280 results about "Device Sensor" patented technology

The invention relates to the technical field of intelligent manufacturing, and discloses an extreme manufacturing process technological parameter optimization method and system fused with machine learning. The method comprises the following steps: acquiring multi-source data from a manufacturing equipment sensor, and fusing to generate a material state vector; inputting a pre-training model to obtain a material coefficient transition trend; judging whether the trend fluctuation amplitude exceeds a preset threshold value or not, and if yes, marking key nodes and extracting feature parameters; for the key nodes, according to the characteristic parameters and the real-time data of the key nodes, a control algorithm is adopted to calculate the parameter adjustment amount; optimizing the control parameters based on the parameter adjustment amount, generating a control instruction sequence and transmitting the control instruction sequence to an actuator; and obtaining adjusted feedback data, comparing the adjusted feedback data with the transition trend, and if the deviation exceeds an allowable range, updating the pre-training model. Through deep fusion of predictive monitoring and intelligent control, accurate optimization and adaptive control of process parameters are realized, the stability of the extreme manufacturing process and the product quality are improved, and the energy consumption and the defect rate are reduced.

The invention discloses an intelligent operation and maintenance scheduling and resource optimization method and system for a new energy power station, and belongs to the technical field of data processing and management.The method comprises the steps that real-time power generation data, equipment sensor data, weather forecast data, a power grid scheduling instruction and electricity market electricity price information of the new energy power station are obtained; a unified data matrix is generated through space-time alignment processing to execute bidirectional feedback prediction, and a corrected power generation plan is generated; a cooperative scheduling decision is generated through a multi-target dynamic balance algorithm, and a personnel dispatching scheme and a material allocation path are generated through real-time path planning; and generating a historical decision data set based on the execution result and the actual execution deviation, and adjusting prediction parameters and scheduling parameters through an incremental learning model. A closed-loop optimization method combining data fusion, collaborative prediction, multi-target scheduling and adaptive learning is adopted, global collaborative optimization of multiple factors such as power generation, operation and maintenance, energy storage and markets can be achieved, and the economic benefit and the intelligent level of overall operation of a power station are improved.

The invention discloses a multi-parameter dynamic intelligent judgment method for the safety state of an operator, and belongs to the technical field of operation safety monitoring. According to the method, by integrating an intelligent wearable device, a sensor and an eye movement tracking device, physiological parameters (such as heart rate, blood pressure, oxyhemoglobin saturation, electroencephalogram signals and the like), behavior parameters (such as action frequency, posture change and the like), psychological parameters (such as pressure level, fatigue degree and the like) and environmental parameters (such as temperature, humidity, noise and the like) of an operator are collected in real time; and a multi-dimensional monitoring system is constructed. Collected data is subjected to cleaning, standardization and feature extraction and then is input into a safety state judgment model based on a bidirectional long short-term memory network (BiLSTM), the real-time safety state of an operator is dynamically analyzed, and low-risk, medium-risk and high-risk three-level early warning results are output.

The invention relates to the technical field of intelligent agricultural planting and breeding, and discloses a digital intelligent agricultural planting and breeding combined system which comprises a multi-source sensing module used for collecting environment data, biological sign data and equipment operation data through a planting area sensor, a breeding area sensor and a circular processing equipment sensor; the data verification module is used for dynamically verifying the collected data based on a preset agronomic mechanism model; the hierarchical digital twinning module is used for carrying out collaborative decision making in a three-level architecture of the edge computing node, the local server and the cloud platform and generating an optimization instruction; a virtual-real decision engine module; and a closed loop execution network module. According to the invention, a lightweight twinborn model is deployed through the edge layer to realize millisecond-level equipment control and guarantee timely response of extreme working conditions, the field area layer coordinates planting and breeding material circulation through a dynamic optimization algorithm, and the cloud layer integrates climate data to generate a carbon sink strategy of block chain evidence storage. And the real-time decision-making precision, the resource utilization efficiency and the carbon economic value are improved through three-level cooperation.

