967 results about "Environmental data" patented technology

This invention discloses an airport FOD (Foreign Object Debris) detection and removal system and method based on multi-source sensing fusion. The system includes a mobile transport chassis, a multi-source heterogeneous sensing unit, a six-degree-of-freedom collaborative robotic arm, a composite cleaning end effector, and an edge computing platform. During operation, environmental data is first collected using LiDAR, millimeter-wave radar, and a binocular camera. Spatiotemporal fusion and state estimation algorithms are used to achieve all-weather, high-precision positioning and attribute calculation of FOD. The composite cleaning end effector integrates a high-negative-pressure suction port, a ring-shaped electromagnetic adsorption array, and a flexible mechanical claw. Subsequently, the system adaptively switches between magnetic suction, suction pickup, or gripping modes based on the material, volume, and shape characteristics of the FOD, and combines a visual closed-loop feedback mechanism to automatically upgrade and verify the cleaning strategy. This invention effectively solves the problems of poor perception robustness under complex weather conditions and insufficient adaptability of single cleaning methods, significantly improving the intelligence level and operational efficiency of airport pavement foreign object removal.

This invention discloses a machine learning-based multi-objective collaborative control system for photovoltaic buildings. The invention relates to the field of building energy conservation and renewable energy utilization technology, and includes: a data acquisition module for real-time acquisition of outdoor environmental data, indoor environmental data, photovoltaic power generation data, and building energy consumption data; and a multi-objective predictive controller, whose input is connected to the data acquisition module, for processing the data input by the data acquisition module based on a machine learning model. This machine learning-based multi-objective collaborative control system for photovoltaic buildings can systematically balance multiple objectives such as power generation, building energy consumption, indoor light environment, and thermal comfort, thereby automatically generating and executing more reasonable and safer control strategies under dynamically changing environments and user needs. This helps to improve the building's energy utilization efficiency and renewable energy production capacity while ensuring the comfort of indoor occupants, achieving synergistic optimization of multi-dimensional performance.

This invention relates to the field of equipment safety diagnosis and data processing technology, specifically a method and system for safety diagnosis of transportation equipment in coal port areas. The method includes: acquiring port environment data and transportation equipment data to generate a disturbance behavior data sequence, and constructing an environmental directional entropy field; generating a disturbance phase vector set of transportation equipment in the environmental directional entropy field; using an information resonance identification model to perform combined calculations on disturbances in all directions of the disturbance phase vector set, performing back-folding inference on resonant disturbance nodes that satisfy the continuous offset resonance condition, and calculating the asymmetric stability index; constructing a disturbance network topology, determining the disturbance propagation path and information impedance, and diagnosing the safety of transportation equipment based on the information impedance of the disturbance propagation path. This invention achieves dynamic safety diagnosis of transportation equipment in coal port areas by performing information resonance identification between the coal port environment and transportation equipment operation data.

This application discloses a method, apparatus, equipment, and storage medium for calculating the moisture content of electrical insulation paperboard, relating to the field of transformer technology. The method for calculating the moisture content of electrical insulation paperboard includes: collecting data on the exposure time and environment of the target transformer body; calculating the moisture absorption rate of the electrical insulation paperboard of the target transformer body based on the exposure time and environmental data combined with a moisture absorption rate calculation model; calculating the stage saturated moisture content of the electrical insulation paperboard of the target transformer body based on the exposure time and environmental data combined with a stage saturated moisture content calculation model; and calculating the moisture content of the electrical insulation paperboard based on the exposure time, moisture absorption rate, and stage saturated moisture content. This method achieves accurate calculation of the moisture content of electrical insulation paperboard based on environmental information and a constructed calculation model without damaging the paperboard, improving the efficiency of moisture content calculation while ensuring real-time performance.

This invention discloses a multifunctional home robot, comprising: a robot body; a central control system disposed on the robot body, the central control system being configured to perform multimodal data fusion and intelligent decision-making; a multimodal sensing unit electrically connected to the central control system for collecting user physiological data, behavioral data, and environmental data; and a life-assistance execution unit connected to the central control system for performing various operational tasks in the home environment according to instructions from the central control system. This multifunctional home robot solves the problem of single-function home service devices by introducing a dual-core architecture of a "life-assistance execution unit" and an "adaptive physiotherapy execution unit." The robot can not only perform household chores such as floor cleaning and tidying up, but also provide professional physiotherapy services. More importantly, the central control system enables the scenario-based linkage between the two.

