133 results about "Environmental indicator" patented technology

The invention relates to the technical field of port ecological construction, and particularly discloses a port ecological construction evaluation method and system, and the method comprises the following steps: collecting dynamic ecological environment data in port construction operation through a multi-source sensor array; performing space-time coupling analysis on the data, calculating a dynamic attenuation rate # imgabs0 # of each dimension environment index by adopting a self-adaptive sliding time window algorithm, adjusting an ecological response time constant theta in combination with hydrodynamic conditions of a sea area around a port, performing synchronous integral calculation on a historical cumulative effect # imgabs1 #, and generating an environment health degree evolution curved surface; constructing a comprehensive evaluation model; and predicting an environmental risk value based on the hybrid prediction model. According to the invention, an intelligent, automatic, safe, reliable, green and low-carbon evaluation solution is provided for ecological construction of the port, and the ecological protection and sustainable development level of the port is remarkably improved.

The invention relates to the technical field of anomaly detection, and discloses a context-aware-driven multi-dimensional anomaly detection early warning method. The method comprises the following steps: collecting real-time context data in a target monitoring scene, and generating an initial feature set containing an environment parameter sequence and a behavior pattern map; a first detection model and a second detection model matched with the scene type are constructed according to the scene types, the first model comprises a dynamic correlation function of environment indexes and abnormal probabilities, and the second model comprises a nonlinear mapping rule of behavior characteristics and risk levels; and based on the real-time context deviation degree and the characteristic fluctuation coefficient, a target model is triggered to generate a dynamic early warning instruction, and the dynamic early warning instruction is pushed to an execution module to adjust a trigger threshold of an abnormal response strategy or a priority of a risk disposal process. According to the method, multi-dimensional data is combined, the adaptability and accuracy of anomaly detection are improved through dynamic model triggering and response strategy adjustment, and the method is suitable for various monitoring scenes.

The invention discloses a power grid toughness evaluation method and system, and the method comprises the steps: inputting the collected monitoring index data and environment index data of a target regional power grid into a pre-trained power grid toughness evaluation model for evaluation and recognition, so as to obtain a toughness evaluation result; the monitoring index data comprises voltage data, current data, harmonic data and power data, and the environment index data comprises temperature and humidity data, line loss data and frequency data; wherein the construction process of the power grid toughness evaluation model comprises the step of respectively training a data expansion process, a feature extraction model, a feature dimension reduction model and a classifier model in the power grid toughness evaluation model, so as to obtain the power grid toughness evaluation model. The evaluation accuracy of the evaluation model is improved by optimizing the training process of the model.

The invention relates to the technical field of remote monitoring, in particular to an urban house safety remote intelligent monitoring system and method, and the system comprises a data collection module, a data processing module, an event-driven analysis module, an early warning notification module, a safety control module and a maintenance scheduling module. According to the invention, by comprehensively collecting temperature, humidity, pressure and photoelectric data, the environment can be monitored more comprehensively, real-time data stream comparison can not only accurately identify tiny changes in the environment, but also rapidly respond through abnormal environment indexes, and when the environment data deviates from a normal range, the system can give an alarm in time, so that the environment is protected. The early warning efficiency and accuracy are improved, the measures of emergency response are automatically adjusted according to risk assessment of different levels, the system can take the most appropriate emergency measures according to the actual situation, for example, a water spraying system is automatically started in the case of a fire, the effectiveness and accuracy of house safety monitoring are effectively improved, and the life and property safety of residents is ensured.

The invention provides an ecological environment evaluation method and device for a coal mine area and a mining area environment evaluation system. The method comprises the steps that a plurality of environmental indexes of a coal mine area are obtained, and the environmental indexes comprise one or more of soil quality, water quality index, vegetation coverage rate, air quality index, noise level, waste water discharge amount, waste gas discharge amount, biodiversity index, heavy metal content and greening coverage area; the relevancy between the environment indexes is obtained, and the relevancy is the changed relevancy degree between any two environment indexes; a first weight coefficient is determined according to the relevancy, and the first weight coefficient and the relevancy are in positive correlation; and performing weighted average at least according to the plurality of environment indexes and the first weight coefficients corresponding to the environment indexes to obtain an evaluation score. According to the scheme, the problem of low accuracy of the evaluation result of the coal mine environment in the prior art is solved.

