474 results about "Selection system" patented technology

The invention provides a shield intelligent auxiliary type selection system and method based on a large language model. The model selection system comprises a data input module, a rule knowledge base module, a large model reasoning module and a result generation module. The integrated decision-making system integrating a rule knowledge base, a deep learning model and expert system logic is constructed for the practical problems of complicated geological conditions, multiple rule constraints, high expert dependency and the like in shield construction, and the system combines a structured model selection rule and historical case data, and has the advantages of intelligence, standardization, self-learning, high efficiency and the like. The problems of low efficiency, high subjectivity, insufficient intelligent degree and the like of the existing shield tunneling machine model selection depending on artificial experience and partial standardized guide are solved, and the transformation of shield construction management from artificial experience to intelligent decision can be promoted.

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for maintaining and restoring context for artificial intelligence chatbots. In some implementations, a system receives a user prompt from a user through a chatbot interface, and the system provides a chatbot response to the user prompt through the chatbot interface. The system provides a control that is associated with the chatbot response on the chatbot interface and is selectable by the user to cause the chatbot response to be saved. In response to user selection with the control, the system saves the chatbot response and corresponding metadata that includes context information used by the one or more AI / ML models to generate the chatbot response. The chatbot interface is configured to display the saved chatbot response a and answer a subsequent user prompt using the context information in the metadata corresponding to the saved chatbot response.

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.

Systems and methods for generating user-specific textual and image-based outputs, in response to a user query, for provision of a matching item are disclosed herein. For example, the system may receive a query that includes a textual description. The system may retrieve a user profile for a user associated with the query. Based on the query and the user profile, the system may generate outputs and images using a machine learning model. Based on the outputs, the system may generate graphical representations. The system may receive a selection of a graphical representation. Based on the selection of the graphical representation, the system may enable access to an item that corresponds to the graphical representation.

The systems and methods disclosed herein receive alphanumeric characters defining operative boundaries for expected model use cases, along with operational data. The expected model use cases share common attributes, which are used by a first AI model to construct observed model use cases from the operational data. Each observed model use case includes features such as a text-based description, expected input and output,? AI model(s) generating the expected output from the input, and / or data supporting the AI models. For each observed model use case, a second AI model maps the alphanumeric characters and features to a risk category, selecting from multiple risk categories based on the level of risk associated with the features. The system identifies criteria for the observed model use case within the alphanumeric characters and generates gaps by comparing the criteria with the features of the observed model use case.

The systems and methods disclosed herein receive alphanumeric characters defining operative boundaries for expected model use cases, along with operational data. The expected model use cases share common attributes, which are used by a first AI model to construct observed model use cases from the operational data. Each observed model use case includes features such as a text-based description, expected input and output, AI model(s) generating the expected output from the input, and / or data supporting the AI models. For each observed model use case, a second AI model maps the alphanumeric characters and features to a risk category, selecting from multiple risk categories based on the level of risk associated with the features. The system identifies criteria for the observed model use case within the alphanumeric characters and generates gaps by comparing the criteria with the features of the observed model use case.

Embodiments of a content recommendation or selection system are described. The system uses a pipeline of machine learning models to select content for a user. In embodiments, a first user model generates a first score of content categories for the user based on short-term user data. A second user model generates a second score of the categories for the user based on long-term user data. The two scores are combined to select the categories to include on the content user interface. In embodiments, new categories are added to the recommendations based on an exploration-exploitation algorithm. In embodiments, content categories are organized on the user interface in a manner to promote neighborhood diversity. Advantageously, the machine learning pipeline enables independent configurability of various objectives of the content recommendation or selection system and reduces the amount of machine learning resources needed to implement the system.

The invention provides a DMP-based crowd selection method and apparatus, and an electronic device, a data management platform pre-processes multi-source user data, generates user features corresponding to the pre-processed data based on a preset rule, converts the obtained user features into user tags based on a pre-constructed tag system, and sends the user tags to the user management platform; the method comprises the following steps: acquiring a user tag, storing the acquired user tag in a distributed storage mode, displaying the stored user tag through an operation interface, converting a circling operation for a target user tag displayed on the operation interface into a rule tree, and circling a target user corresponding to the target user tag based on the rule tree. And associating the circling result with the identification information of the target user corresponding to the circling result to generate a crowd packet, and expanding the obtained crowd packet by adopting a similar crowd expansion algorithm. According to the invention, the problems of complex rule configuration, insufficient real-time performance, limited expansibility and the like of a DMP-based crowd selection system in the prior art can be solved.

Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support intelligent model selection for style-specific digital content generation. For example, a system that provides a digital content generation service may include a trained style detection model may receive reference digital content items from a user and extract a user style embedding that represents a style preference of the user. In some implementations, the reference digital content items may include text documents or images provided or selected by the user. The system may compare the user style embedding to a plurality of model style embeddings that each correspond to a respective generative artificial intelligence (AI) model to generate a ranked list of generative AI models. The system may access one or more highest ranked generative AI models from the ranked list to generate novel digital content based on a prompt from the user.

Systems, computer program products, and methods are described herein for implementing parametric optimization analysis for resource selection. The present invention is configured to determine a first set of requirements associated with a resource exchange agreement; identify one or more non-fungible tokens (NFTs) for one or more categories of past resource exchange agreements based on at least the first set of requirements; extract, from the one or more NFTs, one or more resource descriptors associated with one or more past resource exchange agreements in the one or more categories; predict, using a machine learning subsystem, an optimal resource valuation model for one or more resources that meet the first set of requirements using the one or more resource descriptors and the first set of requirements; and transmit control signals configured to cause a first end-point device to display the optimal resource valuation model.

The invention discloses a logging underground battery selection device, which belongs to the technical field of petroleum underground operation, and comprises a main framework, a battery selection module is arranged outside the main framework, and a rib frame is arranged on one side, far away from the battery selection module, of the outer part of the main framework. According to the invention, the dual-battery selection system, real-time monitoring, intelligent switching and balanced maintenance are adopted, so that the batteries can continuously and stably supply power in a complex and changeable underground environment, and the problems of unstable energy supply and limited cruising ability of a traditional single-battery power supply system are solved; according to the double-battery selection system, by optimizing battery combination and selection strategies, efficient utilization and backup of energy are achieved, the energy requirement of underground equipment under long-time and high-load operation is met, the operation efficiency is improved, the operation safety is enhanced, simultaneous selection and optimization of two sets of batteries are achieved, and the energy utilization rate of the underground equipment is increased. Therefore, the stability and reliability of underground operation are improved.

The invention discloses a coal mining equipment intelligent type selection system and method based on multi-parameter coupling in the technical field of mining equipment type selection. The method comprises the steps that mine geological parameters, mining technical parameters and equipment correlation parameters are received; calculating support parameters and coal cutter parameters based on mine geological parameters, mining technical parameters and equipment associated parameters; calculating parameters of the scraper conveyor based on the parameters of the coal mining machine; calculating reversed loader parameters based on the scraper conveyor parameters; calculating crusher parameters based on the reversed loader parameters; and matching the support parameters, the coal mining machine parameters, the scraper conveyor parameters, the reversed loader parameters and the crusher parameters with the equipment database, screening equipment models meeting a threshold range, and displaying a model selection result. Through the automatic and intelligent model selection design process, the problems that traditional manual model selection is low in efficiency, large in error and the like are solved, and high efficiency and precision of fully-mechanized coal mining equipment model selection are achieved.

The invention provides an anti-quantum identification signature method based on an on-lattice SIS problem. The method comprises the following steps: selecting system safety parameters and other related system parameters; generating a system public parameter and a master key by using the system security parameter and other related system parameters; generating a corresponding public and private key pair by using the user identity ID; the user generates a signature of the message by using the private key; and the verifier verifies the validity of the signature by using the system parameters and the user public key. According to the method, a lattice-based digital signature scheme is constructed by utilizing an SIS difficulty problem on a middle lattice, and quantum attack resistance security is provided for an information system for deploying the algorithm scheme; personal identity information of the user is used for registering and generating public and private keys, the key escrow problem participated by a third party is relieved, and the privacy security of the user is improved.

