CN116359218A - Industrial aggregation area atmospheric pollution mobile monitoring system - Google Patents
Industrial aggregation area atmospheric pollution mobile monitoring system Download PDFInfo
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Abstract
The invention relates to the technical field of environmental monitoring, in particular to a mobile monitoring system for atmospheric pollution in an industrial aggregation area; the monitoring system comprises an unmanned aerial vehicle monitoring module, wherein real-time image information of a preset area is obtained through the unmanned aerial vehicle monitoring module, so that whether pollution gas exists in the preset area or not is judged according to the real-time image information; the weather monitoring module comprises a temperature sensor, a humidity sensor, an air pressure sensor, a wind direction sensor and a wind speed sensor; the data processing module comprises a memory and a processor; the communication module comprises a signal connector, and utilizes meteorological and topographic data to trace the source of the polluted gas, so that the pollution source can be identified, the pollution prevention and control process can be disclosed, and a scientific basis is provided for making and implementing environmental protection measures.
Description
Technical Field
The invention relates to the technical field of environmental monitoring, in particular to a mobile monitoring system for atmospheric pollution in an industrial aggregation area.
Background
Currently, atmospheric pollution has become a global issue. The industrial area is one of important sources of air pollution, at present, in order to monitor the air in an industrial gathering area, a plurality of air monitoring stations are usually arranged in the industrial area, multi-parameter automatic monitoring instruments are arranged in the stations for continuous automatic monitoring, monitoring results are stored in real time and analyzed to obtain relevant data, and the traditional fixed-point air monitoring system can only monitor fixed point positions or areas and cannot comprehensively and real-timely reflect the air quality change condition; and the current atmospheric pollution monitoring system can not quickly and accurately trace the pollution source. In order to solve the technical problems, the invention provides a mobile monitoring system for atmosphere pollution in an industrial aggregation area.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides the mobile monitoring system for the atmospheric pollution of the industrial gathering area.
The technical scheme adopted by the invention for achieving the purpose is as follows:
the first aspect of the invention discloses a mobile monitoring system for atmosphere pollution in an industrial aggregation zone, which comprises:
the unmanned aerial vehicle monitoring module comprises an unmanned aerial vehicle main body and a camera carried on the unmanned aerial vehicle main body, and real-time image information of a preset area is obtained through the unmanned aerial vehicle monitoring module so as to judge whether pollution gas exists in the preset area according to the real-time image information;
the weather monitoring module comprises a temperature sensor, a humidity sensor, an air pressure sensor, a wind direction sensor and a wind speed sensor, and is arranged at a preset position of the industrial gathering area;
the data processing module comprises a memory and a processor, the data obtained by each module is analyzed and processed by the processor, and the data of each module can be stored by the memory;
the communication module comprises a signal connector, and signal interconnection between the modules can be realized through the signal connector.
The invention discloses a control method of an industrial aggregation area atmospheric pollution mobile monitoring system, which is applied to the industrial aggregation area atmospheric pollution mobile monitoring system and comprises the following steps of:
acquiring real-time image information in a preset area, processing the real-time image information to obtain processed real-time image information, and identifying the processed real-time image information to obtain an identification result;
if the identification result is the second identification result, constructing and obtaining a first three-dimensional model diagram based on the processed real-time image information; acquiring second real-time image information in a preset area again at preset time intervals, and constructing a second three-dimensional model diagram based on the second real-time image information;
obtaining the propagation characteristics of the polluted gas based on the first three-dimensional model diagram and the second three-dimensional model diagram; wherein the propagation characteristics include a diffusion path and a diffusion rate;
constructing a knowledge graph, and importing the pollution types of the polluted gas into the knowledge graph for identification so as to identify one or more suspicious pollution sources;
and acquiring remote sensing image information and meteorological data information of an industrial aggregation area, constructing an object-meteorological dynamic model diagram based on the remote sensing image information and the meteorological data information, and importing the suspected pollution source into the object-meteorological dynamic model diagram for analysis so as to determine a final pollution source.
Further, in a preferred embodiment of the present invention, the real-time image information is processed to obtain processed real-time image information, and the processed real-time image information is identified to obtain an identification result, which specifically includes:
processing the real-time image information through mean value filtering, edge detection and image segmentation to obtain processed real-time image information;
acquiring pollution image information corresponding to different atmospheric pollution types through a big data network, constructing a database, and importing the pollution image information corresponding to the different atmospheric pollution types into the database to obtain a characteristic database;
importing the processed real-time image information into the characteristic database, and calculating the similarity between the processed real-time image information and each polluted image information through a SURF algorithm to obtain a plurality of similarities;
comparing a plurality of the similarities with preset similarities, if the similarities are not larger than the preset similarities, indicating that no pollution gas exists in a preset area, and outputting a first identification result; and if at least one similarity is larger than the preset similarity, indicating that the polluted gas exists in the preset area, acquiring the maximum similarity at this time, determining the pollution type of the polluted gas based on the maximum similarity, and outputting a second identification result.
