39590 results about "Environmental chemistry" patented technology

The embodiment of the invention provides an environment monitoring method and system based on 5G. According to the method, a dynamic coupling sensing field is constructed in a 5G network coverage domain, an adaptive topology with nonlinear interaction of parameters is established, and when parameter abnormity is detected, differential geometric reconstruction of a 5G network slice control surface is triggered based on Riemannian curvature change. And constructing a space-time manifold space at the 5G edge computing node, decomposing and extracting a diffusion principal curvature direction, and iteratively solving a propagation rate of an abnormal parameter in combination with a geodesic equation to generate a diffusion leading edge envelope surface. And according to the main curvature distribution characteristics of the diffusion leading edge envelope surface, constructing a dynamic sampling optimizer, generating an exponential decay sampling function, and deploying a directional sensing wavefront. And carrying out conformal tightening processing on the sampling function and the geographic grid to generate an interactive holographic manifold, establishing an aging judgment criterion of an abnormal level based on curvature 2-form characteristics, and triggering a graded early warning pulse wave packet. According to the technical scheme provided by the embodiment of the invention, the real-time performance and precision of environment monitoring are improved.

The invention discloses a land degradation supervision method based on remote sensing monitoring, and relates to the technical field of land degradation monitoring. The method is used for solving the problems of hidden degradation risk identification and dynamic propagation simulation. According to a multi-temporal thermal infrared remote sensing image, daily variation characteristics of surface temperature are extracted through spectral analysis, a recessive salinization risk distribution map is constructed by combining measured data of soil salinity, and interference of vegetation coverage is overcome. And multi-source remote sensing and geographic data are fused, dominant driving factors significantly associated with the salting risk are screened, and the degeneration cause is clarified. And in combination with ecological restoration potential and treatment resource constraints, calculating an antagonism index of a driving factor and the restoration potential, and dividing degradation threat levels. And a natural hydrology and human activity dual-path propagation model is constructed, a risk space-time evolution process is simulated, and a future hotspot migration path and an optimal blocking window are predicted. According to the method, accurate early warning and active prevention and control of land degradation are realized through a technical chain of risk identification, driving analysis, threat grading and dynamic simulation.

A system for the hypothermic transport of biological samples, such as tissues, organs, or body fluids. The system includes a self-purging preservation apparatus to suspend a sample in preservation fluid and perfuse a tissue with preservation fluid. The self-purging preservation apparatus is placed in an insulated transport container having a cooling medium. When assembled, the system allows for transport of biological samples for extended periods of time at a stable temperature.

The invention discloses a multi-parameter in-situ detection data analysis system for a marine ranch, and relates to the technical field of data analysis, the multi-parameter in-situ detection data analysis system comprises a multi-parameter sensor group and a self-adaptive multi-dimensional trigger type sampling control module, the multi-parameter sensor group comprises an environmental parameter sensor and a biological behavior sensor, the environmental parameter sensor is used for measuring real-time environmental parameters of the marine ranch, the biological behavior sensor is used for monitoring biological behavior parameters of the marine ranch in real time, and the self-adaptive multi-dimensional trigger type sampling control module comprises a data preprocessing unit and a dynamic sampling strategy unit. The self-adaptive multi-dimensional trigger type sampling control module is designed, so that sampling modes are dynamically switched according to real-time dual-mode data, environment sudden changes are effectively captured, data redundancy or key change sampling leakage caused by traditional fixed sampling frequency is avoided, monitoring precision is improved, energy optimization is achieved, the service life of equipment is prolonged, and the system is suitable for large-scale popularization and application. And high quality and real-time performance of data are ensured.

The invention relates to the technical field of water treatment intelligent control, in particular to an AI-based intelligent urban water purification and sewage treatment process optimization system and method, and the method comprises the steps: obtaining related sensor data information, and monitoring and recording the change of key data in real time; establishing a water quality change prediction model by using a multiple linear regression model, wherein the water quality change prediction model predicts the water quality change according to the key data change acquired in real time; the dynamic agent AI model adjusts the dosage, and the water treatment chemical / biological reaction kinetics AI model predicts the pollutant degradation condition; a local lightweight AI model optimizer and a multi-target genetic algorithm are combined with energy consumption, medicament cost, processing efficiency and carbon emission to obtain an optimal control model; the optimal control model optimizes the operation parameters of the sewage treatment process according to the pollutant degradation condition. According to the method, the operation parameters of the sewage treatment process are optimized through the optimal control model, the energy consumption, the agent cost and the carbon emission are reduced, and the treatment efficiency is improved.

