396 results about "Intelligent machine" patented technology

A symbolic neuroadaptive control system is disclosed for real-time arbitration, consent, and ethical modulation of artificial intelligence agents operating in wearable computing environments. The system integrates multimodal biometric telemetry—including high-resolution EEG signals—with a symbolic kernel that performs logic-driven arbitration over cognitive, emotional, and ethical states. Using Coq-verified invariants and zero-knowledge biometric consent tokens, the system constructs a deterministic symbolic execution graph, gating AI outputs based on internal user states such as trauma, stress, or intentionality. Unlike conventional black-box BCI models, the invention routes EEG-inferred affective-symbolic tokens through a formal ethics layer that enforces real-time interrupt control, utility bounding, and trust verification. The kernel enables AGI systems to defer or modify behavior based on user-state alignment, granting sovereign agency over all downstream actions. This neuro-symbolic architecture redefines the interface between human cognition and intelligent machines, enabling emotionally conscious, morally verifiable, and symbolically transparent AI governance in dynamic, high-stakes contexts.The present invention relates to artificial intelligence and neurotechnology, specifically to a real-time, neuro-symbolic operating system kernel that converts electroencephalography (EEG) signals into structured symbolic data for use in emotional cognition, ethical prioritization, autonomous agent dispatch, and real-time telecommunications routing. The invention bridges brain-computer interface (BCI) inputs with symbolic AI architectures to enable ethically aligned machine response during cognitively or emotionally intense events.

The invention relates to the technical field of mechanical arms, in particular to a man-machine interaction system of an intelligent mechanical arm with a body. The system decomposes a long time sequence task into sub-tasks through a large language model, and introduces a task acquisition module, a task queue management module, a task re-planning module and a control execution module, uses visual detection to identify gestures and environment events to trigger temporary tasks, and maintains interruptible marks and safety anchor points based on priorities and interruption risks. And during interruption, the mechanical arm with the timestamp and the environment state are collected, during recovery, the states are compared, local re-planning is carried out, transition sub-tasks are automatically generated for failure sub-tasks, and the mechanical arm is controlled to execute after constraint verification. According to the system, on the premise that the structure consistency, the space consistency and the time consistency are guaranteed, safe interruption and efficient recovery in a long-time-sequence task can be achieved, and the autonomy, the real-time performance and the operation safety of the mechanical arm in a man-machine cooperation scene are remarkably improved.

The invention discloses an airport flight quantity and throughput prediction method and system, and relates to the technical field of intelligent airport management. The method comprises the following steps: firstly, determining a future target time period to be predicted, and obtaining related multi-source data according to the future target time period; preprocessing the data and extracting time sequence and non-time sequence features; processing time sequence features by using the trained long-short-term memory neural network model, processing non-time sequence features by using the extreme gradient boosting tree model, and respectively obtaining prediction results; and integrating the outputs of the two types of models through a weighted fusion strategy to obtain an accurate prediction value of the flight sortie and the single-machine passenger capacity, and further calculating to obtain a passenger throughput prediction result. According to the method and the system provided by the invention, through the architecture of multi-source data fusion and hybrid intelligent model cooperation, the prediction precision and reliability are effectively improved, and scientific decision support is provided for airport operation management.

Embodiments provide a user device, UE, of a wireless communication network, the wireless communication network using one or more Artificial Intelligence / Machine Learning, AI / ML, models for one or more use cases, wherein the UE is configured or preconfigured with a plurality of AI / ML models for performing one or more certain operations, and wherein, dependent on one or more criteria, for performing the one or more certain operations, the UE is toswitch from a first AI / ML model to a second AI / ML model, ordeactivate one or more of the plurality of AI / ML models, orswitch from a current operation mode to a new operation mode.

The invention discloses a machine tool running state collection and evaluation method and system, and relates to the field of intelligent machine tools, and the method comprises the steps: collecting machine tool multi-dimensional data through a multi-channel collection device; performing time alignment on different sampling frequency signals by adopting a dynamic synchronization algorithm of clock drift compensation; executing noise suppression, feature extraction and compressed encoding at the edge computing node to generate local state abstract data; sending the abstract data to a central server through a distributed message queue, and realizing multi-application concurrent access based on load dynamic scheduling; and the central server performs fusion analysis on the abstract data by using a multi-modal fusion deep network model, and outputs a health degree index and an overall operation score of the key parts of the machine tool, thereby realizing intelligent evaluation and health prediction of the operation state of the machine tool. The method realizes global perception and intelligent evaluation of the machine tool operation state, and can be widely applied to operation monitoring and health management of high-end numerical control machine tools, complex machining centers and unmanned production lines.

