880 results about "Intelligent machine" patented technology

A system and method for extending AI-enhanced decision platforms with deontic and normative reasoning capabilities that enhance adjustably autonomous decision-making through a novel integration of symbolic and neural approaches. The invention uses hierarchical and fuzzy deontic logic implementations alongside connectionist AI / ML to manage obligations, permissions, and prohibitions while maintaining observer awareness to achieve goals while incorporating knowledge across multiple expert domains. The system employs dynamic event and spatio-temporal knowledge graphs along with debate mechanisms, enabling high-assurance automated reasoning while preserving explainability through neuro-symbolic integration. In at least one embodiment, the invention operates through a federated distributed computational graph architecture that allows for arbitrary scaling while maintaining coherence, consistency and supporting compound workflows. The invention provides a framework for AI systems to make logically consistent, ethically-aware decisions by combining deontic reasoning with multi-agent coordination, token space communications and knowledge, including on intermediate results, enabling automated decision-making for a variety of applications.

The invention provides an intelligent mechanical safety protection system based on industrial Internet of Things equipment, which comprises a dynamic topology modeling module, a risk propagation analysis module, a dynamic protection generation module and a collaborative triggering execution module, the composite topological graph comprises a physical layer and a logic layer; the risk propagation analysis module is used for analyzing a propagation path of the risk along a physical connection edge and a logic dependence edge based on the composite topological graph, and generating a risk propagation full-link sub-graph; the dynamic protection generation module is used for calculating a minimum necessary protection set based on the risk propagation path and generating a hierarchical response strategy; and the cooperative triggering execution module is used for sending a cooperative control instruction to the equipment in the minimum necessary protection set based on the hierarchical response strategy. By adopting the system, a risk propagation path under a complex scene can be automatically identified, and the safety protection level of equipment is improved.

The invention discloses an artificial intelligence machine vision image acquisition system, and the system comprises a multi-mode perception layer which integrates a self-adaptive optical module, inhibits metal reflection, captures a visible light to short wave infrared image, and captures a motion edge; the dynamic adaptive layer adopts an illumination compensation and motion compensation module to dynamically adjust camera parameters and micro displacement compensation, feeds back an illumination trend, outputs a motion vector to the cognitive layer, generates a confrontation sample through a GAN, simulates virtual defects in combination with a physical engine, and expands training data; the cognitive reasoning layer is used for deploying a dynamic routing network, distributing computing resources according to image complexity and optimizing feature extraction efficiency; reducing data deviation through anti-fact analysis, and generating a thermodynamic diagram to explain a detection basis; and the collaborative decision-making layer is used for rapidly screening samples by edge nodes, training a global model by cloud aggregated data, automatically triggering manual rechecking when the confidence coefficient of the model is insufficient, synchronously optimizing a training set and a causal reasoning module by a rechecking result, and improving the labeling efficiency through AR assistance.

The invention provides an intelligent mechanical equipment fault diagnosis method fused with a machine learning algorithm, and the method comprises the steps: obtaining a multi-source signal, and generating a complete machine-level anomaly detection result through dynamic threshold comparison and machine learning anomaly detection model processing; the multi-source signal comprises a vibration signal, a temperature signal and a pressure signal; performing signal decomposition and multi-source parameter fusion analysis processing based on a complete machine level anomaly detection result, and determining a physical subsystem of an anomaly source; time-frequency features are extracted from the operation data of the physical subsystem, and a part-level anomaly positioning result and a microdefect classification result are generated through hierarchical machine learning model processing; and performing multi-granularity data fusion processing based on a complete machine level anomaly detection result, a physical subsystem and a microdefect classification result, and generating a hierarchical diagnosis report. By adopting the method, progressive diagnosis can be carried out on the mechanical equipment hierarchically, and the accuracy of fault diagnosis is improved.

