10479 results about "Pollutant" patented technology

The invention relates to the technical field of offshore area monitoring, in particular to an offshore area pollutant diffusion real-time tracking and traceability monitoring system. The method comprises the following steps: a data perception and fusion unit collects and preprocesses data, carries out space-time calibration and multi-source fusion, and outputs a standardized fusion space-time sequence; the pollutant diffusion simulation and prediction unit calculates a flow velocity field, simulates a diffusion process and predicts a trend; the pollutant traceability positioning unit tracks a pollution path and inverts pollution source parameters; the emergency decision support unit determines a risk level and generates an emergency plan. And through a space-time alignment algorithm and a multi-source fusion mechanism, the data consistency and reliability are improved, a high-quality data basis is provided for subsequent processing, and the monitoring accuracy is guaranteed.

The invention provides a mine water disaster monitoring and early warning method and system based on multi-source heterogeneous data fusion, and the method comprises the steps: collecting multi-source data of a mining area, carrying out the time-space alignment of the multi-source data, and generating a data set, the multi-source data comprising remote sensing data; preprocessing the data, inputting the preprocessed data into a multi-modal fusion network, and extracting surface water body distribution, lithologic permeability, underground water level and structural fracture characteristics to obtain a three-dimensional hydrogeological static model; the hydrological numerical model based on physical driving is coupled with the static model, and the dynamic model is used for simulating the dynamic change of an underground water flow field and a pollutant diffusion path; based on the dynamic model updated in real time, multi-target collaborative evaluation is carried out to evaluate the mining area water resource, ecological and social collaborative effect; and calling an unmanned aerial vehicle to inspect a leakage position or a settlement position in the hydrogeological risk map based on a multi-target collaborative evaluation result. According to the method, the prediction accuracy is improved through the multi-source data.

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 field soil heavy metal pollution health risk dynamic assessment and intelligent early warning system, and relates to the technical field of soil environment monitoring. The system comprises a multi-source data acquisition unit, a data preprocessing unit, a risk calculation engine and a visual interaction terminal. The key technical point is that a dynamic field evolution analysis module and an adaptive grid rendering control module are introduced; the dynamic field evolution analysis module constructs a pollution potential energy field matrix representing a pollutant migration trend based on soil heavy metal concentration and hydrogeological parameters, and calculates a space-time gradient change vector of the pollution potential energy field matrix; and the latter dynamically adjusts the grid local density according to the gradient vector module value, and automatically encrypts the computational nodes in the region with severe risk change. In cooperation with a time sequence prediction deduction and feedback correction mechanism, the method can simulate the dynamic evolution of the pollution plume in the porous medium in real time, solves the problems that a migration rule is difficult to capture and the calculation efficiency of a uniform grid is low in traditional static evaluation, and achieves three-dimensional dynamic risk early warning with high precision and low calculation power consumption.

The invention relates to the technical field of water quality prediction, and discloses an industrial sewage water quality real-time prediction and early warning method and system. According to the method, the depth features of the internal treatment process state of each water quality treatment unit are extracted, so that the problem that the prediction precision of a prediction model is limited due to the fact that the internal deep features cannot be excavated in a traditional method is solved; a migration rule and a response relation of pollutants between every two adjacent water quality treatment units are analyzed through real-time water quality parameters, so that a water quality flow association graph with the water quality treatment units as nodes, pollutant migration paths as edges and cross-unit association strength as edge weights is constructed; the driving effect of the water quality change of the upstream water quality treatment unit on the treatment effect of the downstream water quality treatment unit is quantified, the accurate quantification of the cross-unit dynamic linkage effect is realized, and the problem that the linkage effect is caused by neglecting the transfer and conversion of pollutants among the water quality treatment units in the prior art is solved. Therefore, the water quality prediction accuracy is improved.

The invention relates to the technical field of soil monitoring, in particular to a soil-groundwater pollution risk threshold dynamic adjustment method and system based on an environment model, and the method comprises the steps: collecting soil and groundwater monitoring data, constructing an environment model containing the soil pollution concentration, the groundwater pollution concentration and the pollutant migration rate, obtaining comprehensive pollution characteristic data, extracting main characteristics, and determining the contribution degree of each characteristic to risk assessment; calculating the initial weight of each feature according to the contribution degree, and obtaining a weight distribution scheme; calculating pollution risk values of the soil and the underground water in combination with the pollution load data and the weight values, and preliminarily setting a risk threshold value; monitoring pollutant concentration fluctuation data in real time, predicting a pollutant migration trend through a time sequence analysis algorithm, and obtaining a risk change prediction result; and combining the risk change prediction result and the environmental capacity constraint optimization risk threshold to obtain a dynamically adjusted risk threshold. According to the invention, accurate extraction of pollution characteristics and dynamic adjustment of weights are realized.

