CN116340980A - Water environment pollution analysis management system and method based on big data - Google Patents
Water environment pollution analysis management system and method based on big data Download PDFInfo
- Publication number
- CN116340980A CN116340980A CN202310349883.6A CN202310349883A CN116340980A CN 116340980 A CN116340980 A CN 116340980A CN 202310349883 A CN202310349883 A CN 202310349883A CN 116340980 A CN116340980 A CN 116340980A
- Authority
- CN
- China
- Prior art keywords
- pollution
- microplastic
- water environment
- analysis
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 93
- 238000004458 analytical method Methods 0.000 title claims abstract description 81
- 238000000034 method Methods 0.000 title claims abstract description 29
- 229920000426 Microplastic Polymers 0.000 claims abstract description 145
- 238000009792 diffusion process Methods 0.000 claims abstract description 66
- 238000012544 monitoring process Methods 0.000 claims abstract description 63
- 238000004140 cleaning Methods 0.000 claims abstract description 19
- 239000011538 cleaning material Substances 0.000 claims abstract description 10
- 238000009826 distribution Methods 0.000 claims description 26
- 229920003023 plastic Polymers 0.000 claims description 24
- 239000004033 plastic Substances 0.000 claims description 24
- 238000007726 management method Methods 0.000 claims description 23
- 238000013500 data storage Methods 0.000 claims description 15
- 238000012545 processing Methods 0.000 claims description 13
- 238000004364 calculation method Methods 0.000 claims description 7
- 230000008859 change Effects 0.000 claims description 7
- 238000003860 storage Methods 0.000 claims description 7
- 238000003708 edge detection Methods 0.000 claims description 3
- 239000002699 waste material Substances 0.000 abstract description 2
- 230000008569 process Effects 0.000 description 10
- 241000282414 Homo sapiens Species 0.000 description 7
- 238000011161 development Methods 0.000 description 6
- 239000003344 environmental pollutant Substances 0.000 description 5
- 239000002957 persistent organic pollutant Substances 0.000 description 5
- 231100000719 pollutant Toxicity 0.000 description 5
- 241001465754 Metazoa Species 0.000 description 4
- 230000009471 action Effects 0.000 description 4
- 238000009434 installation Methods 0.000 description 4
- 239000000463 material Substances 0.000 description 4
- 238000001179 sorption measurement Methods 0.000 description 4
- 235000013305 food Nutrition 0.000 description 3
- 238000006424 Flood reaction Methods 0.000 description 2
- 241000237536 Mytilus edulis Species 0.000 description 2
- IISBACLAFKSPIT-UHFFFAOYSA-N bisphenol A Chemical compound C=1C=C(O)C=CC=1C(C)(C)C1=CC=C(O)C=C1 IISBACLAFKSPIT-UHFFFAOYSA-N 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 2
- 238000000354 decomposition reaction Methods 0.000 description 2
- 230000036541 health Effects 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 235000020638 mussel Nutrition 0.000 description 2
- 239000002245 particle Substances 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 102000004190 Enzymes Human genes 0.000 description 1
- 108090000790 Enzymes Proteins 0.000 description 1
- 238000001069 Raman spectroscopy Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006065 biodegradation reaction Methods 0.000 description 1
- 239000000356 contaminant Substances 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 230000002209 hydrophobic effect Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000011065 in-situ storage Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 244000005700 microbiome Species 0.000 description 1
- 150000003071 polychlorinated biphenyls Chemical group 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 239000002910 solid waste Substances 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
- 230000007480 spreading Effects 0.000 description 1
- 238000003892 spreading Methods 0.000 description 1
- 210000002784 stomach Anatomy 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 230000004083 survival effect Effects 0.000 description 1
- 239000002912 waste gas Substances 0.000 description 1
- 239000002351 wastewater Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/18—Water
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/602—Providing cryptographic facilities or services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/64—Protecting data integrity, e.g. using checksums, certificates or signatures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A20/00—Water conservation; Efficient water supply; Efficient water use
- Y02A20/20—Controlling water pollution; Waste water treatment
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Computer Hardware Design (AREA)
- General Engineering & Computer Science (AREA)
- Computer Security & Cryptography (AREA)
- General Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Chemical & Material Sciences (AREA)
- Bioethics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Software Systems (AREA)
- Economics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Development Economics (AREA)
- Analytical Chemistry (AREA)
- Game Theory and Decision Science (AREA)
- Geometry (AREA)
- Evolutionary Computation (AREA)
- Medicinal Chemistry (AREA)
- Pathology (AREA)
- Biochemistry (AREA)
- Food Science & Technology (AREA)
- Immunology (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a water environment pollution analysis management system and method based on big data, and belongs to the field of water environment management. According to the invention, the microplastic pollution condition is traced by analyzing the microplastic content in the water environment, and the diffusion range is predicted by analyzing and monitoring the position pollution diffusion condition, so that the optimal cleaning position is predicted, the treatment of the water environment pollution is promoted, the waste of cleaning materials and pollution leakage are avoided, and the cleaning efficiency of related personnel on the microplastic pollution is improved.
