1074 results about "Environmental sensor" patented technology

The invention discloses a small target recognition method and system for a multi-modal fusion image in a complex environment, and belongs to the technical field of computer vision and image recognition, and the method comprises the steps: obtaining a visible light image, an infrared image and environment sensor data; image registration is carried out on visible light and infrared images, and a multi-scale image feature pyramid is constructed. And respectively extracting visible light and infrared image features to obtain visible light and infrared imaging feature data. And performing multi-modal data fusion on the visible light and infrared imaging feature data based on a cross-modal attention mechanism, and adaptively adjusting a fusion weight based on environmental sensor data to generate fusion features. And performing space-time enhancement processing on the fusion feature to obtain an enhanced fusion feature. And performing target tracking detection on the small target, and outputting position and category information of the small target. According to the method, the small target recognition capability in a severe environment is remarkably improved, and high precision and robustness can still be kept in a foggy, low-visibility and dark scene.

The invention relates to the technical field of wind power generation, discloses an intelligent safety early warning method for wind power hoisting operation, and aims to solve the technical problems of insufficient perception and lack of data fusion and intelligent analysis decision in existing operation safety management. The method is characterized by comprising the following steps: constructing an intelligent terminal integrated with a UWB / IMU / safety belt / environment sensor; deploying a positioning base station network and a signal relay system; a multi-source data fusion intelligent analysis platform is established, and high-precision positioning, behavior recognition and deep learning risk prediction are achieved; and a closed-loop intelligent decision-making and execution system is constructed, and graded early warning, electronic fence, environment linkage and one-key help calling are realized. According to the method, a traditional experience driving mode is innovated into a data driving mode, the safety level and the operation efficiency of wind power hoisting operation are remarkably improved, and the defects of insufficient perception, information isolation and the like are overcome.

The invention relates to the technical field of power equipment monitoring, and discloses a power equipment insulation state online monitoring system. The system comprises a multi-source data fusion module which collects operation parameters, environment sensing and historical insulation degradation record data streams in real time; and the space insulation field intensity distribution module performs field intensity dynamic mapping on the operation parameter data flow in combination with an equipment structure topological graph and an insulation degradation feature library to generate space insulation field intensity distribution feature information. The reference state topology module constructs an insulation state reference topology network through dynamic weight distribution according to element service duration distribution and operation modes; the insulation degradation dynamic detection module analyzes the field intensity deviation degree of the two regions and generates a primary insulation degradation index. When the primary index exceeds the threshold value, the delay response verification module executes continuous time sequence degradation trajectory tracking verification; the insulation state trend prediction module performs multi-step state evolution simulation to obtain predicted field intensity distribution characteristics, and the monitoring compensation optimization module corrects the primary indexes according to the predicted field intensity distribution characteristics and generates optimized insulation degradation indexes.

The invention provides an intelligent inspection risk assessment method and system based on multi-sensor fusion, and the method comprises the steps: obtaining original multi-source data of a transformer substation, the original multi-source data comprises a binocular vision image, infrared thermal image data and environment sensor data, and carrying out the time-space calibration and preprocessing of the original multi-source data, obtaining a multi-source sensor data stream; carrying out feature extraction on the multi-source sensor data stream to obtain a multi-modal feature set, generating a refined semantic mask based on the multi-modal feature set, constructing an initial scene relation graph, calculating a risk level based on a multi-layer perceptron classifier, and generating a risk level evaluation result and a risk distribution graph; outputting a safety distance violation warning and a risk area identifier; and generating comprehensive risk early warning information based on the risk level assessment result, the risk distribution diagram, the safety distance violation warning and the risk area identifier.

The invention relates to the technical field of power load decomposition, and discloses a non-intrusive power load decomposition method and system based on multi-modal feature learning. The method comprises the following steps: synchronously acquiring electric power parameter data of an intelligent electric meter, environmental parameter data of an environmental sensor and use behavior data of user equipment to obtain multi-modal load monitoring data; performing cross-modal feature extraction through a non-negative matrix factorization layer of the first equipment state recognition model to obtain a multi-modal fusion feature vector; carrying out load mode recognition through a first decomposition layer of the first equipment state recognition model to obtain a first decomposition load matrix; performing clustering optimization through a second decomposition layer of the first equipment state recognition model to obtain a second load decomposition matrix; and executing a dynamic fuzzy decision based on the second load decomposition matrix to obtain an equipment operation state identification result. According to the method, the limitation that a traditional method only depends on a single power signal is broken through, and high-precision and high-robustness non-intrusive power load decomposition is achieved.

