3542results about "Particle suspension analysis" patented technology

The invention provides a dust concentration monitoring and treatment method and system for an intelligent furniture production workshop, and relates to the technical field of intelligent furniture, and the method comprises the steps: collecting workshop dust concentration data through arranging a sensor, building a workshop dust concentration distribution three-dimensional model through employing a convolutional neural network, predicting the dust concentrations of different regions and different time periods, and obtaining the dust concentration of the workshop. And according to a prediction result, dividing dust danger level areas. And when the dust concentration exceeds the standard, generating early warning information and sending the early warning information to a manager terminal. And the control center determines the treatment priority according to the dust concentration distribution three-dimensional model, controls the ventilation system and the movable dust removal device to perform graded filtration, optimizes the three-dimensional model on line according to the real-time dust concentration data, and adjusts the treatment strategy. According to the invention, precise monitoring and efficient treatment of workshop dust concentration are realized, dust harm is reduced, and health of workers is guaranteed.

The invention discloses a grinding machine internal part temperature anomaly detection method based on vibration signal analysis, which comprises the following steps that grinding data are collected through sensor deployment, and the sensors comprise a temperature sensor, a vibration sensor, an infrared thermal imaging sensor, a magnetic resistance current sensor and an inductance type particle sensor; carrying out preprocessing and feature extraction on the collected data; model construction and training are carried out based on data of preprocessing and feature extraction; according to the method, the abnormal condition of the temperature of the part is predicted by detecting the vibration signal of the internal part, the content of metal particles in lubricating oil is detected through the oil analysis sensor, and the abrasion degree of the bearing is judged in combination with the vibration signal. And motor current harmonic characteristics are monitored, and overload or rotor imbalance problems are identified.

The invention relates to the technical field of coal mine safety monitoring, in particular to an underground coal mine dust concentration monitoring method and system based on multi-modal data fusion, and the method comprises the steps: synchronously collecting dust concentration time sequence data, dust image data, sound wave signal data and environmental parameters through a multi-modal sensor array deployed in an underground coal mine; carrying out preprocessing and feature extraction on the collected data; predicting the decomposed high-frequency and low-frequency component signals by adopting a long short-term memory neural network and a grey Markov model; when the environment humidity is greater than 80%, carrying out light scattering compensation on the predicted value; inputting various predicted values into an improved D-S evidence theory fusion device, and outputting a fusion dust concentration monitoring value; and when the threshold value is exceeded or the temperature and humidity composite condition is reached, an acousto-optic alarm is triggered and a spraying dust-settling device is started. According to the method, the problems of low precision of a single sensor, multi-source data conflict, high-humidity environment measurement deviation and insufficient time sequence and fusion precision in underground coal mine dust concentration monitoring can be solved.

The invention relates to an air pollution monitoring method based on unmanned aerial vehicle remote sensing and machine learning. According to the method, atmospheric pollutant concentration is monitored in real time by acquiring fixed monitoring stations and satellite remote sensing data, a pollution abnormal signal is generated when the concentration exceeds a preset threshold value, an unmanned aerial vehicle group is scheduled according to the signal to perform three-dimensional pollution data acquisition according to a preset path, and initial pollution distribution data is obtained; and dynamically adjusting a monitoring path and sensor configuration parameters of the unmanned aerial vehicle group by using a reinforcement learning algorithm, collecting real-time monitoring data, performing pollution traceability calculation in combination with a space-time diagram neural network model, generating a pollution source positioning result, then performing a pollution regulation and control instruction, and executing feedback control. By adopting the method, the accuracy of pollution source positioning and the timeliness of regulation and control can be effectively improved, and a scientific basis and technical support are provided for air pollution control.

The invention discloses a solar photovoltaic panel cleaning method and device based on a multi-mode cleaning technology, which can plan a traversal path according to parameters of a photovoltaic panel, primarily remove dust by using a wind blowing and ultrasonic vibration form, plan an energy-saving cleaning path for still existing cleaning points, and improve the cleaning efficiency of the photovoltaic panel. And secondary cleaning is conducted along the cleaning path, dust is adsorbed and cleaned through an electric field generated by the electrode plate, meanwhile, the output parameter set is trained and optimized through a trained deep learning model through input parameters extracted in real time, optimal cleaning parameters are obtained, and intelligent cleaning is achieved.