The invention relates to video coding, more particularly belongs to the technical field of data coding, and particularly relates to a CIM data coding method and system based on an earth subdivision grid, and the method comprises the steps: dividing a target city into a plurality of grid units through employing a preset earth profile grid, generating CIM elements of the target city, and carrying out the coding of the CIM data according to the geometric complexity of the CIM elements, setting an adaptive grid hierarchy selection system of the CIM elements to construct a mapping relationship between the CIM elements and a plurality of grid units, constructing a two-pole index architecture of the CIM elements in a space-time association storage unit by utilizing space-time composite grid codes of the CIM elements, and creating a hierarchy retrieval mechanism of the CIM elements, and in combination with a self-adaptive grid hierarchy selection system, space-time composite grid coding, a space-time associated storage unit and a hierarchy retrieval mechanism, executing data coding processing of the heterogeneous CIM data. According to the method, the retrieval efficiency and the storage quality of the CIM data can be optimized, and the use efficiency of the CIM data is improved.

The invention relates to the technical field of geothermal exploration, in particular to a deep geothermal target area selection system based on artificial intelligence. Obtaining an actual direction vector according to the first principal component vector of the exploration point in the neighborhood of the exploration point and the unit vector of the exploration point in the neighborhood and the centroid of the neighborhood; obtaining a direction consistency index according to the unit vector and the actual direction vector between the exploration point outside the neighborhood and the centroid; constructing a heat source feature sequence from the centroid along the direction of the actual direction vector, and obtaining a change rate; and obtaining a gradient consistency index according to the change rate, the distance characteristics between the exploration points outside the neighborhood and the centroid, the theoretical heat source characteristics and the heat source characteristics of the neighborhood of the exploration points outside the neighborhood. The method comprises the following steps: correcting a clustering measurement distance according to a direction consistency index and gradient consistency, and clustering exploration points to obtain an exploration point cluster; the integrity of heat source area structure analysis and the accuracy of geothermal target area selection are improved.

The invention relates to the technical field of industrial visual intelligence, in particular to an intelligent image selection and switching system fusing visual features and quality scores, which comprises the following steps: receiving multiple paths of video coding streams, inter-frame motion vectors and camera parameters; generating a macro block activeness distribution map based on the video coding stream and the inter-frame motion vector, and performing local window positioning and feature reconstruction on the video coding stream to generate an enhanced video vector; calculating a confidence coefficient mean value and a consistency score of the video coding stream according to the enhanced video vector, and fusing the confidence coefficient mean value and the consistency score with a channel transmission signal-to-noise ratio and a quantization noise increment to generate a video quality score; constructing a multi-criterion optimization model, and setting a feature representation vector for the multi-criterion optimization model; mapping the viewpoint weight based on the inner product of the feature representation vector, and calculating with the video quality score to generate a video switching score; and performing priority ranking based on the video switching score, and triggering a mapping switching instruction. And realizing video image scheduling by fusing the visual features and the quality score.

The invention discloses a distributed grounding fault line selection system and method based on an intelligent grounding device, and relates to the field of grounding fault processing. The first current transformer is used for detecting a zero-sequence current value of a corresponding line; the preprocessor is used for acquiring a zero-sequence current value signal of the current transformer I and transmitting an alarm signal to the intelligent grounding device and the monitoring terminal; the intelligent grounding device is used for receiving the alarm signal, transferring the grounding fault current and determining the phase of the grounding fault; the monitoring terminal is used for receiving the alarm signal and controlling the opening of the circuit breaker; the intelligent grounding device comprises a controller, a first switch, a split-phase switch and a current-limiting resistor, the switches in the split-phase switch correspond to three-phase lines respectively, and the first switch is connected to the current-limiting resistor in parallel; the intelligent grounding device further comprises a three-phase switch and a grounding transformer, the input end of the three-phase switch is connected with the three-phase line, and the output end of the grounding transformer is grounded. The application has the effect of reducing the electric energy consumed by the monitoring terminal.

Systems and methods that communicate, to a streaming device, data that results in a display device of the streaming device displaying first information associated with a first game being approved to be wagered on with fiat currency in a first jurisdiction and a second game not being approved to be wagered on with fiat currency in the first jurisdiction. Responsive to a selection of the first game or the second made via the streaming device, the systems and methods enable / not enable a first client device located in the first jurisdiction to place / not place a fiat currency wager in association with a play of the selected game displayed by the streaming device.

