the domain graph includes one or more domain data schemas. In some examples, the method further includes receiving a source dataset from a source system, and applying the compiler to the source dataset from the source system to generate a domain dataset. In some examples, the domain dataset uses at least one of the one or more domain data schemas. In some examples, the method is performed using one or more processors.

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.

An enrollment system uses a camera to scan an identification document to generate an image. A name of an organization is extracted from the image. The enrollment system obtains one or more Internet domain names associated with the organization. Using a graphical user interface, a prompt is displayed seeking input of an e-mail address of a user of the enrollment system. The input of the e-mail address is received via the graphical user interface. The enrollment system determines that the e-mail address is associated with the one or more Internet domain names. An e-mail is sent to the e-mail address of the user. The enrollment system determines that the user has clicked on a hyperlink in the e-mail using a mobile device that is different from the enrollment system. The enrollment system performs functions to enroll the mobile device in a service plan operated by a mobile network operator.

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 tool wear state identification method and system for cutting of a numerical control machine tool. The method comprises the following steps: acquiring a three-way vibration signal, a power signal and a temperature signal; performing time domain, frequency domain and time-frequency domain multi-domain feature extraction on the three-way vibration signal, the power signal and the temperature signal, and constructing a multi-domain high-dimensional feature vector; performing feature fusion dimension reduction on the high-dimensional feature vector to obtain a feature sample set; obtaining a cutter state recognition model according to the feature sample set; and inputting the acquired multi-source signals into a cutter state identification model to carry out cutter wear state identification of cutting machining of the numerical control machine tool. By the adoption of the technical scheme, various machining conditions and tool types can be better adapted, and the applicability and universality of the recognition system are improved.

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.