Further, in a preferred embodiment of the present invention, a first three-dimensional model map is constructed based on the processed real-time image information, specifically:
performing feature matching processing on the processed real-time image information based on a SIFT algorithm to obtain a plurality of matching points, obtaining pixel values corresponding to the matching points, and eliminating the matching points with the pixel values lower than a preset pixel value to obtain screened matching points;
acquiring coordinate values of the screened matching points in a world coordinate system, and calculating intermediate points between every two matching points according to the coordinate values of the screened matching points to obtain a plurality of intermediate points; converging the screened matching points with the intermediate points to obtain a plurality of dense matching points;
selecting any dense matching point as a construction origin, constructing a three-dimensional space coordinate system based on the construction origin, and importing a plurality of dense matching points into the three-dimensional space coordinate system to obtain three-dimensional coordinate values of the dense matching points, and generating point cloud data of the dense matching points based on the three-dimensional coordinate values;
registering the point cloud data, performing rigid or non-rigid transformation on the point cloud data to enable each point cloud data to be represented by a unified coordinate system, and performing gridding processing on the point cloud data until a curved surface model is generated, so that a first three-dimensional model diagram is constructed and obtained.
Further, in a preferred embodiment of the present invention, the propagation characteristics of the polluted gas are obtained based on the first three-dimensional model map and the second three-dimensional model map, specifically:
acquiring a first matching point pair of the first three-dimensional model diagram based on the point cloud data of the first three-dimensional model diagram, and acquiring a second matching point pair of the second three-dimensional model diagram based on the point cloud data of the second three-dimensional model diagram;
constructing a model fusion space, importing the first three-dimensional model diagram and the second three-dimensional model diagram into the model fusion space, and enabling the first matching point pair and the second matching point pair to coincide in the model fusion space so as to fuse the first three-dimensional model diagram and the second three-dimensional model diagram;
removing the model part, which is overlapped with the first three-dimensional model image and the second three-dimensional model image, from the model fusion space, and reserving the model part, which is not overlapped with the first three-dimensional model image and the second three-dimensional model image, from the model fusion space to obtain a diffusion model of the polluted gas;
calculating a model volume value of the diffusion model through a Montecello algorithm, and based on the model volume value, calculating the diffusion rate of the polluted gas; and carrying out edge detection on the diffusion model through a Canny algorithm to obtain a profile curve of the diffusion model, and obtaining a diffusion path of the polluted gas based on the profile curve.
Further, in a preferred embodiment of the present invention, a knowledge graph is constructed, and the pollution category of the polluted gas is imported into the knowledge graph for identification, so as to identify one or more suspected pollution sources, specifically:
acquiring production information and industry information of each enterprise in an industrial aggregation area, generating a correlation text based on the production information and the industry information, constructing a knowledge graph, and importing the correlation text into the knowledge graph;
obtaining pollution types of pollution gas, importing the pollution types into the knowledge graph, and calculating the association degrees between the pollution types and each association text through a gray association analysis method to obtain a plurality of association degrees;
comparing a plurality of relevance degrees with preset relevance degrees, and extracting relevance texts corresponding to the relevance degrees larger than the preset relevance degrees;
and determining one or more suspicious pollution sources according to the relevance text corresponding to which the relevance is greater than the preset relevance.
Further, in a preferred embodiment of the present invention, remote sensing image information and weather data information of an industrial aggregation area are obtained, an object-weather dynamic model diagram is constructed based on the remote sensing image information and the weather data information, and the suspected pollution source is imported into the object-weather dynamic model diagram for analysis, so as to determine a final pollution source, which is specifically:
Acquiring image information of a preset object through a big data network, and dividing the image information of the preset object into a training set and a verification set;
constructing an identification model based on a deep learning network, importing the training set into the identification model for training, and storing model parameters after the cross loss function is trained to be stable; verifying the recognition model through a verification set until the model parameters meet preset requirements, and outputting the model parameters to obtain a recognition model after training is completed;
acquiring remote sensing image information of an industrial aggregation area on a plurality of time nodes, and importing the remote sensing image information into the recognition model after training for recognition so as to recognize whether a preset object exists in the remote sensing image information;
if the preset object exists in the remote sensing image information, an object model diagram of the preset object is constructed based on the remote sensing image information, and the position information of the preset object on each time node is obtained.
Further, in a preferred embodiment of the present invention, the method further comprises the steps of:
acquiring AR scene graph information of an industrial aggregation area, and constructing an object dynamic model graph of the industrial aggregation area in preset time based on the AR scene graph information, an object model graph of a preset object and position information of the preset object on each time node;
Acquiring meteorological data information of an industrial aggregation area on a preset time node, and importing the meteorological data information on the preset time node into the object dynamic model diagram to obtain an object-meteorological dynamic model diagram of the industrial aggregation area in preset time;
determining the position information of each suspicious pollution source in the object-weather dynamic model diagram, and carrying out weather numerical simulation analysis on each suspicious pollution source in the object-weather dynamic model diagram based on a turbulence theory, so as to obtain simulation propagation characteristics corresponding to each suspicious pollution source;
calculating hash values between the propagation characteristics of the polluted gas and the simulated propagation characteristics corresponding to each suspicious pollution source through a hash algorithm to obtain a plurality of hash values;
constructing a sequence table, importing a plurality of hash values into the sequence table for size sorting, extracting a maximum hash value from the sequence table after sorting is completed, and calibrating a suspicious pollution source corresponding to the maximum hash value as a final pollution source.
