CN116818685B - Environment monitoring method and system based on big data - Google Patents
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Abstract
The application discloses an environment monitoring method and system based on big data, comprising the steps of obtaining newly-added user sharing data in preset time of each social sharing platform through a data tracking model, and distributing the user sharing data to an observation object library and a tracking object library according to the association degree with a target area; continuously updating user sharing data in the observation object library and the tracking object library, and calculating a feature matrix of the user sharing data in the tracking object library; obtaining target data according to the feature matrix, and making a monitoring path of a region corresponding to the target data; and acquiring and analyzing images through the environment monitoring unmanned aerial vehicle to obtain environment pollution state monitoring data of the position corresponding to the target data. The application can effectively find newly added discretely distributed environmental pollution points in the target area, and improves the accuracy and efficiency of environmental monitoring.
Description
Technical Field
The application relates to the technical field of environmental pollution monitoring, in particular to an environmental monitoring method and system based on big data.
Background
In the prior art, environmental pollution is mainly collected and monitored by adopting a satellite remote sensing technology. The satellite remote sensing technology is mainly used for comparing and analyzing electromagnetic wave spectrums reflected by the earth, so as to calculate the conditions of land topography, vegetation, environmental pollution and the like. The satellite remote sensing technology has the advantages of wide coverage, multiple monitoring objects and the like, but the satellite remote sensing technology also has the defects of long remote sensing revisit period, untimely emergency, long information feedback time and the like. And satellite remote sensing technology is mainly applied to environmental monitoring in a selected area, but is not applicable to small-range randomly distributed environmental pollution point monitoring.
With the recent favor of people to outdoor play such as camping and hiking, the accumulation of pollutants such as household garbage generated by play is not easy to degrade, and irreversible environmental damage is easy to cause. And the distribution positions of the newly added pollution points are random, and are not easy to identify and monitor by the existing satellite remote sensing technology. Therefore, a technology capable of timely and effectively monitoring the newly increased pollution points of the environment is needed.
Disclosure of Invention
In view of the above problems, the application provides an environmental monitoring method and system based on big data, which are used for solving the technical problem that the satellite remote sensing technology cannot timely and effectively monitor newly increased environmental pollution points.
In order to achieve the above object, the present inventors provide an environmental monitoring method based on big data, comprising the steps of:
acquiring newly added user sharing data in preset time of each social sharing platform through a data tracking model to obtain a newly added database; analyzing position information in the user sharing data in a newly-added database, calculating the association degree of the user sharing data and a target area of environment monitoring according to the position information, screening out the user sharing data which are obviously inconsistent with the target area, adding the user sharing data with the association degree larger than a first preset value into a tracking object library, and adding the user sharing data with the association degree smaller than the first preset value and larger than a second preset value into an observation object library;
Classifying data according to the types of the user sharing data corresponding to the social sharing platforms, presetting a corresponding feature matrix model for each data classification, and carrying out feature analysis on the user sharing data of different classifications by adopting the corresponding feature matrix model to obtain a corresponding feature matrix; the feature matrix includes: sharing time, a first position feature obtained according to positioning information, a plurality of second position features obtained according to image and character analysis, and a heat feature obtained according to forwarding, collection and praise calculation;
acquiring the latest data of the social sharing platform, and screening out first updated data related to the tracking object library and second updated data related to the observation object library in the latest data; performing feature decomposition on the first update data, and updating the feature matrix of the corresponding user sharing data by using each feature obtained after feature decomposition;
identifying information related to the position in the second updating data, updating the association degree of the user sharing data and the target area according to the identified information related to the position, transferring the user sharing data with the association degree larger than the first preset value to the tracking object library, and updating the feature matrix of the user sharing data according to each feature in the second updating data;
Performing similarity analysis on feature matrixes of the sharing data of all users in the tracking object library, merging the user sharing data with the similarity larger than a preset value, and fusing the corresponding feature matrixes to obtain fused data and corresponding fused feature matrixes; then extracting user sharing data and fusion data with the heat characteristics larger than a preset heat value in the tracking object library to obtain target data;
the first position features in the target data are determined to be monitoring positioning points, and the plurality of second position features in the target data are determined to be monitoring inspection points; calculating and generating a monitoring main path according to the monitoring locating points corresponding to the target data, calculating a monitoring branch path taking the monitoring locating points as the center according to the geographic position relation between the monitoring locating points and a plurality of monitoring inspection points, and formulating a monitoring path of a region corresponding to the target data according to the monitoring main path and the monitoring branch path;
importing the images corresponding to the second position features in the monitoring path and the target data into an environment monitoring unmanned aerial vehicle, and navigating and collecting environment monitoring images according to the monitoring path by the environment monitoring unmanned aerial vehicle; identifying spectrum information in the environment monitoring image, and extracting texture features of the spectrum information to obtain feature data; and comparing the characteristic data with the characteristic data of the historical image before the sharing time to obtain the environmental pollution state monitoring data of the position corresponding to the target data.