The invention discloses a multi-mode reinforcement learning driven PM2.5 chemical component vertical profile inversion system and method. The system comprises a deep reinforcement learning model; the deep reinforcement learning model comprises a state space, an Action action space, a reward function, an Actor network and a Critic network; the deep reinforcement learning model is used for calculating a reward value at the current moment in combination with a reward function according to the difference between a vertical profile predicted value of the concentration of each PM2.5 chemical component output by the Action action space and an actual monitoring value; and according to an evaluation result of the strategy evaluation, continuously performing strategy iteration by using a gradient descent method until the deep reinforcement learning model is converged, and obtaining an optimal target deep reinforcement learning model. An Actor-Critic network dynamic optimization strategy is adopted, minute-level model updating is achieved in combination with edge calculation, and the problems of high-altitude blind areas, insufficient real-time performance and the like of a traditional model are solved.

Filter installations for suspended matter in flowing fluids, including filtration methods, uses and equipment and plants with filter installations. A device reducing the specific particle count of suspended matter by means of an energy input using ultrasound waves stabilized with electronic feedback loops and their harmonics in the fluids or in objects attached thereto. A flow pipe having a wall, which, on its outer side, its inner side, and / or in the wall has pairs of mutually opposite exciters of longitudinal waves and their harmonics, and / or reflectors, opposite the exciters of the flow pipe to a filter, which keeps the specific particle counts of the suspended matter in the filtered fluids to below the detectable limit.

The invention relates to the technical field of urban road construction, in particular to an intelligent flying dust monitoring and dust falling control system for urban road construction, which comprises a flying dust monitoring module, a dust falling control module, a dust falling control module and a dust falling control module, the data fusion processing center is used for processing the monitoring data and generating a dust fall control instruction; the intelligent dust falling execution module is used for triggering a dust falling device according to the instruction; the cloud platform and the mobile terminal are used for storing the monitoring data, visually displaying the flying dust thermodynamic diagram and pushing standard exceeding early warning information to management personnel; data are collected in real time through the flying dust monitoring module, a flying dust pollution source and the diffusion trend of the flying dust pollution source are recognized in combination with the dynamic analysis and prediction capacity of the data fusion processing center, the intelligent dust falling execution module is used for triggering dust falling measures, and meanwhile remote monitoring and early warning are achieved through the cloud platform and the mobile terminal. The problem of response lag in a traditional method is solved, and the control efficiency is improved.

A method for extracting a wear particle feature signal based on segmentation entropy is provided, including obtaining a raw signal to be processed by performing real-time data acquisition using a lubricating oil wear particle monitoring system; obtaining a preprocessed signal by performing low-pass filtering and harmonic interference suppression on the raw signal to be processed; dividing the preprocessed signal into a plurality of time domain sequence segments with a sliding window; calculating segmentation entropy corresponding to each time domain sequence segment, normalizing a segmentation entropy set to obtain normalized segmentation entropy; obtaining an adaptive threshold through curve fitting based on empirical cumulative distribution of normalized segmentation entropy, obtaining a plurality of non-zero discrete time domain signal segments by segmenting the preprocessed signal by the adaptive threshold; and obtaining final extraction results of the wear particle feature signal by excluding residual noise interference through target signal feature recognition indices.

The invention relates to the technical field of mine environment control, and discloses an intelligent environment monitoring and control method in a mine laneway. The method comprises the steps that a real-time environment parameter set of a mine laneway is collected, the set contains gas concentration data, dust concentration data and temperature and humidity data, and real-time capture of laneway environment multi-dimensional parameters is achieved; then, based on the collected real-time environment parameter set, calculating an environment safety evaluation index so as to scientifically and objectively reflect the roadway environment safety condition; a dynamic control strategy is generated according to the environmental safety evaluation indexes, and the strategy comprises ventilation equipment regulation and control parameters and early warning level parameters and can be flexibly adjusted according to environmental changes; and finally, the ventilation equipment regulation and control parameters are sent to a mine ventilation system, and roadway environment regulation and control are executed. According to the method, environmental parameters can be comprehensively monitored, safety risks can be accurately evaluated, ventilation equipment can be flexibly regulated and controlled, the early warning function can be perfected, energy waste can be reduced, labor intensity can be reduced, and underground operation safety and mining efficiency can be guaranteed.