Description
Technical Field
The invention relates to the field of water environment management, in particular to a water environment pollution analysis management system and method based on big data.
Background
Along with the continuous development of economy, the environmental pollution problem is more and more serious, and the ecological environment is an essential material condition for human survival and development, is also the basis of the operation of an economic system, and is a necessary precondition for the economic development. For sustainable development of society and economy, the importance of water environment protection is undoubted, the awareness and the requirement of people on water environment protection are enhanced, and the method is a foundation for promoting sustainable development of human society. The river course is the important resource of people's production life, provides sufficient water source for the resident to be the main passageway of draining floods and draining floods, simultaneously, water environment is the important component of human living environment, and the water environment pollution degree aggravates not only influences people's water demand, still influences people's living environment, brings serious influence to social production life, has restricted social economic development, can say that water environment is the most important factor of human living environment.
Microplastic is a plastic particle with a diameter smaller than 5 mm, and is a main carrier causing pollution. The micro-plastic has small volume, the larger the specific surface area, the stronger the capability of adsorbed pollutants, a large amount of polychlorinated biphenyl, bisphenol A and other persistent organic pollutants exist in the environment, the organic pollutants are often hydrophobic, that is, the organic pollutants are not easy to dissolve in water and are not easy to be diluted by water, once the micro-plastic meets the pollutants, the micro-plastic just gathers to form an organic pollution sphere, and the micro-plastic is equivalent to riding as the pollutants, and can float around in the environment. The wandering micro-plastic is easy to be eaten by low-end food chain organisms such as mussels, zooplankton and the like, cannot be digested, can only exist in the stomach all the time, occupies space, and causes illness and even death of animals; if the microplastic with the organic pollutants is eaten, the pollutants are released under the action of enzymes in organisms, so that the illness state of the microplastic is aggravated. The living beings at the bottom end of the food chain such as mussels, zooplankton and the like can be eaten by upper animals, and the micro-plastics, even the micro-plastics and organic pollutants enter the upper animals, and the harmful substances in the lower animals are only 1 percent, but become 20 percent to the upper layer, so that a large number of living beings eating the micro-plastics can be sick or dead, the living beings at the top end of the food chain are human beings, a large number of micro-plastics can be accumulated in the human body under the enrichment effect, and the small particles which are difficult to digest generate unpredictable harm to the human body.
At present, the cleaning measures of the microplastic are to carry out physical interception through biodegradation and an adsorption film, however, the plastic is extremely dispersed in the environment and is difficult to be biodegraded in a large scale in situ, the consumption of manpower and material resources by regular dredging and film changing is not small, a large amount of manpower and material resources can be wasted due to overlarge installation range of the adsorption film, pollution leakage can be caused due to overlarge installation range of the adsorption film, and the water environment pollution management difficulty is increased.
It appears that it is necessary to trace the source of microplastic pollution in a water environment and to analyze the treatment area according to the spread of microplastic pollution. Therefore, a system and a method for analyzing and managing water environment pollution based on big data are needed.
Disclosure of Invention
The invention aims to provide a water environment pollution analysis management system and method based on big data, which are used for solving the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: a water environment pollution analysis management system based on big data, the pollution analysis management system includes: the system comprises a data acquisition module, a database, a pollution analysis module and an early warning and reminding module;
the output end of the data acquisition module is connected with the input end of the database, the output end of the database is connected with the input end of the pollution analysis module, the output end of the pollution analysis module is connected with the input end of the early warning reminding module, and the output end of the pollution analysis module is connected with the input end of the database; the system comprises a data acquisition module, a database, a pollution analysis module and an early warning reminding module, wherein the data acquisition module is used for acquiring basic data information and monitoring microplastic in a water environment in real time, the database is used for carrying out encryption storage on acquired data and analysis results, the pollution analysis module is used for carrying out analysis processing on the acquired data, and the early warning reminding module is used for carrying out early warning reminding on related personnel according to the analysis results.
Further, the data acquisition module comprises a basic data acquisition unit and a pollution monitoring unit, wherein the basic data acquisition unit is used for acquiring basic data information, the pollution monitoring unit monitors the content and the diffusion condition of microplastic in the water environment in real time through microplastic monitoring equipment, and monitors the water flow speed of the water environment through a water flow sensor.