The invention relates to the technical field of environment monitoring, in particular to an intelligent environment monitoring method and system based on big data, and the method comprises the following steps: collecting real-time environment monitoring data, recording the indexes of air temperature, humidity and pollutant concentration from differential sensors, uploading and updating the data, and carrying out the data verification and format standardization. And obtaining a processed environment data set. According to the method, the volatility of each environmental index is quantified by calculating and analyzing the information entropy, abnormal and rare events in the data are effectively identified, the positioning precision of the environmental abnormality is further improved by recording the abnormal indexes and labeling the place and time, the response can be quickly made, and the accuracy of positioning the environmental abnormality is improved. According to the method, the environment change trend can be accurately identified and predicted, the real-time performance, the accuracy and the prediction capability of environment monitoring are greatly improved through deep processing and analysis of environment monitoring data, and environment management and policy decision are better supported.

The invention discloses an environmental health risk assessment system, electronic equipment and a nonvolatile storage medium. The system comprises an environment image acquisition module which is used for acquiring an environment image of a target area; the image feature extraction module is used for performing feature extraction on the environment image to obtain a first feature corresponding to the streetscape image and a second feature corresponding to the remote sensing image; the environment variable prediction module is used for fusing the first feature and the second feature, and determining a parameter value of an environment variable corresponding to the target area according to a fused feature obtained after fusion; and the comprehensive index determination module is used for determining a comprehensive environment index corresponding to the target crowd object in the environment of the target area according to the parameter value of the environment variable. According to the method and the device, the technical problem that the environmental health risk cannot be effectively assessed due to the fact that a method for measuring and analyzing the environmental variables mainly depends on field monitoring, the efficiency is low and the data coverage is narrow in the prior art is solved.

The invention discloses a water quality monitoring method and system based on large model data analysis, and the method comprises the steps: collecting multi-dimensional environment index real-time data through a remote sensing device and an underwater mobile sensor, completing the noise filtering and format standardization through an edge calculation node, and inputting the data into a machine learning model; the method comprises the following steps: automatically extracting space-time distribution characteristics and intelligently identifying potential abnormal modes, immediately starting multi-parameter correlation analysis when an abnormal degree exceeds a threshold value, constructing a deviation correlation map to accurately invert pollution source space coordinates, further performing time sequence prediction modeling in combination with a historical data sequence, and deducing a future diffusion path track of pollutants. And finally, deeply overlapping and fusing the predicted trajectory and the ecological sensitive area map, quantitatively calculating the comprehensive risk score distribution of each intersection area by using a risk assessment algorithm, automatically generating a visual environment monitoring report, and updating a historical sequence to form closed-loop learning at the same time. According to the invention, the emergency response speed, the traceability accuracy and the risk prevention and control capability of the sudden pollution event of the water environment are greatly improved.

The invention relates to the technical field of ecological environment monitoring, in particular to an ecological environment quality monitoring and early warning method, a product, equipment and a medium. The method comprises the steps of performing correlation analysis on historical environment data and historical influence factor data, and determining influence factor data with strong correlation in each environment index in the historical environment data; establishing a multi-output regression model; obtaining current influence factor data, performing prediction based on the current influence factor data to obtain predicted influence factor data, and inputting the predicted influence factor data into the multi-output regression model to obtain predicted environment data; and analyzing and predicting standard-exceeding environment indexes in the environment data, performing early warning on the standard-exceeding environment indexes, generating an influence factor list corresponding to the standard-exceeding environment indexes, and determining rectification measures based on the influence factor list. According to the invention, timely early warning and rectification guidance can be carried out on the condition that the ecological environment indexes exceed the standard, and scientific and effective decision support can be provided for environmental protection and treatment.