Systems and methods for generating user-specific textual and image-based outputs corresponding to existing items, in response to user queries, are disclosed herein. For example, the system may receive a query that includes a textual description. The system may retrieve a user profile for a user associated with the query. Based on the query, the system may obtain a description of an item. Based on the query, the user profile, and the description, the system may generate an output and an image using a machine learning model. Based on the output and the image, the system may generate a graphical representation of the item. The system may receive a selection of the graphical representation of the item. Based on the selection of the graphical representation, the system may enable access to the first item.

The invention discloses a multi-access network intelligent selection system and method in a 5G network environment, and the system comprises a multi-network state sensing module, a service feature recognition module, an intelligent access decision module, a network switching control module, and a strategy updating and learning module. According to the present service type and service quality demand dynamic optimization, communication performance and user experience are considered, an access decision-making mechanism with an adaptive learning capability is constructed, experience can be accumulated along with time, a strategy can be continuously optimized, the frequency and cost of network switching are controlled, the communication continuity is ensured, unnecessary switching risks are reduced, and the network switching efficiency is improved. The method is suitable for various actual scenes such as a 5G mobile terminal, a vehicle-mounted communication device and an industrial terminal, the communication performance and the overall use experience of the terminal device in the 5G environment under the multi-network access condition are effectively improved, and a basic support is provided for achieving heterogeneous network convergence and intelligent communication.

An adaptive artificial intelligence (AI) control system receives a prompt for an AI system from a user interface component of a software application on a mobile device. The adaptive AI control system determines a current battery level of the mobile device using a battery level monitoring component. The adaptive AI control system then selects a generative AI model of the AI system to use to generate a response for the prompt based on the current battery level using a model selection component. The generative AI model that is selected is one of a plurality of different generative AI models of the AI system which are capable of processing the prompt, each of the plurality of generative AI models having a different level of complexity.

The invention is suitable for the technical field of intelligent recommendation, and provides an intelligent topic selection system based on medical big data, which comprises a user front end, a multi-source data integration module, an intelligent analysis module and a topic selection recommendation module, the intelligent analysis module comprises a natural language processing unit, a knowledge graph construction unit, a trend prediction unit and an innovativeness evaluation unit; the topic selection recommendation module comprises a blank recognition unit, an interdisciplinary association analysis unit, a clinical demand matching unit, a visual analysis unit and a feasibility evaluation unit, the device adopts an intelligent algorithm to realize automatic analysis, and the intelligence of a topic selection system is improved; by integrating multiple types of medical data sources, the data coverage is improved; by establishing a dynamic knowledge base, the latest research progress is tracked, and real-time updating of data is achieved; through visual interaction, a visual data display and operation interface is provided, the topic selection efficiency is improved, the research preparation period is shortened, the topic selection repetition rate is reduced, and the innovativeness is improved.

According to the embodiment of the invention, the instrument type selection method comprises the following steps: demand collection: obtaining a spoken type selection demand input by a user; the demand analysis is used for converting the spoken type selection demand input by the user into a demand of applying a standard instrument term; type selection searching: searching in an instrument type selection knowledge graph based on the analyzed demand to obtain an instrument entity meeting the demand; and performing model selection evaluation, calculating weighted scores of the instrument entities meeting the requirements based on weights of different model selection elements, and obtaining the instrument entities of final model selection, based on the method, customer requirements can be more fully and accurately understood, meanwhile, the entity association ability and retrieval ability of the knowledge graph are further enhanced, and the user experience is improved. Therefore, the instrument type which better meets the requirements of the user is recommended for the user. The invention further provides an instrument type selection system and a storage medium.

The invention discloses a dynamic tool selection system and method based on multi-stage training, and belongs to the field of natural language processing. The method comprises the following steps: acquiring a labeling data set in a specific vertical field through a high-confidence training sample construction module, preprocessing the labeling data set to obtain different types of complex problem sets, generating a positive sample tool and a negative sample tool for each complex problem, and performing reasoning to obtain a high-quality training sample set; a progressive training module is used for multi-stage training of a model, and the model comprises a supervised fine-tuning training stage, a comparative learning stage and a reinforcement learning stage; and performing multi-dimensional evaluation on the trained model by taking the accuracy rate, the recall rate and the F1 score as evaluation indexes through a multi-dimensional evaluation module. Through the synergistic effect of the modules, the question answering ability of the model in the vertical field is effectively improved, particularly, a remarkable breakthrough is made in the aspect of tool selection accuracy, and a reliable technical scheme is provided for application of an intelligent question answering system in the professional field.