Furthermore, the database comprises a data encryption unit and a data storage unit, the data encryption unit encrypts acquired data information and analysis results through an RSA encryption algorithm, so that the data security of the system is ensured, the occurrence of data leakage or data tampering is avoided, the RSA encryption is a public key cryptosystem, the so-called public key cryptosystem is a cryptosystem which uses different encryption keys and decryption keys and is a "computationally infeasible" cryptosystem for deriving the decryption keys from the known encryption keys, and the RSA algorithm security is based on the difficulty of large number decomposition. The difficulty of recovering plaintext from a public key and ciphertext is equivalent to decomposing the product of two large primes; in order to improve the confidentiality strength, the RSA key is at least 500 bits long, 1024 bits are generally recommended, and the key length of the current commercial RSA algorithm is 2048 bits; the RSA algorithm has better security than the symmetric encryption algorithm, but the encryption processing efficiency is not as high as that of the symmetric encryption algorithm due to the higher algorithm complexity. Therefore, when the network transmits important information, two encryption algorithms are often used in a mixed mode. The data storage unit stores collected data information and analysis results through an HDFS data storage mode, the HDFS is a data storage system in Hadoop distributed computation, the data storage system is developed based on the requirement of accessing and processing oversized files through a streaming data mode, in the HDFS, the file reading and writing process is a process of interacting clients, nameNodes and dataNodes together, the HDFS data storage mode can process oversized files and can operate on a relatively cheap commercial machine cluster, the HDFS can well process 'write once and read and write many times' tasks through streaming access data, one data set can be copied into different storage nodes once being generated, and then in response to various data analysis task requests, the analysis tasks can involve most of data in the data set in most cases. Therefore, HDFS requests to read the entire data set more efficiently than reading one record.
Further, the pollution analysis module comprises a source tracing unit and a diffusion analysis unit, wherein the source tracing unit is used for tracing the source of the microplastic pollution according to the collected data, so that related personnel can know the specific situation of the microplastic from the source conveniently, measures are taken, the diffusion analysis unit is used for analyzing the diffusion situation of the microplastic, the related personnel can conveniently clean the pollution, serious pollution problems caused by the large-scale diffusion of the microplastic are avoided, and the cleaning efficiency of the related personnel is improved.
Further, the early warning reminding module comprises a screen display unit and a voice alarm unit, wherein the screen display unit is used for displaying a pollution range and guiding a processing range for related personnel through screen display equipment according to an analysis result, so that the related personnel can conveniently and rapidly clean micro-plastic pollution, the related personnel can be guaranteed to clean the pollution most efficiently, the voice alarm unit is used for broadcasting the related personnel through voice equipment according to the analysis result, the related personnel can be guaranteed to receive warning reminding information in real time, and the problem that pollution is spread in a large range due to untimely measures is avoided.
A water environment pollution analysis management method based on big data comprises the following steps:
s1, basic data information is collected, the content and the diffusion condition of micro plastics in a water environment are monitored in real time through micro plastic monitoring equipment, and encryption storage is carried out;
s2, tracing the microplastic pollution in the water environment according to the acquired basic data information and the microplastic content condition;
s3, predicting the micro plastic pollution diffusion range of each monitoring point according to the collected micro plastic diffusion condition and historical data, and performing prediction analysis on the cleaning area of related personnel;
and S4, according to an analysis result, when microplastic pollution occurs, alarming and reminding related personnel through a display device and a voice device.
Further, in step S2, the following steps are included:
s201, establishing a plane rectangular coordinate system according to the collected water flow direction, the equipment monitoring position information and the content of microplastic in the water environment, wherein the coordinate system can be set by related technicians;
s202, establishing a Gaussian diffusion model through the following formula:
where G (x, y) represents the average microplastic content over a period of time t at the monitoring point (x, y) in the plane, G is represented as the source intensity, which refers to a measure of the intensity of the generated or emitted contaminant, α x and αy Expressed as diffusion parameters on the x-axis and y-axis, v expressed as water flow velocity, (x) 0 ,y 0 ) Expressed as preset pollution source coordinates;
s203, calculating the content value C of the microplastic at the monitoring position according to the content of the microplastic in the water environment monitored at the monitoring position by the following formula:
wherein P is expressed as microplastic content index, Z H High value, Z, expressed as microplastic content limit L Low value, Z, expressed as microplastic content limit H and ZL Is a high-low numerical value which is equal to the content limit value of the microplastic, each level value is a fixed value, P H Pollution index, P, corresponding to the high value expressed as microplastic content limit L A pollution index corresponding to a low-order value expressed as a microplastic content limit value, wherein the pollution index is a dimensionless number, and each level value is a set fixed value;
the source strength G is calculated by the following formula:
wherein T is expressed as time, and S is expressed as monitoring the water environment surface area;
s204, defining a spherical search area beta, wherein the center point (x i ,y i ) Value g after two-stage Taylor expansion of Gaussian diffusion model function * And (3) performing calculation:
the correlation index γ is calculated by the following formula:
wherein ,f(xi )-f(x i +d) is expressed as the actual drop-off content value, f (x) i )-g * Expressed as a predicted drop-in content value between adjacent monitoring points of the microplastic, a correlation index threshold is set to be gamma Threshold value When gamma > gamma Threshold value When the two-stage Taylor expansion representing the Gaussian diffusion model function is an approximate objective function, the change parameter d is correct, otherwise, when gamma is less than or equal to gamma Threshold value When the method is used, the searching is needed to be carried out again, the change parameters are updated continuously, and a final convergence solution is obtained after multiple iterations, so that the preset pollution source coordinate position (x) is deduced reversely through data fitting 0 ,y 0 )。
Further, in step S3, the following steps are included:
s301, according to real-time monitoring of the content and the diffusion of the micro plastics in the water environment by the micro plastic monitoring equipment, obtaining micro plastic data of the same monitoring point at different moments, and establishing a plane rectangular coordinate system; according to the distribution conditions of the microplastic in the water environment obtained by monitoring at different moments, a distribution image is obtained, information of the spatial geographic position is added to the microplastic distribution boundary, and an edge detection algorithm is utilized to extract the profile of the microplastic distribution boundary so as to obtain a microplastic distribution region E;
s302, the micro plastic is diffused in the normal direction and the tangential direction of the micro plastic distribution boundary outline, but the diffusion in the tangential direction does not influence the outline position, so that the diffusion of the outline in the normal direction is only calculated. The normal velocity field for microplastic diffusion is calculated by the following formulaAnd (3) performing calculation:
wherein ,(nx ,n y ) The normal unit vector expressed as the profile line of the microplastic distribution boundary, the velocity field refers to a flow field formed by velocity at discrete points in a space;
the distance function r (t, x, y) is calculated by the following formula:
wherein t is represented as a monitoring moment, (x, y) is represented as a position coordinate of a point in the water environment, and D is represented as a nearest distance from the point (x, y) to a microplastic distribution boundary;
thenAcquiring normal velocity field distribution of micro-plastic diffusion through real-time monitoring data, and carrying out solution on the position of micro-plastic diffusion at the subsequent moment after the continuation of a non-polluted area so as to acquire a micro-plastic diffusion prediction position profile;
s303, obtaining the predicted contour circumference of the t' moment as S according to the micro plastic diffusion predicted position contour t′ According to the collected basic data, the running speed v of the related personnel for installing the micro plastic cleaning material is obtained Dress(s) The processing position is calculated by the following formula:
wherein, deltat 'is expressed as the total time for installing cleaning materials, and the time t' and the corresponding micro plastic diffusion position are obtained by solving and expressed as the optimal cleaning position;
s304, displaying a predicted pollution range to related personnel through display equipment according to the analysis result, and displaying a predicted optimal cleaning position.
Further, in step S4, when micro plastic pollution occurs, the predicted micro plastic pollution source, pollution range and treatment range guidance are displayed to related personnel through the display device, and the related personnel are alerted through the voice device, so that the water environment pollution cleaning efficiency of the related personnel is improved, the micro plastic pollution is prevented from spreading in a large area, the water environment is guaranteed to be tidy, and the health of people is guaranteed.
Compared with the prior art, the invention has the following beneficial effects:
the invention collects basic data information, monitors the content and the diffusion condition of micro plastic in the water environment in real time through micro plastic monitoring equipment, and monitors the water flow speed of the water environment through a water flow sensor; according to the collected data information, the content of the microplastic in the water environment of different monitoring points is analyzed, the microplastic pollution condition is traced, so that related personnel can conveniently and rapidly clean the microplastic pollution source, and more microplastic pollution is avoided; according to the collected data information, the pollution diffusion conditions of the monitoring position at different moments are analyzed, the pollution diffusion range of the monitoring position is predicted, so that the optimal cleaning position is predicted, waste caused by large-scale installation of cleaning materials in advance is avoided, pollution leakage caused by small installation of the cleaning materials is avoided, the cleaning efficiency of related personnel on micro plastic pollution is improved, and the treatment of water environment pollution is promoted.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic diagram of the module composition of a water environment pollution analysis management system based on big data;
fig. 2 is a flow chart of steps of a water environment pollution analysis and management method based on big data.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the present invention provides the following technical solutions: a water environment pollution analysis management system based on big data, the pollution analysis management system includes: the system comprises a data acquisition module, a database, a pollution analysis module and an early warning and reminding module;
the output end of the data acquisition module is connected with the input end of the database, the output end of the database is connected with the input end of the pollution analysis module, the output end of the pollution analysis module is connected with the input end of the early warning reminding module, and the output end of the pollution analysis module is connected with the input end of the database;
the data acquisition module is used for acquiring basic data information and monitoring microplastic in a water environment in real time, the data acquisition module comprises a basic data acquisition unit and a pollution monitoring unit, the basic data acquisition unit is used for acquiring basic data information such as water flow direction, equipment monitoring position, spectrum information and basic physical characteristics of the microplastic, and the pollution monitoring unit monitors the coverage condition and content of the microplastic through microplastic monitoring equipment such as Raman spectrometer, energy spectrometer, microorganism mass spectrometer and the like, monitors the content and diffusion condition of the microplastic in the water environment in real time and monitors the water flow speed of the water environment through a water flow sensor.