The invention relates to the technical field of data management, and discloses a dynamic error management and tracing method for flow line production data, and the method comprises the following steps: collecting multi-source dynamic data in a flow line production process, including equipment sensor data, production parameter data and product quality data; performing real-time dynamic error detection processing based on the multi-source dynamic data, and generating dynamic error characteristic data through a sliding window algorithm and an abnormal value analysis technology; in the dynamic error management and tracing process of flow line production data, equipment sensor data, production parameter data and product quality data are acquired in real time through a multi-source dynamic data acquisition technology, and data integration is performed by using a timestamp alignment algorithm, so that the synchronism and integrity of error detection data are ensured; meanwhile, through real-time dynamic error detection processing, abnormal fluctuation in the production process can be recognized in time, and the real-time performance and reliability of error detection are guaranteed.

The embodiment of the invention provides a form generation method, device and system based on AI enhancement and a storage medium, and relates to the technical field of form generation. The method comprises the steps of generating form structure metadata according to a natural language processing technology, a reinforcement learning algorithm and / or a type security verification rule; according to the form structure metadata, the equipment sensor data and / or the user visual behavior data, the rendering priority of each field is determined; and generating a target form according to the rendering priority and the equipment parameters. By generating the form structure metadata which is reasonable in structure and conforms to the type safety specification, the automation degree and reliability of form design are improved. By fusing form structure metadata, device sensor data and user visual behavior data, a rendering strategy can reflect device characteristics and user attention distribution. The target form is generated by combining the rendering priority with the equipment parameters, so that the form layout is adaptively matched with the terminal equipment, and the loading performance and the interaction experience of the form are improved.

The invention discloses a parallel double-filtering pressure-reducing valve redundancy control system with one used and one standby, which comprises a main filtering pressure-reducing valve and a standby filtering pressure-reducing valve which are oppositely arranged, a switching valve, a sensor module and a controller, and is characterized in that the main filtering pressure-reducing valve comprises a first air inlet and a first air outlet which are communicated with each other; the standby filtering pressure reducing valve comprises a second air inlet and a second air outlet which are communicated with each other, the first air inlet and the second air inlet are both communicated with an air source pipeline, the first air outlet and the second air outlet are communicated with an input port of the switching valve, an output port of the switching valve is used for being connected with downstream equipment, and the sensor module is arranged on the main filtering pressure reducing valve. The sensor module is used for monitoring the pressure condition of the main filtering pressure reducing valve, the controller is connected with the main filtering pressure reducing valve, the standby filtering pressure reducing valve and the sensor module, and the controller can automatically switch a gas path flowing through the device between the main filtering pressure reducing valve and the standby filtering pressure reducing valve according to the pressure condition. The system has the advantages of seamless switching, quick response and the like.

The invention relates to the technical field of power communication network security, in particular to a power communication network risk assessment method based on risk genes and a space-time large model. According to the technical scheme, the power communication network risk assessment method based on the risk gene and the space-time large model comprises the following steps that equipment sensor data, meteorological grid data and service topology data of a power communication transmission network are obtained in real time through a multi-source data acquisition module, and space-time alignment and feature coding are executed; a risk gene map is constructed, the risk gene map comprises a three-layer map structure of an equipment vulnerability gene, an environment threat gene and a business dependence gene, and a cross-space-time risk propagation rule is defined; a multi-modal space-time large model is trained, and a framework combining Transform and a space-time diagram convolutional network is adopted. A biological gene concept is introduced into power communication network risk assessment, gene-level fault traceability is achieved, and risk factors have combinability and interpretability.

The invention relates to the technical field of Beidou inspection data processing, and discloses an automatic inspection data acquisition and space-time association method under Beidou space-time reference. The method comprises the following steps: acquiring historical working condition data of inspection equipment based on nanosecond-level time synchronization and centimeter-level space positioning information of a Beidou system, and constructing a space-time dynamic prediction model; acquiring an equipment sensor measurement value, a moving track point set and a timestamp sequence in real time, and outputting a theoretical inspection value through a model; performing space-time multi-dimensional comparison on the theoretical value and the actual value, analyzing a time accumulation error, a space deviation degree and data flow form consistency, and generating a difference feature tensor; inputting the tensor into a geocoding topological reasoning network, simulating anomaly diffusion and generating an anomaly probability cloud picture in combination with topographic map node attributes and equipment distribution, and delineating an anomaly range; polling parameters are dynamically configured according to the cloud atlas, enhanced collection is started for a high-probability abnormal area, and data credibility inspection is executed on peripheral equipment.