The invention relates to the technical field of remote sensing image recognition, in particular to a remote sensing sewage area recognition method and system based on a graph structure and multi-stage enhancement. The method comprises the steps of performing data preprocessing and representation enhancement on an acquired remote sensing image; performing sewage salient region preliminary screening on the enhanced remote sensing image, including abnormal enhancement mapping construction based on local statistical distribution; pollution candidate graph extraction based on spatial structure prior driving; enhancing the response of the stable region based on a structure consistency enhancing mechanism of the polluted region; high-precision segmentation and identification of the sewage area comprises the following steps: constructing a multi-resolution residual pyramid structure; carrying out fine-grained boundary structure modeling and uncertainty suppression; generating a sewage distribution probability graph and optimizing structural consistency; according to the method, the multi-resolution residual pyramid structure is constructed, image context information under different perception scales is fully mined, and the sensitivity and edge integrity of the model to a sewage area under a complex texture background are remarkably enhanced.

A system for the hypothermic transport of biological samples, such as tissues, organs, or body fluids. The system includes a self-purging preservation apparatus to suspend a sample in preservation fluid and perfuse a tissue with preservation fluid. The self-purging preservation apparatus is placed in an insulated transport container having a cooling medium. When assembled, the system allows for transport of biological samples for extended periods of time at a stable temperature.

The invention discloses a water body new pollutant risk assessment method and system. Belongs to the technical field of environmental science. Comprising the steps of collecting multi-source heterogeneous data to generate an initial data set; performing data fusion in the initial data set by using a weighted Bayesian fusion algorithm and a space-time interpolation method to generate a target data set; importing the target data set into a deep learning model for prediction and obtaining a prediction result; and obtaining a preset early warning condition and combining with the prediction result to judge whether risk early warning needs to be carried out. Through constructing a pollutant data acquisition system and data fusion, the monitoring precision of the new pollutants in the water body is improved. The fusion algorithm is used for data preprocessing, and pollutant detection is comprehensive. And the space-time diffusion process of the pollutants can be accurately modeled.

The invention discloses a landslide prediction and early warning system and method based on a remote sensing technology, and is applied to the technical field of landslide early warning. Comprising a data acquisition module for acquiring multi-source data in a monitoring area; the cloud computing module is used for large-range landslide risk assessment and deformation monitoring; the edge calculation module is used for landslide risk assessment and deformation monitoring of a specific area; the end-cloud cooperative communication module is used for realizing data interaction between the cloud computing module and the edge computing module; the federal learning module is used for realizing cooperative training under data privacy protection of different areas; and the landslide early warning module is used for carrying out early warning based on landslide risk assessment and deformation monitoring results of the cloud computing module and the edge computing module. According to the invention, by using the deep learning model, the end-cloud collaborative architecture and the federal learning driven data fusion framework, efficient, low-cost and high-precision landslide risk prediction is realized.

A system and method for maintaining an oxygen concentration of a biological sample. The oxygen concentration can be maintained by measuring the oxygen concentration within the biological sample and adjusting a rate of an oxygen supplier in response to this measurement. For example, when the oxygen concentration is below a threshold, oxygen can be delivered to the biological sample at a higher rate.

The invention relates to the technical field of soil pollution identification, and discloses a soil heavy metal pollution identification system. A multispectral remote sensing sensing module of the system obtains surface reflectance data and soil in-situ spectral data through a satellite load and a vehicle-mounted mobile platform respectively, and the surface reflectance data and the soil in-situ spectral data are processed by a heterogeneous data fusion gateway to generate multiband spectral response signals. In the pollution risk assessment module, a spatial distribution analysis unit outputs a heavy metal spatial distribution map, a migration risk prediction unit generates a pollution migration probability cloud map in combination with meteorological and hydrological data, and a pollution threshold defining unit outputs a soil remediation safety threshold. In the treatment decision execution module, an in-situ remediation execution unit adjusts passivator injection parameters, a pollution source management and control unit regulates pollution source blocking equipment and collects monitoring signals, and a three-dimensional dynamic early warning platform generates a comprehensive pollution risk index. According to the system, the cooperative operation of soil heavy metal pollution identification, evaluation and treatment is realized.