The database is used for encrypting and storing acquired data and analysis results, the database comprises a data encryption unit and a data storage unit, the data encryption unit encrypts the acquired data information and the analysis results through an RSA encryption algorithm, the data security of the system is guaranteed, the condition of data leakage or data tampering is avoided, the RSA encryption is a public key cryptosystem, the so-called public key cryptosystem is a cryptosystem which uses different encryption keys and decryption keys, the decryption keys are not feasible in calculation by the known encryption keys, and the RSA algorithm security is based on the difficulty of large number decomposition. The difficulty of recovering plaintext from a public key and ciphertext is equivalent to decomposing the product of two large primes; in order to improve the confidentiality strength, the RSA key is at least 500 bits long, 1024 bits are generally recommended, and the key length of the current commercial RSA algorithm is 2048 bits; the RSA algorithm has better security than the symmetric encryption algorithm, but the encryption processing efficiency is not as high as that of the symmetric encryption algorithm due to the higher algorithm complexity. Therefore, when the network transmits important information, two encryption algorithms are often used in a mixed mode. The data storage unit stores collected data information and analysis results through an HDFS data storage mode, the HDFS is a data storage system in Hadoop distributed computation, the data storage system is developed based on the requirement of accessing and processing oversized files through a streaming data mode, in the HDFS, the file reading and writing process is a process of interacting clients, nameNodes and dataNodes together, the HDFS data storage mode can process oversized files and can operate on a relatively cheap commercial machine cluster, the HDFS can well process 'write once and read and write many times' tasks through streaming access data, one data set can be copied into different storage nodes once being generated, and then in response to various data analysis task requests, the analysis tasks can involve most of data in the data set in most cases. Therefore, HDFS requests to read the entire data set more efficiently than reading one record.
The pollution analysis module is used for analyzing and processing collected data, the pollution analysis module comprises a source tracing unit and a diffusion analysis unit, the source tracing unit is used for tracing the source of microplastic pollution according to the collected data, relevant personnel are convenient to know the specific situation of the microplastic from the source, measures are taken, the diffusion analysis unit is used for analyzing the diffusion situation of the microplastic, the relevant personnel are convenient to carry out pollution cleaning, serious pollution problems caused by large-scale diffusion of the microplastic are avoided, and the cleaning efficiency of the relevant personnel is improved.
The early warning and reminding module is used for carrying out early warning and reminding on related personnel according to the analysis result. The early warning reminding module comprises a screen display unit and a voice alarm unit, wherein the screen display unit is used for displaying and guiding a treatment range of pollution ranges of related personnel through screen display equipment such as mobile phones or computers according to analysis results, so that the related personnel can conveniently and rapidly clean micro-plastic pollution, the pollution can be cleaned most efficiently, the voice alarm unit is used for broadcasting the related personnel through voice equipment such as broadcasting or sounding and the like according to the analysis results, the related personnel can receive warning reminding information in real time, and the pollution large-scale diffusion caused by untimely measures is avoided.
A water environment pollution analysis management method based on big data comprises the following steps:
s1, basic data information is collected, the content and the diffusion condition of micro plastics in a water environment are monitored in real time through micro plastic monitoring equipment, and encryption storage is carried out;
s2, tracing the microplastic pollution in the water environment according to the acquired basic data information and the microplastic content condition;
in step S2, the following steps are included:
s201, establishing a plane rectangular coordinate system according to the collected water flow direction, the equipment monitoring position information and the content of microplastic in the water environment, wherein the coordinate system can be set by related technicians, for example, a connecting line of a starting point and an ending point of the monitoring water flow is used as a coordinate axis;
s202, establishing a Gaussian diffusion model through the following formula:
wherein G (x, y) represents the average microplastic content in the time period t at the monitoring point (x, y) in the plane, G represents the source intensity, which refers to the measurement of the intensity of generated or discharged pollutants, including the source intensity of waste gas, waste water, noise, vibration, solid waste and the like, alpha x and αy Expressed as diffusion parameters on the x-axis and y-axis, v expressed as water flow velocity, (x) 0 ,y 0 ) Expressed as preset pollution source coordinates;
s203, calculating the content value C of the microplastic at the monitoring position according to the content of the microplastic in the water environment monitored at the monitoring position by the following formula:
wherein P is expressed as microplastic content index, Z H High value, Z, expressed as microplastic content limit L Low value, Z, expressed as microplastic content limit H and ZL Is a high-low numerical value which is equal to the content limit value of the microplastic, each level value is a fixed value, P H Pollution index, P, corresponding to the high value expressed as microplastic content limit L A pollution index corresponding to a low-order value expressed as a microplastic content limit value, wherein the pollution index is a dimensionless number, and each level value is a set fixed value;
the source strength G is calculated by the following formula:
wherein T is expressed as time, and S is expressed as monitoring the water environment surface area;
s204, defining a spherical search area beta, wherein the center point (x i ,y i ) Value g after two-stage Taylor expansion of Gaussian diffusion model function * And (3) performing calculation:
the correlation index γ is calculated by the following formula:
wherein ,f(xi )-f(x i +d) is expressed as the actual drop-off content value, f (x) i )-g * Expressed as a predicted drop-in content value between adjacent monitoring points of the microplastic, a correlation index threshold is set to be gamma Threshold value When gamma > gamma Threshold value When the two-stage Taylor expansion representing the Gaussian diffusion model function is an approximate objective function, the change parameter d is correct, otherwise, when gamma is less than or equal to gamma Threshold value When the method is used, the searching is needed to be carried out again, the change parameters are updated continuously, and a final convergence solution is obtained after multiple iterations, so that the preset pollution source coordinate position (x) is deduced reversely through data fitting 0 ,y 0 )。
S3, predicting the micro plastic pollution diffusion range of each monitoring point according to the collected micro plastic diffusion condition and historical data, and performing prediction analysis on the cleaning area of related personnel;
in step S3, the following steps are included:
s301, according to real-time monitoring of the content and the diffusion of the micro plastics in the water environment by the micro plastic monitoring equipment, obtaining micro plastic data of the same monitoring point at different moments, and establishing a plane rectangular coordinate system; according to the distribution conditions of the microplastic in the water environment obtained by monitoring at different moments, a distribution image is obtained, information of the spatial geographic position is added to the microplastic distribution boundary, and an edge detection algorithm is utilized to extract the profile of the microplastic distribution boundary so as to obtain a microplastic distribution region E;
s302, the micro plastic is diffused in the normal direction and the tangential direction of the micro plastic distribution boundary outline, but the diffusion in the tangential direction does not influence the outline position, so that the diffusion of the outline in the normal direction is only calculated. The normal velocity field for microplastic diffusion is calculated by the following formulaAnd (3) performing calculation:
wherein ,(nx ,n y ) The normal unit vector expressed as the profile line of the microplastic distribution boundary, the velocity field refers to a flow field formed by velocity at discrete points in a space;
the distance function r (t, x, y) is calculated by the following formula:
wherein t is represented as a monitoring moment, (x, y) is represented as a position coordinate of a point in the water environment, and D is represented as a nearest distance from the point (x, y) to a microplastic distribution boundary;
thenAcquiring normal velocity field distribution of micro-plastic diffusion through real-time monitoring data, and carrying out solution on the position of micro-plastic diffusion at the subsequent moment after the continuation of a non-polluted area so as to acquire a micro-plastic diffusion prediction position profile;
s303, obtaining the predicted contour circumference of the t' moment as S according to the micro plastic diffusion predicted position contour t′ According to the collected basic data, the running speed v of the related personnel for installing the micro plastic cleaning material is obtained Dress(s) The processing position is calculated by the following formula:
wherein, deltat 'is expressed as the total time for installing cleaning materials, and the time t' and the corresponding micro plastic diffusion position are obtained by solving and expressed as the optimal cleaning position;
s304, displaying a predicted pollution range to related personnel through display equipment according to the analysis result, and displaying a predicted optimal cleaning position.
And S4, according to an analysis result, when microplastic pollution occurs, alarming and reminding related personnel through a display device and a voice device.
In step S4, when the microplastic pollution occurs, the predicted microplastic pollution source, pollution range and treatment range guidance are displayed to the relevant personnel through the display device, such as a mobile phone or a computer, for example, the relevant personnel are guided to install the microplastic adsorption film at the predicted position, and the relevant personnel are warned and reminded through the voice device, such as warning sound or voice prompt, for example, so that the water environment pollution cleaning efficiency of the relevant personnel is improved, the microplastic pollution is prevented from being spread in a large area, the water environment is guaranteed to be clean, and the physical health of the people is guaranteed.