The invention solves the technical defects existing in the background technology, and has the following beneficial effects: the system can rapidly identify the pollution type of the polluted gas, improve the operation efficiency of the system and improve the robustness of the system; the method has the advantages that the meteorological and topographic data are utilized to trace the source of the polluted gas, so that the polluted source can be identified, the pollution prevention and control process can be realized, scientific basis is provided for making and implementing environmental protection measures, and the method is beneficial to the responsibility of related departments.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other embodiments of the drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a mobile monitoring system for atmospheric pollution in an industrial aggregation area;
FIG. 2 is a flow chart of a first control method of the industrial aggregation area atmospheric pollution mobile monitoring system;
FIG. 3 is a flow chart of a second control method of the industrial aggregation area atmospheric pollution mobile monitoring system;
FIG. 4 is a flow chart of a third control method of the industrial aggregation area atmospheric pollution mobile monitoring system.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
As shown in fig. 1, a first aspect of the present invention discloses a mobile monitoring system for atmosphere pollution in an industrial aggregation zone, the monitoring system comprising:
the unmanned aerial vehicle monitoring module 101 comprises an unmanned aerial vehicle main body and a camera carried on the unmanned aerial vehicle main body, and real-time image information of a preset area is obtained through the unmanned aerial vehicle monitoring module so as to judge whether pollution gas exists in the preset area according to the real-time image information;
the weather monitoring module 102 comprises a temperature sensor, a humidity sensor, an air pressure sensor, a wind direction sensor and a wind speed sensor, and is arranged at a preset position of the industrial gathering area;
the data processing module 103, which includes a memory and a processor, and is used for analyzing and processing the data obtained by each module, and storing the data of each module through the memory;
The communication module 104 includes a signal connector through which signal interconnection between the modules can be achieved.
The system can be used for rapidly identifying the pollution type of the polluted gas, improving the operation efficiency of the system and improving the robustness of the system; the method has the advantages that the meteorological and topographic data are utilized to trace the source of the polluted gas, so that the polluted source can be identified, the pollution prevention and control process can be realized, scientific basis is provided for making and implementing environmental protection measures, and the method is beneficial to the responsibility of related departments.
The invention discloses a control method of an industrial aggregation area atmospheric pollution mobile monitoring system, which is applied to the industrial aggregation area atmospheric pollution mobile monitoring system, as shown in fig. 2, and comprises the following steps:
s102: acquiring real-time image information in a preset area, processing the real-time image information to obtain processed real-time image information, and identifying the processed real-time image information to obtain an identification result;
s104: if the identification result is the second identification result, constructing and obtaining a first three-dimensional model diagram based on the processed real-time image information; acquiring second real-time image information in a preset area again at preset time intervals, and constructing a second three-dimensional model diagram based on the second real-time image information;
S106: obtaining the propagation characteristics of the polluted gas based on the first three-dimensional model diagram and the second three-dimensional model diagram; wherein the propagation characteristics include a diffusion path and a diffusion rate;
s108: constructing a knowledge graph, and importing the pollution types of the polluted gas into the knowledge graph for identification so as to identify one or more suspicious pollution sources;
s110: and acquiring remote sensing image information and meteorological data information of an industrial aggregation area, constructing an object-meteorological dynamic model diagram based on the remote sensing image information and the meteorological data information, and importing the suspected pollution source into the object-meteorological dynamic model diagram for analysis so as to determine a final pollution source.
Further, in a preferred embodiment of the present invention, the real-time image information is processed to obtain processed real-time image information, and the processed real-time image information is identified to obtain an identification result, which specifically includes:
processing the real-time image information through mean value filtering, edge detection and image segmentation to obtain processed real-time image information;
acquiring pollution image information corresponding to different atmospheric pollution types through a big data network, constructing a database, and importing the pollution image information corresponding to the different atmospheric pollution types into the database to obtain a characteristic database;
Importing the processed real-time image information into the characteristic database, and calculating the similarity between the processed real-time image information and each polluted image information through a SURF algorithm to obtain a plurality of similarities;
comparing a plurality of the similarities with preset similarities, if the similarities are not larger than the preset similarities, indicating that no pollution gas exists in a preset area, and outputting a first identification result; and if at least one similarity is larger than the preset similarity, indicating that the polluted gas exists in the preset area, acquiring the maximum similarity at this time, determining the pollution type of the polluted gas based on the maximum similarity, and outputting a second identification result.
The preset area is an area with suspicious polluted air, and is obtained in advance through a remote sensing technology or fixed-point monitoring equipment. The method comprises the steps of shooting real-time image information of a preset area at a plurality of angles through a camera on the unmanned aerial vehicle, and preprocessing the real-time image information through image processing technologies such as mean value filtering, edge detection and image segmentation to improve the quality of images, so that the processed real-time image information is obtained. The polluted image information comprises the characteristics of colors, shapes, textures and the like corresponding to different types of polluted air. Then calculating the similarity between the processed real-time image information and each piece of polluted image information through a SURF algorithm (acceleration robust feature detection algorithm), specifically, calculating the similarity of the characteristics such as color, shape, texture and the like between the processed real-time image information and each piece of polluted image information, and if the similarity is not greater than the preset similarity, indicating that no polluted gas exists in a preset area; if at least one of the similarities is greater than the preset similarity, it is indicated that a pollution gas exists in the preset area, the maximum similarity is obtained at this time, and a pollution type of the pollution gas is determined based on the maximum similarity, for example, if the similarity between the processed real-time image information and a certain pollution image information in the characteristic database is 99%, at this time, the pollution type of the pollution air in the preset area can be determined according to the pollution type corresponding to the pollution image information. The method can quickly identify the pollution type of the polluted gas through simple algorithm steps, and can improve the operation efficiency of the system and the robustness of the system.