The invention belongs to the technical field of pollution tracing, and discloses a distributed water body pollution tracing method and system, and the method comprises the steps: constructing a plurality of directed node pairs according to a topological structure of each partition node in a monitored water area; according to the pollutant concentration data in the monitored water area, determining a time delay interval of a pollutant concentration peak value between each directed node pair; determining an effective node pair of which the time delay interval meets the space-time constraint from the plurality of directed node pairs, and constructing a plurality of backtracking paths according to the effective node pair; performing particle tracking simulation on the pollutant concentration data to obtain a plurality of simulation paths, and determining a particle intersection area of pollutants according to the plurality of simulation paths; and determining a pollution source area according to the plurality of backtracking paths and the space intersection of the particle intersection area, and further determining traceability positioning. According to the method, the backtracking path is constructed through dynamic time-delay analysis, high-precision identification of the pollution source is realized by combining particle tracking and gridding intersection positioning, and the problems of insufficient space-time dynamics and large positioning deviation of a traditional method are solved.

The invention provides an underground water pollutant concentration prediction method and system based on machine learning, and relates to the technical field of underground water pollutant concentration prediction.The method comprises the steps that historical data, hydrogeological parameters, meteorological data, human activity data and geochemical parameters of underground water pollutant concentration of a target area are preprocessed; dividing a training set, a verification set and a test set; constructing a preset resolution feature set based on a geochemical mechanism; selecting an adaptive machine learning model according to data characteristics and coupling a physical mechanism; performing hyper-parameter tuning by adopting Bayesian optimization, and supplementing small sample data in combination with transfer learning to complete model training; predicting the underground water pollutant concentration of the target area by using the trained model, and outputting a pollutant concentration prediction result with an uncertainty interval; the invention provides a technical scheme for predicting the concentration of underground water pollutants, which is efficient, accurate and high in adaptability.

A regenerative filter that includes a filter housing having inlet and outlet zones; a fluid path provided between the inlet and outlet zones; a plurality of filter elements each having an outer surface filter media applied thereto and functioning to filter particulate or contaminants from the fluid path. The filter elements are disposed in an monolithic array. An inlet port arrangement is disposed at the filter housing leading to the inlet zone and is constructed and arranged to direct the fluid path into the filter housing in a manner so as to maintain a controlled turbulence at the inlet zone. The inlet port arrangement comprises a pair of inlet ports that are arranged in juxtaposition at the inlet zone and each having one end where the fluid flow enters. Embodiments that are considered as falling under the scope of the present invention include 1. Single inlet / two or more outlets; 2. Two or more inlets / single outlet, or 3. Two or more inlets and two or more outlets.

The invention discloses an environment pollution detection data processing system and method based on the Internet of Things, relates to the technical field of data analysis and evidence tracing, and aims to solve the problems of data isomerism, time-space correlation analysis splitting and insufficient propagation path recognition precision in the prior art. According to the system and the method, multi-source information such as Internet of Things monitoring data and environment management data is collected, the time trend, the incidence relation and the spatial distribution characteristics of pollutants are extracted after preprocessing, an environment pollution data chain with time, space and logic three-dimensional fusion is constructed, then pollution sources are positioned, a propagation path and an influence range are analyzed, and a real-time environment pollution analysis result is obtained. And a result is visually presented. According to the invention, the precision and reliability of pollution traceability are improved, and a systematic solution is provided for precise treatment of environmental pollution.

The invention belongs to the crossing field of environmental engineering and hydrology and hydrodynamics, and particularly relates to a traceability method based on coupling hydrodynamics and a pollutant degradation equation, which comprises the following steps: firstly, acquiring and preprocessing multi-source heterogeneous monitoring data, then selecting a one-dimensional Saint-Venant equation or a two-dimensional shallow water equation according to a water body form to solve a hydrodynamic field, and finally, determining the hydrodynamic field. A multi-component convection-diffusion-reaction equation is coupled to simulate pollutant migration and transformation; an LSTM module is introduced to identify suspected pollution events, pollution source parameters are inverted through a two-channel framework of PDE constraint optimization and Bayesian inference, uncertainty is quantified, model parameters are updated online in combination with data assimilation, and finally the uncertainty is quantified and verified. The method considers traceability precision, efficiency and compliance, supports multiple water bodies and multiple data sources, and is suitable for complex water body pollution traceability.