Example 1:
if the actual drop content value between adjacent monitoring points of the micro plastic is 2, the predicted drop content value between adjacent monitoring points of the micro plastic is 2.5, and the related index isIf the related index threshold is set as gamma Threshold value =0.5, then γ>γ Threshold value Second-order taylor representing gaussian diffusion model functionThe expansion is an approximate objective function; if the related index threshold is set as gamma Threshold value =1, then γ<γ Threshold value The searching is needed again, the change parameters are updated continuously, and a final convergence solution is obtained after multiple iterations, so that the preset pollution source coordinate position (x) is deduced reversely through data fitting 0 ,y 0 );
If related personnel install the running speed v of the micro plastic cleaning material Dress(s) =5, at time t' pastPredicted contour perimeter S t' =100, remind relevant personnel that this contour position is the clearance position through display device, clear up little plastics pollution, when little plastics pollution spreads to this place, relevant personnel just install the clearance material.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. A water environment pollution analysis management system based on big data is characterized in that: the pollution analysis management system includes: the system comprises a data acquisition module, a database, a pollution analysis module and an early warning and reminding module;
the output end of the data acquisition module is connected with the input end of the database, the output end of the database is connected with the input end of the pollution analysis module, the output end of the pollution analysis module is connected with the input end of the early warning reminding module, and the output end of the pollution analysis module is connected with the input end of the database; the system comprises a data acquisition module, a database, a pollution analysis module and an early warning reminding module, wherein the data acquisition module is used for acquiring basic data information and monitoring microplastic in a water environment in real time, the database is used for carrying out encryption storage on acquired data and analysis results, the pollution analysis module is used for carrying out analysis processing on the acquired data, and the early warning reminding module is used for carrying out early warning reminding on related personnel according to the analysis results.
2. The big data-based water environment pollution analysis management system according to claim 1, wherein: the data acquisition module comprises a basic data acquisition unit and a pollution monitoring unit, wherein the basic data acquisition unit is used for acquiring basic data information, the pollution monitoring unit monitors the content and the diffusion condition of microplastic in the water environment in real time through microplastic monitoring equipment, and monitors the water flow speed of the water environment through a water flow sensor.
3. The big data-based water environment pollution analysis management system according to claim 2, wherein: the database comprises a data encryption unit and a data storage unit, wherein the data encryption unit encrypts collected data information and analysis results through an RSA encryption algorithm, and the data storage unit stores the collected data information and analysis results through an HDFS data storage mode.
4. The water environment pollution analysis management system based on big data according to claim 3, wherein: the pollution analysis module comprises a source tracing unit and a diffusion analysis unit, wherein the source tracing unit is used for tracing the source of the microplastic pollution according to the collected data, and the diffusion analysis unit is used for analyzing the diffusion condition of the microplastic.
5. The big data-based water environment pollution analysis management system according to claim 4, wherein: the early warning reminding module comprises a screen display unit and a voice alarm unit, wherein the screen display unit is used for displaying a pollution range and guiding a processing range of related personnel through screen display equipment according to an analysis result, and the voice alarm unit is used for broadcasting the related personnel through voice equipment according to the analysis result.
6. A water environment pollution analysis and management method based on big data is characterized in that: comprises the following steps:
s1, basic data information is collected, the content and the diffusion condition of micro plastics in a water environment are monitored in real time through micro plastic monitoring equipment, and encryption storage is carried out;
s2, tracing the microplastic pollution in the water environment according to the acquired basic data information and the microplastic content condition;
s3, predicting the micro plastic pollution diffusion range of each monitoring point according to the collected micro plastic diffusion condition and historical data, and performing prediction analysis on the cleaning area of related personnel;
and S4, according to an analysis result, when microplastic pollution occurs, alarming and reminding related personnel through a display device and a voice device.
7. The water environment pollution analysis and management method based on big data as claimed in claim 6, wherein the method is characterized in that: in step S2, the following steps are included:
s201, establishing a plane rectangular coordinate system according to the collected water flow direction, the equipment monitoring position information and the content of microplastic in the water environment;
s202, establishing a Gaussian diffusion model through the following formula:
wherein G (x, y) represents the average microplastic content in the time period t at the monitoring point (x, y) in the plane, G represents the source intensity, alpha x and αy Expressed as diffusion parameters on the x-axis and y-axis, v expressed as water flow velocity, (x) 0 ,y 0 ) Expressed as preset pollution source coordinates;
s203, calculating the content value C of the microplastic at the monitoring position according to the content of the microplastic in the water environment monitored at the monitoring position by the following formula:
wherein P is expressed as microplastic content index, Z H High value, Z, expressed as microplastic content limit L Low value, P, expressed as microplastic content limit H Pollution index, P, corresponding to the high value expressed as microplastic content limit L A pollution index corresponding to a low value expressed as a microplastic content limit;