Further, in a preferred embodiment of the present invention, a first three-dimensional model map is constructed based on the processed real-time image information, as shown in fig. 3, specifically:
s202: performing feature matching processing on the processed real-time image information based on a SIFT algorithm to obtain a plurality of matching points, obtaining pixel values corresponding to the matching points, and eliminating the matching points with the pixel values lower than a preset pixel value to obtain screened matching points;
s204: acquiring coordinate values of the screened matching points in a world coordinate system, and calculating intermediate points between every two matching points according to the coordinate values of the screened matching points to obtain a plurality of intermediate points; converging the screened matching points with the intermediate points to obtain a plurality of dense matching points;
s206: selecting any dense matching point as a construction origin, constructing a three-dimensional space coordinate system based on the construction origin, and importing a plurality of dense matching points into the three-dimensional space coordinate system to obtain three-dimensional coordinate values of the dense matching points, and generating point cloud data of the dense matching points based on the three-dimensional coordinate values;
s208: registering the point cloud data, performing rigid or non-rigid transformation on the point cloud data to enable each point cloud data to be represented by a unified coordinate system, and performing gridding processing on the point cloud data until a curved surface model is generated, so that a first three-dimensional model diagram is constructed and obtained.
It should be noted that, after the matching point of the processed real-time image information is obtained by the SIFT algorithm (scale invariant feature transform algorithm), the matching point often has the phenomena of loss and distortion, and if the first three-dimensional model map (the three-dimensional model map of the polluted gas) is directly constructed by the obtained matching point, the obtained model has the phenomena of local loss, unsmooth curved surface and distortion, so that the accuracy of the obtained first three-dimensional model map is lower, and further the subsequent evaluation result is affected. In the method, after the matching points are obtained, the matching points with the pixel values lower than the preset pixel values are removed firstly, so that the matching points which do not meet the requirements are removed, and the screened matching points are obtained; and then carrying out dense processing on the screened matching points to obtain more matching points, so as to obtain dense matching points, and constructing a first three-dimensional model diagram through three-dimensional software (such as SolidWorks, PROE) based on the point cloud data of the dense matching points. The method can supplement lost and distorted matching points to obtain more matching points, so that a more accurate three-dimensional model diagram of the polluted gas is constructed, the reliability of a monitoring result can be improved, the system can rapidly obtain dense matching points without complex operation, the operation speed of the system can be improved, the modeling speed is improved, and the working efficiency is further improved.
In addition, after the first three-dimensional model diagram is constructed, acquiring second real-time image information in a preset area through a camera on the unmanned aerial vehicle again at a preset time interval, and then constructing through the second real-time image information to obtain a second three-dimensional model diagram, wherein the second three-dimensional model diagram represents a real-time three-dimensional model diagram of the polluted gas after the preset time interval. The construction principle of the second three-dimensional model diagram is the same as that of the first three-dimensional model diagram, and will not be described here.
Further, in a preferred embodiment of the present invention, the propagation characteristics of the polluted gas are obtained based on the first three-dimensional model map and the second three-dimensional model map, specifically:
acquiring a first matching point pair of the first three-dimensional model diagram based on the point cloud data of the first three-dimensional model diagram, and acquiring a second matching point pair of the second three-dimensional model diagram based on the point cloud data of the second three-dimensional model diagram;
constructing a model fusion space, importing the first three-dimensional model diagram and the second three-dimensional model diagram into the model fusion space, and enabling the first matching point pair and the second matching point pair to coincide in the model fusion space so as to fuse the first three-dimensional model diagram and the second three-dimensional model diagram;
Removing the model part, which is overlapped with the first three-dimensional model image and the second three-dimensional model image, from the model fusion space, and reserving the model part, which is not overlapped with the first three-dimensional model image and the second three-dimensional model image, from the model fusion space to obtain a diffusion model of the polluted gas;
calculating a model volume value of the diffusion model through a Montecello algorithm, and based on the model volume value, calculating the diffusion rate of the polluted gas; and carrying out edge detection on the diffusion model through a Canny algorithm to obtain a profile curve of the diffusion model, and obtaining a diffusion path of the polluted gas based on the profile curve.
It should be noted that, the first pairing point pair and the second pairing point pair are pairing points between two models, the pairing point pair is set in advance, and the pairing point pair can be understood as a positioning reference of the models. The model fusion space is a virtual space constructed by three-dimensional software, and a plurality of grid spaces exist in the space. After a diffusion model of the polluted gas is obtained, calculating a model volume value of the diffusion model through a Monte Carlo algorithm (Monte Carlo algorithm), and calculating the diffusion rate of the polluted gas after a preset time interval according to a model difference value from a first three-dimensional model diagram and a second three-dimensional model; and performing edge detection by a Canny algorithm (Canni edge detection algorithm) to obtain a profile curve of the diffusion model, and analyzing and obtaining a diffusion path of the polluted gas after a preset time interval according to the profile curve. The method can be used for rapidly and accurately analyzing the propagation characteristics of the polluted gas according to the dynamic change model of the polluted gas.