The invention discloses an operation and maintenance manipulator intelligent control method and system based on visual identification, and relates to the technical field of intelligent manipulator control, and the method comprises the steps: collecting RGB image data and depth image data of an operation and maintenance operation area, and obtaining a standardized image matrix and a mapping relation matrix; inputting the standardized image matrix into an improved ResNet residual network model, generating a comprehensive feature descriptor, and calculating a spatial position coordinate and an attitude angle of the target equipment based on the mapping relation matrix; based on the current joint angle state of the manipulator, an improved Jacobian matrix inverse kinematics algorithm is used for solving a target angle sequence of each joint, a preset operation mode library is matched based on the comprehensive feature descriptor, and a grabbing force parameter and a motion speed parameter are determined; and converting the target angle sequence into a control instruction, and sending the control instruction to each joint driver of the manipulator to drive the manipulator to complete action planning. According to the invention, full-process automation from environment perception to task execution is realized.

Various embodiments include methods for generating image datasets for training an artificial intelligence machine learning (AI / ML) optical character recognition (OCR) model. Image processing may be performed on a plurality of roadway images to identify street signs within the images and generate a dataset of sign images categorized into sign variants of the same shape, color, pictogram, and characters. An OCR model may process sign images to obtain OCR results for images of each sign variant. An aggregation process may be performed on the OCR results for all sign images within each sign variant to identify a ground truth OCR result for each sign variant. The ground truth OCR result may be used to automatically label all sign images of each sign variant to produce an OCR model training dataset. The produced training dataset may then be used to retrain the initial AI / ML OCR model and / or train other AI / ML OCR models.

The invention discloses an intelligent mechanical arm control system, and relates to the technical field of intelligent mechanical arms, the system comprises a sensor data acquisition module, a multi-modal data fusion module, an environment and target prediction module, an intelligent decision and control module and a control execution and feedback module; through a cooperative working mechanism of the environment and target prediction module and the intelligent decision-making and control module, future state change of a target object can be accurately predicted, possibility is provided for pre-judgment operation of the mechanical arm, then a decision-making optimization algorithm and a multi-target optimization function are applied, an optimal motion strategy is planned for the mechanical arm, and the optimal motion strategy is provided for the mechanical arm. The system further has the real-time feedback and adjustment capacity, it is ensured that the mechanical arm can automatically adapt to changes in the complex dynamic environment, the operation precision and efficiency of the mechanical arm are improved, the capacity of the mechanical arm for adapting to the complex environment and dealing with emergencies is enhanced, and the production efficiency and safety are improved.

The invention relates to the technical field of intelligent mechanical arm control, and discloses a mechanical arm track optimization method and system based on deep learning and a fuzzy algorithm, and the method comprises the steps: building a kinematic model of a mechanical arm, determining the working space of the mechanical arm, carrying out the high-density random sampling, and generating a three-dimensional point cloud picture of a reachable region at the tail end of the mechanical arm; constructing a path planning model, designing a state space and an action space, and constructing a reward function; time-impact double-target optimization is carried out on the tail end path point sequence, a smooth joint trajectory is constructed, and balance between the shortest trajectory execution time and the minimum joint impact is achieved on the premise that speed and acceleration constraints are met; and tracking control is carried out on the trajectory, external disturbance and unmodeled dynamics are estimated and compensated in real time, a parameter adaptive law is designed, and the trajectory tracking precision of the system in a complex environment is improved. The autonomy, the accuracy and the anti-interference capability of the hot-line work mechanical arm in a complex environment are improved.

The invention discloses an intelligent machine construction method and system for ground concrete base troweling, and relates to the technical field of intelligent construction.The intelligent machine construction method comprises the steps that an intelligent robot collects sensing data of a concrete base in a construction area in real time; the sensing data is analyzed through a preset algorithm to generate a construction strategy, and troweling operation is automatically completed; in the troweling operation process, the intelligent robot transmits real-time sensing data to the cloud platform, and a closed-loop monitoring system of construction quality is formed. Through preset path planning, parameter adjustment and an image recognition algorithm, accurate matching of troweling operation with the base layer state is achieved, troweling uniformity is improved, manual dependence is reduced, perception data in the robot operation process are uploaded to the cloud platform in real time, comparative analysis and feedback control are carried out, and the troweling efficiency is improved. Quality problems can be recognized and corrected in real time, construction data in the whole process can be traced, and remote construction supervision is visualized.