The invention discloses an environment index monitoring method and system for a networked storage cabinet, and the method comprises the steps: collecting environment index data in a plurality of storage cabinets through employing a low-power-consumption sensor array, enabling the environment indexes to comprise the temperature, humidity, illumination and gas concentration of the storage cabinets, carrying out the preprocessing of the environment index data, and obtaining a pre-processed environment index data; the preprocessing comprises denoising, normalization and feature extraction; sending the preprocessed environment index data to a monitoring terminal, and dynamically adjusting a communication frequency band in a sending process according to real-time channel quality by adopting a self-adaptive frequency hopping mechanism; based on the received environment index data, the monitoring terminal adopts an offline layered reinforcement learning model to carry out environment anomaly detection, and the model comprises bottom-layer perception, middle-layer reasoning and high-layer decision; and if the detection result is environmental anomaly, constructing a space-time diagram structure of the storage cabinet network, and performing multi-cabinet collaborative analysis by adopting a double-current diagram converter fusion network to realize environmental anomaly correlation analysis among the plurality of storage cabinets.

The invention discloses a screen projection connection method and device, a medium and equipment, and the method comprises the steps: firstly determining an environment index used for indicating a connection environment with a main connection end, and then carrying out the precise matching of a target scanning mode for preferentially scanning a set target channel set according to the environment index, and scanning based on the target scanning mode and establishing projection screen connection with the main connection end. Compared with a traditional full-channel scanning mode, the mode of preferentially scanning the target channel set in combination with the actual connection environment can avoid time waste on a large number of invalid channels, reduce scanning delay and repeated attempts caused by channel interference, and remarkably improve scanning efficiency.

The invention relates to the field of pumped storage power station engineering, in particular to an efficient ventilation, waste dissipation and cooling system and method for an underground cavern group, and the system comprises a distributed environment sensing network, a partitioned ventilation execution unit, a partitioned cooling execution unit and a control unit. The prediction model is adopted to predict the future environment or load change trend, and the system coordination control model evaluates the influence of a control instruction on the overall state of the system; by comprehensively considering a real-time measured value, a future predicted value and an evaluation result of a system coordination control model and considering multiple targets of an environment index and operation energy efficiency, an optimization control instruction capable of cooperatively regulating and controlling each partition execution unit is determined and executed.

The invention discloses an intelligent fire prevention and control and operation and maintenance optimization method and system for an electric power supply network, and the method comprises the steps: collecting temperature, humidity and smoke concentration data in real time through a sensor network, generating a comprehensive environment index data flow, and obtaining a fire initial signal through abnormality judgment; calling a medium selection model, and outputting an optimal environment-friendly insulating medium and release parameters thereof by querying an environment-friendly medium and insulating characteristic parameter library and performing matching calculation based on the equipment type; utilizing a response speed optimization algorithm to generate an accurate release instruction sequence containing a release path, a dose and a time sequence; controlling the medium atomization device to synchronously release the medium according to the instruction; and collecting equipment protection and power supply continuity data, performing risk assessment to generate an efficiency index, and updating the prevention and control strategy library to optimize a subsequent decision. Through intelligent decision making and closed-loop optimization, power supply continuity and equipment safety are guaranteed to the maximum extent while rapid and accurate fire extinguishing is achieved, and transformation from post-disaster disposal to operation and maintenance level prevention and control is completed.

The utility model belongs to the technical field of experimental research, and particularly relates to an experimental device for researching a subsurface zone pollutant migration rule, which comprises a water injection component, a column body, a filling medium and a detection component, the column body is of a cylindrical cavity structure, the upper end and the lower end of the column body are each provided with a water injection opening, and the two water injection openings are each communicated with one output end of the water injection assembly; the filling medium is arranged in the column body and is used for simulating a soil layer structure from surface water to underground water; the detection assembly comprises a processor, an environment monitoring module and a pollutant collection module; the environment monitoring module is used for monitoring different depth environment indexes in the filling medium; and the pollutant collection module is used for collecting pollutants at different depths in the filling medium at regular time and transmitting the pollutants to the processor for analysis. The device can avoid the interference of external environmental factors, and provides reliable reference data for accurately analyzing the migration rule of the subsurface zone pollutants in the vertical direction.

The invention discloses a method for constructing a health influence prediction model based on multi-source air data coupling, and relates to the technical field of air quality health monitoring, and the method comprises the steps: obtaining multi-source air environment data in a selected space as an environment index, and obtaining a multi-source physiological signal of a person in the selected space as a physiological index; on the basis of the feature vectors of the environment indexes and the physiological indexes, the coupling relation between the air environment features and the physiological features is constructed, modeling is conducted on the nonlinear coupling relation between the air features and the physiological features through a time sequence deep learning model, and the action rule of air pollution accumulative exposure on dynamic changes of the physiological indexes is captured. Through feature extraction and cross-modal coupling, the comprehensive influence rule of air pollution on the blood pressure, blood oxygen, body temperature, heart rate, heart rate variability and blood glucose physiological indexes of the human body can be revealed, and dynamic prediction of health risks can be achieved through a deep learning model.