The invention particularly relates to a water pollution intelligent traceability system based on a three-dimensional fluorescence spectrum, which relates to the technical field of environmental monitoring, and comprises a water sample collection and three-dimensional fluorescence data acquisition module, a data preprocessing and PARAFAC component decomposition module, a multi-source pollution characteristic fingerprint database module and an intelligent comparison and pollution source analysis module. According to the method, standard acquisition and preprocessing are adopted, and the original data quality is ensured by means of scattering rejection, instrument correction and the like; pure fluorescence components are decomposed through a PARAFAC algorithm, and feature extraction reliability is guaranteed through triple verification. In the comparison link, spectrum similarity, concentration proportion difference and space-time hydrological constraint multi-round screening are adopted, and irrelevant pollution sources are removed; and then the contribution proportion is quantified, and Monte Carlo simulation, water quality and specific pollutant cross validation are carried out, so that the crossing from suspected tracing to quantitative responsibility investigation is realized.

The invention provides an ecological environment dynamic monitoring method based on big data, and relates to the technical field of environmental pollution detection, and the method comprises the steps: collecting multi-source sensor data of a fixed monitoring station in an urban region, and carrying out the time alignment and space registration of the multi-source sensor data, thereby generating a standardized environmental monitoring data set. And on the basis of the data set, constructing a pollutant transmission network between the monitoring points to obtain a regional pollutant distribution characteristic graph, calculating a diffusion trend coefficient of pollutants in combination with regional wind direction data, constructing a pollutant migration path network, and generating a key supervision region map. According to the method, the pollutant concentration distribution can be dynamically partitioned, and the mobile monitoring equipment is scheduled to carry out gradient type point distribution monitoring, so that the accurate positioning of the pollution source is realized; the accuracy of pollutant diffusion trend analysis and the precision of pollution source positioning are improved, dynamic supervision of key areas is facilitated, the efficiency of environment monitoring and the fine management level are improved, and therefore the risk of environmental pollution is effectively reduced.

The invention discloses an unknown pollutant non-target identification method and system based on mass spectrum and a generative model, and is applied to the technical field of environmental monitoring and analytical chemistry. The method comprises the following steps: collecting mass spectrum data of a water body sample to be detected; preprocessing the original mass spectrum data, and outputting standardized features; inputting the standardized features into a pre-trained generative model to generate a plurality of candidate molecular formulas; using chemical and physical constraints to eliminate candidate molecular formulas which do not conform to rules; executing a rule-driven algorithm to obtain candidate pollutant molecular structures; carrying out comprehensive scoring and sorting on candidate pollutant molecular structures through chemical prior and environmental prior; and semi-quantitative or relatively quantitative concentration determination is carried out. According to the method, a rule-driven expert system and a data-driven generation model are combined, unknown pollutants which do not exist in a standard library can be effectively recognized and analyzed, and full-process automatic processing from original mass spectrum data to pollutant structures and concentrations is achieved.

The invention relates to a territorial space planning data monitoring evaluation method and system, and the method comprises the steps: obtaining multi-modal data, constructing an atmospheric diffusion model based on the multi-modal data, reversely positioning a pollution source, and obtaining a pollutant responsibility matrix; carrying out space grid fusion calculation through the pollutant responsibility matrix and the carbon sink data to generate a carbon source-carbon sink distribution diagram representing a territorial space carbon balance pattern; based on the regional characteristics of the carbon source-carbon sink distribution diagram, generating a territorial space carbon planning scheme through a low-carbon guiding algorithm; constructing a disaster model based on the multi-modal data, and carrying out space coupling analysis on a disaster risk area and a high-carbon sink potential area output by the disaster model to obtain a land space carbon restoration scheme; a digital twinborn platform is constructed according to multi-modal data, an atmospheric diffusion model and a disaster model, an expected carbon sink increment value of a land space carbon planning scheme is deduced and verified through the digital twinborn platform, and iteration of the implementation scheme is carried out.