In order to solve the technical problems, the application also provides another technical scheme:
an environmental monitoring system based on big data, comprising:
the data collection module is used for obtaining newly added user sharing data in preset time of each social sharing platform through the data tracking model to obtain a newly added database; analyzing position information in the user sharing data in a newly-added database, calculating the association degree of the user sharing data and a target area of environment monitoring according to the position information, screening out the user sharing data which are obviously inconsistent with the target area, adding the user sharing data with the association degree larger than a first preset value into a tracking object library, and adding the user sharing data with the association degree smaller than the first preset value and larger than a second preset value into an observation object library;
the data analysis module is used for classifying data according to the types of the user sharing data corresponding to the social sharing platforms, presetting a corresponding feature matrix model for each data classification, and carrying out feature analysis on the user sharing data of different classifications by adopting the corresponding feature matrix model to obtain a corresponding feature matrix; the feature matrix includes: sharing time, a first position feature obtained according to positioning information, a plurality of second position features obtained according to image and character analysis, and a heat feature obtained according to forwarding, collection and praise calculation; acquiring the latest data of the social sharing platform, and screening out first updated data related to the tracking object library and second updated data related to the observation object library in the latest data; performing feature decomposition on the first update data, and updating the feature matrix of the corresponding user sharing data by using each feature obtained after feature decomposition; identifying information related to the position in the second updating data, updating the association degree of the user sharing data and the target area according to the identified information related to the position, transferring the user sharing data with the association degree larger than the first preset value to the tracking object library, and updating the feature matrix of the user sharing data according to each feature in the second updating data; performing similarity analysis on feature matrixes of the sharing data of all users in the tracking object library, merging the user sharing data with the similarity larger than a preset value, and fusing the corresponding feature matrixes to obtain fused data and corresponding fused feature matrixes; then extracting user sharing data and fusion data with the heat characteristics larger than a preset heat value in the tracking object library to obtain target data;
The monitoring path generation module is used for determining the first position features in the target data as monitoring positioning points and determining the plurality of second position features in the target data as monitoring patrol points; calculating and generating a monitoring main path according to the monitoring locating points corresponding to the target data, calculating a monitoring branch path taking the monitoring locating points as the center according to the geographic position relation between the monitoring locating points and a plurality of monitoring inspection points, and formulating a monitoring path of a region corresponding to the target data according to the monitoring main path and the monitoring branch path;
the monitoring module is used for importing the images corresponding to the second position features in the monitoring path and the target data into an environment monitoring unmanned aerial vehicle, and the environment monitoring unmanned aerial vehicle performs navigation according to the monitoring path and acquires environment monitoring images; identifying spectrum information in the environment monitoring image, and extracting texture features of the spectrum information to obtain feature data; and comparing the characteristic data with the characteristic data of the historical image before the sharing time to obtain the environmental pollution state monitoring data of the position corresponding to the target data.
Compared with the prior art, the environmental monitoring method based on big data in the technical scheme can obtain information related to the target area of environmental monitoring by counting and analyzing the user sharing data of each social sharing platform, obtain a feature matrix of the user sharing data through counting and analyzing, and obtain the position features and the heat features of the user sharing data according to the feature matrix. The specific location of the environmental monitoring can be initially determined based on the heat signature and the location information. And dynamically updating the user sharing data in the observed object library and the tracked object library according to the latest data of the continuously analyzed social sharing platform so as to discover the specific position of the new environment monitoring. According to the technical scheme, the monitoring path of the environment monitoring unmanned aerial vehicle can be made according to the position features in the target data, the collected outdoor activity track diagram is combined with the monitoring path to obtain a more reasonable real-time navigation path, pollutants near the specific position can be found as far as possible, and accidents of the environment monitoring unmanned aerial vehicle during monitoring of multiple obstacle road sections such as forests can be avoided.
The foregoing summary is merely an overview of the present application, and may be implemented according to the text and the accompanying drawings in order to make it clear to a person skilled in the art that the present application may be implemented, and in order to make the above-mentioned objects and other objects, features and advantages of the present application more easily understood, the following description will be given with reference to the specific embodiments and the accompanying drawings of the present application.
Drawings
The drawings are only for purposes of illustrating the principles, implementations, applications, features, and effects of the present application and are not to be construed as limiting the application.
