CN-121978286-A - Ambient air quality monitoring system based on internet of things perception

CN121978286ACN 121978286 ACN121978286 ACN 121978286ACN-121978286-A

Abstract

The invention relates to the technical field of intersection of environment monitoring and the Internet of things technology, and particularly discloses an environment air quality monitoring system based on the perception of the Internet of things. The system comprises an acoustic radar subsystem, a pollutant concentration sensing network, an atmospheric fluid mechanics calculation engine, an Internet of things data fusion platform and a three-dimensional migration track visualization terminal, wherein the acoustic radar subsystem utilizes urban background noise to invert a local wind field and a turbulence structure, the pollutant concentration sensing network acquires multi-parameter gas data in real time, the atmospheric fluid mechanics calculation engine is coupled with a microclimate field to dynamically simulate a pollutant diffusion track, the Internet of things data fusion platform realizes multi-source data time-space alignment and feature fusion, and the visualization terminal presents a three-dimensional migration path and a vortex aggregation area. According to the technical scheme, the air detention area can be accurately identified under the windless or weak wind condition, the hardware cost is reduced, the difference between monitoring data and human body feeling is bridged, and scientific decision support is provided for urban environment management.

Inventors

Yin Zuobin
CHEN MENGJIE
ZHANG JUAN
Guang Haijun
FU XIJUN
Zhai Yinhuan
FANG MEIHUA
ZHU JINYAN

Assignees

成都鸿翔环卫服务有限公司

Dates

Publication Date: 20260505
Application Date: 20260407

Claims (10)