The invention relates to the technical field of robot perception and data generation technologies, in particular to a first-view-angle drilling method and device based on memory enhancement and a storage medium, and the method comprises the steps: extracting a plurality of target frames from a historical video stream stored in a space intelligent machine, obtaining a plurality of memory elements based on the plurality of target frames, performing three-dimensional reconstruction on each target frame, constructing a target world model, planning a first visual angle track of the target robot in the target world model, performing imaging simulation on the target robot, performing real-time rendering on a first visual angle video of the target robot, and performing real-time rendering on a second visual angle video of the target robot based on the same time axis and action script as the first visual angle video. A third-person video corresponding to the space intelligent machine is generated, and a drilling result is output; according to the method, the long-term memory data of the space intelligent machine is ingeniously used, high-quality drilling data can be quickly generated, the reliability of the first-view video is improved, and a reliable basis is provided for training and testing of a robot algorithm.

The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. There is disclosed a first method for reporting user equipment (UE) artificial intelligence (AI) / machine learning (ML) capability to a network. The first method comprises: transmitting, to the network, an indication of the UE AI / ML capability. There is also disclosed a second method for reporting network AI / ML capability to a UE. The second method comprises: transmitting, to the UE, an indication of the network AI / ML capability.

The invention provides a drilling and blasting method tunnel section contour and blast hole position automatic design method, and relates to the technical field of tunnel engineering, and the method comprises the following steps: S1, determining the type of a tunnel section contour; s2, performing parameterized representation and automatically drawing a contour line based on the tunnel section contour type; s3, based on the drawn tunnel section contour, conducting parameterized arrangement of cut holes, peripheral holes, bottom plate holes and auxiliary holes, and generating coordinates of various blast holes; automatically designing blast hole positions through different types of tunnel section contours; and the arrangement of the blast holes is converted into the generation of coordinate points, so that the requirements of intelligent mechanical equipment for tunnel excavation in the drilling and blasting method on the coordinates of the blast holes are met, and finally, the intelligent and refined blast hole arrangement of a tunnel excavation site in the drilling and blasting method is realized. The problems that in the contour drawing and blast hole arrangement link, manual lofting is often low in efficiency and large in error are solved, and it is avoided that the excavation section deviates from the design, the over-excavation or under-excavation phenomenon occurs, and the construction quality is affected.

The invention discloses an industrial intelligent control system and method, relates to the technical field of industrial automatic control, and mainly aims to solve the problems of low data cross-layer interaction efficiency, system global optimization, poor safety, poor expansion capability, difficulty in function upgrading and difficulty in fusion of AI control and traditional control in the existing industrial intelligent control scheme. The system comprises a basic control layer, an intelligent I / O layer and an intelligent control layer. The basic control layer comprises an extended I / O cabinet, a common controller and a main control network A / B; the intelligent I / O layer comprises an intelligent I / O cabinet and an intelligent I / O switch; the intelligent control layer comprises an intelligent controller, a credibility regulator, a hyper-fusion module and an intelligent middle table, an end-edge-cloud intelligent collaborative architecture is constructed, the computing power of a DCS is enhanced, the development collaborative safety capability of an intelligent algorithm of the system is improved, and safety fusion and smooth transition of AI intelligent control and traditional control are promoted.

The invention relates to the technical field of numerical control machine tool intelligent manufacturing and industrial data collection, in particular to a machining data collection method and system applied to a numerical control machine tool, and the method comprises the following steps: S1, the classification and access scheme design of the numerical control machine tool: dividing the numerical control machine tool into a class I intelligent new machine tool, a class II semi-intelligent machine tool and a class III old dumb equipment according to the digitization capability, a classification access scheme of combination of a general protocol adapter, a serial port-Ethernet converter and a non-intrusive sensor is respectively adopted to realize data access of heterogeneous and old equipment; uniform access of 10 + brand numerical control machine tools is supported, the data intercommunication rate of heterogeneous equipment reaches 100%, the problem of'information isolated island 'is solved, six types of equipment states are recognized, and the OEE calculation precision is improved to + / -2% and is improved by 5 times compared with a traditional method.