The invention provides a multi-dimensional coupling water quality evaluation method and system for drainage of a sewage treatment plant, and the method comprises the steps: obtaining water response data of a drainage port of the sewage treatment plant in a flow velocity fluctuation environment, so as to generate an acoustic feature vector related to a treatment process of the sewage treatment plant; carrying out association processing on the acoustic feature vector, the chemical parameter variation and the temperature gradient variation to generate a multi-dimensional coupling data set so as to establish a mapping relation between sound wave spectrum features and water quality discharge standard grades, and identifying association between a multi-parameter variation mode and the processing process so as to generate an analysis result; according to the mapping relation and the analysis result, an evaluation control parameter set is generated, the multi-dimensional coupling data set is dynamically corrected, and a water quality evaluation result associated with the treatment process is generated. And the monitoring reliability and the evaluation accuracy under complex working conditions are improved.

The invention provides a monitoring method for a deep-sea mining plume concentration and deposition rate monitoring device, which comprises a seabed base frame, the seabed base frame is a cubic frame structure, and the upper part of the seabed base frame is fixedly provided with an ADCP acoustic monitoring module and a sediment trapping module. According to the technical scheme, comprehensive monitoring of diffusion concentration and deposition flux of the seabed mining plume is achieved by combining acoustics, sediment trapping and other multi-source monitoring means, the system can adapt to deep-sea high-pressure and long-term operation environments, and important data support is provided for environmental impact assessment and ecological protection of seabed mining.

According to the dynamic traffic environment monitoring method and device provided by the embodiment of the invention, the mobile sampling of pollutants such as particulate matters, nitrogen oxides and carbon monoxide is realized by constructing a power-assisted bicycle environment monitoring and collecting network. And the cloud computing platform performs abnormal value identification based on time sequence clustering, constructs a pollutant concentration distribution model through a multivariate convolutional neural network, and generates diffusion trend prediction in combination with a pollutant migration model. And intelligent scheduling of monitoring resources is realized by adopting a deep reinforcement learning algorithm, and an optimized inspection scheme is generated according to the pollution risk level, the vehicle position and the electric quantity. According to the method, the defects of the traditional technology in the aspects of monitoring coverage, data analysis, resource scheduling and the like are effectively overcome, and the accuracy and the practical value of traffic environment monitoring are remarkably improved.

The invention relates to the field of pollution source analysis, and discloses a formaldehyde pollution source analysis and ozone influence quantification method and system for coupling a receptor model and a box-type model, and the method comprises the steps: collecting the concentration of formaldehyde and other pollutants in urban atmosphere in real time through online monitoring equipment, and carrying out abnormal value marking and data standardization processing; performing source analysis on the processed data based on a positive definite factor matrix method to generate a pollution source contribution matrix; converting the source contribution matrix into a molecular concentration input file which can be recognized by the box-type model; embedding a free radical circulation mechanism into the box-type model, and executing photochemical simulation under different pollution source scenes; and generating a pollution source contribution heat map based on a simulation result, and outputting management and control suggestions through a deep learning model. The method has the following effects: by coupling the receptor model and the box type model, not only can different pollution sources of formaldehyde be determined, but also the influence of each pollution source on ozone generation through photochemical reaction can be analyzed.

The air quality intelligent monitoring system based on sensing data feedback comprises a sensing array module, a preprocessing module, a risk assessment module, an energy consumption decision module, a mode switching module, a data fusion module and a linkage module. The sensing array module generates an environment parameter signal; the preprocessing module generates a standardized characteristic signal; the risk assessment module receives the standardized characteristic signal and forms an early warning instruction signal; the energy consumption decision module analyzes the change gradient of the standardized feature vector and generates an activation instruction according to the vector stability; the mode switching module generates a state configuration signal and feeds back the state configuration signal to the sensing array module; fusing the data to generate a diffusion prediction signal; the linkage module generates an emergency braking instruction and transmits the emergency braking instruction back to the data fusion module. The intelligent air quality monitoring system based on sensing data feedback can solve the problem that intelligent linkage of multi-source pollution monitoring, dynamic energy saving and real-time early warning of industrial places is poor.