Disclosed is a media element, comprising a media substrate, a gelled material adhered to the media substrate, and a plurality of microcapsules suspended in the gelled material. Each microcapsule encapsulating an environmental indicator material in a non-activated configuration and releasing the environmental indicator material when ruptured, the environmental indicator material configured, in response to exposure to a predetermined environmental condition, to change from an initial state to a second state producing an observable effect. The plurality of microcapsules prevent an occurrence of the observable effect while in the non-activated configuration and allow the occurrence of the observable effect when the environmental indicator material is exposed to the predetermined environmental condition after at least a portion of the plurality of microcapsules are ruptured.

The invention discloses a road carbon emission built environment influence assessment method and system based on a multi-source trajectory. The method comprises the steps of obtaining and preprocessing multi-source vehicle trajectory data, urban road network data, street view data and interest point data; traffic carbon emission of different pollutants on the road scale is calculated, multi-dimensional representation of the urban built environment is carried out, and built environment indexes on the road scale are calculated; an evaluation model of the influence of the urban built environment on the road carbon emission is constructed, training and prediction are carried out, an importance sequence of built environment indexes is obtained, and correlation analysis is carried out on the road carbon emission and the urban built environment; and evaluating the influence of the road carbon emission construction environment. According to the method, the urban road carbon emission is quantified and evaluated in combination with the multi-source vehicle trajectory data and various built-up environment indexes, the maximum built-up environment index for the road carbon emission is found, the influence degree and the action mechanism of the maximum built-up environment index are researched, and the method has good benefits in the fields of traffic carbon emission reduction, urban planning and the like.

Disclosed is a process to manufacture an environmental sensor. The process includes dispensing a dry powder material to a first area on a media element, wherein the powder material includes a plurality of microcapsules, each microcapsule encapsulating an environmental indicator material. The process further includes dispensing an adhesive to the media element. After the powder material and the adhesive have been dispensed to the media element, the powder material is in contact with the dispensed adhesive, and the adhesive holds the powder material in position.

The invention relates to the technical field of oil and gas field environment monitoring, and particularly discloses a digital intelligent risk assessment method, platform and equipment for an oil and gas field environment. The method comprises the following steps: acquiring environmental monitoring data of an oil and gas field, the environmental monitoring data comprising pollutant concentration data and meteorological data; the environment monitoring data is preprocessed; constructing an environmental protection regulation knowledge graph; matching the preprocessed environmental monitoring data with the environmental protection regulation knowledge graph to generate a matching result; evaluating the environmental risk level of the oil and gas field according to the matching result; and outputting an environmental risk assessment result according to the environmental risk level. According to the method, through data preprocessing and knowledge graph matching, automatic compliance assessment and risk quantification of environmental indexes are realized, the timeliness and accuracy of risk identification are improved, preventive intervention is supported, and the method is suitable for green development and management of oil and gas fields.

This invention provides a method for simulating heavy non-aqueous phase liquids in groundwater by coupling a mathematical model and an optimization algorithm. The steps include: acquiring dynamic change data of groundwater, environmental indicator change data, and DNAPL concentration data; analyzing the hydrogeological conditions of the site and the migration and transformation process of DNAPL pollution; performing simulation based on a coupled multiphase flow and solute transport model; constructing a simulation system coupled with the mathematical model and optimization algorithm; simulating and evaluating the DNAPL transport process in groundwater based on the acquired data; and obtaining simulation results consistent with actual monitoring data. This invention is programmed using Python and uses open-source programs to achieve full-process simulation of different phases of DNAPL pollution. Simultaneously, the model parameters are corrected based on the optimization algorithm, ultimately yielding optimal simulation results consistent with actual monitoring data, laying the foundation for further in-depth DNAPL simulation work.