Systems and methods are disclosed for automated generation, validation, and deployment of infrastructure-as-code (IaC) using a multi-agentic artificial intelligence / machine learning (AI / ML) architecture. An extraction agent parses multimodal artifacts (e.g., diagrams and / or configuration data) to derive or generate infrastructure set-up parameters. A coding agent generates IaC units based on the extracted parameters. A validation agent determines the syntactic and semantic compliance of the generated IaC, classifying the IaC as executable, non-executable, or identifying corrective actions. A deployment agent transmits executable IaC to target computing environments and manages automated provisioning of the IaC in the target environments. In cases of validation failure, error indicators are provided to a user (e.g., via an agentic chat bot) for clarification or correction, enabling iterative refinement.

The invention discloses a measuring point table matching method, device and equipment and a medium, and belongs to the technical field of intelligent cabins of ships. The method comprises the following steps: acquiring a to-be-matched measuring point table, and converting to-be-matched measuring point information in the to-be-matched measuring point table into a first vector; acquiring a plurality of second vectors according to a preset standard measuring point vector database; calculating the similarity between the plurality of second vectors and the first vector, and determining a first candidate set based on the similarity; reordering the first candidate set based on semantic correlation to determine a second candidate set; and performing reasoning matching on the second candidate set through the large language model, and determining a matching result in the second candidate set. According to the method, the matching accuracy is improved by means of semantic analysis, the method does not need to depend on a predefined rule through a large language model, the method has the universality of adapting to ship intelligent cabin systems and comprehensive monitoring systems developed by different manufacturers, and the problem that in the related technology, it is difficult to consider high efficiency, accuracy and universality at the same time in measuring point matching is effectively solved.

The invention relates to the technical field of machine room monitoring, in particular to an intelligent machine room monitoring system and method based on the Internet of Things. According to the method, the visible light and infrared images are fused, so that the detection precision and robustness of the target in a complex environment are improved, and the limitation of single-mode imaging in illumination variation, shielding or extreme environments is effectively overcome. Meanwhile, by combining RFID identity recognition and space-time correlation analysis of a visual track, accurate matching of the personnel and the equipment is realized, the identity recognition accuracy and behavior discrimination reliability of the target personnel, tools or equipment are remarkably improved, and the risk of misinformation and missing report is effectively reduced. According to the invention, localization processing is carried out on the multi-source data through edge calculation, network transmission delay and bandwidth pressure are effectively reduced, and the response speed and stability of the system are improved.

The invention relates to the technical field of predictive maintenance and health management of industrial equipment, in particular to an intelligent electromechanical integrated monitoring system based on the industrial internet, which comprises a data acquisition module, a data processing module, a data processing module and a data processing module, and is characterized in that the data acquisition module acquires an equipment dynamic operation parameter set, inherent physical attribute parameters and historical maintenance data of target electromechanical equipment; calling preset process topology data, economic loss data and maintenance cost data; the state evaluation module iteratively calculates an accumulated degradation index representing the health condition of the equipment; the importance quantification module calculates a process importance coefficient representing the criticality of the equipment in the production line based on the process topology data; the risk calculation module generates a production risk degree which quantifies the shutdown influence; the decision generation module generates a dynamic maintenance decision instruction; according to the invention, systematic risk cognition from the individual equipment to the overall production line is realized, extensive management of all equipment is avoided, and allocation of maintenance resources is more strategic and targeted.