The invention relates to the technical field of data processing, in particular to a raised dust concentration data acquisition method and system for pavement construction, and the method comprises the steps: analyzing the fluctuation of the raised dust concentration in each time sequence segment, and analyzing the stability index of environmental data in each time sequence segment to obtain the influence index of the environmental data; analyzing the relative change rate and change trend of the dust raising concentration in each time sequence section to obtain the final abnormal degree of the dust raising state in each time sequence section; and taking the final abnormal degree as a data weight of the flying dust concentration in the corresponding time sequence segment to obtain a weighted flying dust concentration. According to the method, different weights are given to different dust concentration data, so that the influence of noise data is reduced, the importance of unstable data when abnormity occurs in the construction process is improved, and the accuracy of the collected dust concentration data is guaranteed.

The invention relates to the technical field of environmental science, in particular to a box-type model-based inversion method for methane sulfonic acid particles in marine droplet aerosol, which comprises the following steps of: 1, acquiring satellite remote sensing data, ground observation data and laboratory simulation data, and carrying out data standardization processing; 2, dividing the target sea area into a plurality of sub-areas as independent box bodies; 3, calculating the discharge flux of the marine droplet aerosol based on the dimethyl sulfide concentration and the wind speed, and updating the spatial and temporal distribution of the discharge source; 4, embedding a chemical reaction path for oxidizing dimethyl sulfide on the surface of the aerosol into the box-type model to generate methanesulfonic acid, and introducing the specific surface area of the aerosol to correct the reaction rate; and step 5, outputting spatio-temporal distribution of the concentration of the methane sulfonic acid, and comparing measured data for verification. The accuracy of aerosol source item estimation is improved, the fusion efficiency of multi-source data is improved, and the adaptive capacity of the model in a complex environment is enhanced.

The invention discloses a graded detection method and system for an air-breathing smoke-sensing fire in a complex environment, and relates to the technical field of fire-fighting early warning, and the method comprises the steps: recognizing a ventilation opening of a monitoring region in the complex environment, and arranging air-breathing smoke-sensing equipment on the ventilation opening; state monitoring is conducted on the ventilation opening, and working mode parameters of the ventilation opening are obtained and comprise the ventilation starting and stopping state, the wind speed and the ventilation direction; and analyzing to obtain a self-adaptive strategy matched with the working mode parameters to control the sucking pump to drive the air-breathing smoke sensing equipment to collect smoke gas samples, performing multi-time sequence grading detection, and outputting a fire early warning signal. According to the invention, the technical problem that the existing fire detection technology is difficult to accurately collect smoke samples and give out early warning in time in places with complex ventilation structures and obvious air disturbance is solved, and the air-breathing smoke sensing equipment is arranged at the ventilation opening and the sampling strategy is dynamically adjusted; and the accuracy of smoke collection and the real-time performance of fire early warning are improved.

The invention relates to the technical field of dust concentration monitoring, in particular to a dust concentration monitoring method and system in a feed processing scenario, and the method comprises the steps: collecting dust concentrations and environment parameters of all preset positions at all moments in a feed processing area to form a dust concentration sequence and an environment parameter sequence; determining an environmental action index of the dust concentration at each preset position at the current moment; determining a dust smooth characteristic value of each preset position at the current moment according to the prominence degree of all the environmental parameters of each preset position at the current moment in all the preset positions and the fluctuation degree of the dust concentration sequence of each preset position in combination with the environmental action index; and monitoring the dust concentration at the current moment by utilizing the dust concentration and the dust smoothness characteristic value of each preset position at the current moment. Therefore, the accuracy of monitoring the dust concentration is improved.

The invention relates to the technical field of sensors, and discloses a detection and management method and system for purification equipment. The method comprises the following steps: carrying out three-phase coupling dynamic modeling on a gas phase flow state, a particle phase movement track and a sensor phase interface characteristic in the purification equipment to obtain a gas mass transfer characteristic parameter; performing response characteristic analysis on a real-time detection signal of a sensor array in the purification equipment to obtain a sensor calibration compensation parameter; based on the sensor calibration compensation parameters and the gas mass transfer characteristic parameters, deposition kinetics analysis of pollutants on the surface of the sensor is carried out, and equipment operation state evaluation data are obtained; and performing spatial layout optimization on the sensor array according to the equipment operation state evaluation data and the gas flow consistency constraint condition to obtain an optimal layout scheme and generate a staged adaptive management control instruction. According to the invention, self-adaptive adjustment and intelligent switching of the sensor management strategy are realized, and the detection performance and reliability of the purification equipment sensor system are significantly improved.