The invention relates to the technical field of data integration, and particularly discloses a perfluoro / polyfluoroalkyl substance multi-source exposure data integration risk assessment system, which is characterized in that environmental exposure data of perfluoro / polyfluoroalkyl substances is acquired through a multi-source environmental sensor array, and a PFAS multi-mode exposure feature database is established; carrying out pollution source isotope fingerprint analysis, and obtaining source contribution rate distribution maps of three pollution sources of industrial emission, surface runoff and atmospheric settlement through a nonlinear source analysis algorithm; constructing a three-dimensional geographic information dynamic migration model according to the source contribution rate distribution map, and generating a multi-medium dynamic migration flux matrix; a composite risk assessment model is established based on the multi-medium dynamic migration flux matrix, probability risk assessment is executed in combination with an ecological toxicity threshold database, and a space gridding risk grade map is output; the method not only fills the blank of the prior art in the aspects of multi-medium dynamic modeling and nonlinear source analysis, but also provides powerful technical support for environmental pollution control and ecological risk prevention and control.

The invention discloses a carbon fiber composite material surface modification spraying system and a control method thereof. The system comprises a multi-axis robot, a plasma spray gun, a contact angle measuring probe, a 3D line laser scanner, a hyperspectral imager, an environment sensor and a controller. According to the method, the plasma power and the robot speed are adjusted in real time through contact angle measurement and plasma treatment feedback control, so that the CFRP surface can accurately reach a target value, and the coating adhesive force is improved; 3D line laser scanning and hyperspectral imaging are combined, and the posture, the distance, the wet film thickness and the component uniformity of the spray gun are monitored in real time; based on a prediction model fusing a physical model and a neural network, a self-adaptive fuzzy PID control algorithm is adopted, and the coating flow and the track posture of a spray gun are accurately regulated and controlled in real time. The problems of weak coating binding force, uneven thickness, orange peel, sagging and serious coating waste in traditional spraying are effectively solved, and self-adaptive, high-quality and green spraying of workpieces with complex curved surfaces is achieved.

A system and method for generating patient care instructions based on real-time sensor and medical data. The method includes receiving time-stamped sensor data from a sensor network comprising motion, occupancy, and environmental sensors, and receiving medical data associated with a patient, including medical conditions, treatment history, medication data, and biometric data. The sensor data is enriched with room-specific information, and activity pattern data is generated in real time using a pattern recognition model. The activity pattern data includes mobility, sleep patterns, medication adherence, statistical measures, temporal patterns, and correlations with medical data. Anomalies indicating potential health risks are detected by comparing current activity patterns with baseline data. A prediction model, trained on historical patient data, assesses the patient's health and generates care instructions accordingly. The care instructions are securely delivered to patient devices, caregiver applications, or automated medication dispensing systems, enabling timely interventions and continuous patient monitoring.

The invention relates to the technical field of computer systems based on specific calculation models, and discloses a control method and system for an intelligent calculation center comprehensive energy supply device, and the method comprises the steps: collecting the operation data of energy supply equipment and an environment sensor in real time through an SCADA system, and carrying out the preprocessing; based on the collected data and simulation data generated by a simulation environment, performing offline mixed training on the reinforcement learning model to generate an energy scheduling strategy; inputting a real-time state into the trained reinforcement learning model to generate a preliminary scheduling instruction, performing security verification and interpretability analysis by using a large language model, and optimizing a strategy; and fusing the preliminary scheduling instruction with the suggestion of the large language model, generating a final scheduling command through the energy router control unit, and issuing the final scheduling command to the energy supply equipment for execution. The problems that in the prior art, black box decision making and simulation are not accurate, and experience is difficult to solidify are solved, and the purposes of decision making transparency, simulation high fidelity and experience structuring are achieved.