Further, in a preferred embodiment of the present invention, a knowledge graph is constructed, and the pollution category of the polluted gas is imported into the knowledge graph for identification, so as to identify one or more suspected pollution sources, specifically:
acquiring production information and industry information of each enterprise in an industrial aggregation area, generating a correlation text based on the production information and the industry information, constructing a knowledge graph, and importing the correlation text into the knowledge graph;
obtaining pollution types of pollution gas, importing the pollution types into the knowledge graph, and calculating the association degrees between the pollution types and each association text through a gray association analysis method to obtain a plurality of association degrees;
comparing a plurality of relevance degrees with preset relevance degrees, and extracting relevance texts corresponding to the relevance degrees larger than the preset relevance degrees;
and determining one or more suspicious pollution sources according to the relevance text corresponding to which the relevance is greater than the preset relevance.
The production information includes the type of operation of the enterprise, for example, the enterprise is to produce rubber products, steel products, etc. The industry information includes enterprise geographic locations, drain locations, and the like. When the association degree is larger than the preset association degree, the homology of the current pollution gas and enterprises corresponding to the association text is indicated, if the current pollution gas is polluted by the organic plastic gas, the enterprises producing the organic plastic in the industrial gathering area are identified at the moment, and one or more suspicious pollution sources are determined.
Further, in a preferred embodiment of the present invention, remote sensing image information and weather data information of an industrial aggregation area are obtained, an object-weather dynamic model diagram is constructed based on the remote sensing image information and the weather data information, and the suspected pollution source is imported into the object-weather dynamic model diagram for analysis, so as to determine a final pollution source, as shown in fig. 4, specifically:
s302: acquiring image information of a preset object through a big data network, and dividing the image information of the preset object into a training set and a verification set;
s304: constructing an identification model based on a deep learning network, importing the training set into the identification model for training, and storing model parameters after the cross loss function is trained to be stable; verifying the recognition model through a verification set until the model parameters meet preset requirements, and outputting the model parameters to obtain a recognition model after training is completed;
s306: acquiring remote sensing image information of an industrial aggregation area on a plurality of time nodes, and importing the remote sensing image information into the recognition model after training for recognition so as to recognize whether a preset object exists in the remote sensing image information;
S308: if the preset object exists in the remote sensing image information, an object model diagram of the preset object is constructed based on the remote sensing image information, and the position information of the preset object on each time node is obtained.
It should be noted that, the preset objects include objects that can move flexibly, such as automobiles, bicycles, motorcycles, pedestrians, and the like, if the preset objects exist in the remote sensing image information, the characteristics of the preset objects existing in the remote sensing image are extracted to obtain characteristic points, then an object model diagram of the preset objects is constructed based on three-dimensional software through the characteristic points, and position information of the preset objects on each time node is obtained. By the method, the flexibly movable object of the industrial gathering area in the preset time period can be identified, so that a diffusion topographic model map which is more similar to reality can be built later.
Further, in a preferred embodiment of the present invention, the method further comprises the steps of:
acquiring AR scene graph information of an industrial aggregation area, and constructing an object dynamic model graph of the industrial aggregation area in preset time based on the AR scene graph information, an object model graph of a preset object and position information of the preset object on each time node;
Acquiring meteorological data information of an industrial aggregation area on a preset time node, and importing the meteorological data information on the preset time node into the object dynamic model diagram to obtain an object-meteorological dynamic model diagram of the industrial aggregation area in preset time;
determining the position information of each suspicious pollution source in the object-weather dynamic model diagram, and carrying out weather numerical simulation analysis on each suspicious pollution source in the object-weather dynamic model diagram based on a turbulence theory, so as to obtain simulation propagation characteristics corresponding to each suspicious pollution source;
calculating hash values between the propagation characteristics of the polluted gas and the simulated propagation characteristics corresponding to each suspicious pollution source through a hash algorithm to obtain a plurality of hash values;
constructing a sequence table, importing a plurality of hash values into the sequence table for size sorting, extracting a maximum hash value from the sequence table after sorting is completed, and calibrating a suspicious pollution source corresponding to the maximum hash value as a final pollution source.
It should be noted that, a corresponding AR scene graph exists at each geographic location, where the AR scene graph generally includes a terrain-building model graph at a geographic location, and the AR scene graph may be obtained by map software; however, the AR scene graph is not a real-time graph, and is different from reality, for example, flexible moving objects such as automobiles, bicycles, motorcycles, pedestrians and the like existing on an industrial gathering area do not exist in the AR scene graph. And constructing an object dynamic model diagram of the industrial gathering area in the preset time by three-dimensional software based on the AR scene diagram information, the object model diagram of the preset object and the position information of the preset object on each time node, wherein the object dynamic model diagram is a real-time scene model diagram of the industrial gathering area in the preset time. And then acquiring weather data information of the industrial gathering area on a preset time node, and importing the weather data information on the preset time node into the object dynamic model diagram to obtain an object-weather dynamic model diagram of the industrial gathering area in the preset time, wherein the object-weather dynamic model diagram represents a model diagram of the industrial gathering area combined with real-time weather in the preset time, such as wind speed, wind direction, wind pressure, temperature and the like of the industrial gathering area on a certain time node. The method comprises the steps of carrying out numerical simulation on an meteorological field by using a vertical meteorological model and a horizontal meteorological model through comprehensive meteorological and topographic data, calculating and analyzing movement of air flow and diffusion of chemical substances to predict transmission paths, time and space distribution of each suspicious pollution source in a preset time period, and comparing the transmission paths, time and space distribution of each suspicious pollution source in the preset time period with the propagation characteristics of the polluted gas, so that a final pollution source is determined. The method utilizes meteorological and topographic data to trace the source of the polluted gas, can help identify the pollution source, reveal and prevent the pollution process, provides scientific basis for making and implementing environmental protection measures, and is beneficial to the responsibility of related departments.