A method and device for transmitting or receiving improved codebook-based channel state information in a wireless communication system are disclosed. A method performed by a terminal in a wireless communication system, according to one embodiment of the present disclosure, comprises the steps of: receiving one or more channel state information (CSI)-reference signals (RS) from a network; and transmitting, to the network, one or more CSI reports including information indicating a first precoding matrix based on the one or more CSI-RSs and a channel quality indicator (CQI) based on a second precoding matrix, wherein the first precoding matrix is included in a set of precoding matrices defined on the basis of one or more predetermined codebooks, and the second precoding matrix can be determined on the basis of the first precoding matrix and one or more pieces of artificial intelligence (AI) / machine learning (ML) model-related information.

The invention discloses a machine room energy-saving operation and maintenance management method and system, and particularly relates to the technical field of machine room management. Computing resources are integrated in combination with a virtualization technology, virtual machine allocation is dynamically adjusted, migration flow is optimized by adopting an intelligent network bandwidth scheduling mechanism, and migration efficiency is improved; by combining a temperature and humidity control system and equipment operation temperature anomaly detection, the operation states of the air conditioner and the cooling equipment are dynamically optimized, energy consumption is reduced, early warning is performed based on the equipment operation temperature and the network load condition through a fault prediction model, equipment faults are prevented, and fuzzy logic control is adopted; based on the energy consumption efficiency and the fault prediction result, resource configuration is dynamically adjusted, the energy efficiency of the whole machine room is optimized, the utilization rate of computing resources is effectively improved, the influence of network migration on services is reduced, the energy consumption of the machine room is reduced, the equipment stability is improved, the management cost is optimized, and efficient, energy-saving and intelligent machine room operation and maintenance management is achieved.

The invention relates to the technical field of intelligent mechanical arm control, in particular to a mechanical arm grabbing method and system based on image processing, and the method comprises the steps: collecting an image of a target object needing to be grabbed by a mechanical arm through a visual sensor, carrying out the preprocessing and multi-scale decomposition of the collected image of the target object, screening a region of a potential grabbing point, and carrying out the recognition of the target object; and generating a capture point candidate area map. According to the invention, through processing and multi-scale decomposition of the target object image, noise is eliminated, an illumination area is balanced, a clearer target feature image is generated, and environmental adaptability is enhanced. And in combination with texture change detection and gradient analysis, the feature intensity and boundary characteristics of the object are accurately extracted, and more accurate grabbing point screening is realized. Furthermore, the three-dimensional surface form projection is in a two-dimensional form, and the stability and operability of the grabbing area are improved by optimizing the boundary symmetry and curvature fluctuation.

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 relates to the technical field of intelligent airport logistics, in particular to an airport luggage flow group intelligent AI-driven digital twinning low-carbon scheduling system, which comprises a physical sensing layer, a digital twinning engine layer, a group intelligent decision-making layer, a block chain evidence storage layer and a dynamic scheduling execution layer, and forms a'sensing-modeling-decision-evidence storage-execution 'closed loop; the physical sensing layer collects equipment and luggage state data; the digital twinborn engine layer constructs a total-factor twinborn body to realize synchronous mapping and carbon accounting of a physical system; the group intelligent decision-making layer adopts an improved carbon sensitive ant colony algorithm to generate a multi-objective optimization strategy of total energy consumption, residence time and load balance; the block chain evidence storage layer ensures credibility and traceability of the carbon data; and the dynamic scheduling execution layer converts the decision into a control signal and corrects simulation and actual deviation. According to the method, low-carbon, high-efficiency and reliable luggage scheduling is realized, and the method is suitable for green operation of a smart airport.

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.