The present disclosure provides water-soluble film packages composed of a water-soluble film, where the packages include: one or more compartment(s) including dehydrated microbes. The disclosure further provides methods of production of the water-soluble film packages, and methods of application thereof. The water-soluble packages of the disclosure enable convenient and accurate application of microbial dry powders to plants, promote uniform dispersion of powdered microbes, enhance microbial shelf life, and enhance the survival of microbes on seed, while being environmentally sustainable.

The invention relates to the field of landfill methane emission remote sensing monitoring, and discloses a landfill methane emission flux measuring and calculating method and device and a medium, and the method comprises the following steps: based on regional satellite TROPOMI observation data, carrying out qualitative analysis on annual average methane emission signals of a landfill, and determining a target landfill according to a qualitative analysis result; based on data of a satellite-borne hyperspectral imager, the methane emission flux of the target landfill is quantitatively measured and calculated, and the position of a methane emission hot spot area in the target landfill is determined; and positioning the position of an emission source in the target landfill through an active methane laser detection lens carried by an unmanned aerial vehicle platform, and independently calculating the methane emission flux of the target landfill by adopting an upper and lower section method. Based on the method, the annual average methane emission condition of the landfill can be rapidly and accurately evaluated, the methane emission flux of the whole landfill is obtained, and the internal emission hot spot area of the landfill is positioned.

The invention provides a refuse landfill methane emission monitoring method and system for air-ground combined monitoring, ground sampling points are selected by combining topographic features, possible emission hot spot areas and historical operation information of a refuse landfill, and areas which are difficult to reach by ground sampling can be realized through aerial survey air sampling; by integrating ground sampling and aerial survey, high-resolution methane concentration data can be obtained, and meanwhile interference of flowing wind on hot spot area recognition is reduced; by combining ground surface sampling data and aerial survey data, a Getis-Ord Gi * statistical method and a nuclear density estimation method are adopted to carry out bimodal hot spot area identification, a methane emission source area can be positioned more accurately, the precision and efficiency of methane emission monitoring of the refuse landfill are remarkably improved, and the method is suitable for popularization and application. The methane emission source of the refuse landfill is accurately and efficiently found, the emission amount of the methane emission source is evaluated, targeted suggestions are provided for operation of the landfill, and the method has important environmental protection and economic values.

The invention relates to the technical field of environment monitoring, solves the problem that a monitoring system and method cannot meet the requirements for accuracy and real-time performance of animal behavior research and climate environment research in a plateau area at the same time, and particularly discloses a monitoring system and method integrating environment monitoring and animal monitoring recognition. Comprising a sensing system, a data transmission system and a data processing system, the sensing system is used for collecting environment and animal data of a monitoring site, and the data processing system is used for receiving and processing the data and analyzing the change trend and the correlation influence degree of the environment data and the animal data; the monitoring method comprises the following steps: acquiring environment and animal data through the environment monitoring network and the animal behavior sensing network; data is transmitted to the data processing system in real time through the data transmission system; the data processing system analyzes the dynamic change trend of the environment data and the animal data; and generating early warning information and triggering a protection measure execution instruction. The method can be used for monitoring the plateau environment and animals, and is high in accuracy and high in real-time performance.

A system for the hypothermic transport of biological samples, such as tissues, organs, or body fluids. The system includes a self-purging preservation apparatus to suspend a sample in preservation fluid and perfuse a tissue with preservation fluid. The self-purging preservation apparatus is placed in an insulated transport container having a cooling medium. When assembled, the system allows for transport of biological samples for extended periods of time at a stable temperature.