The present invention is directed to a software-based technology system for managing and executing high volumes of work on complex projects across multiple organizations involving multiple users that results in superior outcomes at significantly reduced time and cost. Litigation is the initial application of this invention; each case is handled as a project. As applied to litigation, the system of the invention empowers a novel method of end-to-end case assessment, planning, execution and management by integrating tools, processes and procedures allowing for more informed decision-making and deliberate execution of all case-related actions as well as enhanced coordination and communication among all constituents, resulting in efficient achievement of targeted outcomes. The system provides intuitive, menu-driven functionality for inputting, processing and utilizing vast amounts of information, including company and case-specific information, attorney discretionary evaluation and input, document templates and monitoring and reporting requirements. A threat algorithm cross-correlates via multivariate analysis critical data points with customized company-specific information and business logic to generate a numeric threat score as an output. The system also predicts the expected settlement range with a high degree of precision for a specific case as the output of one or a combination of multiple artificial intelligence (AI) / machine learning algorithms. The system then generates a unique case strategy (which includes an expected outcome, including projected outcome, Case Plan and estimated attorneys' fees and costs). System logic learns from thousands of examples of similar cases and directs highly effective and efficient execution of a specific Case Plan providing each attorney and staff member assigned a role in a case with a prioritized task list that is updated in real time as other users complete tasks and litigation priorities change. For each user, the system integrates assigned tasks across multiple cases and multiple client portfolios into a single, prioritized task list. The execution features become increasingly valuable given the volume and complexity of projects across multiple organizations and users, allowing users to focus on their area of expertise and avoid the wasted time and distraction of trying to determine what they should be working on or recording notes to communicate what they have been doing since the system automatically performs these functions and directs the various users' next steps while monitoring their progress against the plan. Integration of administrative functions, including automated time tracking, billing and reporting without the need to rely on users to update this information, further increases quality and efficiency of legal services. Key objectives of the system of the present invention include eliminating gaps between business objectives and litigation strategy and optimizing communication and coordination of case-related tasks and priorities in a fully automated fashion, which are all achieved by leveraging data and technology to enable a fundamentally different approach that becomes smarter and more precise via machine learning and predictive modeling.

The invention discloses a ship electromechanical equipment fault diagnosis system and method based on transfer learning, and the system comprises an equipment data preprocessing layer, an equipment fault diagnosis model, and an optimized fault diagnosis model layer. The equipment data preprocessing layer comprises a working condition data acquisition unit, a time sequence data processing unit and a working condition feature fusion unit; the equipment fault diagnosis model comprises a source domain, a target domain, a fault attention unit and a fault diagnosis unit; a feature mapping network is trained by adopting a transfer learning method through electromechanical equipment working condition features to obtain an equipment fault diagnosis model; aligning the characteristics of the working condition characteristic vectors distributed in the source domain with the characteristic vectors of the target domain through a gradient inversion method to obtain an equipment fault network; the method can be directly deployed to an intelligent cabin system of the unmanned ship, real-time fault diagnosis of electromechanical equipment is realized, and a powerful guarantee is provided for safe operation of the unmanned ship.

Provided is a process, including: executing, with the computer system, tasks in the workflow with AI agents; detecting, with the computer system, that a human is needed to assist with a given task among the tasks in the workflow after one of the AI agents attempts the given task; selecting, with the computer system, the human from among a set of humans based on a profile of the human and the given task and sending a message that causes the selected human to perform the given task; and receiving, with the computer system, the selected human's completion of the given task and inputting the human completion of the given task, or input, to one of the AI agents performing a downstream task in the workflow; and storing, with the computer system, a result of completing the workflow in memory.

This invention relates to the field of intelligent machine tool technology, specifically to an intelligent machine tool equipped with a transverse movement mechanism, including an up-and-down transverse movement mechanism, a left-and-right transverse movement mechanism, a left-and-right micro-movement mechanism, and a pressure mold. The left-and-right transverse movement mechanism is mounted on the outer side of the up-and-down transverse movement mechanism, and the left-and-right micro-movement mechanism is mounted on the outer side of the left-and-right transverse movement mechanism. The pressure mold is located on the top of the left-and-right micro-movement mechanism. The core of this invention lies in eliminating the independent dedicated mounting plate and instead directly connecting the mold-moving assembly to the machine head frame. By eliminating redundant mounting plates and auxiliary connectors, the overall mass and inertial load of the moving parts are effectively reduced, which is beneficial to improving the dynamic response performance and motion accuracy of the system. Secondly, the direct connection method significantly compresses the structural space, making the layout more compact and significantly improving space utilization. Based on the simplification of the overall structure, the miniaturization and lightweight design of the machine head module are realized, providing a structural foundation for the deployment and performance improvement of the equipment in a limited space.