The invention relates to the technical field of fire coal control, and discloses a thermal power plant fly ash floating black online detection control method and system, and the method comprises the steps: 1, employing microwave, infrared and laser sensors for cooperative work, detecting carbon dielectric characteristics through microwave dust interference resistance, identifying thermal radiation difference through infrared, and analyzing particle distribution through laser; 2, on the basis of the detection requirements in the step 1, triple protection of air curtain barrier, ultrasonic cleaning and hydrophobic coating is designed, dust condensation is blocked, and it is ensured that the light transmittance of the sensor is larger than 90%; the invention provides a multi-mode sensing fusion-anti-interference current stabilization-closed loop optimization full-link solution for the industrial problem of signal distortion caused by optical window dew formation and dust deposition, airflow disturbance and environmental interference in fly ash floating black online detection. The light transmittance of the optical window is greater than 93% by combining triple protection of air curtain barrier, ultrasonic cavitation stripping and nano hydrophobic coating.

A sample preparation instrument with an integrated device for estimating a concentration of white blood cells in a specimen is described. The sample preparation instrument receives a specimen for which a sample is to be prepared for analysis. The integrated device can implement an optical method, an electrical resistance method, or a flow cytometry method, for example, to estimate the white blood cell concentration in the specimen. For some types of analysis, a sample volume of the specimen is dependent on the white blood cell concentration. Therefore, the sample preparation instrument can automatically determine and adjust the sample volume of the specimen based on the estimated white blood cell concentration prior to adding additional reagents, such as labeling reagents and / or lytic reagents, to prepare the sample.

The invention discloses an intelligent monitoring method for an argon recovery system, particularly relates to the technical field of gas purification adsorbent state monitoring, and is used for solving the problem of adsorbent recessive failure early warning lag caused by dependence on tail gas average concentration monitoring of an existing argon recovery system. The method comprises the following steps: collecting regeneration tail gas concentration time sequence data in real time, and extracting a slope change rate of a desorption rate rising section and an area integral value of a desorption peak attenuation section; constructing an area integral value sequence and calculating a symmetric deviation degree; identifying a pressure pulse disturbance time period to obtain effective time period data; calculating the fluctuation abnormality of the slope change rate offset and the symmetry deviation degree based on the effective data; when the two regeneration periods simultaneously exceed the threshold value and three continuous regeneration periods meet the condition, an adsorbent replacement early warning signal is generated, and early-stage accurate early warning of adsorbent performance degradation is achieved.

The invention discloses a BIM-based bridge high pier construction safety monitoring method and system, and relates to the technical field of BIM, a dust concentration prediction vector P is calculated through an environment feature set T, high-precision prediction of dust diffusion concentration is realized, and the dust distribution condition of a construction site is more prospective. Then, according to the scheme, the dust fall parameter set D is formulated in combination with the dynamic change of the dust concentration, the dynamic dust fall strategy is executed, the spraying scheme can be adjusted according to the real-time environment, the dust fall efficiency is effectively improved, and the problems of water resource waste and uneven coverage existing in a traditional spraying mode are solved. Besides, through a feedback mechanism of a dust concentration error D, an environment safety index ZS is calculated and compared with a preset construction area trigger threshold ST, iterative optimization of a dust fall parameter set D is achieved, it is ensured that the construction environment is always in a controllable range, and potential safety hazards caused by excessive dust are avoided.

The utility model provides an air purification filter performance detection and verification device, including a detection component, which includes a bottom plate, a mixing box, a first sealing sleeve, a feeding pipe, a blower, a groove, and a screw. The utility model places the air purification filter in the first sealing sleeve, starts the drive motor to drive the connecting block to move through the screw, and the connecting block drives the second sealing sleeve to move through the connecting box, and the air purification filter is fixed and sealed by the first sealing sleeve and the second sealing sleeve, and dust is added to the mixing box through the feeding pipe. The blower moves the first sealing sleeve of the dust in the mixing box to the inside of the air purification filter, and then sprays it into the dust storage box along the air outlet of the air purification filter through the second sealing sleeve, the connecting pipe and the hose. The dust sensor can give the concentration of the dust test to the connecting pipe, and transmit the data to the processor for processing. After processing, the data is displayed through the display screen, and the operation is simple and convenient.