The invention discloses an intelligent Internet of Things home energy management system, and relates to the technical field of intelligent homes, the system comprises a multi-dimensional data sensing module which uses a sensor and an intelligent equipment interface to collect human physiological data, obtains environment and external data through an environment sensor and a weather API, and sends the environment and external data to a cloud server; historical physiological records, environmental regulation logs and user feedback data are called, timestamps and space coordinate information are added to all kinds of data at the same time, and the data are converted into data vectors and uploaded to a system database; through the multi-dimensional data sensing module, human body physiological data, environmental data and external data can be comprehensively collected, and a physiological state prediction model and a health risk identification model exclusive to a user are constructed in combination with historical records and user feedback, so that the personalized level of health management is improved, and the health risk of the user is improved. And through a chaos embedding biological rhythm prediction formula and a dynamic Bayesian health risk assessment formula, accurate prediction of the physiological state of the user and identification of the health risk are realized.

The invention discloses a vehicle monitoring method based on frame difference and deep learning fusion, and relates to the technical field of vehicle monitoring, cameras and environment sensors are deployed in a monitoring area, and videos and multi-source data are acquired by means of vehicle-road cooperation; a self-adaptive frame difference method is used, morphology and optical flow estimation are matched, a threshold value is determined according to the environment, and vehicle features are extracted; constructing a deep convolutional neural network with an attention mechanism, and training a model by using various data in combination with migration and reinforcement learning; fusing the two types of features based on a graph attention network to form high-quality fusion features; a space-time diagram convolutional network is combined with an LSTM to track a vehicle and predict a trajectory, a behavior pattern library is constructed to judge abnormity, and classification analysis is performed in combination with an SVM and a knowledge graph. According to the invention, the frame difference and deep learning are fused, the monitoring accuracy is improved, and the vehicle can be accurately identified and detected; the real-time performance is enhanced, the data is quickly processed, and the environmental influence is reduced; traffic management is assisted, and a safe and efficient traffic environment is created.

The invention belongs to the technical field of medical risk early warning, and discloses a medical risk dynamic early warning and intervention decision-making system based on a multi-mode Internet of Things and AI, which comprises a sensing layer, a network layer, a data processing layer, an AI analysis layer and an application layer, the sensing layer comprises a wearable device, an environment sensor, a medical device, a non-contact monitoring device and an implantation device; the network layer constructs a data transmission channel and is provided with an edge computing node; the data processing layer adopts a multi-modal data fusion engine, is responsible for data alignment, data cleaning and feature extraction, and constructs a knowledge graph; the AI analysis layer comprises a dynamic risk early warning model and an intervention decision engine; the application layer is provided with a three-level early warning board, an intelligent intervention terminal and a block chain evidence storage platform. According to the invention, the early warning window is advanced, and the recognition accuracy is improved; the system promotes a medical monitoring mode to be changed from post response to active defense, the medical accident rate is further reduced, and a technical foundation is provided for constructing a new generation of smart hospitals.

A portable device for agricultural applications, consisting of: a microcontroller-based processing unit (101); a plurality of integrated environmental sensors (102), including temperature, humidity and barometer sensors; wireless communication modules (103) supporting Bluetooth, Wi-Fi and LoRa protocols; a GPS module (104) for geolocation; a display interface (105) with touch and haptic feedback; wherein the device is configured to communicate with nearby edge computing nodes to receive localized agricultural predictions based on real-time environmental data.

The invention relates to the technical field of monitoring systems, and discloses a logistics storage monitoring system based on artificial intelligence, and the system comprises a data collection module which is composed of an environment sensor array, a medicine batch identification unit and a space positioning device which are disposed on a storage three-dimensional shelf, the environment sensor array collects temperature, humidity and vibration parameters in real time, and the medicine batch identification unit stores unique codes and batch attribute data of medicines. According to the method, the dynamic association model of the medicine batch-environment sensor is established, and the spatial interpolation algorithm and the three-dimensional coordinate mapping technology are combined, so that the accurate association of the abnormal event and the medicine batch is realized. When an environment abnormity occurs in a certain area, the system can quickly position the batch of the affected medicine and generate the tracing map, and compared with a scheme in which environment data is disjointed from the position of the medicine in the prior art, the problems that the abnormity tracing efficiency is low and the high-value medicine is easy to deteriorate are solved.