The application discloses a carbon metering method and device based on measured data and a medium, comprising the following steps: obtaining emission data through a pre-set sensing network, determining energy consumption data of an enterprise, and obtaining environmental data through a pre-set integrated sensor; fusing the emission data, the energy consumption data and the environmental data to integrate data of different sources into a pre-set time coordinate system, and spatially aggregating the fused data according to production units to determine a comparison model; determining direct emission according to the emission data, and determining indirect emission according to the energy consumption data; comparing the direct emission and the indirect emission through the comparison model to determine emission deviation, comparing the emission deviation with a pre-set deviation threshold; if the emission deviation is greater than the deviation threshold, determining deviation reasons according to equipment operation logs and the environmental data, determining review data points according to the deviation reasons, and performing review metering according to the review data points.

The present application relates to the technical field of chemical process equipment control, and particularly relates to a polyformaldehyde polymerization auxiliary agent feeding system, which comprises a collection module, a buffer module, a metering module, a control module, a conveying module, a blocking module and a connecting module. The collection module acquires auxiliary agent attribute parameters and environmental data in real time; the buffer module maintains a micro-positive-pressure inert environment through a nitrogen sealing unit to prevent material oxidation; the metering module generates high-precision data by monitoring quality changes using a liquid loss balance; the control module dynamically adjusts the conveying rate based on a fuzzy PID algorithm; the conveying module uses a micro gear pump to reduce internal leakage; the blocking module absorbs fluid pulsation through a pulse damping unit; and the connecting module has a flexible pipeline structure to block mechanical vibration transmission. The modules cooperate to realize the precise conveying of several milliliters to tens of milliliters of auxiliary agent per hour, improve the feeding stability and precision, and ensure the polyformaldehyde polymerization reaction effect.

The application relates to a photovoltaic power station power generation amount prediction method and device and a photovoltaic demonstration system. Power generation data and environmental data collected by a photovoltaic power station are acquired, data preprocessing and feature extraction are performed on the power generation data and the environmental data, and feature parameters are obtained. Prediction is performed according to the feature parameters and a trained integrated model, prediction power is obtained, the integrated model is obtained by combining feature parameters and target variables in a structured training database and performing multi-algorithm weighted integrated training, the prediction power is analyzed to obtain prediction power generation amount per unit time and output, and the prediction power generation amount and actual power generation amount are updated to a historical database, and the integrated model is optimized through the updated historical database. The actual power generation data and the environmental data of the photovoltaic power station are collected for feature extraction, and the integrated model trained by using multi-algorithm weighted integration is used for prediction, the influence of environmental factors on power generation prediction is reduced, and the prediction accuracy is improved.

This invention provides a method for simulating and predicting urban bird species diversity based on convolutional neural networks, comprising: acquiring current environmental data of the city to be tested; predicting and generating a spatial distribution map of bird species diversity in the city based on the current environmental data using a target bird species diversity prediction model; wherein, during model training, discrete bird observation vector data is aggregated into a grid using spatial indexing and sliding window techniques, and unique bird species values ​​within the window are deduplicated to generate bird species diversity sample data for model training, and the spatial distribution characteristics of bird species are determined based on historical bird observation datasets; an initial convolutional neural network model is constructed and trained to obtain the target bird species diversity prediction model. This improves the accuracy and adaptability of bird species diversity prediction.

The application discloses a kind of water ecological restoration plant community management method and system based on neural network, method includes: based on SOFM neural network, water body stratification detection is carried out to target water area, and stratification result is obtained;According to stratification result, dynamic hydrological data sampling is carried out to each water layer using water body monitoring equipment, and the sampling result of each water layer is obtained;According to aquatic plant distribution data and aquatic animal distribution data, corresponding biological information and water layer natural ecological environment data are called to construct target database;Based on BP neural network, according to target database, determine the dynamic change rule of target water area within a predetermined time;Judge whether the dynamic change rule meets the preset restoration target, according to the judgment result, based on ecological restoration principle, calculate the plant species and corresponding quantity required for each water layer, and obtain the calculation result;According to the calculation result, the plant community of target water area is managed using plant management equipment.

This invention discloses a short-term intelligent numerical weather prediction method and system based on environmental perception, belonging to the field of meteorological management technology. The system includes a data acquisition and fusion module, an intelligent zoning and feature mining module, a short-term forecasting and risk assessment module, and a visualization and focusing module. The data acquisition and fusion module collects and fuses environmental data from meteorological radar and weather stations, as well as GIS maps. The intelligent zoning and feature mining module divides the data into grids based on the GIS map, analyzes the rainfall synergy between grids based on historical environmental data to construct analysis groups, and selects and constructs an impact set for each analysis group. The short-term forecasting and risk assessment module fits near-term precipitation intensity relationships and short-term precipitation intensity relationships based on the impact sets of each analysis group. The visualization and focusing module calculates and outputs the near-term and short-term predicted precipitation intensities for each analysis group based on the relationships, identifies key areas of concern using topographic and hydrological models, and enhances visualization.