In the drawings of the specification:
FIG. 1 is a flow chart of a big data based environmental monitoring method according to an embodiment;
FIG. 2 is a flow chart of keyword reliability calculation according to an embodiment;
FIG. 3 is a flow chart of the environment monitoring unmanned aerial vehicle navigation according to an embodiment;
FIG. 4 is a flowchart of a process for capturing images of an environmental monitoring unmanned aerial vehicle according to an embodiment;
FIG. 5 is a schematic diagram of an environmental monitoring device based on big data according to an embodiment;
reference numerals referred to in the above drawings are explained as follows:
10. a data collection module; 20. a data analysis module; 30. a monitoring path generation module; 40. a monitoring module;
500. an environmental monitoring system based on big data;
Detailed Description
In order to describe the possible application scenarios, technical principles, practical embodiments, and the like of the present application in detail, the following description is made with reference to the specific embodiments and the accompanying drawings. The embodiments described herein are only for more clearly illustrating the technical aspects of the present application, and thus are only exemplary and not intended to limit the scope of the present application.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of the phrase "in various places in the specification are not necessarily all referring to the same embodiment, nor are they particularly limited to independence or relevance from other embodiments. In principle, in the present application, as long as there is no technical contradiction or conflict, the technical features mentioned in each embodiment may be combined in any manner to form a corresponding implementable technical solution.
Unless defined otherwise, technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application pertains; the use of related terms herein is for the purpose of describing particular embodiments only and is not intended to limit the application.
In the description of the present application, the term "and/or" is a representation for describing a logical relationship between objects, which means that three relationships may exist, for example a and/or B, representing: there are three cases, a, B, and both a and B. In addition, the character "/" herein generally indicates that the front-to-back associated object is an "or" logical relationship.
In the present application, terms such as "first" and "second" are used merely to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual number, order, or sequence of such entities or operations.
Without further limitation, the use of the terms "comprising," "including," "having," or other like open-ended terms in this application are intended to cover a non-exclusive inclusion, such that a process, method, or article of manufacture that comprises a list of elements does not include additional elements in the process, method, or article of manufacture, but may include other elements not expressly listed or inherent to such process, method, or article of manufacture.
As in the understanding of "review guidelines," the expressions "greater than", "less than", "exceeding" and the like are understood to exclude this number in the present application; the expressions "above", "below", "within" and the like are understood to include this number. Furthermore, in the description of embodiments of the present application, the meaning of "a plurality of" is two or more (including two), and similarly, the expression "a plurality of" is also to be understood as such, for example, "a plurality of" and the like, unless specifically defined otherwise.
In the description of embodiments of the present application, spatially relative terms such as "center," "longitudinal," "transverse," "length," "width," "thickness," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "vertical," "top," "bottom," "inner," "outer," "clockwise," "counter-clockwise," "axial," "radial," "circumferential," etc., are used herein as a basis for the description of the embodiments or as a basis for the description of the embodiments, and are not intended to indicate or imply that the devices or components referred to must have a particular position, a particular orientation, or be configured or operated in a particular orientation and therefore should not be construed as limiting the embodiments of the present application.
Unless specifically stated or limited otherwise, the terms "mounted," "connected," "affixed," "disposed," and the like as used in the description of embodiments of the application should be construed broadly. For example, the "connection" may be a fixed connection, a detachable connection, or an integral arrangement; the device can be mechanically connected, electrically connected and communicated; it can be directly connected or indirectly connected through an intermediate medium; which may be a communication between two elements or an interaction between two elements. The specific meaning of the above terms in the embodiments of the present application can be understood by those skilled in the art to which the present application pertains according to circumstances.
Referring to fig. 1 to 5, the present embodiment provides an environmental monitoring method and system based on big data. The environmental monitoring method and the system for big data can be used for analyzing and obtaining the possible newly increased environmental pollution points in the target area of environmental monitoring based on the user sharing data shared by a plurality of social sharing platforms such as WeChat video signals, weChat public signals, weChat books, fast hands and the like, generating a more detailed monitoring path based on the user sharing data, monitoring along the monitoring path by the environmental monitoring unmanned aerial vehicle, comprehensively covering the area which is possibly polluted near the newly increased environmental pollution points, effectively avoiding the obstacles on the monitoring path, and avoiding the accidents such as collision of the environmental monitoring unmanned aerial vehicle.
As shown in fig. 1, the environmental monitoring method based on big data in this embodiment includes the following steps:
s101, obtaining newly-added user sharing data in a preset time of each social sharing platform, wherein the method specifically comprises the following steps: acquiring newly added user sharing data in preset time of each social sharing platform through a data tracking model to obtain a newly added database; analyzing position information in the user sharing data in the newly-added database, calculating the association degree of the user sharing data and a target area of environment monitoring according to the position information, screening out the user sharing data which are obviously inconsistent with the target area, adding the user sharing data with the association degree larger than a first preset value into a tracking object library, and adding the user sharing data with the association degree smaller than the first preset value and larger than a second preset value into an observation object library. The social sharing platform comprises a WeChat video signal, a reddish book, a beep knotweed, a tremble sound and a fast hand. The data tracking model can acquire information such as sharing time (namely release time) and position of sharing data of users, and taking a reddish book as an example, the data tracking model can acquire the sharing time of sharing data of each user, and can acquire positioning information, images such as photos and videos, text descriptions, comments in comment areas and the like in an electronic map. The data tracking model can acquire comment information of text description and comment areas, can also capture the shared video, and then acquire information in an image through image recognition, or split the video into a plurality of image frames, and then recognize all or part of the image frames to acquire the information in the image. When the association degree is calculated, the position information obtained in the user sharing information can be input into a geographic information base for searching, or the image in the user sharing information is compared with the image in the target area, so that the association degree is calculated.