The invention relates to a solid waste traceability analysis method based on a knowledge graph, and the method comprises the steps: completing the time-space collection, denoising standardization, unified alignment and geocoding through collection and fusion of multi-modal data, such as a structured circulation document, a GPS track, an RFID signal, video monitoring and an environment sensor, and constructing a node-level confidence probability model; and the abnormal node state is cooperatively corrected by using a multi-modal signal, the solid waste flow direction knowledge graph is dynamically updated by a corrected result, global traceability and service decision feedback of an abnormal path are realized, and the judgment accuracy of the abnormal traceability of the solid waste flow direction is effectively improved in combination with continuous model iteration and knowledge closed-loop optimization.

The invention relates to the technical field of intelligent traffic, in particular to a subway traffic operation decision adjustment system and method based on a large visual screen, and the system comprises a perception data module, a data processing center, a large visual screen interaction platform, an intelligent instruction decision engine, and a dynamic adjustment unit. Wherein the sensing data module collects train positioning data, platform monitoring video streams, gate passing data and environment sensor data in real time; integrating and analyzing the data by the data processing center, and mining potential modes, association relationships and abnormal conditions in the data; the visual large-screen interaction platform presents a line network congestion thermodynamic diagram, equipment fault positioning and passenger flow prediction trend in real time; the intelligent instruction decision engine is used for dynamically generating a train shift adjustment strategy and an emergency dispersion scheme; and the dynamic adjusting unit dynamically adjusts the electronic guide screen and the platform broadcast, and dynamically dispatches the train. Therefore, the problems of lagging abnormal response, unreasonable resource allocation, low efficiency and the like in the prior art are solved.

The invention relates to the technical field of power construction safety monitoring, and discloses an automatic safety monitoring system and method for a power construction area, and the system comprises a multi-source sensing and fusion module which is used for collecting and fusing multi-type data from video equipment, an environment sensor and wearable equipment, and forming time sequence sensing data in a unified format; the dynamic behavior recognition module is used for recognizing the behavior category of the construction personnel based on the sensing data and forming a behavior sequence; and the high-dimensional risk tensor construction module is used for carrying out joint modeling on the behavior sequence, the construction area position, the environment state and the time dimension to construct a risk semantic tensor. According to the method, accurate recognition of construction risks is realized through multi-source data fusion and spatio-temporal behavior modeling, and early warning perspectiveness is improved in combination with dynamic risk tensor prediction; and efficient utilization of prevention and control resources and continuous evolution of the system are ensured through multi-objective optimization strategy generation and a closed-loop adaptive mechanism.

The invention relates to the crossing field of biothermodynamics and dynamic environment control, and discloses a space thermal microclimate multi-target regulation and control method, device and system, a medium and a product. The method comprises the following steps: acquiring physiological parameters (including effective dressing thermal resistance and local sweat gland density of posture correction) of a passenger, environment space-time parameters and light environment parameters through a biological characteristic sensor, an environment sensor and an optical sensor; constructing a dynamic human body thermal comfort model library, and adopting a feature coding-parameter decoding mechanism to match a target thermal comfort model; generating a hierarchical regulation strategy based on the difference between the local comfortable temperature and humidity interval and the real-time thermal environment sensing parameter; and the refrigerant circulation system, the fluid circulation system and the optical radiation system are cooperatively controlled to realize local microclimate regulation and control. According to the method, the problems of inaccurate thermal comfort modeling and low multi-system cooperation efficiency in a transient environment are solved, the energy efficiency ratio is improved, and the comfort of passengers in a cabin is remarkably improved.

The invention provides a battery car dynamic charging optimization management system based on battery health state evaluation, and belongs to the technical field of battery car charging management. The system comprises a data acquisition module which is connected with an environment sensing device and a battery management system and acquires environment and battery parameters in real time; the data storage module is used for storing an initial training data set, a charging and discharging data set and a battery aging feature library; the model building module is used for building a battery health state evaluation model through a migration strategy and building a response model through a modeling strategy; the health state evaluation module is used for extracting associated features with a critical damage threshold value as a core through a mapping algorithm based on the battery parameters, the environmental parameters and an aging feature library; and the charging strategy generation module comprises a strategy generation unit, a reference parameter generation unit and a dynamic correction unit. The system effectively improves the adaptability of battery car charging, guarantees the efficient and stable operation of the battery, and remarkably improves the safety and prolongs the service life of the battery.