The invention discloses a guide path display method and device for a virtual scene, computer equipment and a medium, and the method comprises the steps: collecting the current behavior data of a user through a VR equipment sensor, and analyzing the intention of the user according to the current behavior data; according to the user intention and the virtual scene information, a current path is dynamically generated in a path planning mode; and generating a special effect guide path based on the current path, and generating semantic path guide information by adopting a multi-modal semantic fusion mode. According to the invention, visual guidance for user navigation is realized, and the path display intuition and immersion experience are improved.

Systems and methods described herein facilitate the tracking of events relating to property. For example, in certain embodiments, a system includes a property event detection device (e.g., smart home device, sensor device, and so forth) configured to detect activity (e.g., motion, light, sound, and so forth) proximate the property. The system also includes a property event tracking system configured to receive data (e.g., images, videos, facial recognition, movement recognition, object tracking, sound signatures, wireless data streams, and so forth) relating to the activity from the property event detection device, to determine whether the activity relates to a property event (e.g., home service, and so forth) for the property, and to store data relating to the property event in a distributed ledger (e.g., blockchain network) upon determination that the activity relates to the property event for the property.

The invention relates to a crane operation monitoring method and device, equipment and a storage medium. The method comprises the following steps: collecting signal data of a crane through an edge node equipment sensor, analyzing the signal data into structured frame data, dividing the structured frame data into a plurality of samples to be identified based on a preset time window, determining state score values of the samples to be identified based on a locally deployed self-encoding model, and verifying the state score values of the samples to be identified according to a preset verification rule. The to-be-recognized sample with the state score value smaller than a preset threshold value is verified to obtain a verification result, and if the verification result represents that the crane operation corresponding to the to-be-recognized sample is the operation of an alarm event, alarm information is fed back to a preset user, and end-to-end real-time monitoring and abnormal protection of the crane operation state are achieved; and quick response is ensured when the operation of the crane is abnormal.

The invention relates to the technical field of sensors, and discloses an array type flexible pressure sensor and a data transmission method thereof, and a wearable device, the sensor comprises a flexible transverse conductive layer, a flexible longitudinal conductive layer, a flexible pressure-sensitive material layer, a data selector, a divider resistor and a single-chip microcomputer; the flexible transverse conductive layer is located at the top of the flexible pressure-sensitive material layer; the flexible longitudinal conductive layer is located at the bottom of the flexible pressure-sensitive material layer; a pressure-sensitive sensor is formed at the crossed position of the flexible longitudinal conducting layer and the flexible transverse conducting layer; the data selector is respectively connected with an output port of the flexible transverse conducting layer, an output port of the flexible longitudinal conducting layer and the single chip microcomputer; the divider resistor is connected with an output port of the flexible transverse conductive layer; the single-chip microcomputer is used for controlling the array type flexible pressure sensors to switch rows and columns and collecting voltage values between the pressure-sensitive sensors and the divider resistors. The method can be suitable for fabric pressure measurement and other scenes needing flexible sensing, and occupation of hardware resources is reduced.

The invention discloses a home health management method and device, computer equipment and a storage medium, belongs to the technical field of artificial intelligence, and is applied to a home health management scene of old people. According to the method, multi-dimensional data such as sleep, behavior habits and physiological indexes are collected through a multi-device sensor, and the accuracy and consistency of the data are ensured through time synchronization and consistency verification processing; then, health state parameters are generated through a pre-trained health assessment model, the change trend of the health state is analyzed, potential abnormal health events are recognized, personalized reminding information is generated through an intelligent triggering mechanism for the abnormal events, and the personalized reminding information is pushed to user interaction equipment in a mode suitable for the cognition and operation ability of old people; and the system is guided to timely respond to health management requirements. Meanwhile, the system continuously monitors the operation feedback of the user, calculates the participation index of the user, and dynamically adjusts the reminding frequency and content to realize the self-optimization of the interaction strategy if the responsivity is insufficient.