To provide a function capable of checking whether or not abnormal information that is not in compliance with the regulation at each country is contained in information which is registered in an information processing system that processes information on an amount of emission of carbon dioxide, etc., and on an environmental index.SOLUTION: An information processing system includes: a receiving unit that receives information relating to an environment; an obtaining unit that obtains predetermined provision information; a checking unit that checks whether or not abnormality information not in compliance with the predetermined provision information is contained in the information relating to the environment; and an output unit that outputs the check result by the checking unit.SELECTED DRAWING: Figure 93

The invention belongs to the field of environment monitoring, and particularly relates to an environment monitoring method and system based on a large model, and the method comprises the steps: S1, collecting multi-source data which comprises environment monitoring data, geographic and spatio-temporal data and priori knowledge data; s2, carrying out preprocessing and non-uniform data interpolation on the multi-source data, and generating time-space aligned gridding feature data; a space-time attention mechanism is utilized to extract space-time correlation features from the gridding feature data, a pre-constructed environment knowledge graph is fused to perform knowledge enhancement, the features after knowledge enhancement are reused through a residual network, and depth features are output; dynamically adjusting the weight of the prediction layer based on the real-time monitoring data, and outputting an environment index prediction result of the target region in a future time period; and S3, generating an environment quality grade and early warning information according to an environment index prediction result, and generating a pollution cause traceability report based on a feature contribution degree analysis result. The problems of low prediction precision, poor interpretability and weak dynamic adaptability in environmental monitoring are solved.

The invention provides an enterprise environment influence assessment method, device and equipment and a computer storage medium, and relates to the technical field of environment assessment, and the method comprises the steps: collecting environment index original data with a timestamp, and constructing a multi-source environment data set in combination with enterprise resource consumption data and emission factor data; carrying out credibility evaluation and weighted fusion on the data set, inputting the fused data into a pre-training evaluation model, and outputting a comprehensive environmental impact evaluation value; the contribution degree of each input feature is analyzed and calculated through model attribution, key factors are identified based on metadata, and a customized improvement measure list of priority ranking is generated in combination with a measure knowledge base and a multi-objective optimization algorithm; the invention further provides a matched portable detection device, electronic equipment and a storage medium, dynamic quantification, accurate attribution and closed-loop optimization of enterprise environment influences are achieved, and the method is suitable for intelligent environment management of high-emission industrial enterprises.

The invention discloses a pilot boarding and off-wheel risk early warning method and system under a severe sea condition, and relates to the technical field of ship piloting, and the method comprises the steps: obtaining a historical pilot boarding and off-wheel data set of a target piloting boat and a target guided boat; identifying overlapping influence areas under different severe sea conditions and corresponding severe sea condition feature sets; analyzing real-time sea condition characteristics according to preset motion parameters and preset sea condition environment indexes; after matching, extracting a matching overlapping influence region; collecting the wave spectrum data of the region and carrying out iterative analysis; and if the wave spectrum iteration features of the target area do not meet the preset requirements, sending out a landing-off wheel risk early warning instruction. The technical problem that in the severe sea condition, based on related data of an alarm device and a ship, a traditional wheel boarding and leaving risk monitoring means is difficult to carry out early warning on the risk in the wheel boarding and leaving process of the pilot is solved, and the technical effects that accurate early warning on the wheel boarding and leaving risk of the pilot in the severe sea condition is achieved, and the monitoring reliability is improved are achieved.

To provide a system configured to omit registration of data on activity for calculating emissions of a supply chain or data on activity for calculating an environmental indicator.SOLUTION: An information processing system includes: a storage unit which may store a numerical value of first activity related to direct greenhouse gas emissions produced by a business entity, a numerical value of second activity related to indirect greenhouse gas emissions produced by the business entity, a numerical value of third activity related to greenhouse gas emissions produced by other business entities located upstream or downstream in a supply chain of the business entity, and a numerical value related to an environmental indicator; and a management unit which manages predetermined categories related to the first activity, the second activity and / or the third activity, in association with predetermined items related to the environmental indicator.SELECTED DRAWING: Figure 76