The invention belongs to the technical field of air pollution monitoring, and particularly relates to an air pollution monitoring system and method based on big data. According to the method, the pollutant diffusion topological structure is constructed through collection degree fusion of the multi-source environmental data, the prediction accuracy of air pollution monitoring is effectively improved, and when the pollutant diffusion topological structure is constructed, the influence of meteorological parameters and geographic information on a pollutant transmission path is comprehensively considered, so that the prediction accuracy of the air pollution monitoring is improved. The method enables a corresponding prediction mechanism to more accurately describe a pollutant space-time evolution rule in an air environment, introduces a prediction error-based iterative optimization mechanism after a prediction result is output, and compares the pollutant prediction concentration with the actual monitoring concentration in real time, thereby achieving the real-time prediction of the pollutant. And a diffusion weight coefficient and a diffusion probability matrix in the pollutant diffusion topological structure are adjusted, so that a corresponding prediction mechanism can maintain high-precision prediction capability, and relatively accurate data support is provided for early warning of an air environment.

The invention discloses a drainage basin nitrogen and phosphorus pollution prediction method based on rainfall runoff migration, and relates to the technical field of water quality pollution prediction, and the method comprises the steps: accurately obtaining land utilization and water body distribution through a remote sensing technology, carrying out the space division of a pollution source through combining a nitrogen and phosphorus load coefficient, and constructing a rainfall runoff model. The runoff volume and time and space distribution under different rainfall events are reflected, and the spatial dynamic change of pollutants is captured through a hydrodynamic model, in combination with a convection diffusion equation and by introducing a conversion model of nitrogen and phosphorus in various forms. By integrating remote sensing data, rainfall runoff simulation, hydrodynamic force and nitrogen and phosphorus form transformation, the space-time migration and transformation process of nitrogen and phosphorus pollution in a drainage basin is described, so that the coupling relation between pollution source distribution and runoff power is revealed, the conveying and diffusion rule of pollutants in a river network is dynamically reflected, and the drainage effect is improved. And refining to transformation evolution of different nitrogen and phosphorus forms, and generating intuitive concentration distribution and thermodynamic diagrams through spatial interpolation.

The invention discloses an atmospheric pollution remote sensing intelligent monitoring system and method based on multi-modal data, and belongs to the technical field of atmospheric pollution remote sensing monitoring. Comprising a multi-source heterogeneous data acquisition and preprocessing module, a dynamic adaptive modal fusion module, a sudden change scene adaptive module, a three-dimensional pollution inversion enhancement module, a space-time diagram convolution traceability module and a weak signal pollution source collaborative sensing module. According to the method, the concentration contribution values of a plurality of pollution sources are superposed, the three-dimensional distribution of pollutants in space and time is accurately simulated, a visual basis is provided for pollution source positioning and diffusion path analysis, the synchronization of an inversion result and a real-time environment state is ensured through dynamic parameter updating, and the spatial resolution and prediction precision of pollution monitoring are remarkably improved.

The invention discloses an oxidation-reduction free radical synergistic electrochemical wastewater treatment method and system. The method comprises the following steps: collecting pollutant concentration in wastewater and electrochemical reactor state parameters in real time through a sensor array; analyzing a pollutant change trend based on the data, and determining an optimization target value of an active substance generation proportion in combination with historical data; the trained classification model is used for classifying the water body state, and whether dynamic adjustment is needed or not is judged; if necessary, calculating the state parameter correction according to the deviation between the target value and the actual value, and adjusting the operation of the reactor; and monitoring the adjusted active substance proportion and degradation efficiency, and dynamically updating the optimization target according to the adjusted active substance proportion and degradation efficiency and the pollutant trend to realize continuous self-adaptive regulation and control. The intelligent closed-loop control of the reaction parameters is realized, the stability of the treatment efficiency is obviously improved, and the energy consumption and the risk of secondary pollution are effectively reduced.

The invention discloses a drainage basin water body heavy metal pollution prediction system based on multi-modal attention, and the system comprises a data collection module which is used for collecting multi-modal data related to drainage basin water body heavy metal pollution, and a preprocessing module which is used for carrying out the standardization processing of the obtained multi-modal data. The multi-modal attention fusion module is used for performing feature extraction and cross-modal interaction on the preprocessed multi-modal data to generate fusion features; the dynamic prediction module is used for outputting a spatial-temporal distribution prediction result of the heavy metal pollutant concentration in the drainage basin by constructing a spatial-temporal coupled prediction model; according to the method, the cross-modal interaction accuracy is improved by dynamically focusing the key association information of the multi-modal data through the intra-modal and inter-modal attention mechanism, meanwhile, the data-driven prediction model is constructed, the prediction precision of the high-risk area is optimized through the weighted loss function, the prediction error is effectively reduced, and the prediction efficiency is improved. And high-precision dynamic prediction of heavy metal pollution under the watershed scale is realized.