The invention discloses a task scheduling method based on MES system collaboration, and belongs to the technical field of task scheduling. The invention relates to a task scheduling method based on MES (Manufacturing Execution System) system collaboration, which is characterized by comprising the following steps of: S1, carrying out production data acquisition and standardization processing; s2, sensing and predicting a production resource state; s3, performing task scheduling constraint modeling; s4, cooperative scheduling calculation; s5, carrying out visualization and explanation generation on the scheduling scheme; s6, task issuing and execution feedback; and S7, historical data accumulation and scheduling strategy optimization. According to the method, the production resource state is predicted through a time sequence prediction model, a Bayesian model and a skill matching model based on the equipment sensor data, the material state and the personnel scheduling information which are acquired in real time.

An apparatus and methods for offsetting carbon emissions of a transport are provided. Apparatus may include a computing device, sensors, a display, and a memory communicatively connected to a processor to store sensor signals transmitted from the sensors, wherein the sensors are configured to detect a phenomenon; preprocess the sensor signals, wherein the preprocessing comprises translate the signal detections into electronic signals; generate transport data as a function of the electronic signals and the detected phenomenon; receive the transport data associated with a transport vehicle; calculate a first carbon emission metric based on the transport data; determine if the first carbon emission metric exceeds an impact threshold; generate an offset element as a function of the first carbon emission metric and the impact threshold, wherein the offset element comprises instructions for a second transport to reduce a second carbon emission metric; and display the offset element through a GUI.

The invention discloses a 5G-based workshop digital visualization method and system, and the method comprises the steps: obtaining collection data from a workshop equipment sensor, and obtaining a compressed data flow; transmitting the compressed data stream to a cloud processing platform, judging the integrity of the data fragments, and if the data fragments are complete, storing the data fragments to a distributed database to obtain a structured data set; for the structured data set, obtaining an equipment state feature set; performing dynamic mapping on the virtual model and the physical equipment through a space-time calibration mechanism according to the equipment state feature set, judging whether a mapping error is lower than a preset threshold value or not, and if the mapping error is lower than the threshold value, updating the virtual model to obtain a real-time synchronous digital twin model; and extracting equipment collaboration information from the real-time synchronous digital twinborn model to obtain visual presentation data of the production process. According to the invention, real-time monitoring and accurate fault prediction of the equipment cooperation state are realized, and the production efficiency and the intelligent level of equipment management are improved.

The invention belongs to the technical field of engineering construction, and particularly relates to a full-process construction method for a marching type deductive project, which comprises the following steps of: acquiring construction foundation, construction equipment, technical standard, historical similar projects, cultural content associated data and performance plot data of the deductive project; constructing a project risk early warning library by using a fusion early warning algorithm, storing the project risk early warning library in a cloud data center, issuing an early warning threshold and data to an edge computing gateway, and completing communication adaptation with a construction equipment sensor, a deduction equipment controller and a team terminal; the process is divided into stages through a WBS algorithm, a task generation algorithm is used for issuing a marching type construction task book, deploying three IMUs, collecting construction data and comparing the construction data with a risk early warning library threshold value for each stage, and if the threshold value is exceeded, early warning is triggered, and the construction task book is updated; and after construction is finished, stage acceptance and whole-process rechecking are carried out, and the project experience library is updated. Therefore, the problems of difficulty in project management and control, weak monitoring capability and the like in the prior art are solved.

The invention provides a business processing method of a smart factory platform based on a Beidou grid code, and the method comprises the steps: carrying out the multi-stage grid subdivision of a factory physical space through the Beidou grid code, and generating a unique grid code; the grid coding adopts an integer format to represent position information; equipment sensor data, personnel positioning data and business logic data are bound with the grid codes, and a space-time joint index table is constructed; performing real-time deviation correction on the positioning data through a grid neighborhood topological relation and personnel moving speed constraint to generate a high-precision positioning track; and mapping the service logic to the grid codes through a rule engine, and triggering automatic operation according to grid attributes. Traditional floating point latitude and longitude coordinates are replaced by integer coding, data calculation efficiency is optimized through bit operation, cross-level business association and fast path planning are supported, and business response efficiency is improved.