This disclosure relates to the collection of data for the management and monitoring of AI / ML models. In one described embodiment, a method performed by user equipment (UE) includes receiving from a network node a first instruction for model management decisions and a second instruction for model performance reporting, and, if the first instruction does not indicate model activation and the second instruction enables model performance reporting, running the model and reporting model performance to the network node without using the output from the model to control communication with the network node. Various other methods, including corresponding network node methods, are disclosed.

The present invention relates to an artificial intelligence Four Pillars of Destiny (Bazi) fortune-telling apparatus and method, a recording medium containing a computer program capable of executing this method, an artificial intelligence Four Pillars of Destiny fortune-telling service system and method using the same, more specifically, to an apparatus and method that improves the accuracy of Four Pillars of Destiny fortune-telling by utilizing artificial intelligence (AI) machine learning, deep learning, and large language models (LLM) to analyze past Four Pillars of Destiny readings, thereby enabling users to improve their understanding of their own Four Pillars of Destiny fortune-telling, a recording medium containing a computer program, and a service system and method using the same. An artificial intelligence-based Four Pillars of Destiny (Bazi) analysis device according to an embodiment of the present invention is characterized in that, in response to input Four Pillars of Destiny information, a computing device including at least a processor outputs Four Pillars of Destiny analysis data through Four Pillars of Destiny analysis and analysis, and in order to improve the accuracy of Four Pillars of Destiny analysis, the data analyzed based on answers (question and answer) obtained through a multi-stage questioning process, is comprehensively analyzed (descriptive statistics and inference statistics) by combining physiognomy (image), palm lines - palmistry (image), voice / voice physiognomy (voice) data and the Four Pillars of Destiny of people with similar Four Pillars of Destiny, and then utilizes artificial intelligence (machine learning and deep learning, etc.).

The present invention discloses an integrated research and development system for high-throughput preparation and statistical mapping characterization of materials, comprising: a high-throughput preparation module, a high-throughput characterization module, an automatic control module and a statistical mapping data processing module; the high-throughput preparation module is used for preparing a multi-component combinatorial-sample; the high-throughput characterization module comprises a plurality of different high-throughput characterization devices; the automatic control module comprises a special sample box, a sample moving platform, an intelligent mechanical arm and a synchronous control system; and the statistical mapping data processing module is used for constructing a statistical mapping constitutive model corresponding to position mapping according to the composition, microstructure and performance data of the combinatorial-sample. The present invention integrates multiple functions, has high automatic control level, improves the experimental speed and experimental efficiency.

A method performed by a distributed unit of an access network node is disclosed. The method includes receiving, from a central unit of the access network node, input data for inferring an artificial intelligence / machine learning (AI / ML) model; and inferring the AI / ML model.

Systems and methods for tuning hyperparameters of a model includes: receiving a multi-criteria tuning work request for tuning hyperparameters of the model of the subscriber to the remote tuning service, wherein the multi-criteria tuning work request includes: a first objective function of the model to be optimized by the remote tuning service; a second objective function to be optimized by the remote tuning service, the second objective function being distinct from the first objective function; computing a joint tuning func-tion based on a combination of the first objective function and the second objective function; executing a tuning opera-tion of the hyperparameters for the model based on a tuning of the joint function; and identifying one or more proposed hyperparameter values based on one or more hyperparam-eter-based points along a convex Pareto optimal curve.

The invention belongs to the field of intelligent mechanical equipment, and particularly relates to a posture-adjustable double-station storage platform. A multi-station storage platform is core intelligent equipment for industrial and agricultural production and is extremely widely applied to the fields of manufacturing, logistics, medical instruments, automobiles and parts, low-altitude economy and the like. Aiming at the problems that a single-layer storage framework is low in space utilization efficiency, low in unit area operation density and difficult to meet the high-density deployment requirement in the prior art, the invention provides the posture-adjustable double-station storage platform, the posture-adjustable double-station storage platform is provided with an upper storage station and a lower storage station, and each station has a translational freedom degree and a rotational freedom degree. The pose adjustment of the bearing platform is realized by adopting a hinge mechanism; the design of an elastic rotating pair and an elastic buffer layer is introduced into the positioning and clamping mechanism, so that the damage to stored materials can be effectively avoided while the positioning precision is ensured; the double-path collaborative operation is realized through the dynamic opening and closing baffle device, so that the task throughput and the space utilization rate in unit time are greatly improved.