The invention discloses an application of an electronic waste pollution human body exposure fingerprint spectrum in exposure risk prediction, and the human body exposure fingerprint spectrum is composed of more than three of exposure markers of 2, 4, 6-tribromophenol, 2, 4, 5-trichlorophenol, 4-bromophenol, 1-hydroxypyrene and bisphenol A. The invention further discloses an application of the electronic waste pollution human body exposure fingerprint spectrum in exposure risk prediction. According to the method, a systematic method of urine pollutant exposure omics is adopted for the first time, comprehensive screening and characterization of pollutant exposure markers related to specific components and by-products are achieved in electronic waste exposure worker groups, and the method is the most systematic organic pollutant exposure research known in the field at present.

The invention relates to the field of intelligent manufacturing, and provides a bio-pharmaceutical wastewater cooperative treatment method and system based on a graph neural network. The method comprises the following steps: monitoring a biopharmaceutical process in real time; performing dynamic aggregation on the real-time monitoring data to obtain molecular structure information, microbial community network information and a topological relation among process links in a pharmaceutical process to obtain a dynamic heterogeneous knowledge graph; analyzing interaction rules of different node types in the dynamic heterogeneous knowledge graph by adopting a hierarchical learning mechanism through a multi-modal graph neural network, and predicting migration paths of pollutants and / or pollutant detection indexes in the biopharmaceutical process according to the interaction rules; based on the key index change trend in the migration path, multi-objective optimization decision making is carried out, an optimization control strategy for the biopharmaceutical process is generated, intelligent source tracing and optimization treatment of biopharmaceutical wastewater are achieved, the wastewater treatment efficiency is improved, and the wastewater treatment effect is improved.

The invention relates to an intelligent regulation and control system for multi-phase conversion of water quality of a gate-controlled river reach, in particular to the field of water treatment, and realizes accurate dynamic perception of the multi-phase conversion process of the water quality of the gate-controlled river reach by constructing a high-fidelity digital twinborn body and fusing a real-time data assimilation technology; by means of a time-space diagram neural network agent model based on an attention mechanism, the system can predict a complex time-space pattern of pollutant concentration field and phase evolution under different regulation and control strategies in an ultra-fast manner; on the basis, a multi-agent reinforcement learning algorithm is adopted to automatically generate a globally optimal gate group coordinated regulation scheme, and multiple targets of water quality improvement, ecological protection, engineering operation and the like are effectively coordinated; and finally, a theoretical strategy is reliably converted into a physical action through rolling optimization and a closed-loop execution mechanism, and adaptive adjustment can be performed according to environmental feedback, so that the emergency response speed of sudden water pollution events, the scientificity of regulation and control decision and the intelligent level of water quality management of the whole river network are comprehensively improved.

The invention relates to the technical field of flood purification treatment, and discloses a flood percolation and purification system based on artificial intelligence. A flood data acquisition unit of the system acquires physical characteristics and chemical components of flood in real time, extracts multi-dimensional water quality indexes, identifies pollution concentration abnormal areas through a neural network, and generates an infiltration medium pollution load distribution diagram; a percolation layer state analysis unit calls high-load area data, recognizes percolation layer blockage and saturation segments in combination with historical percolation efficiency, and generates a percolation efficiency attenuation interval; the purification medium regulation and control unit extracts the purification efficiency and the regeneration state according to the attenuation interval, judges the medium failure condition and generates a purification medium dynamic adjustment strategy; and the purification process execution unit calls the regulation and control parameters, detects the pressure change and the pollutant interception amount, identifies the interlayer synergistic effect strength, and generates a purification medium backwashing instruction to control the start and stop of a purification layer high-pressure backwashing pump after reaching the standard.

The invention belongs to the technical field of organic pollution treatment, and relates to a biochar-LDH-nZVI-GO composite material for organic pollution remediation and a preparation method and application thereof.The preparation method comprises the steps that graphene oxide is ultrasonically dispersed in deionized water, magnesium salt and aluminum salt are added to form uniform suspension liquid, then the uniform suspension liquid is mixed with biochar suspension liquid, and the biochar-nZVI-GO composite material is obtained; carrying out pH (Potential of Hydrogen) regulation and a hydrothermal crystallization reaction, so as to prepare a biochar-Mg / Al-LDH-GO compound; and dispersing the compound in an organic solvent, sequentially adding FeSO4. 7H2O and a NaBH4 solution in an inert atmosphere to carry out a reduction reaction, and finally washing and drying to obtain the biochar-LDH-nZVI-GO composite material. The material shows high-efficiency removal capacity on various organic pollutants such as trichloroethylene, chlorobenzene and polychlorinated biphenyl, is suitable for high-efficiency green remediation of organic pollutants in soil and water, and has good popularization and application prospects.