The invention discloses a monitoring device and a monitoring method for construction organization, belongs to the technical field of intelligent construction safety monitoring, and mainly solves the problems of monitoring blind areas, insufficient fixed visual angle coverage and difficulty in field long-term power supply caused by dynamic shelters in the construction process. According to the technical scheme, a camera is fixed to a double-shaft electric holder with a horizontal rotating and pitching adjusting mechanism, a processor, a communication unit and a battery are arranged in a base, a solar panel is arranged on the top of the base, and an electric lifting column is connected with the base and the holder; the environment sensor group is mounted on the camera; the solar panel is connected with the battery and supplies power to the whole device; the processor calculates a target monitoring path for eliminating a blind area in combination with a preset three-dimensional coordinate of a key area, generates a control signal to drive the holder and the lifting column to move, and dynamically adjusts the path execution frequency and the movement speed of each mechanism according to the electric quantity of a battery and the solar power. The device is mainly used for dynamic dead-corner-free safety monitoring of key areas of a building construction site.

The invention relates to the technical field of power system state monitoring and operation and maintenance, and discloses a power distribution network equipment state intelligent sensing and predictive maintenance decision-making method, system, equipment and medium, and the method comprises the steps: collecting equipment operation data, monitoring data and environment data through a power distribution automation system, an online monitoring device, an environment sensor and other channels; performing time synchronization and quality verification on the multi-source data; extracting multi-dimensional characteristic parameters reflecting the running state of the equipment, and performing normalization processing and fusion analysis on the characteristics; establishing an equipment state evaluation model by using a deep learning algorithm, and carrying out quantitative scoring on the current health condition of the equipment; constructing a time sequence prediction model, and predicting the future degradation trend and residual life of the equipment; and establishing a multi-objective optimization model, and generating an optimal maintenance decision scheme. The whole process is from data acquisition, state perception and fault prediction to decision optimization, and intelligent management of power distribution network equipment is realized.

The invention discloses a child activity data desensitization processing method and system based on differential privacy, and relates to the technical field of privacy data protection, and the method comprises the steps: carrying out the time synchronization and unit normalization of multi-source data from a wearing device and an environment sensor, and building a unified multi-dimensional time sequence matrix; then, introducing a change point detection method to dynamically segment the time series data, so that the data is divided into event segments with clear behavior semantics; a sensitivity scoring mechanism is designed, factors such as activity types, time, places and behavior frequencies are comprehensively considered, privacy risk quantification is performed on each event segment, adaptive privacy budget allocation is implemented based on a scoring result, and key protection of sensitive data segments and efficient utilization of conventional data segments are realized; after differential privacy noise is injected, time sequence consistency correction and cross-feature correlation correction are combined. Therefore, differentiated and refined protection of the child activity data is realized, and privacy protection and data availability are effectively balanced.

The invention discloses a fish school photoacoustic feature recognition method based on a multi-modal deep network, and the method comprises the steps: 1, obtaining data: obtaining acoustic data and visual data; 2, acoustic data preprocessing and feature engineering; 3, visual data preprocessing and labeling alignment are carried out; 4, constructing a sample and organizing a data set; step 5, designing a multi-mode branch network; step 6, carrying out cross-modal fusion and joint representation; step 7, performing multi-task output and a loss function; and step 8, model training and evaluation. According to the invention, by fusing acoustic, optical and environmental sensor data, an acoustic-vision-environment three-branch feature extraction network is constructed, adaptive fusion of multi-modal features is realized, the problems of low fish school recognition precision, difficulty in alignment and fusion of multi-modal data and the like in a complex marine environment are effectively solved, and the accuracy of fish school recognition is improved. The method is suitable for real-time fish school monitoring and analysis tasks of unmanned ships, buoys and fishery administration monitoring platforms.

The invention discloses a sensor-based GIL pipe gallery expansion joint on-line monitoring system and method, and relates to the technical field of power equipment monitoring, and the method comprises the steps: collecting key state information and environmental parameters of an expansion joint through the deployment of an expansion joint sensor and an environmental sensor, and carrying out the preprocessing; performing deep fusion on expansion joint sensor data and environment sensor data, constructing a comprehensive state evaluation and prediction model, and analyzing the influence of different factors including environment, mechanical faults and material aging on the expansion joint state; correcting state parameters for environmental factors; for mechanical faults, fault types, positions and severity are identified, and corresponding early warning is triggered; aiming at material aging, monitoring and predicting performance degradation and residual life in real time, and formulating a maintenance strategy; the model performance is evaluated and updated regularly, an early warning threshold value is adjusted dynamically, and self-adaptive early warning is achieved; a data integration and visualization platform is developed, and visual display and interactive analysis of monitoring data are achieved.