This invention discloses a method and system for monitoring and assessing the marine aquaculture environment using IoT big data. The method includes: collecting multi-dimensional environmental parameters such as dissolved oxygen, nitrogen and phosphorus nutrients, and pH value through IoT sensing devices; performing parallel reception and status verification through a high-concurrency marine environmental data processing platform; using a multi-dimensional time-series data adaptive clustering algorithm to perform time-series segmentation and similarity clustering of the parameters; quantitatively assessing water quality status based on the clustering results using a eutrophication assessment model; and then dynamically predicting stress intensity and duration using an environmental stress response prediction model, combined with parameters adapted to the growth cycle of aquaculture organisms. The platform integrates the assessment and prediction results to generate a structured report including parameter change trajectories, level indicators, and predicted trend curves, achieving precise monitoring and assessment of the marine aquaculture environment throughout the entire process, providing reliable technical support for aquaculture environment regulation.

The application discloses a power supply field service smart cabin intelligent management and control method and system based on multi-source fusion, relates to the technical field of electric power Internet of Things and smart operation and maintenance, and comprises the following steps: triggering RFID batch scanning through radio frequency field micro disturbance, and performing time sequence consistency verification on the scanning result to obtain tool unique identification information and current in-place state data; collecting environment data in the smart cabin, time aligning and fusing the environment data and the tool in-place state data in an event-driven mode to generate a multi-source perception data set; performing health state analysis on the tool according to the multi-source perception data set, optimizing the storage position and access path of the tool according to the analysis result, and outputting a tool storage and taking scheduling strategy; and the application solves the problems of low tool state perception accuracy, poor multi-source data fusion quality, inability to identify health evolution trend and unreasonable storage path optimization in the power supply field service smart cabin.

A method and a system for online integrated environmental monitoring, assessment, and impact visualization of projects are disclosed. The method includes receiving project site data representing the geographical boundary of a project in a virtual environment and collecting real-world environmental data from fixed and mobile monitoring devices. Model data, modeling parameters, and digital ground data associated with the project are retrieved and unified through a unified connector interface to form a unified input data. The unified input data is processed through one or more impact modeling entities to assess environmental impacts, including air quality, noise, water quality, and ecological effects. Visualizations of the environmental impact determined are transmitted to an environmental assessment planner interface for display. The method and the system provide for real-time integration, efficient impact modeling, and multi-dimensional visualization, enabling stakeholders to evaluate and monitor environmental effects with enhanced precision and accessibility in a seamless digital environment.

This application relates to the field of big data acquisition technology and provides a carbon-saving deodorization control method and system based on online gas monitoring. The method includes: deploying a corresponding sensor array according to a preset sensor deployment strategy to collect gaseous environmental data, obtaining a multi-dimensional spatiotemporal dataset; extracting features from the multi-dimensional spatiotemporal dataset using a multi-layer convolutional neural network to obtain a diffusion feature set of odor pollutants; constructing an odor pollutant diffusion model based on the diffusion feature set; simulating the probability distribution of the diffusion path of the odor pollutants using a Monte Carlo algorithm based on the odor pollutant diffusion model to generate a diffusion topology; determining the control priority of a preset emission valve based on the diffusion topology; and controlling the opening and closing state of the preset emission valve according to the control priority to ensure that the concentration of the odor pollutants meets a preset concentration condition. This method helps improve the prediction accuracy of the diffusion path of gaseous pollutants.

The application is suitable for the technical field of warehouse intelligent management, and particularly relates to a smart warehouse whole-process scheduling method and system based on the Internet of Things. The method comprises the following steps: acquiring warehouse area and facility installation information, determining detection points according to the facility installation information, and collecting environmental data based on the detection points; partitioning the warehouse area based on the environmental data, and determining micro-environmental areas; acquiring in-out cargo information and known cargo information in each micro-environmental area, determining reserved cargo and pending cargo according to the in-out cargo information and the known cargo information, and generating a cargo rearrangement scheme based on the reserved cargo and the pending cargo; optimizing the cargo rearrangement scheme, determining a final scheme, generating a scheduling instruction based on the final scheme, and sending the scheduling instruction to a to-be-scheduled device; and the process of determining the rearrangement path within the domain and the rearrangement path outside the domain can apply a variety of existing rules for random combination, and finally converges the scheme through the optimization process, with extremely high flexibility.