The invention relates to the technical field of water environment risk monitoring, in particular to a method for evaluating the algal bloom risk of a water body. The method comprises the following steps: collecting historical monitoring data of a to-be-evaluated water body, wherein the historical monitoring data comprises blue-green algae abundance data, water quality data and hydrological data; analyzing the correlation between the cyanobacteria abundance or chlorophyll a concentration and the water quality and hydrological data of the to-be-evaluated water body; hydrological and water quality parameters with the highest correlation with the cyanobacteria abundance or chlorophyll a concentration are screened out; hydrology and water quality parameters of a water body to be evaluated are taken as predictive variables, and cyanobacteria abundance or chlorophyll a concentration is taken as a response variable to construct a Bayesian network model; the weight of each parameter in the Bayesian network model is calculated, and the algal bloom risk probability that the cyanobacteria abundance exceeds a specific threshold value under the given parameter condition is calculated according to the weights. According to the invention, the scene-based probability deduction of the stable period and the dynamic period is realized through the double-branch Bayesian network model, so that the accuracy and timeliness of algal bloom risk assessment are improved.

A system for the hypothermic transport of biological samples, such as tissues, organs, or body fluids. The system includes a self-purging preservation apparatus to suspend a sample in preservation fluid and perfuse a tissue with preservation fluid. The self-purging preservation apparatus is placed in an insulated transport container having a cooling medium. When assembled, the system allows for transport of biological samples for extended periods of time at a stable temperature.

The invention discloses a cleaning control method and system based on an electronic cleaning agent, and the method comprises the steps: obtaining the distribution characteristics of pollutants and a material sensitivity judgment result through obtaining the micron-sized particle distribution data, chemical residue concentration data and surface roughness parameters of the pollutants on the surface of an electronic workpiece; according to the pollutant distribution characteristics and the material sensitivity judgment result, matching a candidate formula from a preset cleaning agent formula database, calculating pollutant removal rate predicted values of different concentration and time combinations through a linear regression algorithm, and determining optimized cleaning agent formula parameters and cleaning strength control parameters; and in the cleaning process, the residual quantity of pollutants and the surface damage degree are collected in real time, if the residual quantity of the pollutants exceeds a preset threshold value T1 or the surface damage degree exceeds a preset threshold value T2, cleaning agent injection pressure and cleaning time parameters are dynamically adjusted, and updated cleaning execution parameters are generated. Accurate and efficient cleaning of the surface of the electronic workpiece is achieved.

The invention provides a marine ranch water quality parameter multi-model fusion dynamic prediction method and system, and relates to the technical field of water quality prediction, and the method comprises the steps: obtaining data through multiple monitoring platforms, building a combined prediction model through LSTM, PSO-SVM and a random forest regression model, and dynamically adjusting the combined weight according to the prediction error of each model, and accurate prediction of the dissolved oxygen concentration is realized. Compared with single model prediction, the advantages of multiple models are fused, and the prediction precision is improved; the adaptability and robustness of the model are enhanced by a strategy of dynamically adjusting the weight; according to the method, the time sequence analysis and the early warning threshold are combined, the early warning of the oxygen deficit risk is realized, the position and range of the oxygen deficit risk area can be determined according to the spatial distribution characteristics of the prediction result, the accurate oxygenation of the oxygenation equipment is guided, the water quality safety of the marine ranch is effectively guaranteed, and the economic loss caused by oxygen deficit is avoided.

The present disclosure provides a landslide identification method, a device and a storage medium based on multi-path feature fusion, and relates to the field of geological hazard monitoring and early warning. The device and storage medium are used to implement the method. The beneficial effects of the present disclosure are as follows: a landslide identification method based on multi-path is provided, deep feature-level interaction among different types of landslide image data is achieved, high-resolution feature information is preserved, landslide identification accuracy is significantly improved, the computational costs are reduced, the real-time performance of landslide identification is significantly enhanced and the real-time monitoring and early warning are achieved.

The present invention discloses a method for detecting and applying C1-C2-alkyl-substituted phenol isomers, which relates to the technical field of environmental detection and includes: selecting a corresponding sample pretreatment process to prepare a test sample according to the petroleum content range in the sample; under a mobile phase of methanol-5.0-6.5 mmol / L ammonia water, using LC-MS to detect the test sample to obtain the concentration of the first component and the standardized concentrations of co-eluted 3,5-dimethylphenol and 2,6-dimethylphenol; under a mobile phase of acetonitrile-4.0-10.0 mmol / L ammonia water, using LC-MS to detect the test sample to obtain the concentration of 3,5-dimethylphenol, or the standardized concentrations of co-eluted 3,5-methylphenol and 3-ethylphenol, or the standardized concentrations of co-eluted 2,6-dimethylphenol and 2,4-dimethylphenol; under a mobile phase of methanol-5.0-6.5 mmol / L ammonia water or acetonitrile-4.0-10.0 mmol / L ammonia water, using LC-MS to separately detect the calibration samples of the co-eluted components, and calculate the conversion factor of the co-eluted components and the concentration of the other component. The present invention realizes the accurate quantification of phenol and 12 C1-C2-alkyl-substituted phenol isomers.