In addition, the control method of the industrial aggregation area atmospheric pollution mobile monitoring system further comprises the following steps:
acquiring the energy consumption rate of the unmanned aerial vehicle at each temperature value through a big data network, constructing a prediction model based on a convolutional neural network, and importing the energy consumption rate of the unmanned aerial vehicle at each temperature value into the prediction model for training to obtain a trained prediction model;
acquiring real-time working temperature information of the unmanned aerial vehicle, and inputting the real-time working temperature information into the prediction model to obtain the real-time energy consumption rate of the unmanned aerial vehicle;
acquiring a current residual energy value of the unmanned aerial vehicle, and calculating the longest working time of the unmanned aerial vehicle according to the current residual energy value and the real-time energy consumption rate;
acquiring real-time position information of the unmanned aerial vehicle, and acquiring the residual working time of the unmanned aerial vehicle based on the real-time position information and a preset flight task;
and if the remaining working time is greater than the maximum working time, searching out a charging node in the industrial aggregation area, and transmitting the charging node to the unmanned aerial vehicle control terminal.
It should be noted that, according to the actual situation, the energy consumption of the unmanned aerial vehicle's battery under different temperatures is inconsistent, through this method can carry out simulation analysis to unmanned aerial vehicle's energy consumption change according to unmanned aerial vehicle's real-time operational environment temperature for can adjust the time node that the battery charges according to real-time environment's change at unmanned aerial vehicle, and then supply energy to unmanned aerial vehicle in advance. The preset flight tasks comprise information such as time for completing the flight tasks, flight paths and the like, and the preset flight tasks are set in advance by a user and are imported into a data storage of the unmanned aerial vehicle.
In addition, the control method of the industrial aggregation area atmospheric pollution mobile monitoring system further comprises the following steps:
acquiring charging time required by charging of the unmanned aerial vehicle; acquiring a residual work task of the unmanned aerial vehicle, and acquiring the time required by the unmanned aerial vehicle to complete the residual task based on the residual work task;
acquiring a latest working time node of the unmanned aerial vehicle based on a preset flight task of the unmanned aerial vehicle, and judging whether the sum of time required by the unmanned aerial vehicle to complete the residual task and charging time required by charging is larger than the latest working time node;
if the current unmanned aerial vehicle is larger than the current unmanned aerial vehicle, the current unmanned aerial vehicle is transmitted to the unmanned aerial vehicle closest to the current unmanned aerial vehicle, and the current unmanned aerial vehicle is completed through the unmanned aerial vehicle closest to the current unmanned aerial vehicle.
It should be noted that, if the unmanned aerial vehicle currently executing the task needs to be supplemented with electric energy, it is determined whether the sum of the time required for the unmanned aerial vehicle to complete the remaining task and the charging time required for charging is greater than the latest working time node, and if so, it is indicated that the unmanned aerial vehicle cannot complete the task in time. The method can improve the working cooperativity of the unmanned aerial vehicle in the industrial aggregation area, so that the unmanned aerial vehicle is more reasonable in the monitoring process, and the monitoring task can be completed on time.
In addition, the control method of the industrial aggregation area atmospheric pollution mobile monitoring system further comprises the following steps:
acquiring propagation characteristics and pollution types of polluted gas, determining key search words based on the propagation characteristics and the pollution types, and searching a big data network based on the key search words to obtain an associated prevention and treatment scheme;
acquiring historical control success rates corresponding to all control schemes, and comparing the historical control success rates corresponding to all control schemes with preset success rates;
extracting a control scheme with a history control success rate greater than a preset success rate to obtain a control scheme after primary screening, obtaining control properties of the control scheme corresponding to the control scheme after primary screening, and removing the control scheme after primary screening, with the control properties corresponding to the preset control properties, to obtain a control scheme after secondary screening;
acquiring the control success rate corresponding to the control scheme after the secondary screening, constructing a ranking table, importing the control success rate corresponding to the control scheme after the secondary screening into the ranking table for size ranking, and extracting the maximum control success rate after ranking is finished;
And taking the secondarily screened control scheme corresponding to the maximum control success rate as a final control scheme, and outputting the final control scheme.