S102, analyzing a feature matrix of user sharing data in a tracking object library, wherein the feature matrix specifically comprises the following steps: classifying data according to the types of the user sharing data corresponding to the social sharing platforms, presetting a corresponding feature matrix model for each data classification, and carrying out feature analysis on the user sharing data of different classifications by adopting the corresponding feature matrix model to obtain a corresponding feature matrix; the feature matrix includes: sharing time, a first position feature obtained according to positioning information, a plurality of second position features obtained according to image and character analysis, and a heat feature obtained according to forwarding, collection and praise calculation. The feature matrix comprises a plurality of rows and each row comprises a plurality of columns, wherein each row corresponds to one main feature, and each column corresponds to a sub-feature under the main feature, so that the feature of sharing data of a user can be comprehensively represented through the feature matrix. For example, in one embodiment, the feature matrix includes three main features of location, text description and heat, and the main features of location include more than two sub-features of electronic positioning information, text description location information, etc., the text description includes text description of the content posted by the information publisher and comment information of other users in the comment area, and multiple sub-features of reply information of the information publisher in the comment area, etc.; the main feature of the heat degree comprises a plurality of sub-features of collection, forwarding, praise and the like. The data classification of the user sharing data type corresponding to the social sharing platform mainly comprises user sharing information of plain text, user sharing information of image combined words, user sharing information of video combined words and the like. Because each classified information has different characteristics, a corresponding feature matrix is set to analyze different types of user analysis information.
S103, updating user sharing data in a tracking object library and an observation object library, wherein the method specifically comprises the following steps: acquiring the latest data of the social sharing platform, and screening out first updated data related to the tracking object library and second updated data related to the observation object library in the latest data; performing feature decomposition on the first update data, and updating the feature matrix of the corresponding user sharing data by using each feature obtained after feature decomposition; and identifying information related to the position in the second updating data, updating the association degree of the user sharing data and the target area according to the identified information related to the position, transferring the user sharing data with the association degree larger than the first preset value to the tracking object library, and updating the feature matrix of the user sharing data according to each feature in the second updating data.
S104, screening out target data, which specifically comprises the following steps: performing similarity analysis on feature matrixes of the sharing data of all users in the tracking object library, merging the user sharing data with the similarity larger than a preset value, and fusing the corresponding feature matrixes to obtain fused data and corresponding fused feature matrixes; and then extracting the user sharing data and the fusion data of which the heat characteristics are larger than a preset heat value in the tracking object library to obtain target data. The feature matrix is subjected to similarity analysis, wherein the similarity analysis is mainly obtained by superposing the similarity of all the features, and the similarity is especially mainly based on position information. If the two users share information to point to the same place (for example, scenic image of the same place), the similarity of the two users share information is considered as a percentage, if the users share data without specific positioning information or other position information, the similarity can be calculated by analyzing the similarity of objects contained in the image or the presence of the same place in the text. When the arrays are fused, corresponding main features and sub-features in the feature matrices can be mutually overlapped. Through user sharing data merging and feature matrix merging, the calculation amount of subsequent data processing can be reduced, and target data can be more prominent and easier to identify.
S105, calculating a monitoring path, which specifically comprises the following steps: the first position features in the target data are determined to be monitoring positioning points, and the plurality of second position features in the target data are determined to be monitoring inspection points; and calculating a monitoring branch path taking the monitoring locating point as a center according to the geographic position relation between the monitoring locating point and a plurality of monitoring inspection points, and formulating a monitoring path of a region corresponding to the target data according to the monitoring main path and the monitoring branch path.
S106, calculating environmental pollution state monitoring data, wherein the method specifically comprises the following steps of: and importing the images corresponding to the second position features in the monitoring path and the target data into an environment monitoring unmanned aerial vehicle, and navigating and collecting environment monitoring images by the environment monitoring unmanned aerial vehicle according to the monitoring path.
According to the environmental monitoring method based on big data, the user sharing data of each social sharing platform is counted and analyzed, information related to a target area of environmental monitoring can be obtained, the feature matrix of the user sharing data is obtained through counting and analysis, and the position features and the heat features of the user sharing data are obtained according to the feature matrix. The specific location of the environmental monitoring can be initially determined based on the heat signature and the location information. And dynamically updating the user sharing data in the observed object library and the tracked object library according to the latest data of the continuously analyzed social sharing platform so as to discover the specific position of the new environment monitoring.
In an embodiment, the method further comprises the steps of: identifying spectrum information in the environment monitoring image, and extracting texture features of the spectrum information to obtain feature data; and comparing the characteristic data with the characteristic data of the historical image before the sharing time to obtain the environmental pollution state monitoring data of the position corresponding to the target data. And extracting various marks in the characteristic data and various marks in the characteristic data of the historical image, respectively calculating the contrast of the marks by using a contrast algorithm, and counting the contrast to obtain corresponding pollution indexes, wherein the pollution indexes are positively related to the contrast.