The invention relates to the technical field of water and soil conservation monitoring, and discloses a remote sensing image-based water and soil conservation monitoring method, which comprises the following steps of: 1, integrating optical, infrared and microwave remote sensing data, and constructing a soil information atlas; step 2, capturing soil humidity change in each season by using long-time-sequence data according to the soil information, and determining the influence of rainfall, snow melting and evaporation on the reflectivity; step 3, training data of each wave band through a deep neural network according to the influence information of the reflectivity; through an adaptive learning algorithm and a model parameter regular updating mechanism, the problem that in the prior art, characteristic spectrums of wet soil, vegetation and bare soil are overlapped due to seasonal changes is effectively solved, after the seasonal modulation factors are introduced, the system can accurately capture influences of natural factors such as rainfall, snow melting and evaporation on the soil reflectivity, and the soil reflectivity is improved. And model parameters are adaptively adjusted, so that the interference of reflectivity fluctuation on a monitoring result in wet and drought states is eliminated, and the stability of long-term monitoring is ensured.

The invention relates to the field of water quality monitoring analysis, in particular to a multi-modal water quality monitoring data intelligent processing method and system. The method comprises the following steps: obtaining a water quality monitoring parameter stream and a water quality monitoring image, and carrying out adaptive processing to obtain a filtering optimization parameter stream and an optimization monitoring image; performing water quality state evolution perception according to the adaptive filtering optimization parameter flow, and constructing a water quality state evolution graph; and performing biological index response analysis and water quality ecological influence evaluation based on the water quality state trend evolution graph to obtain a water quality evaluation report. And carrying out pollutant spatial distribution change analysis based on the optimized monitoring image, and executing final water quality monitoring evaluation according to a water quality evaluation report. The invention provides efficient and accurate water quality status analysis.

The invention discloses a forest carbon reserve and carbon sink monitoring and evaluating method based on a multi-source remote sensing technology. The method comprises the following steps: multi-source remote sensing data acquisition: respectively acquiring optical remote sensing data and radar remote sensing data; tree species classification and identification: carrying out tree species classification on the forest in the research area to realize tree species scale space distribution information extraction; for different tree species, biomass model parameters obtained through field investigation are combined, a biomass estimation model based on the tree species is established, model input parameters are biomass model parameters, and vegetation biomass is output; calculating the carbon reserve: calculating the vegetation carbon reserve by utilizing the inverted vegetation biomass and combining the carbon content coefficients of different tree species; estimating the soil carbon reserve, and finally obtaining the total carbon reserve of the forest ecosystem: calculating the carbon reserve variation of the forest ecosystem at different time points based on the multi-source remote sensing data of the long-term sequence. According to the invention, large-range, high-precision and continuous forest carbon reserve and carbon sink monitoring and evaluation considering tree species difference is realized.

A fire retarding biochemical liquid composition including: a dispersing agent realized in the form of a major amount of water, for dispersing alkali metal ions dissolved in the water; a major amount of a first fire retarding agent realized in the form of a first alkali metal salt of a nonpolymeric saturated carboxylic acid, specifically, tripotassium citrate, for providing metal potassium ions dispersed in the water when the at least one alkali metal salt is dissolved in the water to form a mixed retardant solution; a minor amount of a second fire retarding agent realized in the form of a second alkali metal salt of a nonpolymeric saturated carboxylic acid, specifically, sodium benzoate (SB), for providing metal sodium ions dispersed in the water when the at least one alkali metal salt is dissolved in the mixed retardant solution, for functioning as a secondary fire retardant agent and also inhibiting surface corrosion reactions involving metals contacting the mixed retardant solution, including specific metals, namely 2024-T3 Aluminum, 4130 Steel, Yellow Brass, and Az31B Magnesium; a minor amount of thickening agent in the form of a biomolecular polymer consisting of polysaccharides, for increasing the viscosity of the mixed retardant liquid during application operations; a minor amount of coloring agent in the form of fugitive color dye powder, or non-fugitive pigment powder, for imparting a visible color (e.g. red or green) when the fire retarding biochemical liquid composition is applied to a surface to be protected against fire, as water molecules in the retardant liquid coating evaporate to the environment during drying, to form a thin fire retardant coating deposited on the treated surfaces having a characteristic visible color, and comprising potassium and sodium salt crystals mixed within the polysaccharides of the biomolecular polymer. Wet and dry concentration formulations of the fire retarding biochemical liquid composition are disclosed for use with both serial and ground delivery methods and apparatus.