The invention discloses a port logistics carbon emission monitoring system based on the Internet of Things and a computer, and the system comprises a multi-modal data collection and transmission module which carries out the full-amount collection of the multi-dimensional data of equipment, sensors and environment in a port area through a hierarchical heterogeneous communication network; the edge intelligent processing and resource scheduling module is deployed at the local of a port, performs priority dynamic allocation and real-time processing on the acquired data, and uploads aggregated data; the anomaly detection and carbon flow modeling module is used for carrying out anomaly detection based on a space-time diagram neural network and constructing an operation chain carbon flow model integrated with a port production operation system; the digital twinning driven monitoring network optimization module is used for constructing and operating a communication-environment digital twinning model and dynamically optimizing the layout of the monitoring network; the accounting decision and intelligent optimization module is used for performing carbon footprint tracing based on a task-level accounting unit and generating a low-carbon optimization strategy; and the visualization and user interaction module is used for providing a visualization display and decision support interface of the carbon emission data.

The invention discloses an AI-based dialogue type zero-trust permission policy generation method and system, and the method comprises the steps: receiving user request information which comprises term information; the term ambiguity in the user request is analyzed through the domain knowledge graph, and an intention confidence score is obtained; when the intention confidence score is lower than a preset threshold value, generating an interactive clarification option; determining a user clear intention according to the user clear option answer; performing multi-modal verification on the clear intention of the user; and generating a permission minimization strategy based on a multi-modal verification result. Through enterprise-level knowledge graph analysis term ambiguity, semantic analysis accuracy is improved, closed options are generated at low confidence, user intentions, behavior characteristics of text input and authenticity of equipment sensor data verification intentions are dynamically defined, social worker attacks are prevented, and space-time limited permission strategies are generated in real time based on verification results. And the meanings of the terms in the user request are distinguished, so that the situation of excessive permission granting or wrong rejection is avoided.

The invention relates to the technical field of power management, and discloses a digitized sensing method, system and device for power demands in a park, and the key points of the technical scheme are that the method comprises the steps: S1, deploying related equipment, including arranging an intelligent electric meter, an environment sensor and an equipment sensor, and constructing a data collection network; s2, accessing a data source interface, and collecting power data and related data in a classified manner; s3, preprocessing the collected data, including denoising, abnormal value detection and missing value completion; s4, a hierarchical multi-modal power operation model is constructed, a multi-dimensional cross validation mechanism is introduced, each power monitoring object is included by at least two models with different feature weights, the models are trained, verified and tested, and a finished product model is obtained; s5, applying the finished product model, and obtaining predicted power data; and comparing the actual electric power data with the predicted electric power data, checking difference data, analyzing abnormal reasons, performing targeted processing, and performing parameter updating on a finished product model.

Electronic surveillance article systems reducing the number and likelihood of false alarms are provided. Such systems include two read zones, with a second read zone having an associated RFID reader configured to detect an RFID device at a trigger threshold. The trigger threshold may be set or modified in view of a value of a sensor of an RFID device (sensing a capacitance or dielectric permittivity or temperature or degree of movement, for example), the number of times the RFID device is detected in the first read zone, or whether the RFID device is detected in the first read zone under predetermined conditions. Such systems may also or alternatively initiate a response (e.g., modifying the trigger threshold or the amount of power transmitted by an RFID reader) when an RFID guard device associated with a piece of infrastructure in the first read zone is detected in the second read zone.

Systems and methods for decentralized group audio communication may include a secondary device which receives a first periodic signal broadcast from a primary device, which information for participation in a group communication session. The secondary device may detect a signal from an on-device sensor indicative of speech input at the secondary device, transmit a slot allocation request to the primary device in response to detecting the signal indicative of speech input, receive a second periodic signal assigning a communication slot to the secondary device for broadcasting information to the device group, and broadcast, during the assigned communication slot, audio signals representative of speech captured at the secondary device, for receipt by other devices within the device group.

Sensor device 1 for spatially resolved determination of the position of a pressure application 9, with a circuit carrier 2, wherein the circuit carrier 2 has a deformable connecting material 3 with conductive particles 4 for electrical connection of rows 5 and columns 6, wherein the circuit carrier 2 has on one planar side a row structure 5 as a first contact structure 5 with several individual electrically conductive rows 5, wherein the circuit carrier 2 has on the other planar side a column structure 6 as a second contact structure 6 with several individual electrically conductive columns 6, wherein at least one of the contact structures 5, 6 is formed on the circuit carrier 2, wherein the connecting material 3 has a first electrical resistance in the ground state without pressure application 9, wherein the connecting material 3 has a second electrical resistance in the connected state under pressure application 9.with an evaluation module, wherein the evaluation module determines a characteristic value of the electrical resistance of the connecting material 3 and, based on the characteristic value, determines the position of the pressure application 9 on the sensor device 1.