The contrast of various marks in the characteristic data and the characteristic data of the historical image is calculated, so that the change of the environmental characteristic in the area within a period of time can be obtained more clearly.
As shown in fig. 4, in an embodiment, the extracting the texture feature of the spectrum information to obtain feature data includes: s401, carrying out wavelength analysis and noise reduction treatment on the spectrum information to obtain spectrum wavelength; s402, calculating characteristic coefficients of the spectrum wavelengths; s403, carrying out normalization processing on the spectrum wavelength according to the characteristic coefficient to obtain characteristic data of the spectrum information.
The embodiment can be applied to the case where the pollutant distribution area is large and the environmental monitoring unmanned aerial vehicle is high, and in this case, the kind of pollutant, such as heavy metal pollution or plastic pollution, can be confirmed through the characteristic data in the spectrum information obtained in the embodiment.
In other embodiments, the captured environmental monitoring images may be identified by an artificial intelligence model to identify whether a particular contaminant is included therein. The household garbage such as plastic packaging bags, packaging boxes and the like can be identified through an artificial intelligent model. In the embodiment, the possible newly-increased pollution points in the target area are obtained by analyzing the user sharing data of the social sharing platform, so that the main pollutants are household garbage of people and the like, and the pollutants in the position area can be effectively identified through the artificial intelligent model.
In an embodiment, the identifying the information related to the location in the second update data, and updating the association degree between the user sharing data and the target area according to the identified information related to the location includes:
presetting a name library of each geographical position name in the target area of the environmental monitoring; extracting text in the second updated data, performing word segmentation on the text to obtain a word segmentation text, and comparing the word segmentation text with each geographic position name in the name library to obtain keywords which are the same as or similar to the geographic position names; carrying out semantic analysis on the context related content of the keywords, obtaining the reliability score of the keywords according to the semantic analysis result, and updating the keywords with the reliability score exceeding a preset value into corresponding position information of the user sharing data; and calculating the association degree between the user sharing data and the target area by adopting the updated position information.
As shown in fig. 2, the performing semantic analysis on the context-related content of the keyword, and obtaining the reliability score of the keyword according to the semantic analysis result includes:
and S201, screening out the context content related to the keywords, wherein the context content comprises the same or similar text input by other users in the comment area, and the reply text of the text where the keywords are located is cited or replied by the other users in the comment area.
S202, counting the repeated occurrence times of the same or similar texts of the keywords, the times of the texts where other users refer to the keywords, and the times of the texts where the keywords are approved by other users; and setting different weights for the statistics times respectively.
And S203, carrying out weighted summation on the times to calculate the reliability of the keyword, wherein the reliability is positively correlated with the weighted summation value of each time.
In this embodiment, by analyzing the context of the keyword in the second update data, the reliability of the keyword (for example, the place name) can be obtained, so that the user sharing data associated with the target area can be more accurately identified from the observation object library. For example, in the sharing data of a certain user, the location information is not specifically described in the sharing content, but in the comment area, other users speak a place (i.e. a keyword), and in this embodiment, the context content related to the place may be analyzed to obtain whether the place is identified by the information publisher or by a plurality of other users, if so, the place name in the comment area may be used as the location feature of the sharing data of the user, and the relevance with the target area may be further calculated.
As shown in fig. 3, in an embodiment, the navigation of the environment monitoring unmanned aerial vehicle according to the monitoring path and the acquisition of the environment monitoring image include:
s301, establishing a track library, wherein outdoor activity track diagrams related to the target area are collected in the track library. Outdoor activity track diagrams can be obtained through APP such as "two-step outdoor assistant".
S302, calculating a branch navigation path, which specifically comprises the following steps: acquiring an outdoor activity track graph related to each monitoring positioning point in the monitoring path from the track library, calculating the coincidence ratio of the monitoring branch path and the outdoor activity track, and cutting out the part of the outdoor activity track graph, the coincidence ratio of which is larger than a preset value, so as to obtain a branch navigation path; and importing the branch navigation path to the environment monitoring unmanned aerial vehicle.
S303, navigation and image acquisition, specifically comprising: and the environment monitoring unmanned aerial vehicle positions according to each monitoring positioning point in the monitoring path and navigates by the monitoring main path, and the branch navigation path is adopted for navigation and image acquisition after the monitoring positioning point is reached.
S304, monitoring coverage calculation, which specifically comprises: judging whether the environment monitoring unmanned aerial vehicle covers the monitoring patrol point or not by comparing whether the image adopted on the branch navigation path is consistent with the image corresponding to the monitoring patrol point or not; the image corresponding to the monitoring inspection point is the image corresponding to the second position feature. In addition, in the embodiment, the monitoring path of the environment monitoring unmanned aerial vehicle can be made according to the position features in the target data, the collected outdoor activity track diagram is combined with the monitoring path to obtain a more reasonable real-time navigation path, so that pollutants near specific positions are found as much as possible, the outdoor activity track is relatively good in passing, the number of obstacles is fewer, the environment monitoring unmanned aerial vehicle is more suitable for low-altitude flight, and accidents such as collision of the environment monitoring unmanned aerial vehicle during monitoring of multiple obstacle road sections such as forests can be avoided.
