1132 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.

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.

The invention provides an intelligent mechanical arm control system scheme based on multi-model cooperation, and belongs to the field of automation and intelligent control. The system is divided into four parts of man-machine interaction, detection and perception, task decoupling planning and program generation, and robot execution operation. Man-machine interaction is carried out, a user inputs an instruction in a text or voice format, and instruction processing is carried out through voice recognition and a natural language translation model; detecting and sensing, and identifying and positioning a target captured by the camera by using a multi-modal large model; task decoupling planning and program generation: analyzing the user instruction and the perception information into an executable program by applying a task planning model and a program translation model; and checking and correcting the program by using the program checking model, and driving the mechanical arm to complete a task. According to the system scheme, variable environments can be dealt with, complex language instructions can be understood, intelligent control over the mechanical arm is achieved, and the multi-task execution capacity and the intelligent level of the mechanical arm are remarkably improved.

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 a mechanical arm operation method based on a multi-mode large visual language model, which comprises the following steps of: firstly, acquiring image data of a mechanical arm operation scene through a visual sensor, and acquiring a user instruction in combination with a voice recognition module or a text input module; and inputting the natural language instruction and the visual scene data into a multi-modal large visual language model for multi-modal task analysis. The model identifies the category, position and attribute of a target object in a scene, and generates a specific operation sequence in combination with an input instruction. And then, according to the operation sequence generated through analysis, the system directly drives the mechanical arm to execute corresponding tasks including specific actions such as grabbing, moving and placing of the target object, and in the task execution process, the mechanical arm sequentially completes the actions according to the operation sequence generated in advance. The problems that in the prior art, a mechanical arm is inaccurate in complex task instruction analysis, poor in operation flexibility and adaptability and poor in real-time response capacity in a dynamic environment are effectively solved.

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.

The invention discloses a manipulator clamping force control method and device based on motor driving, and relates to the field of intelligent manipulators. Comprising the following steps that the mass of a to-be-clamped object is obtained, and basic friction force is calculated; collecting object shape characteristic parameters such as the maximum boundary length, the outer surface area and the volume, and calculating a shape influence coefficient; then, acquiring surface characteristic parameters such as average curvature, irregularity and projection area, calculating a surface concave-convex coefficient, and generating a shape complexity index in combination with the shape influence coefficient; then, acquiring material characteristic parameters such as hardness and elastic modulus, determining and correcting a basic friction coefficient, and calculating friction force; and finally, the instantaneous speed of the clamping task of the manipulator is obtained, the impact load is calculated, the basic friction force is corrected based on the parameters, the accurate clamping force is obtained, and therefore the accurate control over the clamping force of the manipulator is achieved. According to the method, the clamping effect and safety are improved, and the method is suitable for various clamping scenes.

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 relates to the technical field of geological survey, in particular to a potential landslide hidden danger identification method based on rock mass deterioration characteristics. The technical problems that in the prior art, in landslide monitoring, the monitoring range is limited, data collection is not comprehensive, and early warning timeliness is insufficient are solved; according to the technical scheme, the potential landslide hidden danger identification method based on the rock mass degradation characteristics comprises the steps that feature extraction is conducted on a preprocessed image through a convolutional neural network, the rock mass degradation characteristics are identified, and the identified degradation characteristics are converted into numerical value characteristics; compared with the problems of limited monitoring range, incomplete data collection and insufficient early warning timeliness in landslide monitoring in the prior art, the intelligent machine vision technology is used for identifying the rock mass degradation characteristics, and the sensor network is deployed in combination with the Internet of Things technology to monitor the rock mass state in real time; the cloud computing platform is used for data analysis and processing, and finally intelligent early warning of the landslide hidden danger is achieved.

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 user equipment (UE) mobility management method executable by a UE. The UE receives configuration of UE-based mobility management and transmits artificial intelligence (AI) / machine learning (ML) model-inferred one or more parameters of UE-based mobility management based on the configuration to assist a handover operation of the UE. The one or more parameters comprise UE-predicted UE trajectory of the UE.

A wireless terminal which communicates over a radio interface with a radio access network comprises receiver circuitry and processor circuitry. The receiver circuitry is configured to receive, over the radio interface from the radio access network, at least a first message which concerns implementation of an Artificial Intelligence / Machine Learning (AI / ML) Functionality / feature for the wireless terminal. The processor circuitry is configured: to make a determination that the reception of the first message requires one of the following: an update of an existing model for the Artificial Intelligence / Machine Learning (AI / ML) Functionality / feature; a download of a new model for the Artificial Intelligence / Machine Learning (AI / ML) Functionality / feature for a particular functionality or feature; and to make a determination that a pause maybe necessary to implement the Artificial Intelligence / Machine Learning (AI / ML) Functionality / feature. In an example optional implementation, the processor circuitry may also be further configured to generate a pause notification message.

The invention is suitable for the technical field of intelligent sorting, and particularly relates to a method and system for generating a sorting task based on image recognition, and the method comprises the steps: constructing an intelligent sorting system, and constructing a sorting system model based on the intelligent sorting system; operating the intelligent sorting system to obtain a cargo monitoring image, and performing shape analysis on the cargo monitoring image to generate a cargo entity model; identifying cargo label information, determining a sorting path, simulating a conveying path according to the sorting path, and determining sorting time; and the initial state of the sorting mechanical arm is read, a mechanical arm sorting task is generated, and the mechanical arm sorting task is sent to the corresponding sorting mechanical arm. The sorting mechanical arms are independently controlled, the control instructions are sent to the sorting mechanical arms, operation of the multiple sorting mechanical arms can be achieved through one data processing center, the sorting efficiency is improved, goods sorting can be achieved under the condition that high-cost intelligent mechanical arms are used, and the operation cost is reduced.

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 relates to the technical field of industrial safety, in particular to an intelligent machine tool fire extinguishing system which comprises a fire detection module, a data processing module, a fire type recognition module, a fire extinguishing control module, a man-machine interaction module and a power failure guarantee module. Wherein the fire detection module is used for monitoring multi-dimensional data of a working environment of a machine tool in real time; the data processing module is used for identifying different types of fire features; the fire type identification module is used for identifying the specific type of the fire; the fire extinguishing control module automatically selects a corresponding fire extinguishing medium; the man-machine interaction module is used for sending an alarm signal to an operator when a fire disaster occurs; and the power-off guarantee module is used for automatically cutting off a machine tool power supply when a fire occurs. According to the invention, through the technologies of intelligent fire identification, accurate fire source positioning and timely power cut-off, the fire extinguishing efficiency is effectively improved, early fire control is ensured, and secondary disasters are prevented.

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.