Systems, methods, and devices for monitoring sanitation events in a medical environment are described herein. A method includes receiving, at a monitoring device, a first signal indicative of the presence of a user device; receiving, at the monitoring device, a second signal generated by a sensor configured to detect activation of a sanitation device; using both information from the first signal and information from the second signal as inputs to a model; and calculating, based on an output of the model, an infection risk score.

The application provides a device sensor machining surface treatment device, belonging to the technical field of machining, which comprises a base and a top plate connected with each other through a stand, a dust collection fan and a dust collection box fixedly installed on the top of the connecting plate, an exhaust port of the dust collection fan in communication with the inside of the dust collection box, and a suction port of the dust collection fan fixedly and communicatively provided with a suction nozzle; the side of the dust collection box is provided with an opening and fixedly connected with a first filter screen, the first filter screen is matched with the outer contour of the side of the dust collection box; a second filter screen is slidingly arranged in the dust collection box and slidingly matched with the first filter screen, the second filter screen is provided with an elastic reset member, and the communication between the first filter screen and the dust collection box can be realized through the sliding of the second filter screen. When a certain amount of dust is attached to the surface of the second filter screen, the wind force brought by the dust collection fan can push the second filter screen to slide, so that the first filter screen is in communication with the inside of the dust collection box, thereby avoiding the influence of the dust attached to the second filter screen on the exhaust of the dust collection box.

The application discloses a kind of respiratory support equipment sensor self-calibration method, comprising the following steps: S1, first sensor obtains first oxygen concentration N1, second sensor obtains second oxygen concentration N2;S2, the difference of first oxygen concentration N1 and second oxygen concentration N2 is calculated, and it is judged: if |N1-N2|<K, then execute step S3, if |N1-N2|>K, then execute step S4, K value is set threshold value;S3, obtain initial average concentration and the initial average concentration N0 as output concentration;S4, obtain several oxygen concentrations N1, N2, N3......Ni corresponding to several sensors, and carry out data fitting to it, obtain concentration optimal solution Ny, and the concentration optimal solution Ny is used as output concentration;S5, select the two sensors of measured oxygen concentration closest to optimal solution Ny as working sensor.The application solves the problem of low parameter accuracy caused by the error of sensor measured data or the error of sensor itself.

The application discloses a kind of power equipment control management system and method based on big data analysis, specifically related to power equipment management technical field, including power equipment sensor deployment module, power equipment sensor data acquisition module, power equipment health state management module, power equipment energy efficiency optimization module, power equipment fault early warning module and power equipment fault evaluation module, the operating state data of each monitoring point of power equipment is collected by sensor in the application, power equipment health index is calculated, power equipment health management is carried out, power equipment energy efficiency optimization index is calculated, and power equipment energy-saving optimization processing is carried out, with clear management priority, analyze power equipment fault early warning coefficient, by comparing fault early warning coefficient with preset value, secondary comparison of sensor data and safety threshold, a double verification mechanism is built, the management accuracy and efficiency of power equipment are improved, and the safe and stable operation of power system is ensured.

The invention discloses an industrial equipment full life cycle fault prediction and health management method and system, and the method comprises the steps: constructing an end-to-end unified network architecture, and achieving the direct interconnection of equipment, a sensor and a platform. And building an equipment full-information dimension digital mapping model, and completing real-time synchronization of the physical equipment and the digital model. The process, state and quality data of equipment operation are comprehensively collected, and high-quality feature information for health assessment is generated through storage, exception elimination, standardization, noise reduction and feature extraction processing. A physical mechanism and a data driving method are fused to construct a health assessment model, and feature information and working condition parameters are input to calculate a health index. And pre-judging the remaining service life by adopting a time sequence prediction model, setting a multi-stage early warning threshold value, triggering early warning, and pushing early warning information. And matching an optimal operation and maintenance scheme, recording maintenance data, updating the model and the database, constructing a cross-scene fault knowledge system, and iteratively optimizing, evaluating and predicting the model. The equipment reliability is improved, and the operation and maintenance cost is reduced.