19 results about "Environmental indicator" patented technology

This invention discloses a computer-based method for evaluating the complexity of the working environment of unmanned ground vehicles (UGVs). First, it determines environmental complexity evaluation indicators and establishes an evaluation indicator system. Then, it acquires and normalizes multi-source environmental data using onboard sensors and a deep learning model. Next, it determines the weights of static environmental indicators using the Analytic Hierarchy Process (AHP) and calculates the static environmental complexity index using a nonlinear mapping function. Simultaneously, it establishes a potential energy function incorporating distance, speed, and predicted collision time, calculating the total environmental potential energy to output the dynamic environmental complexity index. For the state evolution complexity evaluation indicator, it constructs a state evolution complexity quantification model based on state change entropy, outputting the state evolution complexity index. Finally, it outputs the working environment complexity index by establishing a multi-dimensional coupled complexity model. This invention achieves real-time quantification and evaluation of working environment complexity, providing reliable data for autonomous decision-making, path planning, and safety control of UGVs.

This application relates to a method, system, equipment, and medium for assessing the ESG resilience of heating facilities. The method includes: first, acquiring facility type data and dynamic environmental indicators; then, filtering and extracting environmental performance parameters and social demand indicators for heating scenarios to form a preliminary screening dataset; using a random forest algorithm to calculate the impact degree of dynamic environmental factors, obtaining a subset of high-risk environmental performance; extracting governance factor data and training a risk prediction model using a support vector machine algorithm; based on the equilibrium point output by the model, iteratively optimizing to obtain factor balance index data; simultaneously acquiring emission relationship index data and using a random forest algorithm for regression analysis to construct an emission relationship model; finally, extracting allocation fairness features, matching social demand to form an assessment framework, and integrating data to determine the adjustment results for the heating facility resilience assessment. This method can accurately reflect the actual resilience level of heating facilities and improve the scenario adaptability and generalization ability of the assessment method.

The application provides a kind of water environment multi-index data space-time field establishment and early warning method, comprising the following steps: S1, detection data acquisition: obtain environmental detection data;S2, time segmentation: based on detection time information, environmental detection data is divided in multiple time slices;S3, uniform grid construction: based on the coordinate position of all detection points, construct uniform two-dimensional space grid;S4, spatial interpolation reconstruction: environmental index rasterization space-time field is generated on two-dimensional space grid using spatial interpolation method;S4, standardization processing: based on the average and standard deviation of interpolation result calculation standardization processing, to dimensionless rasterization space-time field;S6, multivariate two-dimensional function construction: based on the dimensionless rasterization space-time field of each environmental index, construct multivariate two-dimensional function data object;S7, multivariate function principal component analysis, obtain spatial characteristic function, principal component score and variance contribution;S8, baseline modeling and anomaly detection, output abnormal early warning information.

The application belongs to the technical field of pollutant detection and environmental protection, and discloses a detection method and application of cephalosporin in atmospheric environmental medium, which comprises the following steps: S1, determining key environmental indicators; S2, sampling atmospheric particulates; S3, extracting cephalosporin; S4, batch detection and data analysis. The application also discloses the application of the detection method in environmental and health risk assessment. The application proposes a specific and feasible key detection scheme with low cost through a whole-process strategy of'screening key environmental indicators, optimizing sampling network, sample pretreatment enrichment and purification, high-efficiency separation, high-sensitivity detection, method verification and risk assessment', develops and standardizes the detection method, and qualitatively and quantitatively detects different generations of structural characteristics, so as to improve the detection efficiency, reduce the detection cost, improve the overall monitoring capability of atmospheric environmental safety, and be applicable to activities such as atmospheric environmental health risk assessment.

