106 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 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 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.

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.

The invention provides an energy efficiency evaluation method and device for a high-energy-consumption enterprise under a low-carbon target, and belongs to the field of data processing. The method comprises the steps of collecting current operation data of a target high-energy-consumption enterprise; the current operation data comprises environment data; the environmental data comprises water pollution data, carbon emission, light pollution data, vegetation area and noise pollution data; constructing a simulation operation model according to the current operation data; calculating simulation operation data; and calculating a carbon emission index value based on the simulation operation data and the carbon emission evaluation index, and further performing energy efficiency evaluation according to the simulation operation data when the carbon emission index value satisfies a preset condition. According to the method, the environmental indexes directly or indirectly influencing the carbon absorption amount can be comprehensively considered, and a new carbon emission evaluation index is generated based on the influence factors, so that simulation data of a high-energy-consumption enterprise under a low-carbon target is obtained, energy efficiency evaluation is performed on the high-energy-consumption enterprise, and green transformation of the enterprise is promoted.

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.

The present application belongs to the technical field of environmental protection monitoring, and particularly relates to an environmental protection monitoring and inspection robot, which comprises a shell, a monitoring rotating module is installed at the upper end of the shell, and a high-definition camera and an infrared thermal imager are respectively installed at the left and right sides of the monitoring rotating module. The present application can realize the collection of omnibearing visible light and infrared images, and through visual intelligent algorithm analysis technology, the robot can autonomously identify various machines and equipment in a factory, automatically classify pollution source types, monitor various environmental indexes of an industrial enterprise production line in the process of robot intelligent inspection and monitoring, divide the early warning level based on good or extreme weather, and link with the equipment to assist in decision-making of whether the operation of each process of the industrial enterprise and the equipment continues, strengthen the supervision of the operation of the enterprise workshop, ensure the rationalization of production in the workshop, and improve the informatization and intelligentization level of the ecological environmental bureau in monitoring the environmental quality.

The embodiment of the invention provides a control method of a control switch, the control switch, a system, equipment and a medium, and the method comprises the steps: receiving a plurality of environment parameters collected by a sensor, determining a target parameter of current adjustment from each environment parameter according to a preset environment index and priority of each environment parameter, determining a first device group for adjusting the target parameter, wherein the first device group comprises at least one smart home device; and according to a pre-stored mapping relationship between the environmental parameters and light colors, obtaining and indicating an indicator lamp of the control switch to display a target color corresponding to the target parameter, meanwhile, enabling the control switch to respond to the control operation, and controlling the first equipment group according to the control operation, therefore, the user can directly operate to realize adjustment of the target parameters without thinking and active switching, the steps of manual operation required by the user in a complex smart home environment are reduced, and the user experience and the control efficiency are remarkably improved.

The invention discloses a method and related equipment for predicting available cadmium and methane emission of soil under a dry-wet alternating condition, and belongs to the technical field of agricultural information, and the method comprises the following steps: obtaining soil physicochemical property data of a to-be-predicted target area under the dry-wet alternating condition; inputting the data into a soil environment index prediction model to obtain a soil environment index prediction result; wherein the soil environment index prediction model is constructed on the basis of an available cadmium prediction model and a methane emission prediction model; the effective-state cadmium prediction model and the methane emission prediction model are obtained by training a training data set. By inputting the soil physicochemical property data into the soil environment index prediction model, the available cadmium content and methane emission of the soil under alternation of wetting and drying can be predicted at the same time, the prediction efficiency and prediction precision can be effectively improved, the manpower and material resource cost is reduced, scientific guidance data is provided for targeted regulation and control of the soil physicochemical property, and the method is suitable for popularization and application. And the practicability of large-scale application is improved.

The invention discloses a DC power transmission line lightning stroke risk assessment method and device, terminal equipment and a storage medium, and relates to the field of power transmission lines. The method comprises the following steps: according to historical lightning stroke times, historical rainfall, historical fault times, historical fault indexes calculated by first preset weight coefficients, material operation age limit, large-span and large-height-difference data, lightning protection facility coverage data, power transmission line length, tower span length and power transmission line characteristic indexes calculated by second preset weight coefficients, calculating a power transmission line characteristic index; the method comprises the following steps of: calculating a ground lightning density range, a soil resistivity value, topographic and topographic data, a tower altitude, a climatic environment index calculated by each third preset weight coefficient, a tower inspection frequency, a technician level, lightning protection data, and an operation and maintenance management index calculated by each fourth preset weight coefficient; and determining the lightning stroke risk level of the to-be-assessed line according to the lightning stroke risk assessment coefficient obtained through calculation according to the corresponding fifth weight coefficients. According to the invention, line lightning stroke disaster risk assessment is realized.

Environmental monitors for monitoring a predetermined environmental exposure, for example, an historical heat exposure such as a cumulative ambient heat exposure and / or a peak ambient heat exposure may include an environmental indicator material and a security material. The environmental monitor may be configured to attach to a host product for monitoring the predetermined environmental exposure of the host product, and additionally serve as an anti-counterfeiting indicator.

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 invention provides a building decoration material environmental protection property detection and rating system. Relates to the technical field of building and decoration material detection, and comprises a multi-source detection unit which forms a composite detection channel through an electrochemical sensing coupling detection technology, and the composite detection channel detects key environmental protection indexes of building decoration materials to generate key environmental protection index data. According to the environment-friendly property detection and rating system for the building decoration material, the environment-friendly property of the building decoration material is comprehensively detected through a multi-source electrochemical detection technology in combination with methods such as infrared spectrum sensing, differential pulse voltammetry and electrochemical impedance spectroscopy. The innovative detection can evaluate the key environmental protection indexes of the material in multiple dimensions, such as formaldehyde emission, volatile organic compound concentration, heavy metal element content and radioactivity, so that the accuracy and comprehensiveness of the detection result are ensured, and the scientificity of environmental protection material evaluation is effectively improved.