Technologies for providing probes-as-services to customers of a cellular network are described. One method receives, from a customer, a request for a probe-as-a-service in a slice of the cellular network, the slice being associated with the customer. The method generates a set of probe collectors to collect network analytic data associated with the slice of the cellular network. Using the set of probe collectors, the method collects input data (e.g., user data usage, events metrics, counters, logs, etc.). The method aggregates the input data from each of the plurality of data sources in the cellular network to obtain combined data. The method generates, using at least one artificial intelligence (AI) / machine learning (ML) model, the network analytic data based on the combined data. The method provides the network analytic data to an application associated with the customer.

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 provides a machine authority management method based on a customer autonomous decision-making mechanism, comprising the following steps: firstly, acquiring a current account authority configuration set of a target machine, the current account authority configuration set comprising a plurality of authority level nodes and corresponding operation range identifiers, associated account identifiers and verification features, then performing authority feature extraction on the current account authority configuration set; generating a dynamic decision node set representing permission change trigger conditions and verification paths on the basis of a preset autonomous decision model according to hierarchical constraint features and operation behavior features of the permission level nodes, and calling a permission verification model to perform multi-dimensional matching processing on the related characteristics and the node set, generating a permission change verification result, updating a current account permission configuration set according to the permission change verification result, and finally synchronizing the updated configuration set to a target machine operation control module to enable the permission to take effect, thereby realizing flexible and intelligent machine permission management.

The invention provides an intelligent machine tool cutter wear state recognition and machining parameter adjustment method and system, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: collecting the working condition data of a machine tool machining process, carrying out the sliding window segmentation, denoising and normalization processing, extracting a time sequence feature sequence through a bidirectional gating circulation unit, and carrying out the recognition of a machining parameter; the method comprises the following steps: acquiring an intrinsic mode component by combining Hilbert-Huang transform decomposition, extracting a frequency domain feature sequence by utilizing four-layer spatial pyramid pooling, determining a fusion weight based on mutual information entropy to obtain a tool wear state feature vector, and inputting the feature vector into a pre-training extreme learning machine to predict the wear loss and the residual life. And determining an initial adjustment scheme in combination with observation sharing information of a parallel agent structure, predicting a processing quality score through a capsule network and feeding back the score as a reward signal, and obtaining an optimization correction strategy for adjusting working parameters of a machine tool.

A monitoring apparatus of an AI / ML model includes: a receiver configured to receive an uplink reference signal transmitted by a terminal equipment; and processor circuitry configured to perform performance monitoring on the AI / ML model by using the uplink reference signal.

The invention provides an unmanned aerial vehicle cluster intelligent opportunistic routing method based on multi-agent deep reinforcement learning, and aims to optimize a data transmission path in an unmanned aerial vehicle cluster and improve communication efficiency. The method comprises the following steps: firstly, establishing a communication channel model and a motion model of an unmanned aerial vehicle cluster, and constructing a distributed partial observation Markov decision process model; based on the model, a multi-agent near-end strategy optimization reinforcement learning algorithm is adopted to carry out routing decision training, and after training is completed, a collaborative strategy of the agents is loaded into an airborne computer of the unmanned aerial vehicle, so that intelligent opportunistic routing is realized. In the training process, the unmanned aerial vehicle selects a proper next-hop node to optimize data forwarding by continuously collecting neighbor information and making a local decision. Through the method, the unmanned aerial vehicle cluster can dynamically adapt to a complex environment, and the end-to-end transmission efficiency of a network and the successful transmission rate of data packets are remarkably improved.

The invention relates to the technical field of intelligent robots, and discloses an automobile cabin intelligent robot arm device which comprises a multi-axis robot arm, a controller, an in-automobile space sensing device and an intelligent interaction device, and the multi-axis robot arm, the controller, the in-automobile space sensing device and the intelligent interaction device are all installed in an automobile cabin. The robot arm comprises a base rotating motor, an arm motor, a palm motor, a capacitance sensor, a pressure sensor, a USB / Type-C interface, a wireless charging interface, a magnetic attraction interface and the like. The in-vehicle space sensing device comprises a camera, a millimeter wave radar and the like. According to the intelligent robot arm device of the automobile cabin, the intelligent robot arm is controlled through voice or keys and the like, and the robot arm replaces personnel to complete object taking operation at a long distance or a view blind area position; distraction caused by the fact that a driver takes objects towards the back row can be avoided, and driving safety is improved. Meanwhile, the intelligent robot arm is controlled through voice or keys and the like, and the robot arm replaces personnel to complete service operations such as article storage, article fixing, games and caring; and the intelligent experience of the cabin is improved.