The application relates to a deep learning-based low-temperature brittleness prediction method for wood components. The method comprises the following steps: acquiring temperature and humidity time series data of an environment in which a target wood component is located and performing standardization preprocessing to generate a standardized environment data sequence; based on the standardized environment data sequence, a temperature and humidity coupling derived index is calculated, and a multi-dimensional feature sequence is generated in combination with original time series statistical features of the standardized environment data sequence; the multi-dimensional feature sequence is intercepted according to a first preset sliding time window to generate a plurality of feature matrices, and each feature matrix is assigned a label to generate a deep space-time sequence sample; the deep space-time sequence sample is input into a trained deep space-time prediction model to generate a continuous risk probability curve of the target wood component occurring brittle fracture in a preset future time period. The method can deeply integrate time series environment data, scientifically quantify the temperature and humidity coupling effect, and significantly improve the timeliness and accuracy of risk early warning.

This invention belongs to the field of intelligent transportation, specifically relating to a MaaS-IoT integrated method and system for crowd risk perception and intervention. The method first collects environmental data and travel orders for the target area through IoT devices and an online platform; then, it constructs a path decision model based on the Bandit algorithm; combining shared and unique feature vectors to achieve path recommendation. Next, it constructs a crowd risk event dataset based on historical data and generates an easily searchable vector database, and fine-tunes the LLM to obtain a crowd risk inference model. Then, it tokenizes the time-series data, and uses the crowd risk inference model to perform chain reasoning and Monte Carlo-Shapley attribution to generate corresponding results; finally, it generates intervention instructions based on the outputs of the two models, and issues them for execution after verification. This invention solves the problem that existing methods struggle to achieve timely risk perception and efficient intervention in public places where crowds gather.

The present application relates to the field of livestock and poultry intelligent breeding and environmental control, in particular to a sheep house environment adaptive regulation system for Nanjiang yellow sheep breeding, comprising a data acquisition device, an environment control device, a feeding execution device and a controller; the controller receives real-time environmental data of a target environment and real-time physiological behavior data of a target breeding object, generates a physiological stress load value through a time integral algorithm, and inputs the physiological stress load value and the real-time physiological behavior data into a neural network model containing a time sequence processing structure to output an effective feeding capacity, a nutrient absorption efficiency and an expected feed conversion rate; when the effective feeding capacity is lower than a standard feeding capacity, a target intervention combination strategy composed of an environment compensation weight and a feeding adjustment weight is generated with the condition of maximum expected feed conversion rate, and then a feeding end control instruction and an environment control end control instruction are generated and closed-loop feedback adjusted to realize self-adaptive closed-loop control of the sheep house environment and the Nanjiang yellow sheep.

This invention relates to the field of data monitoring technology, specifically to an intelligent control method for workshop air purification based on environmental data. The method involves: obtaining upward trend points and concentration inflection points based on changes in concentration data sequences; identifying concentration rise periods and concentration fall periods based on the distribution characteristics of concentration inflection points; identifying suspected abnormal data points based on the differences in the trends of concentration rise and fall periods and the local data differences at the upward trend points; determining the air pollution risk level based on the quantity, interval, and concentration change characteristics of the suspected abnormal data points; and adaptively adjusting a preset concentration anomaly threshold based on the air pollution risk level to obtain an adaptive threshold. This invention adjusts ventilation power based on the adaptive threshold, thereby reducing the power consumption of the air ventilation system while improving the efficiency of air purification in the workshop.

The application relates to the field of traditional Chinese medicine diagnosis and treatment, and discloses a disease acupoint rash image analysis system based on a body surface gridding coordinate system. The system comprises a system data acquisition module, a body surface gridding coordinate system construction module, a rash image feature extraction and analysis module, a dynamic pathological correlation analysis module and a dynamic risk early warning module. The back body surface image, physiological signals, clinical indexes and environmental data of a patient are collected. Then, the image data is used to calibrate anatomical mark points, and grid unit codes are stored by division. The gridded body surface data is subjected to multi-dimensional color space conversion and cross-modal feature fusion to obtain quantitative rash image feature data. The rash image dynamic deviation feature set is obtained by matching a preset database according to the rash image feature data, physiological signals, clinical index data and current environmental data, the network is constructed to predict disease conversion probability and analyze acupoint correlation. Finally, the visual analysis data is obtained, a heat map and ranking are generated, and early warning information is output according to the comparison result, thereby providing strong support for traditional Chinese medicine diagnosis and treatment.