In an embodiment, the environmental monitoring unmanned aerial vehicle transmits the acquired environmental monitoring image to a background in real time, and the environmental pollution state monitoring data is obtained by the background calculation; when the environmental pollution state exceeds a preset index, the background generates a positioning instruction and sends the positioning instruction to the environmental monitoring unmanned aerial vehicle; the environment monitoring unmanned aerial vehicle responds to the positioning instruction, and the environment monitoring unmanned aerial vehicle is accurately positioned and the accurate positioning information is returned to the background; the background marks the accurate positioning information as a pollution treatment point. The positioning is performed through more positioning satellites during accurate positioning, longitude and latitude information and height information can be positioned, so that more accurate three-dimensional positioning is realized, and a pollution treatment point distribution map can be directly generated according to the accurate positioning information after the positioning.
As shown in fig. 5, in one embodiment, a big data based environmental monitoring system 500 is provided, the big data based environmental monitoring system 500 comprising:
the data collection module 10 is configured to obtain newly added user sharing data in a preset time of each social sharing platform through a data tracking model, so as to obtain a newly added database; analyzing position information in the user sharing data in a newly-added database, calculating the association degree of the user sharing data and a target area of environment monitoring according to the position information, screening out the user sharing data which are obviously inconsistent with the target area, adding the user sharing data with the association degree larger than a first preset value into a tracking object library, and adding the user sharing data with the association degree smaller than the first preset value and larger than a second preset value into an observation object library;
The data analysis module 20 is configured to perform data classification according to types of user sharing data corresponding to each social sharing platform, preset a corresponding feature matrix model for each data classification, and perform feature analysis on user sharing data of different classifications by adopting the corresponding feature matrix model to obtain a corresponding feature matrix; the feature matrix includes: sharing time, a first position feature obtained according to positioning information, a plurality of second position features obtained according to image and character analysis, and a heat feature obtained according to forwarding, collection and praise calculation; acquiring the latest data of the social sharing platform, and screening out first updated data related to the tracking object library and second updated data related to the observation object library in the latest data; performing feature decomposition on the first update data, and updating the feature matrix of the corresponding user sharing data by using each feature obtained after feature decomposition; identifying information related to the position in the second updating data, updating the association degree of the user sharing data and the target area according to the identified information related to the position, transferring the user sharing data with the association degree larger than the first preset value to the tracking object library, and updating the feature matrix of the user sharing data according to each feature in the second updating data; performing similarity analysis on feature matrixes of the sharing data of all users in the tracking object library, merging the user sharing data with the similarity larger than a preset value, and fusing the corresponding feature matrixes to obtain fused data and corresponding fused feature matrixes; then extracting user sharing data and fusion data with the heat characteristics larger than a preset heat value in the tracking object library to obtain target data;
A monitoring path generating module 30, configured to determine the first location feature in the target data as a monitoring location point, and determine the plurality of second location features in the target data as monitoring inspection points; calculating and generating a monitoring main path according to the monitoring locating points corresponding to the target data, calculating a monitoring branch path taking the monitoring locating points as the center according to the geographic position relation between the monitoring locating points and a plurality of monitoring inspection points, and formulating a monitoring path of a region corresponding to the target data according to the monitoring main path and the monitoring branch path;
the monitoring module 40 is configured to import the image corresponding to the second position feature in the monitoring path and the target data into an environmental monitoring unmanned aerial vehicle, and the environmental monitoring unmanned aerial vehicle navigates according to the monitoring path and acquires an environmental monitoring image.
The monitoring module 40 is further configured to identify spectral information in the environmental monitoring image, and extract texture features of the spectral information to obtain feature data; and comparing the characteristic data with the characteristic data of the historical image before the sharing time to obtain the environmental pollution state monitoring data of the position corresponding to the target data.
In one embodiment, the data analysis module 20 performs the identifying the information related to the location in the second update data, and updating the association degree of the user sharing data with the target area according to the identified information related to the location includes:
presetting a name library of each geographical position name in the target area of the environmental monitoring; extracting text in the second updated data, performing word segmentation on the text to obtain a word segmentation text, and comparing the word segmentation text with each geographic position name in the name library to obtain keywords which are the same as or similar to the geographic position names; carrying out semantic analysis on the context related content of the keywords, obtaining the reliability score of the keywords according to the semantic analysis result, and updating the keywords with the reliability score exceeding a preset value into corresponding position information of the user sharing data; and calculating the association degree between the user sharing data and the target area by adopting the updated position information.