Disclosed is a thermal transfer ribbon for use with a media processing device. The thermal transfer ribbon includes a carrier layer and a transfer layer operatively coupled to the carrier layer. The transfer layer includes an environmental indicator material encapsulated in a plurality of microcapsules. The plurality of microcapsules prevent the environmental indicator material from producing an observable effect in response to exposure to a predetermined environmental condition. The environmental indicator material is released from the plurality of microcapsules in response to the plurality of microcapsules being ruptured. After the environmental indicator material is released and exposed to the predetermined environmental condition, the environmental indicator material produces the observable effect. The observable effect results from a change in the environmental indicator material from an original state to a second state in response to the predetermined environmental condition.

The application discloses a shale organic matter enrichment master control factor evaluation method and system, including: collecting shale sample geochemical data to construct an original data set; obtaining a standardized feature subset after preprocessing and constructing a multi-dimensional feature matrix, wherein the independent variable is a paleoenvironment index, and the dependent variable is total organic carbon content; grouping all samples according to geological sections, and training a random forest model for each section; using a SHAP model to interpret each section model, calculating the quantitative contribution percentage of each paleoenvironment index and determining the master control factor of the section; obtaining a contribution percentage set of each section, and comparing and analyzing the dynamic evolution law of the contribution percentage of each index with the stratigraphic stage. Through the method, the qualitative sorting to quantitative attribution is realized.

This invention relates to the field of edge computing technology, specifically to a groundwater pollution monitoring method and system based on edge computing. The method includes the following steps: acquiring groundwater sequence data and calculating unit-time fluctuation intensity parameters; comparing threshold intervals to generate sampling frequencies; collecting physicochemical indicators to generate deviation difference sets and extracting urgency weight parameters; combining the remaining computing power of edge nodes to generate task scheduling strategies; collecting environmental indicators and writing them into a deviation accumulation matrix and accumulating them row by row; and comparing with set risk thresholds to generate pollution risk levels. In this invention, by adaptively matching the sampling frequency based on the fluctuation intensity state, the fixed interval blind zone is effectively eliminated and abrupt signals are captured. Combining multi-dimensional deviation features and node computing power to construct an allocation index to generate a scheduling strategy, dynamic task allocation and computing power balancing are completed at the edge, avoiding link congestion caused by redundant baseline transmission. Based on the accumulation matrix, pollution trends are tracked, achieving proactive assessment.

This invention discloses a method for assessing enterprise ESG environmental indicators based on IoT data, belonging to the field of intelligent industrial environmental management technology. It addresses the technical problems of ambiguous correlations between production factors and environmental indicators, lack of scientific basis for anomaly judgment, and low efficiency in source tracing and handling in existing technologies. The method establishes a ledger for the coding of equipment, processes, and ESG environmental indicators throughout the entire production process; collects IoT data to filter core correlation groups; builds a three-level correlation mapping table and establishes a dynamic update and traceability mechanism; establishes a threshold ledger for different operating conditions; constructs an abnormal feature library of enterprise ESG environmental indicators; and uses threshold comparison and cosine similarity methods to determine true anomalies; generates a source tracing list based on the three-level correlation mapping table; filters abnormal elements hierarchically; and integrates information to form an anomaly root cause location report. This invention achieves standardized and quantitative binding of production factors and ESG indicators, improves the rationality and accuracy of anomaly judgment, and effectively enhances the efficiency and scientific basis of enterprise ESG environmental indicator assessment and anomaly handling.

A data logging device includes an activation circuit, an environmental sensor, a processing module, and an environmental indicator. The activation circuit is operably coupled to place the data logging device in a ready mode based on an activation signal. The environmental sensor is operable to sense an environmental condition associated with the data logging device in response to a log command to produce sensed data. The processing module is operably coupled to generate the log command; receive the sensed data; determine whether the sensed data triggers an environmental violation; and, when the sensed data triggers the environmental violation, generate an environmental violation signal. The environmental indicator is operably coupled to provide a visual indication of the environmental violation based the environmental violation signal.