The present disclosure provides a sample generation method, a machine learning model training method, an information evaluation method, an apparatus, a device, a storage medium and a program product, relates to the technical field of computer and data processing, and particularly relates to the technical field of artificial intelligence, machine learning, big data, data mining and the like. The specific implementation scheme is as follows: for any one content publishing subject, determining target information according to the publishing content of the content publishing subject; determining a label for each content publishing subject according to the target information; and for any one content publishing subject, generating a subject sample according to the label.

Disclosed are methods, network data analysis functions (NWDAF), and network functions (NF) for enhanced wireless device (WD) latency performance analysis to support application layer artificial intelligence / machine learning (AI / ML) operations. In one embodiment, the method in the NWDAF includes collecting WD latency performance inputs from a network function (NF), wherein the latency performance inputs include multiple transmit data volume values ​​and transmit time values. The method includes determining a set of latency performance analyses to support a federated learning process, wherein the set of latency performance analyses includes at least one of latency predictions and latency statistics relating to uplink and downlink transmissions between the NWDAF and the NF, at least in part based on the latency performance inputs. The method also includes transmitting the set of latency performance analyses to the NF.

This disclosure provides a method, apparatus, electronic device, storage medium, and computer program product for obtaining business configuration parameters, relating to the field of artificial intelligence technology, particularly to the fields of artificial intelligence, machine learning, intelligent search, and intelligent recommendation technology. The specific implementation scheme is as follows: acquiring traffic request data; inputting the feature data of the traffic request data into at least one set of target models to obtain the output parameters of each set of target models; the output parameters of each set of target models represent the business indicator gain resulting from changing the business processing of the traffic request data from using the original business configuration parameters corresponding to each set of target models to using the candidate business configuration parameters corresponding to each set of target models; based on the business indicator gain of each set of target models, determining the target business configuration parameters from the original business configuration parameters and candidate business configuration parameters corresponding to each set of target models. According to this disclosure, accurate acquisition of target business configuration parameters can be achieved.

The invention discloses an intelligent unstacking, bag breaking and quantitative feeding system and method, and relates to the technical field of intelligent machinery, the intelligent unstacking, bag breaking and quantitative feeding system comprises a double-layer conveying unit, an automatic bag breaking mechanism and a moped, a transfer conveyor is arranged on the right side of the double-layer conveying unit in parallel, and a fixed visual camera is arranged over the double-layer conveying unit on the right side. According to the intelligent unstacking, bag breaking and quantitative charging system and method, the double-layer conveying unit is arranged to be of a double-layer conveying structure, full tray materials are conveyed through the upper layer, and empty trays are conveyed through the lower layer, so that the occupied space of the device is greatly saved; all the structures are matched with one another to achieve automation of the whole process of automatic unstacking, bag breaking and feeding, the fixed visual camera is arranged, so that the device can automatically recognize the abnormal state of non-full stacks such as half stacks and intelligently judge the positions of the materials, meanwhile, automatic quantitative adding can be achieved, and the system can be connected with an upper computer and conduct quantitative adding according to requirements.

1. Name of the product in this design: AI Companion Robot Dog (Xiao Zhi Ling Dong). 2. Purpose of this design: For use in intelligent robot dogs. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: 3D view 1.

This invention discloses a fingertip tactile sensor and its sensing method capable of motion recognition and three-dimensional force measurement. The sensor comprises, from top to bottom, a stacked sensing electrode layer, a pressure-sensitive layer, and a grounding layer. The sensing electrode layer consists of four... x - y The device consists of four symmetrically distributed sensing electrodes arranged in pairs along a central axis. A common electrode forms the grounding layer, with its lower surface attached to the fingertips of the intelligent robotic arm. In the non-contact state, the four sensing electrodes are activated simultaneously or sequentially, forming three asymmetrical sensing units with the common electrode. This asymmetrical design effectively enables the recognition of robotic arm movements. In the contact state, the four sensing electrodes are activated sequentially, forming four sensing units with the common electrode. Three-dimensional pressure-shear force measurement is achieved by calculating the difference between two sensing units in the same measurement direction. This invention is used for non-contact movement recognition and three-dimensional force measurement after contact with an object during robotic arm operation, improving operational flexibility and accuracy.