Finally, it should be noted that, although the embodiments have been described in the text and the drawings, the scope of the application is not limited thereby. The technical scheme generated by replacing or modifying the equivalent structure or equivalent flow by utilizing the content recorded in the text and the drawings of the specification based on the essential idea of the application, and the technical scheme of the embodiment directly or indirectly implemented in other related technical fields are included in the patent protection scope of the application.
Claims (9)
1. The environment monitoring method based on big data is characterized by comprising the following steps:
acquiring newly added user sharing data in preset time of each social sharing platform through a data tracking model to obtain a newly added database; analyzing position information in the user sharing data in a newly-added database, calculating the association degree of the user sharing data and a target area of environment monitoring according to the position information, screening out the user sharing data which are obviously inconsistent with the target area, adding the user sharing data with the association degree larger than a first preset value into a tracking object library, and adding the user sharing data with the association degree smaller than the first preset value and larger than a second preset value into an observation object library;
classifying data according to the types of the user sharing data corresponding to the social sharing platforms, presetting a corresponding feature matrix model for each data classification, and carrying out feature analysis on the user sharing data of different classifications by adopting the corresponding feature matrix model to obtain a corresponding feature matrix; the feature matrix includes: sharing time, a first position feature obtained according to positioning information, a plurality of second position features obtained according to image and character analysis, and a heat feature obtained according to forwarding, collection and praise calculation;
Acquiring the latest data of the social sharing platform, and screening out first updated data related to the tracking object library and second updated data related to the observation object library in the latest data; performing feature decomposition on the first update data, and updating the feature matrix of the corresponding user sharing data by using each feature obtained after feature decomposition; identifying information related to the position in the second updating data, updating the association degree of the user sharing data and the target area according to the identified information related to the position, transferring the user sharing data with the association degree larger than the first preset value to the tracking object library, and updating the feature matrix of the user sharing data according to each feature in the second updating data;
performing similarity analysis on feature matrixes of the sharing data of all users in the tracking object library, merging the user sharing data with the similarity larger than a preset value, and fusing the corresponding feature matrixes to obtain fused data and corresponding fused feature matrixes; then extracting user sharing data and fusion data with the heat characteristics larger than a preset heat value in the tracking object library to obtain target data;
The first position features in the target data are determined to be monitoring positioning points, and the plurality of second position features in the target data are determined to be monitoring inspection points; calculating and generating a monitoring main path according to the monitoring locating points corresponding to the target data, calculating a monitoring branch path taking the monitoring locating points as the center according to the geographic position relation between the monitoring locating points and a plurality of monitoring inspection points, and formulating a monitoring path of a region corresponding to the target data according to the monitoring main path and the monitoring branch path;
and importing the images corresponding to the second position features in the monitoring path and the target data into an environment monitoring unmanned aerial vehicle, and navigating and collecting environment monitoring images by the environment monitoring unmanned aerial vehicle according to the monitoring path.
2. The big data based environmental monitoring method of claim 1, wherein the steps of identifying information related to a location in the second update data, and updating the association degree of the user sharing data with the target area according to the identified information related to the location include:
presetting a name library of each geographical position name in the target area of the environmental monitoring; extracting text in the second updated data, performing word segmentation on the text to obtain a word segmentation text, and comparing the word segmentation text with each geographic position name in the name library to obtain keywords which are the same as or similar to the geographic position names; carrying out semantic analysis on the context related content of the keywords, obtaining the reliability score of the keywords according to the semantic analysis result, and updating the keywords with the reliability score exceeding a preset value into corresponding position information of the user sharing data; and calculating the association degree between the user sharing data and the target area by adopting the updated position information.
3. The big data based environmental monitoring method of claim 2, wherein said performing semantic analysis on the context-related content of the keyword and obtaining a reliability score of the keyword according to the semantic analysis result comprises:
the context content related to the keywords is screened out, wherein the context content comprises the same or similar texts which are input by other users in the comment area and appear in the context, and reply texts of the texts in which the keywords are cited or replied by the other users in the comment area;
counting the repeated occurrence times of the same or similar texts of the keywords, the times of the texts of the keywords which are referenced by other users, and the times of the texts of the keywords which are approved by other users; and setting different weights for the statistics times respectively, and carrying out weighted summation on the times to calculate the reliability of the keyword.
4. The big data based environmental monitoring method of claim 1, further comprising the steps of: identifying spectrum information in the environment monitoring image, and extracting texture features of the spectrum information to obtain feature data;
and extracting various marks in the characteristic data and various marks in the characteristic data of the historical image, respectively calculating the contrast of the marks by using a contrast algorithm, and counting the contrast to obtain corresponding pollution indexes, wherein the pollution indexes are positively correlated with the contrast.