Firstly, searching in a big data network based on the propagation characteristics and the pollution types of the polluted gas to obtain all relevant prevention and treatment schemes corresponding to the current polluted gas, and then obtaining one-time screened prevention and treatment schemes with the prevention and treatment success rate which is not more than the prevention and treatment scheme corresponding to the preset success rate so as to ensure the prevention and treatment success rate; wherein the control property comprises physical control and chemical control, and the preset control property is chemical control; and eliminating the control scheme with chemical properties to obtain a control scheme after secondary screening, further reducing the influence on the ecological environment on the use amount of chemical control articles, and realizing the harmonious development of society, economy and environment. And then screening out the maximum control success rate by all the secondarily screened control schemes, and taking the secondarily screened control scheme corresponding to the maximum control success rate as a final control scheme. According to the method, the optimal control scheme can be automatically screened out in a big data network according to the propagation characteristics and pollution types of the current polluted gas, the use of chemical control products can be reduced on the premise of ensuring the control success rate, the influence of ecological environment can be further reduced, and the harmonious development of society, economy and environment is realized.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the above-described integrated units of the present invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
The foregoing is merely illustrative embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think about variations or substitutions within the technical scope of the present invention, and the invention should be covered. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (8)
1. An industrial aggregation zone atmospheric pollution mobile monitoring system, the monitoring system comprising:
the unmanned aerial vehicle monitoring module comprises an unmanned aerial vehicle main body and a camera carried on the unmanned aerial vehicle main body, and real-time image information of a preset area is obtained through the unmanned aerial vehicle monitoring module so as to judge whether pollution gas exists in the preset area according to the real-time image information;
the weather monitoring module comprises a temperature sensor, a humidity sensor, an air pressure sensor, a wind direction sensor and a wind speed sensor, and is arranged at a preset position of the industrial gathering area;
the data processing module comprises a memory and a processor, the data obtained by each module is analyzed and processed by the processor, and the data of each module can be stored by the memory;
The communication module comprises a signal connector, and signal interconnection between the modules can be realized through the signal connector.
2. A control method of an industrial aggregation area atmospheric pollution mobile monitoring system, which is applied to the industrial aggregation area atmospheric pollution mobile monitoring system as claimed in claim 1, and is characterized by comprising the following steps:
acquiring real-time image information in a preset area, processing the real-time image information to obtain processed real-time image information, and identifying the processed real-time image information to obtain an identification result;
if the identification result is the second identification result, constructing and obtaining a first three-dimensional model diagram based on the processed real-time image information; acquiring second real-time image information in a preset area again at preset time intervals, and constructing a second three-dimensional model diagram based on the second real-time image information;
obtaining the propagation characteristics of the polluted gas based on the first three-dimensional model diagram and the second three-dimensional model diagram; wherein the propagation characteristics include a diffusion path and a diffusion rate;
constructing a knowledge graph, and importing the pollution types of the polluted gas into the knowledge graph for identification so as to identify one or more suspicious pollution sources;
And acquiring remote sensing image information and meteorological data information of an industrial aggregation area, constructing an object-meteorological dynamic model diagram based on the remote sensing image information and the meteorological data information, and importing the suspected pollution source into the object-meteorological dynamic model diagram for analysis so as to determine a final pollution source.
3. The control method of the industrial aggregation area atmosphere pollution mobile monitoring system according to claim 2, wherein the real-time image information is processed to obtain processed real-time image information, and the processed real-time image information is identified to obtain an identification result, specifically:
processing the real-time image information through mean value filtering, edge detection and image segmentation to obtain processed real-time image information;
acquiring pollution image information corresponding to different atmospheric pollution types through a big data network, constructing a database, and importing the pollution image information corresponding to the different atmospheric pollution types into the database to obtain a characteristic database;
importing the processed real-time image information into the characteristic database, and calculating the similarity between the processed real-time image information and each polluted image information through a SURF algorithm to obtain a plurality of similarities;
Comparing a plurality of the similarities with preset similarities, if the similarities are not larger than the preset similarities, indicating that no pollution gas exists in a preset area, and outputting a first identification result; and if at least one similarity is larger than the preset similarity, indicating that the polluted gas exists in the preset area, acquiring the maximum similarity at this time, determining the pollution type of the polluted gas based on the maximum similarity, and outputting a second identification result.
4. The control method of the industrial aggregation area atmospheric pollution mobile monitoring system according to claim 2, wherein the first three-dimensional model diagram is constructed based on the processed real-time image information, specifically:
performing feature matching processing on the processed real-time image information based on a SIFT algorithm to obtain a plurality of matching points, obtaining pixel values corresponding to the matching points, and eliminating the matching points with the pixel values lower than a preset pixel value to obtain screened matching points;
acquiring coordinate values of the screened matching points in a world coordinate system, and calculating intermediate points between every two matching points according to the coordinate values of the screened matching points to obtain a plurality of intermediate points; converging the screened matching points with the intermediate points to obtain a plurality of dense matching points;
Selecting any dense matching point as a construction origin, constructing a three-dimensional space coordinate system based on the construction origin, and importing a plurality of dense matching points into the three-dimensional space coordinate system to obtain three-dimensional coordinate values of the dense matching points, and generating point cloud data of the dense matching points based on the three-dimensional coordinate values;
registering the point cloud data, performing rigid or non-rigid transformation on the point cloud data to enable each point cloud data to be represented by a unified coordinate system, and performing gridding processing on the point cloud data until a curved surface model is generated, so that a first three-dimensional model diagram is constructed and obtained.