The application discloses a water quality anomaly dynamic correlation gain identification method based on machine learning, relates to the technical field of water environment monitoring, and comprises data acquisition, model construction, training and application steps. Based on a multi-order gain coupling activation mechanism, the following core steps are used to complete water quality anomaly identification and tracing: a three-dimensional coupling activation mechanism library of environment-index-gain is constructed, a dynamic coupling correlation network is generated based on the mechanism library, and a special calculation model is designed to quantize coupling gain activation. In the application, the multi-order gain coupling activation mechanism, the three-dimensional coupling activation mechanism library, the dynamic coupling correlation network, the 128-bit coupling fingerprint coding and the CGED-NN neural network are used to solve the problems of static modeling, neglect of environmental regulation and multi-order coupling in the prior art, which lead to water quality anomaly misjudgment, tracing failure and inability to cope with complex pollution scenarios. The application realizes accurate identification of water quality anomaly, efficient tracing, early prediction and dynamic adaptation of monitoring points, and reduces monitoring cost.

This invention relates to a method and system for monitoring the diffusion of toxic waste gas based on machine learning, comprising the following steps: S1, collecting environmental indicator data in real time using gas sensors and meteorological sensors deployed in the monitoring area, wherein the environmental indicator data includes at least pollutant concentration, wind speed, wind direction, ambient temperature, and humidity; S2, constructing a pollution source feature vector P=(p1,p2) based on the environmental indicator data collected in step S1, wherein p1 represents the pollutant emission intensity index calculated based on real-time monitoring data, and p2 represents the type identifier obtained based on the chemical composition analysis of pollutants; S3, calculating the risk score of each monitoring point using an improved entropy weight-TOPSIS comprehensive evaluation algorithm; S4, generating toxic waste gas diffusion early warning information based on the calculated risk score and combined with a dual threshold judgment mechanism, thereby better preventing environmental pollution and safety accidents caused by toxic waste gas leakage.

We provide technology that supports the design of products that can evaluate environmental performance throughout their entire lifecycle. [Solution] The design support system includes a storage unit that stores product component configuration information, alternative parts information, usage scenario information that defines product usage scenarios, regulatory information that specifies regulations concerning the product, improvement item information that includes improvement items for the product's environmental indicators, and constraint information that the product must satisfy; an improvement item identification unit that identifies target values ​​for environmental indicators that must be satisfied throughout the product's lifecycle according to at least one of the usage scenarios, regulations, and improvement items; an alternative parts identification unit that identifies one or more alternative parts that can be replaced while satisfying the constraints; a calculation unit that identifies predicted values ​​for the environmental indicators of the substituted product; and an output unit that outputs alternative parts whose predicted values ​​for environmental indicators exceed the target values.

This invention belongs to the field of bridge engineering safety assessment technology, specifically a differentiated safety assessment method for cross-sea cable-stayed bridges and suspension bridges in frozen coastal areas of northern China. The method includes the following steps: constructing a differentiated assessment index system, acquiring multi-source heterogeneous data, determining the weights of differentiated indicators, establishing a differentiated safety assessment model, conducting assessments, outputting assessment results, and generating maintenance recommendations. For the structural stress characteristics of cable-stayed bridges and suspension bridges, a multi-level assessment index system containing common and characteristic indicators is constructed respectively. The common indicators include at least environmental indicators, and the characteristic indicators are set according to the key stress-bearing components of cable-stayed bridges and suspension bridges. The assessment method involved in this invention is highly targeted, accurate, and practical.

The invention relates to the technical field of pollution traceability, and discloses a multi-scale coupled drainage basin pollution step-by-step traceability method, device and equipment and a medium, and the method comprises the steps: obtaining an initial simulation result of each sub-drainage basin outputted by a target SWAT model; determining a key sub-basin based on the initial simulation result; calling an APEX model to simulate by taking the key simulation result corresponding to the key sub-basin as a boundary condition to obtain an intermediate simulation result of the key sub-basin; performing grid unit division on the sub-region of the intermediate simulation result; determining a grid environment index of each grid unit; taking the initial simulation result and the intermediate simulation result as dependent variables, taking the grid environment index as an independent variable, and constructing a quantitative relationship between the grid environment index and the pollution load capacity; constructing a spatial distribution map based on the quantitative relationship; and tracing the target pollution load capacity of the target grid unit based on the spatial distribution diagram. According to the scheme, step-by-step traceability from the drainage basin to the sub-drainage basin and then to the grid can be realized, the problem of traceability intermediate fault in the prior art is solved, and the traceability accuracy is improved.