The invention provides a sludge concentration and quality selection system, a control method and a sewage treatment system and method.The sludge concentration and quality selection system is applied to an integrated reaction precipitation device inoculated with activated sludge and used for receiving sludge discharged by the integrated reaction precipitation device and concentrating or screening the sludge. Comprising a sludge pre-concentration tank and a sludge quality selection device, and the sludge concentration and quality selection system has at least two working modes including a sludge quality selection mode and a sludge concentration mode. According to the sludge concentration and quality selection system, the control method and the sewage treatment system and method, sludge of the integrated reaction precipitation device can be concentrated and then discharged, the concentration of residual sludge is increased, and the subsequent sludge treatment efficiency is improved; the sludge in the integrated reaction precipitation device can be directionally screened, and the sludge with poor settling performance is discharged, so that the water treatment efficiency and effect are improved.

Systems and methods for on-demand creation, evaluation and / or deployment of machine learning (ML) models for computing the value of a predictive trait. System operations include detecting, at a predictive trait user interface (UI), a selection of a predictive trait of a plurality of predictive traits and a selection of a configuration setting for the predictive trait. System operations further include executing, using an orchestrator, an onboarding flow that retrieves user data, a training workflow that generates a trained predictive trait model, and / or an inference workflow that runs the trained predictive trait model to compute predictive trait values for users in a test set. System operations further include generating and displaying, via the predictive trait UI, explanations associated with the trained predictive trait model, the computed predictive trait values, and the test set. The system further transmits computed predictive values to an audience management service for audience generation or user profiling.

The invention discloses a data management system for franchise owners. The data management system comprises a unified master file management module, a customer feedback module, a service center module and a master file center module, the unified master file management module is used for classified storage of alliance provider master data; the customer feedback module is used for collecting evaluation information of the client to the store and supervising the store and the alliance owner to which the store belongs according to the evaluation information; the service center module is used for collecting order placing data information of the client, generating a work order according to the order placing data information of the client, and matching the work order with a store of a franchise owner to generate an order; the main file center module comprises a center selection system, a new store system, an ERP system and a B I system. According to the method, multi-label storage is carried out on the alliance owner information, the stores and store commodities of the alliance owners are analyzed from multiple aspects, and accurate guidance is provided for store operation of the alliance owners, so that the store operation risk of the alliance owners is reduced.

A system and method are provided a selection, optionally dynamic, of a subset of sensors in a system for determining a location of a remote device relative to an object.

The systems and methods disclosed herein receive alphanumeric characters defining operative boundaries for expected model use cases, along with operational data. The expected model use cases share common attributes, which are used by a first AI model to construct observed model use cases from the operational data. Each observed model use case includes features such as a text-based description, expected input and output,? AI model(s) generating the expected output from the input, and / or data supporting the AI models. For each observed model use case, a second AI model maps the alphanumeric characters and features to a risk category, selecting from multiple risk categories based on the level of risk associated with the features. The system identifies criteria for the observed model use case within the alphanumeric characters and generates gaps by comparing the criteria with the features of the observed model use case.

The invention belongs to the technical field of outpatient service management, and discloses a generative AI-based outpatient service management method and system, and the method comprises the steps: obtaining the basic information, symptom description and historical medical record data of a patient when the patient sees a doctor through an AI outpatient service management system, and carrying out the semantic understanding and preliminary triage prediction of the symptom of the patient through a generative AI model, and generating possible disease types and corresponding department suggestions. According to the invention, semantic understanding and triage prediction are carried out on the symptoms of the patient through the generative AI, and intelligentization of the appointment process is realized. Patients do not need to manually select cumbersomely, and the system automatically generates department suggestions, so that the appointment time is greatly shortened, and the convenience and efficiency of appointment of the patients are improved. The constructed patient priority evaluation model and the department resource state model enable the calculation of the reservation time to be more scientific and reasonable, coordinate the time arrangement of the reservation and the preliminary diagnosis stage, effectively avoid reservation conflicts and long-time waiting, and improve the overall efficiency and accuracy of outpatient reservation.