The method provides for securely harvesting and distributing trusted metadata from devices to artificial intelligence (Al) systems. It enables use of cryptographic hashes and signatures on data for supply chain provenance. It is an agentless method to enhance application security by design, and data protection with data authenticity and confidentiality in device to upstream services communications and data sharing. It helps securely harvest, filter, and forward device metadata to webhooks for AI / ML driven data analytics.

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 provides an AI artificial intelligence machine learning system and an intelligent gateway, and relates to the technical field of AI artificial intelligence control, and the system comprises a communication module which is in bidirectional connection with an air conditioning unit, a security and protection sensor array and an intelligent illumination network in a building through a plurality of Internet of Things protocols, collects equipment operation data flow in real time, and sends the data flow to a server; comprising an air conditioner compressor current waveform, a security alarm code and lighting equipment light intensity time sequence data; and the edge calculation module is used for extracting an air conditioner energy consumption characteristic value, a security alarm signal and an environment temperature and humidity data set according to the equipment operation data flow, and storing the processed structured data set to a simulation object memory area through a simulation object classification algorithm. Through cooperation of multiple modules, real-time acquisition and processing of multi-source equipment data, analysis of personnel and environment information, intelligent generation of control instructions and adaptation to various equipment through protocol conversion are realized, so that the building environment comfort, safety and energy utilization efficiency are improved, and system compatibility and emergency response capability are enhanced.

A user device, UE, for a wireless communication network, is described. The UE is configured or preconfigured to be handed over from the source cell of the wireless communication network to a target cell of the wireless communication network. The UE uses at least one Artificial Intelligence / Machine Learning, AI / ML, handover, HO, model or at least one AI / ML HO functionality for performing one or more of the following: selecting from a set of target cells one or more target cells suitable for a handover, and / or determining a suitability of one or more target cells for a handover.

The invention discloses an intelligent set top box video processing system based on multimode decoding, which relates to the field of terminal video processing, and comprises the following steps: an original data stream processing module outputs a scene complexity grading label through a spectral clustering algorithm; the video metadata analysis module is used for analyzing metadata of an original video stream in real time and identifying a content type identifier; the edge-cloud cooperative decoding module allocates the high-complexity video frames to edge nodes for decoding, allocates the low-complexity video frames to a local terminal for decoding, and outputs a decoded video frame sequence; the video restoration module detects and restores picture distortion caused by transmission frame loss or signal interference; the dynamic code rate control module dynamically distributes the code rate weight of each block-level area according to the scene complexity grading labels; and the energy efficiency self-optimization module dynamically controls the states of the heating sheet and the idle decoding channel to adjust the core voltage and the decoding frame rate of the CPU. The method has the advantages of low power consumption, strong adaptability to weak network smoothness through multi-scene high-definition cloud edge collaboration.

The invention relates to the technical field of network security, in particular to a security operation full-process automatic closed-loop method and system driven by a security agent, and the method comprises the following steps: collecting network behavior data, including network traffic, system logs and user behavior data, from various network devices, security devices and log systems in real time; performing cleaning, denoising and standardization processing on the original data by using a natural language processing technology; the method has the beneficial effects that the full-process automatic closed-loop management from threat detection, analysis, response to feedback is realized by constructing the security agent and combining technologies such as a large language model, artificial intelligence, machine learning, natural language processing (NLP) and the like. The security agent can monitor network behaviors in real time, automatically identify abnormal behaviors, generate security early warning and automatically execute a response strategy, so that the efficiency and accuracy of security operation are remarkably improved.