5. The big data based environmental monitoring method of claim 1, wherein the environmental monitoring drone navigating according to the monitored path and acquiring environmental monitoring images comprises:
establishing a track library, wherein outdoor activity track diagrams related to the target area are collected in the track library;
acquiring an outdoor activity track graph related to each monitoring positioning point in the monitoring path from the track library, calculating the coincidence ratio of the monitoring branch path and the outdoor activity track, and cutting out the part of the outdoor activity track graph, the coincidence ratio of which is larger than a preset value, so as to obtain a branch navigation path; importing the branch navigation path to the environment monitoring unmanned aerial vehicle;
the environment monitoring unmanned aerial vehicle carries out positioning according to each monitoring positioning point in the monitoring path and carries out navigation according to the monitoring main path, the branch navigation path is adopted for navigation and image acquisition after the monitoring positioning points are reached, and whether the environment monitoring unmanned aerial vehicle covers the monitoring inspection point or not is judged by comparing whether the images adopted on the branch navigation path are consistent with the images corresponding to the monitoring inspection point or not; the image corresponding to the monitoring inspection point is the image corresponding to the second position feature.
6. The environmental monitoring method based on big data according to claim 1, wherein the environmental monitoring unmanned aerial vehicle transmits the collected environmental monitoring image to a background in real time, and environmental pollution state monitoring data is obtained by calculation of the background; when the environmental pollution state exceeds a preset index, the background generates a positioning instruction and sends the positioning instruction to the environmental monitoring unmanned aerial vehicle; the environment monitoring unmanned aerial vehicle responds to the positioning instruction, and the environment monitoring unmanned aerial vehicle is accurately positioned and the accurate positioning information is returned to the background; the background marks the accurate positioning information as a pollution treatment point.
7. The big data based environmental monitoring method of claim 1, wherein the social sharing platform comprises a WeChat video number, a redbook, a beep, a tremble, and a fast hand.
8. An environmental monitoring system based on big data, comprising:
the data collection module is used for obtaining newly added user sharing data in preset time of each social sharing platform through the data tracking model to obtain a newly added database; analyzing position information in the user sharing data in a newly-added database, calculating the association degree of the user sharing data and a target area of environment monitoring according to the position information, screening out the user sharing data which are obviously inconsistent with the target area, adding the user sharing data with the association degree larger than a first preset value into a tracking object library, and adding the user sharing data with the association degree smaller than the first preset value and larger than a second preset value into an observation object library;
The data analysis module is used for classifying data according to the types of the user sharing data corresponding to the social sharing platforms, presetting a corresponding feature matrix model for each data classification, and carrying out feature analysis on the user sharing data of different classifications by adopting the corresponding feature matrix model to obtain a corresponding feature matrix; the feature matrix includes: sharing time, a first position feature obtained according to positioning information, a plurality of second position features obtained according to image and character analysis, and a heat feature obtained according to forwarding, collection and praise calculation; acquiring the latest data of the social sharing platform, and screening out first updated data related to the tracking object library and second updated data related to the observation object library in the latest data; performing feature decomposition on the first update data, and updating the feature matrix of the corresponding user sharing data by using each feature obtained after feature decomposition; identifying information related to the position in the second updating data, updating the association degree of the user sharing data and the target area according to the identified information related to the position, transferring the user sharing data with the association degree larger than the first preset value to the tracking object library, and updating the feature matrix of the user sharing data according to each feature in the second updating data; performing similarity analysis on feature matrixes of the sharing data of all users in the tracking object library, merging the user sharing data with the similarity larger than a preset value, and fusing the corresponding feature matrixes to obtain fused data and corresponding fused feature matrixes; then extracting user sharing data and fusion data with the heat characteristics larger than a preset heat value in the tracking object library to obtain target data;
The monitoring path generation module is used for determining the first position features in the target data as monitoring positioning points and determining the plurality of second position features in the target data as monitoring patrol points; calculating and generating a monitoring main path according to the monitoring locating points corresponding to the target data, calculating a monitoring branch path taking the monitoring locating points as the center according to the geographic position relation between the monitoring locating points and a plurality of monitoring inspection points, and formulating a monitoring path of a region corresponding to the target data according to the monitoring main path and the monitoring branch path;
the monitoring module is used for guiding the monitoring path and the image corresponding to the second position feature in the target data into the environment monitoring unmanned aerial vehicle, and the environment monitoring unmanned aerial vehicle carries out navigation and acquires environment monitoring images according to the monitoring path.
9. The big data based environmental monitoring system of claim 8, wherein,
the data analysis module executing the identifying the information related to the position in the second updating data, and updating the association degree of the user sharing data and the target area according to the identified information related to the position includes:
Presetting a name library of each geographical position name in the target area of the environmental monitoring; extracting text in the second updated data, performing word segmentation on the text to obtain a word segmentation text, and comparing the word segmentation text with each geographic position name in the name library to obtain keywords which are the same as or similar to the geographic position names; carrying out semantic analysis on the context related content of the keywords, obtaining the reliability score of the keywords according to the semantic analysis result, and updating the keywords with the reliability score exceeding a preset value into corresponding position information of the user sharing data; and calculating the association degree between the user sharing data and the target area by adopting the updated position information.
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