The invention discloses a sewage treatment information intelligent management method, system and equipment based on the Internet of Things, and relates to the technical field of sewage treatment information management, and the method comprises the steps: 1, collecting multi-source water quality data, initializing sensor equipment, and carrying out the primary processing and caching of the data; step 2, carrying out edge intelligent prediction on pollutant concentration and pollution load sudden increase; step 3, establishing a prediction and control response model, introducing a control strategy generation mechanism based on a rule engine and priority tradeoff, and calculating a dosing dynamic sensitivity value; 4, adjusting frequency conversion of a fan, a dosing module and a reflux pump, and feeding back, collecting and uploading in real time; and step 5, performing artificial intelligence strategy center verification and a digital twinborn simulation strategy, analyzing multi-plant load scheduling and implementing optimization measures, and performing visual interaction in real time. The problem of energy and medicament waste caused by lack of a feed-forward pre-judgment mechanism is solved.

The invention relates to the technical field of data traceability, and discloses an air quality prediction and pollution traceability method and system based on multi-source data coupling, and the method comprises the steps: carrying out the data standardization processing of pre-obtained meteorological data, pollution source data, emission source data and landform data, and generating air quality standardization data, pollutant distribution simulation is carried out on the air quality standardized data based on a preset atmospheric chemical model, then pollution concentration prediction is carried out on the currently monitored atmospheric environment, an obtained pollution prediction result is compared with a preset pollution concentration threshold value, rechecking is carried out, and a final high-pollution early warning signal is obtained. And carrying out refined emission analysis on the pollution prediction result and the air quality standardized data to obtain pollution emission data, and carrying out pollution tracing on the air quality standardized data by adopting a path optimization algorithm to obtain a pollution source position. According to the invention, the accuracy of air quality prediction and pollution tracing based on multi-source data coupling can be improved.

The invention relates to the technical field of atmospheric dust pollution prevention and environmental protection, and discloses a multi-objective optimization layout method for dust monitoring points of a bulk cargo port, which comprises the following steps: processing historical meteorological data and port geographic information, and calculating the comprehensive evaluation concentration of each candidate grid point in combination with a pollutant diffusion model; constructing a multi-target nonlinear mixed integer programming model taking maximization of environment sensitive point coverage and minimization of concentration interpolation deviation as target functions, wherein the model comprehensively considers multiple reality constraints; solving the model by adopting an improved genetic algorithm to obtain an optimal solution set capable of forming tradeoff among different targets; and carrying out quantitative evaluation on the solution set through preset monitoring area coverage rate and monitoring accuracy indexes, and determining an optimal monitoring point position layout scheme. According to the invention, a scientific and quantitative optimized layout scheme can be provided for bulk cargo port dust monitoring, and the reliability and spatial representativeness of a monitoring network are improved.

Systems, methods, and computer-readable media for calibrating air quality sensors are provided. A sensor node includes a sensor node printed circuit board, a sensor module, and a communication module. The sensor node printed circuit board manages power of the sensor node circuitry, the sensor module, and the communication module such that power is provided from a primary power supply supplemented by a secondary power supply. The sensor module includes a plurality of air quality sensors to measure the concentration of air pollutants. The sensor module may be replaceable. The communication module may communicate air quality measurements to and receive configurations from a data management platform, which may perform processes to improve the accuracy of the air quality measurements.

The invention relates to the technical field of sewage treatment supervision, and discloses a sewage plant supervision system based on digital twinning. The system comprises an operation mirror image generation module which collects data in real time and generates a digital twin mirror image of the sewage treatment facility; the virtual equipment state library construction module is used for extracting key equipment parameters based on the digital twin mirror image and dynamically updating a virtual equipment state library; the strategy optimization module is used for analyzing a sewage flow state and a pollutant migration path and generating a virtual operation strategy optimization parameter set; the abnormal response engine module is used for monitoring abnormal fluctuation of key nodes and generating an abnormal response intervention data set; and the efficiency improvement module is used for optimizing virtual processing path regulation and control parameters and generating a pollutant removal efficiency improvement scheme. According to the system, dynamic monitoring, intelligent optimization and quick response of the whole sewage treatment process are achieved through the digital twinning technology, supervision accuracy and timeliness are improved, and the system adapts to complex and changeable operation conditions.