The invention relates to a method and a device for optimizing water quality evaluation based on interpretable machine learning. The method comprises the following steps: constructing a water quality data set; the contribution degree of the environmental indexes is determined through principal component analysis, and scores of the environmental indexes are obtained based on a penalty mechanism deviating from the optimal range so as to calculate water quality indexes; performing k-fold cross validation on the plurality of machine learning models, and selecting a prediction model according to the comprehensive evaluation value; evaluating the influence of each environmental index on the water quality index by adopting a plurality of importance evaluation methods to obtain comprehensive importance; and gradually inputting the environment indexes into the prediction model for training according to the sequence of gradually decreasing comprehensive importance, and determining an optimal prediction model according to the amplitude of increasing the performance of the model. According to the method, the monitoring purpose is achieved through a better environmental index combination, unnecessary environmental indexes are reduced, and the water quality evaluation efficiency is remarkably improved on the premise that high accuracy is guaranteed; the optimal prediction model can effectively reduce the monitoring cost and improve the operability.

The present application relates to a kind of ecological island building fill unit, fill unit middle is provided with through hole, fill unit includes shell surface layer, middle structural layer and internal filling layer;Shell surface layer is made of neutral material, to ensure that the sea bottom water quality and related environmental indicators where fill unit is after sinking to bottom are not affected;Structural layer uses high-performance corrosion-resistant material, for bearing the water head pressure after fill unit sinking to bottom, unit stacking transmission pressure and mutual collision force, the whole outside of structural layer is covered by shell surface layer, and internal full-closed cavity is formed;Filling layer uses the full-closed cavity surrounded by structural layer, and fills crushed construction waste.The present application has the advantages of simple structure, large specific weight, easy production and transportation, etc.

The invention belongs to the technical field of cup holders, and discloses a big data analysis-based intelligent dust removal environmental impact assessment method, which comprises the following steps of: acquiring multi-source data, and integrating the multi-source data into a unified data model by using a space-time alignment technology to obtain a comprehensive data set of environment and dust removal system operation; performing data preprocessing on the multi-source data in the comprehensive data set, and performing feature extraction to obtain feature data; establishing an environmental impact assessment model based on the feature data; utilizing the trained model to evaluate the influence of the dust removal measures on the environment; the evaluation result is displayed in a chart form; the time sequence diagram shows the change of the environment indexes before and after the dedusting measures are implemented. According to the invention, through big data fusion and an advanced causal inference model, the defect that the influence of natural meteorological change cannot be eliminated by a traditional front-back comparison method is overcome, and the real contribution of dust removal measures to environment improvement can be quantified more scientifically and accurately.

The invention provides a soft measurement method for dioxin emission of grate furnace MSWI process based on simplified deep forest regression of residual fitting mechanism. The highly toxic pollutant dioxin (DXN) generated in the solid waste incineration process is a key environmental index which must be subjected to control. The rapid and accurate soft measurement of the DXN emission concentration is an urgent affair for reducing the emission control of the pollutants. The method comprises the following steps: firstly, carrying out feature selection on a high-dimensional process variable by adopting mutual information and significance test; then, constructing a simplified deep forest regression (SDFR) algorithm to learn a nonlinear relationship between the selected process variable and the DXN emission concentration; and finally, designing a gradient enhancement strategy based on a residual error fitting (REF) mechanism to improve the generalization performance of a layer-by-layer learning process. The method is superior to other methods in the aspects of prediction precision and time consumption.

The invention discloses a lake water quality multi-parameter real-time monitoring method based on the Internet of Things. The method comprises the following steps: acquiring environmental indexes through a data acquisition device, and binding the environmental indexes with position depth information to generate three-dimensional data; cleaning data by adopting distributed calculation and marking anomalies, extracting normal data subsets, and reporting and converging the normal data subsets into regional feature parameters; dividing monitoring zones based on geographical and hydrological characteristics, and analyzing a parameter trend to construct a local dynamic sensing model; when it is detected that parameter linkage is abnormal, auxiliary parameter weights are analyzed through fusion learning to generate comprehensive monitoring indexes; and extracting full-depth data, performing dimension reduction to obtain global perception link data, and finally generating an optimized layout scheme through classification prediction analysis. According to the invention, multi-parameter and multi-depth accurate real-time monitoring and dynamic early warning of lake water quality are realized, the layout of monitoring equipment is optimized through data driving, and the monitoring efficiency and the management scientificity are obviously improved.