1. Environmental air quality monitoring system based on thing networking perception, including acoustic radar subsystem, pollutant concentration sensing network, atmospheric fluid power calculation engine, thing networking data fusion platform and three-dimensional migration orbit visual terminal, its characterized in that: The acoustic radar subsystem is configured to be deployed at a plurality of sensing nodes in an urban street canyon area, each sensing node is integrated with a microphone array, and a local atmospheric turbulence structure and a wind field vector field are inverted by collecting propagation signals of urban environment background noise in the air and combining an acoustic tomography algorithm; The pollutant concentration sensing network is composed of distributed multi-parameter gas sensors and is used for collecting concentration data of various atmospheric pollutants in a target area in real time and synchronously transmitting the concentration data to the data fusion platform of the Internet of things; the atmospheric fluid mechanics calculation engine is used for coupling the microscopic meteorological field data obtained by calculating the fluid mechanics model and acoustic inversion and calculating the migration track and the detention area of the pollutants in the three-dimensional space; The Internet of things data fusion platform is used for receiving multi-source heterogeneous data from the acoustic radar subsystem and the pollutant concentration sensing network, performing space-time alignment, noise filtering and feature fusion, and generating comprehensive environmental situation information containing coupling relation between pollutant concentration distribution and microclimate field; the three-dimensional migration track visualization terminal is connected with the Internet of things data fusion platform and is used for displaying a three-dimensional migration path, an eddy current gathering area and a well-ventilated area of pollutants in a visual mode and assisting environmental management decisions and public health early warning.
2. The ambient air quality monitoring system based on internet of things perception according to claim 1, wherein the acoustic radar subsystem comprises: a high-sensitivity microphone array configured as an omnidirectional pickup structure having a broadband response characteristic covering low-frequency wind vibration to medium-high frequency traffic noise for capturing acoustic features in an urban background sound field; The acoustic signal preprocessing unit is connected with the high-sensitivity microphone array and is configured for executing cross-correlation operation to acquire delay information of acoustic wave propagation between different sensing nodes, filtering unsteady noise interference from background sound by utilizing the self-adaptive echo cancellation module and the environment characteristic extraction submodule, and retaining steady acoustic characteristics related to wind fields and traffic flows; The local edge deduction module is internally provided with the acoustic tomography algorithm, is configured to calculate the equivalent sound velocity distribution of sound waves on a propagation path based on the time delay information, establishes an observation equation set, and performs deconvolution processing on observation data by using a Bayesian inversion framework to separate wind speed vector components and turbulence kinetic energy distribution parameters in a local area; the high-sensitivity microphone array adopts a multi-aperture distribution configuration, each sensing node comprises at least four capacitance microphone units which are arranged in a tetrahedron mode, or a plane square matrix which is composed of a plurality of pickup units, and the high-sensitivity microphone array is configured to detect by utilizing a passive sound source which exists naturally and is incoherent in urban environment, wherein the passive sound source comprises broadband noise generated by wind and building edge friction, tire noise generated by vehicle running and power system noise.
3. The ambient air quality monitoring system based on internet of things perception according to claim 1, wherein the contaminant concentration sensing network comprises: The sensing nodes are distributed, and each sensing node is internally integrated with a multi-parameter gas sensor, a signal conditioning circuit, a wireless transmission module, a self-calibration unit and an environment temperature and humidity compensation unit; the multi-parameter gas sensor is used for monitoring various components including nitrogen dioxide, sulfur dioxide, carbon monoxide, ozone, fine particles and inhalable particles; the self-calibration unit is configured to automatically correct sensitivity attenuation caused by sensor aging by comparing readings of adjacent sensing nodes under similar weather conditions; The environment temperature and humidity compensation unit corrects the gas concentration measured value in real time by utilizing a built-in temperature and humidity sensor; Each sensing node in the pollutant concentration sensing network and the sensing node of the acoustic radar subsystem are configured to be deployed co-located, mounted on the same physical support and share the same geographic position coordinates, so as to ensure that the pollutant concentration sampling points are strictly aligned with voxel grids inverted by the microclimate field in space coordinates.
4. The system of claim 1, wherein the data fusion platform of the internet of things employs a distributed logic architecture of edge-cloud collaboration, comprising: a data receiving gateway configured to perform data format normalization processing to convert original code streams from different communication protocols into a unified structured environment object; The time-space alignment processor is configured to assign a millisecond-level time stamp to each piece of data by utilizing the high-precision time service signal, and convert all sensing values into a unified local coordinate system of a city according to the geographic information of the sensing node; The feature fusion server is configured to adopt a multi-sensor fusion strategy, perform weighted average on a plurality of redundant observation values in the same area, eliminate measurement drift of a single sensor, and finish primary data cleaning and feature extraction in an edge computing unit close to a sensing node; the situation awareness database is configured to store key environmental features and is provided for cloud to conduct global fusion and long-term trend modeling.
5. The ambient air quality monitoring system based on internet of things perception according to claim 1, wherein the atmospheric fluid computing engine is physically hosted on a high performance computing server cluster, comprising: A data driven solver coupled to a computational fluid dynamics model, the computational fluid dynamics model being a lightweight solver of a reduced form of a base Yu Nawei-stokes equation set; The solver is configured to receive a real-time wind field vector from the acoustic radar subsystem as an inlet boundary condition and a source term constraint, discretize solving a control equation by a finite volume method or a Boltzmann method, and calculate a stress balance state of pollutant particles in complex street valley topography, wherein the stress balance state comprises drag force, pressure gradient force and gravity, and a Lagrange migration track of the particles is deduced; For gaseous pollutants, the calculation engine is configured to determine its scalar concentration field evolution trend in space by solving a convection-diffusion equation; the solver divides the target monitoring space into a plurality of three-dimensional voxel units in the inversion process, wind field parameters of each voxel unit serve as variables to be solved, and the sum of squares of differences between simulated sound propagation time and actually measured sound propagation time is enabled to reach the minimum value through an iterative optimization algorithm.
6. The ambient air quality monitoring system based on internet of things perception according to claim 1, wherein the three-dimensional migration trajectory visualization terminal comprises: the graphic rendering engine supports a hardware acceleration technology, is configured to render a particle flow field and an isosurface structure in real time, and displays the flowing state of pollutants in a building gap and vortex residence formed on a wind-avoiding surface; the three-dimensional geographic information system module is loaded with a three-dimensional model of the urban high-precision building and is used for realizing the integrated display of geographic space data; The early warning decision logic unit is configured to automatically identify an air dead circulation zone, wherein the air dead circulation zone is defined as a closed circulation zone with the wind field speed lower than a preset wind speed threshold value and the pollutant concentration continuously higher than a preset concentration threshold value, and the air dead circulation zone is marked as a high-risk early warning color on a three-dimensional interface; The visual terminal supports the process of backtracking pollutant diffusion by time slicing, and generates health early warning information according to a preset environmental safety threshold value to assist in urban ventilation corridor planning and pollution source management and control.
7. The ambient air quality monitoring system based on internet of things perception according to claim 1, further comprising a feedback closed loop control unit connecting the atmospheric fluid dynamics calculation engine with the acoustic radar subsystem: When the atmospheric fluid mechanics calculation engine recognizes that a complex turbulence structure appears in the area and calculation uncertainty is increased, the feedback closed-loop control unit sends a control instruction to an acoustic sensing node at a corresponding position; The control instructions are used to trigger the sensing node to increase the acoustic sampling frequency or to activate a directional sound source transmitter integrated on the sensing node to enhance the acoustic inversion accuracy of the region by transmitting controlled acoustic pulses of known frequency.
8. The ambient air quality monitoring system based on internet of things perception according to claim 1, further comprising: the vehicle-mounted or unmanned aerial vehicle mobile monitoring unit is provided with a mobile acoustic detector and a miniature chemical sensor and is configured to realize dynamic positioning through a Beidou navigation system and sample on a preset inspection path; The mobile monitoring unit returns data to the data fusion platform of the Internet of things through a mobile communication link and is used for filling a perception blank area outside the coverage range of the fixed monitoring network; The mobile monitoring unit adopts a path planning algorithm based on information gain, utilizes preliminary situation information fed back by a fixed sensing node to identify a subarea with severe concentration gradient change or high flow field inversion uncertainty, and carries out encryption sampling on the subarea.
9. The system for monitoring the quality of the ambient air based on the perception of the internet of things according to claim 1, wherein the intelligent hub with self-learning capability is deployed in the internet of things data fusion platform: The intelligent hub comprises a recurrent neural network model configured to identify a typical pattern of contaminant diffusion under meteorological combinations through learning of historical monitoring data and corresponding meteorological conditions; When the similar meteorological mode appears again, the platform invokes the simulation result with similar history as an initial value so as to shorten the convergence time of the atmospheric fluid computing engine; The calculation engine also adopts a physical constraint neural network method, a Navier-Stokes equation is used as a part of a loss function to be embedded into a deep learning model, and real-time approximate solution of the three-dimensional migration track of the pollutant is realized on the premise of keeping physical consistency.
10. The ambient air quality monitoring system based on internet of things perception according to claim 1, further comprising: The multidimensional situation association analysis platform is configured to execute pollution tracing logic, and the position and emission intensity of a pollution source are reversely calculated by constructing an accompanying equation of pollutant migration and utilizing the measured concentration gradient and the flow direction of a wind field; The multidimensional situation association analysis platform is internally integrated with an expert knowledge base, wherein the expert knowledge base comprises pollution emission characteristic fingerprints of different industries and traffic modes and is configured to match measured chemical component proportions with the characteristic fingerprints so as to position a pollution source; And the interactive early warning response terminal supports an augmented reality display mode, is configured to superimpose a virtual pollutant migration track and a concentration cloud picture on a real live-action image, executes different data desensitization and visualization strategies according to the role of a receiver, provides a detailed parameter billboard for a decision maker, and provides environment evaluation and clean air corridor suggestion based on chromaticity bar distinction for the public.