This invention discloses a humanoid robot for dispensing medicine in a pharmacy and its walking control method. The humanoid robot includes a walking base, a torso, a humanoid robotic arm mechanism, a humanoid robotic hand mechanism, a humanoid head, and a behavior control unit. The torso is connected to the top of the walking base, and the humanoid robotic arm mechanism is connected to opposite sides of the torso. The humanoid robotic hand mechanism for grasping medicine is connected to the side of the humanoid robotic arm mechanism away from the torso. A storage box is provided on the top of the walking base. The humanoid robotic arm mechanism is movable. A humanoid head is connected to the top of the torso, and the humanoid head is equipped with a lidar for collecting high-altitude environmental data. The walking base is equipped with an environmental sensing device for collecting near-ground environmental data. The behavior control unit is used to construct a global environmental model based on the high-altitude and near-ground environmental data; obtain a preset trajectory route based on the global environmental model; and control the walking base to move according to the preset trajectory route.

Systems, methods, and computer-readable storage media for forecasting the impact of climate change, and more specifically to the impact on water quality and / or quantity. The system receives, from a plurality of sensors within a predefined geographic area, environmental data. The system normalizes the environmental data and executes an artificial intelligence algorithm, where inputs to the artificial intelligence algorithm include the normalized environmental data, and outputs of the artificial intelligence algorithm include environmental risks, consequences, and probabilities associated with at least one environmental event. The system then modifies a planned project using the environmental risks, consequences, and probabilities associated with the at least one environmental event.

We provide the system. [Solution] A means for receiving operational data and verbal data, and for storing the information, A means for analyzing the stored information and identifying the subject's behavioral patterns and emotions, A means for generating behavioral improvement suggestions suitable for the subject based on the aforementioned analysis results, Means for notifying the aforementioned suggestion, A means of collecting environmental data in real time using a visual device and providing suggestions for improving situational responses based on the analyzed data, A system that includes this.

The present application belongs to the technical field of environmental data analysis, and particularly relates to a method for predicting heavy metal concentration in soil colloid solution based on a machine learning model. Colloid extraction is performed on heavy metal contaminated soil samples, and characteristic variables of the soil colloid solution are determined. The characteristic variables are standardized and coded, and the preprocessed characteristic variables are respectively input into pre-trained machine learning models for predicting the concentrations of As, Cd, Pb and Zn in the soil colloid solution to obtain predicted values corresponding to the heavy metal concentrations. The pre-trained machine learning models for As and Pb are two-stage models, the first stage being a release probability classification model and the second stage being a release amount regression model. The pre-trained machine learning models for Cd and Zn are single-stage regression models. The present application provides a method for predicting heavy metal concentration in soil colloid solution based on a machine learning model, which provides a reliable technical method for accurately predicting heavy metal release characteristics.

This application belongs to the field of virtual art restoration technology, specifically providing a deep learning-based virtual art restoration system. The system primarily analyzes historical images and environmental data of the artifact and restoration materials to train an aging prediction model and simulate long-term appearance changes. By quantitatively comparing the differences and stability of their aging trajectories, it calculates the long-term compatibility score for each restoration material and automatically selects the optimal material to generate the final virtual restoration image. This application effectively solves the problem in existing technologies where it is difficult to predict significant visual differences that may appear between restoration materials and the artifact itself after long-term natural aging, ultimately leading to insufficient durability of the restoration results. It achieves scientific prediction and optimized selection of the long-term visual compatibility of restoration schemes, significantly improving the reliability and stability of restoration results and reducing the risk of restoration failure due to material aging incompatibility.

Solar panel system and method for self-cleaning The disclosure covers a solar panel system (10) comprising: at least one solar panel (1); a self-cleaning mechanism (2) for the at least one solar panel (1); one or more panel sensors (4) configured to measure at least one performance parameter linked to an energy-production efficiency of the solar panel (1); environmental sensors configured to measure environmental data; a data acquisition module (6) configured to obtain the at least one performance parameter and the environmental data as a function of time; a control system (8) for controlling the self-cleaning mechanism (2), the control system (8) comprising: one or more processor configured to: predict a future weather pattern based on the obtained environmental data; and determine, based on the at least one performance parameter and the predicted future weather pattern, a time tA at which to activate the self-cleaning mechanism (2). The present disclosure also relates to a computer-implemented method for determining when a cleaning mechanism (2) of a solar panel (1) is to be activated.

We provide the system. [Solution] A means that enables multiple information processing modules to cooperate with each other on a distributed communication structure, A means for securely sharing data between these information processing modules using encryption technology, Each information processing module has a means to individually perform a specific analysis task, A means of integrating environmental data in real time and providing decision support information, A means of displaying results on the user's terminal and dynamically updating the information, A system that includes this.