5. The control method of the industrial aggregation area atmospheric pollution mobile monitoring system according to claim 2, wherein the transmission characteristics of the polluted gas are obtained based on the first three-dimensional model diagram and the second three-dimensional model diagram, specifically:
acquiring a first matching point pair of the first three-dimensional model diagram based on the point cloud data of the first three-dimensional model diagram, and acquiring a second matching point pair of the second three-dimensional model diagram based on the point cloud data of the second three-dimensional model diagram;
constructing a model fusion space, importing the first three-dimensional model diagram and the second three-dimensional model diagram into the model fusion space, and enabling the first matching point pair and the second matching point pair to coincide in the model fusion space so as to fuse the first three-dimensional model diagram and the second three-dimensional model diagram;
Removing the model part, which is overlapped with the first three-dimensional model image and the second three-dimensional model image, from the model fusion space, and reserving the model part, which is not overlapped with the first three-dimensional model image and the second three-dimensional model image, from the model fusion space to obtain a diffusion model of the polluted gas;
calculating a model volume value of the diffusion model through a Montecello algorithm, and based on the model volume value, calculating the diffusion rate of the polluted gas; and carrying out edge detection on the diffusion model through a Canny algorithm to obtain a profile curve of the diffusion model, and obtaining a diffusion path of the polluted gas based on the profile curve.
6. The control method of an industrial aggregation area atmosphere pollution mobile monitoring system according to claim 2, wherein a knowledge graph is constructed, and the pollution category of the polluted gas is imported into the knowledge graph for identification, so as to identify one or more suspected pollution sources, specifically:
acquiring production information and industry information of each enterprise in an industrial aggregation area, generating a correlation text based on the production information and the industry information, constructing a knowledge graph, and importing the correlation text into the knowledge graph;
Obtaining pollution types of pollution gas, importing the pollution types into the knowledge graph, and calculating the association degrees between the pollution types and each association text through a gray association analysis method to obtain a plurality of association degrees;
comparing a plurality of relevance degrees with preset relevance degrees, and extracting relevance texts corresponding to the relevance degrees larger than the preset relevance degrees;
and determining one or more suspicious pollution sources according to the relevance text corresponding to which the relevance is greater than the preset relevance.
7. The method for controlling an atmospheric pollution mobile monitoring system of an industrial aggregation area according to claim 2, wherein the method comprises the steps of obtaining remote sensing image information and weather data information of the industrial aggregation area, constructing an object-weather dynamic model diagram based on the remote sensing image information and the weather data information, and importing the suspected pollution source into the object-weather dynamic model diagram for analysis to determine a final pollution source, specifically:
acquiring image information of a preset object through a big data network, and dividing the image information of the preset object into a training set and a verification set;
constructing an identification model based on a deep learning network, importing the training set into the identification model for training, and storing model parameters after the cross loss function is trained to be stable; verifying the recognition model through a verification set until the model parameters meet preset requirements, and outputting the model parameters to obtain a recognition model after training is completed;
Acquiring remote sensing image information of an industrial aggregation area on a plurality of time nodes, and importing the remote sensing image information into the recognition model after training for recognition so as to recognize whether a preset object exists in the remote sensing image information;
if the preset object exists in the remote sensing image information, an object model diagram of the preset object is constructed based on the remote sensing image information, and the position information of the preset object on each time node is obtained.
8. The method for controlling an industrial aggregation area atmospheric pollution mobile monitoring system according to claim 7, further comprising the steps of:
acquiring AR scene graph information of an industrial aggregation area, and constructing an object dynamic model graph of the industrial aggregation area in preset time based on the AR scene graph information, an object model graph of a preset object and position information of the preset object on each time node;
acquiring meteorological data information of an industrial aggregation area on a preset time node, and importing the meteorological data information on the preset time node into the object dynamic model diagram to obtain an object-meteorological dynamic model diagram of the industrial aggregation area in preset time;
determining the position information of each suspicious pollution source in the object-weather dynamic model diagram, and carrying out weather numerical simulation analysis on each suspicious pollution source in the object-weather dynamic model diagram based on a turbulence theory, so as to obtain simulation propagation characteristics corresponding to each suspicious pollution source;
Calculating hash values between the propagation characteristics of the polluted gas and the simulated propagation characteristics corresponding to each suspicious pollution source through a hash algorithm to obtain a plurality of hash values;
constructing a sequence table, importing a plurality of hash values into the sequence table for size sorting, extracting a maximum hash value from the sequence table after sorting is completed, and calibrating a suspicious pollution source corresponding to the maximum hash value as a final pollution source.
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| CN117171223A (en) * | 2023-11-02 | 2023-12-05 | 北京建工环境修复股份有限公司 | Microorganism culture scheme recommendation method and system in microorganism repair process |
| CN117171223B (en) * | 2023-11-02 | 2024-02-06 | 北京建工环境修复股份有限公司 | Microorganism culture scheme recommendation method and system in microorganism repair process |
| CN117610438A (en) * | 2024-01-24 | 2024-02-27 | 广东智环创新环境科技有限公司 | Volatile organic pollutant diffusion simulation and tracing method and system |
| CN117610438B (en) * | 2024-01-24 | 2024-05-28 | 广东智环创新环境科技有限公司 | Volatile organic pollutant diffusion simulation and tracing method and system |
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