To provide a thermal sensation estimation device that can more accurately estimate the thermal sensation of living organisms. [Solution] A biological information acquisition unit acquires biological information of living organisms within the observation range from signals obtained by a transmitting / receiving unit that transmits and receives radar waves. A temperature sensor measures the temperature within the observation range. An environmental sensor measures at least one environmental indicator within the observation range, including humidity, atmospheric pressure, illuminance, carbon dioxide concentration, oxygen concentration, noise level, wind speed, wind direction, and PM2.5 concentration. Based on the biological information acquired by the biological information acquisition unit, the temperature measurement values ​​acquired by the temperature sensor, and the environmental indicators measured by the environmental sensor, a thermal sensation estimation unit estimates the thermal sensation of living organisms within the observation range.

This invention relates to the field of gas concentration sensing and detection technology, and provides an automatic baseline correction algorithm for a gas concentration sensor, comprising the following steps: starting the gas concentration sensor until a special environmental indicator is 0; if the temperature and humidity meet preset conditions, entering the ASC process; if the preset conditions are not met, exiting the ASC process; setting sampling count parameters and sampling period, and collecting gas concentration samples within a preset time period, calculating the average value and / or the difference between the maximum and minimum values ​​of the samples to determine the stability of the ambient gas concentration; if the ambient gas concentration is stable and the average value exceeds a preset offset threshold, it is considered that zero-point drift has occurred, the baseline parameters are corrected, a baseline correction is completed, and the ASC process restarts; if the current ambient gas concentration is unstable, the ASC cache is cleared and the ASC process restarts. This effectively improves the long-term stability and measurement accuracy of the gas concentration sensor, and enhances application reliability.

This invention discloses a deep learning-based multi-dimensional environmental evaluation method for integrated air-space and space (ALAN) cities, comprising the following specific steps: S1: multi-source data acquisition and preprocessing; S2: light source target detection; S3: functional area identification; S4: light environment index calculation and multi-modal feature fusion; S5: light pollution risk assessment; S6: intelligent early warning system output. This invention proposes a weakly supervised learning method for light pollution level classification, requiring minimal manual annotation to train the risk assessment model, and combines random forest feature importance analysis to achieve quantitative modeling of light pollution risk influencing factors. This method effectively alleviates the problem of insufficient large-scale labeled data, can adaptively assess light pollution risk based on the characteristics of different cities and regions, and has good generalization performance.

This invention discloses a method for assessing enterprise ESG environmental indicators based on IoT data, belonging to the field of intelligent industrial environmental management technology. It addresses the technical problems of ambiguous correlations between production factors and environmental indicators, lack of scientific basis for anomaly judgment, and low efficiency in source tracing and handling in existing technologies. The method establishes a ledger for the coding of equipment, processes, and ESG environmental indicators throughout the entire production process; collects IoT data to filter core correlation groups; builds a three-level correlation mapping table and establishes a dynamic update and traceability mechanism; establishes a threshold ledger for different operating conditions; constructs an abnormal feature library of enterprise ESG environmental indicators; and uses threshold comparison and cosine similarity methods to determine true anomalies; generates a source tracing list based on the three-level correlation mapping table; filters abnormal elements hierarchically; and integrates information to form an anomaly root cause location report. This invention achieves standardized and quantitative binding of production factors and ESG indicators, improves the rationality and accuracy of anomaly judgment, and effectively enhances the efficiency and scientific basis of enterprise ESG environmental indicator assessment and anomaly handling.