The invention relates to the technical field of sewage detection, provides an intelligent detection method for suspended particles in coking sewage based on image deep learning, and aims to solve the problems of detection lag and low precision in the prior art. According to the technical scheme, the method comprises the following steps: synchronously acquiring sound wave and image data through a high-frequency ultrasonic transducer and an industrial camera, and extracting acoustic parameters and optical characteristic parameters; using a dual-channel convolutional neural network to fuse features, and combining a cross-modal attention mechanism to perform dynamic weighting; and outputting a particle concentration classification result and a particle size distribution prediction value through a multi-task learning model, and performing online learning to compensate environmental interference factors. The device is mainly used for realizing real-time and high-precision suspended particle monitoring and automatic process adjustment of coking sewage treatment.

The invention belongs to the technical field of air pollution detection, and particularly relates to an air environment pollution degree detection system and method based on big data. According to the method, detection and early warning of the air environment pollution degree are achieved by mining and analyzing the multi-source environment data, and a dynamic weight distribution strategy is adopted when the pollution comprehensive index in the monitoring area is determined, so that the calculation precision of the pollution comprehensive index is effectively improved, the accuracy and timeliness of air quality monitoring are improved, and the method is suitable for popularization and application. Besides, by constructing the space-time incidence matrix and calculating the correlation strength coefficient, the correlation between abnormal types can be automatically identified, a scientific basis is provided for emergency response, meanwhile, a bimodal early warning mechanism provided by the invention can distinguish instantaneous anomalies and normalized anomalies, and the accuracy of emergency response is improved. The problems of resource waste and untimely emergency response caused by false alarm or missing alarm are avoided, and the continuity and stability of air quality monitoring are ensured.

The invention provides a PM2.5 (Particulate Matter 2.5) concentration prediction method based on a VMD-TCN-BiGRU-SA model. The method comprises the following steps: acquiring a PM2.5 concentration data set and preprocessing the data set; calculating the Spearman correlation coefficient of the related characteristics in the pre-processed PM2.5 concentration data set and the PM2.5 concentration; variational mode decomposition is carried out on PM2.5 concentration data in the data set after the features which are poor in correlation with the PM2.5 concentration are removed, and n intrinsic mode function components IMF and a residual component RES are obtained; all components obtained through variational mode decomposition are combined into the preprocessed PM2.5 concentration data set; dividing the merged data set into a training set and a test set; a PM2.5 concentration prediction model of the TCN-BiGRU-SA is constructed; the model comprises a TCN network, a BiGRU network and a sparse attention module which are sequentially connected in series. Inputting the training set into the model; and inputting the test set into the trained model to obtain a PM2.5 concentration prediction result. According to the method, the prediction result of the PM2.5 concentration is more accurate, and the generalization ability of the model is improved.

The invention provides a mobile base station and unmanned aerial vehicle cooperative monitoring system and method, relates to the technical field of unmanned aerial vehicles, and aims to solve the problems that an existing unmanned aerial vehicle monitoring system is low in single unmanned aerial vehicle operation efficiency and cannot meet the requirement for simultaneous execution of multiple tasks, and a traditional monitoring base station is limited in coverage range. And the method is difficult to adapt to complex construction environments or large-range monitoring scenes. The system comprises a moving and carrying module, a positioning detection module, a data acquisition module, a data processing module and the like, high-precision positioning, rotary multi-position parking apron and multi-task unmanned aerial vehicle cooperative work capability are integrated, the unmanned aerial vehicle has task synchronous execution capability, a base station has autonomous moving capability, and the system is suitable for complex terrains and high-precision unmanned aerial vehicles. And an environment monitoring module and a solar charging system are integrated, so that the long-term operation capability is enhanced. Through hardware integration innovation, the cooperative operation efficiency of the multiple unmanned aerial vehicles is improved, efficient linkage of the base station and the unmanned aerial vehicles is achieved, and the intelligent level of environment monitoring and construction management is improved.

A system for detecting plastics, macro-plastics, micro-plastics and nano-plastics in a maritime, estuary or river environment includes multiple detection devices and a data processing device. The detection devices detect the plastics, macro-plastics, micro-plastics, and nano-plastics using different detection wavelengths. The data processing device includes a communication interface and a processor. The communication interface receives detection signals from the detection devices and the processor determines locations of the plastics, the macro-plastics, the micro-plastics and the nano-plastics in the environment based at least in part on the received detection signals within a common coordinate system.