A sensor cleaning system for cleaning an environment sensor includes: an air cleaning system configured to clean an environment sensor by spraying compressed air; a washer fluid cleaning system configured to clean the environment sensor by spraying washer fluid onto the environment sensor; a suction cleaning system configured to suck around the environment sensor; and a controller configured to control the air cleaning system, the washer fluid cleaning system, and the suction cleaning system.

The invention discloses environment early warning and tampering detection equipment and method for a long-term storage container, and the equipment comprises an electronic container terminal integrated on the container, and the electronic container terminal is provided with a main control unit, and a bistable electronic display screen, a wireless communication unit, a multi-sensor unit and a power supply unit which are connected with the main control unit. The multiple sensor unit includes a tamper detection sensor for detecting a first opening of the container and an environmental sensor for monitoring environmental parameters. And the main control unit displays an abnormal warning when the environment exceeds a threshold value, and generates an irreversible opened state mark and permanently displays opening information according to the tampering signal. The terminal is connected with the cloud server and the user terminal through wireless communication, and identity and state information storage and basic authentication interaction are achieved. According to the invention, dynamic anti-counterfeiting, complete and traceable storage environment recording and active early warning functions can be provided.

The invention provides an intelligent transportation device for fish fry keep-alive transportation and a water treatment process, and the device comprises a heat preservation box body, an environment sensor, a fish fry state indirect monitoring sensor, and a flexible buffer type self-adaptive isolation device, an intelligent environment regulation and control module and a dynamic buffer device which are detachably connected with the heat preservation box body, the intelligent environment regulation and control module comprises a water temperature regulation module, a water quality regulation module and a gradient environment adaptation module, the gradient environment adaptation module automatically regulates values of all parameters according to a preset environment parameter change curve, and meanwhile, the change rate and the water treatment process of the environment parameters are regulated in real time according to data fed back by the water quality regulation module; and the dynamic buffer device dynamically adjusts the damping coefficient of the buffer unit according to the pressure and acceleration data in the cold storage transportation device. The device can accurately match the optimal fish fry transportation environment with the adaptive rhythm of the marine ranching environment, greatly reduces the stress damage of the fish fry in the transportation process, and improves the adaptive capacity of the fish fry to the target environment after transportation.

According to the PLC thermal control system of the heat exchange station, various high-precision sensors, such as a temperature sensor, a pressure sensor, a flow sensor and an outdoor environment sensor, are arranged at key positions of the heat exchange station, and operation parameters in the station and external environment data are collected in real time. The data can be quickly transmitted to the PLC control unit, the PLC preliminarily processes and analyzes the data, and multiple energy devices of the heat exchange station are intelligently dispatched in combination with a multi-energy complementary cooperative regulation and control mechanism. For example, when it is predicted that the heat load requirement is high, the system can preferentially start the gas-fired boiler with the high efficiency and coordinate auxiliary energy equipment such as the ground source heat pump and the air source heat pump to work cooperatively, so that it is ensured that the heat load requirement is met, and meanwhile the energy utilization efficiency is improved to the maximum extent.

The invention discloses a photovoltaic field slope deformation real-time monitoring and early warning system, and particularly relates to the technical field of photovoltaic power station safety monitoring. The system comprises a data acquisition module, a data processing and analysis module, an early warning judgment module and an early warning information management module. The data acquisition module acquires multi-source monitoring data through a tilt angle sensor, a GNSS displacement monitoring station and an environment sensor which are deployed on site; the data processing and analysis module adopts a Kalman filtering and machine learning fusion algorithm to carry out fusion processing and trend prediction on the data; the early warning judgment module performs intelligent judgment based on a dynamic multi-level threshold value and a rule; and the early warning information management module executes a graded and channel-divided information issuing strategy according to the early warning level. According to the invention, real-time accurate sensing, intelligent analysis, research and judgment and efficient early warning information management of the deformation state of the photovoltaic field slope are realized, and the active protection capability of power station safety management is improved.