The invention discloses a plot pollution monitoring, predicting and early warning system and method, and relates to the technical field of environment monitoring and pollution early warning, and the method comprises the steps: obtaining multi-source data of a target plot; performing preprocessing, feature extraction and fusion on the multi-source data; inputting the multi-source data into the dynamic pollution diffusion model to obtain a pollutant concentration field of the target plot; jointly inputting the fusion data and the pollutant concentration field into a land parcel pollutant concentration prediction model for prediction to obtain a pollutant concentration prediction value of the target land parcel in a future target time period; comparing the pollutant concentration predicted value of the target land parcel in the future target time period with the early warning threshold values of all levels, and generating an early warning instruction of the corresponding level according to a comparison result; generating corresponding early warning information according to the early warning instruction; and executing corresponding early warning measures according to the early warning information. According to the invention, through multi-dimensional data integration and model optimization, scientific assessment and hierarchical management and control of pollution risks can be realized.

The invention discloses biochar coupled bacteria-algae granular sludge as well as a culture method and application thereof. According to the invention, in an optical sequencing batch reactor, activated sludge in a secondary sedimentation tank of a sewage treatment plant, chlorella and biochar are utilized for culturing to obtain biochar coupled bacteria-algae granular sludge, and pollutants such as COD, NH4 < + >-N, PO4 < 3->-P and antibiotics (such as enrofloxacin) contained in aquaculture wastewater can be efficiently removed by utilizing the biochar coupled bacteria-algae granular sludge under the condition that the salinity is 0.3%. Besides, the biochar coupled bacteria-algae granular sludge is good in stability, when the biochar coupled bacteria-algae granular sludge is used for treating antibiotic pollutants, the situation that the removal rate of the antibiotic pollutants is reduced along with the prolonging of time is avoided, pollutants such as COD, NH4 < + >-N, PO4 < 3-P > and antibiotics can be efficiently and stably removed, and the biochar coupled bacteria-algae granular sludge has good application prospects. The method has a good application prospect in aquaculture wastewater treatment.

The invention relates to the technical field of microorganisms, and provides a low-temperature-resistant heterotrophic nitrification-aerobic denitrification pseudomonas and application thereof, the strain is pseudomonas (Pseudomonas sp.) SW1, and the preservation number is CCTCC (China Center for Type Culture Collection) NO: M 20221797. The low-temperature-resistant heterotrophic nitrification-aerobic denitrification bacterium Pseudomonas sp. SW1 has the characteristic of low temperature resistance, can well grow at 4-10 DEG C, and has relatively strong heterotrophic nitrification-aerobic denitrification nitrogen removal capability, and the enrichment culture of the strain is carried out in a relatively low nitrogen source, and the condition is extremely consistent with the application scene of the strain in domestic sewage.

The invention relates to a multi-sensor fusion positioning method and device based on environmental characteristics. The method comprises the following steps: respectively acquiring RTK data, laser radar data and IMU data; converting the RTK data into RTK positioning data, converting the laser radar data into laser radar positioning data, and obtaining IMU positioning data according to IMU output; monitoring the quality state of the output signal of each sensor in the current environment, and calculating to obtain a corresponding quality score; constructing and forming an input matrix according to the quality score of each sensor, mapping the input matrix to a high-dimensional feature space through an embedded layer and adding a position code, inputting the input matrix into a Transform model for time sequence modeling and extracting environment dynamic features, and finally outputting a real-time fusion weight of each sensor; and performing weighted fusion by using the real-time fusion weight to obtain final pose output. The method has the advantages of being simple in implementation method, high in positioning precision and stability, high in environmental adaptability and robustness and the like.