Description

Ambient air quality monitoring system based on internet of things perception Technical Field The invention belongs to the technical intersection field of environment monitoring and the Internet of things, and particularly relates to an environment air quality monitoring system based on the perception of the Internet of things. Background With the rapid iteration of the internet of things perception technology, air quality monitoring based on environmental big data has become a core supporting means for improving ecological management level in smart cities. The traditional monitoring system realizes real-time acquisition of the concentration of main atmospheric pollutants by establishing a sensing network of a coverage area, and provides basic data reference for environmental quality evaluation and pollution source supervision. Aiming at the atmospheric state sensing of complex microenvironments such as urban street canyons and the like, a monitoring system is required to be capable of deeply fusing atmospheric physical parameters and microcosmic meteorological features, so that the technical span from isolated point source concentration acquisition to global dynamic field sensing is realized. The existing air quality monitoring technology is mostly dependent on concentration sampling points in discrete distribution, and is difficult to analyze a detention area or an eddy current area formed by pollutants in a local space, so that a monitoring value cannot accurately explain logic dislocation between pedestrian sense organs and data standard reaching. The traditional system lacks a high space-time resolution detection means for a microscopic wind field under a steady meteorological condition, is difficult to capture a nonlinear atmospheric turbulence structure, and restricts the accuracy of pollutant diffusion tracing and migration path prediction. The existing monitoring scheme is highly dependent on expensive hardware wind measuring equipment, an algorithm model is not organically integrated with an acoustic tomography and computational fluid mechanics mechanism, real-time reconstruction of a three-dimensional migration track of pollutants cannot be realized on the premise of considering economy, and an environmental air quality monitoring system based on the perception of the Internet of things is expected. Disclosure of Invention The invention aims to provide an ambient air quality monitoring system based on the perception of the Internet of things, which can solve the problems in the background technology. In order to achieve the above purpose, the technical scheme adopted by the invention is as follows: Environmental air quality monitoring system based on thing networking perception includes acoustic radar subsystem, pollutant concentration sensing network, atmospheric fluid power calculation engine, thing networking data fusion platform and three-dimensional migration track visualization terminal: The acoustic radar subsystem is configured for being deployed at a plurality of sensing nodes in an urban street canyon area, each node is integrated with a high-sensitivity microphone array, and a local atmospheric turbulence structure and a wind field vector field are inverted by collecting propagation signals of urban environment background noise in the air and combining an acoustic tomography algorithm; the pollutant concentration sensing network is composed of distributed multi-parameter gas sensors and is used for collecting concentration data of various atmospheric pollutants in a target area in real time and synchronously transmitting the concentration data to the data fusion platform of the Internet of things; The atmospheric fluid mechanics calculation engine is used for coupling the microscopic meteorological field data obtained by calculating the fluid mechanics model and acoustic inversion and calculating the migration track and the detention area of the pollutants in the three-dimensional space; The data fusion platform of the Internet of things receives multi-source heterogeneous data from the acoustic radar subsystem and the pollutant concentration sensing network, performs space-time alignment, noise filtering and feature fusion, and generates comprehensive environmental situation information containing coupling relation between pollutant concentration distribution and microclimate fields; the three-dimensional migration track visualization terminal is connected with the Internet of things data fusion platform and is used for displaying a three-dimensional migration path, an eddy current gathering area and a well-ventilated area of pollutants in a visual mode and assisting environmental management decisions and public health early warning. Preferably, the acoustic radar subsystem utilizes wind noise and traffic noise naturally existing in urban environments as passive sound sources, does not need to additionally transmit sound wave signals, and reconstructs wind speed vectors and turbulent k