the source strength G is calculated by the following formula:
wherein T is expressed as time, and S is expressed as monitoring the water environment surface area;
s204, defining a spherical search area beta, wherein the center point (x i ,y i ) Value g after two-stage Taylor expansion of Gaussian diffusion model function * And (3) performing calculation:
the correlation index γ is calculated by the following formula:
wherein ,f(xi )-f(x i +d) is expressed as the actual drop-off content value, f (x) i )-g * Expressed as a predicted drop-in content value between adjacent monitoring points of the microplastic, a correlation index threshold is set to be gamma Threshold value When gamma is>γ Threshold value When the two-stage Taylor expansion representing the Gaussian diffusion model function is an approximate objective function, the change parameter d is correct, otherwise, when gamma is less than or equal to gamma Threshold value When the method is used, the searching is needed to be carried out again, the change parameters are updated continuously, and a final convergence solution is obtained after multiple iterations, so that the preset pollution source coordinate position (z) is reversely deduced through data fitting 0 ,y 0 )。
8. The water environment pollution analysis and management method based on big data as claimed in claim 7, wherein the method is characterized in that: in step S3, the following steps are included:
s301, according to real-time monitoring of the content and the diffusion of the micro plastics in the water environment by the micro plastic monitoring equipment, obtaining micro plastic data of the same monitoring point at different moments, and establishing a plane rectangular coordinate system; according to the distribution conditions of the microplastic in the water environment obtained by monitoring at different moments, a distribution image is obtained, information of the spatial geographic position is added to the microplastic distribution boundary, and an edge detection algorithm is utilized to extract the profile of the microplastic distribution boundary so as to obtain a microplastic distribution region E;
s302, a method for diffusing micro plastic through the following formulaTo the velocity fieldAnd (3) performing calculation:
wherein ,(nx ,n y ) A normal unit vector expressed as a microplastic distribution boundary contour;
the distance function r (t, x, y) is calculated by the following formula:
wherein t is represented as a monitoring moment, (x, y) is represented as a position coordinate of a point in the water environment, and D is represented as a nearest distance from the point (x, y) to a microplastic distribution boundary;
thenAcquiring normal velocity field distribution of micro-plastic diffusion through real-time monitoring data, and carrying out solution on the position of micro-plastic diffusion at the subsequent moment after the continuation of a non-polluted area so as to acquire a micro-plastic diffusion prediction position profile;
s303, obtaining the predicted contour circumference of the t' moment as S according to the micro plastic diffusion predicted position contour t' According to the collected basic data, the running speed v of the related personnel for installing the micro plastic cleaning material is obtained Dress(s) The processing position is calculated by the following formula:
the delta t 'is expressed as the total time for installing cleaning materials, and the time t' and the corresponding micro plastic diffusion position are obtained through solving and expressed as the optimal cleaning position;
s304, displaying a predicted pollution range to related personnel through display equipment according to the analysis result, and displaying a predicted optimal cleaning position.
9. The water environment pollution analysis and management method based on big data as claimed in claim 8, wherein the method is characterized in that: in step S4, according to the analysis result, when the microplastic pollution occurs, the predicted microplastic pollution source, pollution range and processing range guidance are displayed to the related personnel through the display device, and the related personnel are warned and reminded through the voice device.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202310349883.6A CN116340980B (en) | 2023-04-04 | 2023-04-04 | Water environment pollution analysis management system and method based on big data |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202310349883.6A CN116340980B (en) | 2023-04-04 | 2023-04-04 | Water environment pollution analysis management system and method based on big data |
Publications (2)
Publication Number | Publication Date |
---|---|
CN116340980A true CN116340980A (en) | 2023-06-27 |
CN116340980B CN116340980B (en) | 2023-09-05 |
Family
ID=86883904
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202310349883.6A Active CN116340980B (en) | 2023-04-04 | 2023-04-04 | Water environment pollution analysis management system and method based on big data |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN116340980B (en) |
Citations (16)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
KR20130104661A (en) * | 2012-03-15 | 2013-09-25 | 김준현 | Mmultidimensional system for modeling water quality |
CN106830353A (en) * | 2017-01-18 | 2017-06-13 | 亿利生态修复股份有限公司 | Water pollution processing method and equipment |
JP6158967B1 (en) * | 2016-02-05 | 2017-07-05 | インダストリー アカデミック コーポレーション ファウンデーション ケミョン ユニバーシティIndustry Academic Cooperation Foundation Keimyung University | Environmental pollution prediction system and method |
CN108492007A (en) * | 2018-03-02 | 2018-09-04 | 交通运输部水运科学研究所 | A kind of marine eco-environment damage causality determination method |
CN110085281A (en) * | 2019-04-26 | 2019-08-02 | 成都之维安科技股份有限公司 | A kind of environmental pollution traceability system and method based on feature pollution factor source resolution |
CN112417788A (en) * | 2020-11-30 | 2021-02-26 | 重庆市生态环境大数据应用中心 | Water environment pollution analysis system and method based on big data |
US20210389293A1 (en) * | 2020-06-12 | 2021-12-16 | Chinese Research Academy Of Environmental Sciences | Methods and Systems for Water Area Pollution Intelligent Monitoring and Analysis |
CN114527206A (en) * | 2022-01-25 | 2022-05-24 | 长安大学 | Method and system for tracing groundwater pollution by sulfonamides antibiotics |
CN114757687A (en) * | 2022-05-07 | 2022-07-15 | 合肥先进产业研究院 | Atmospheric pollutant tracing system and method based on big data technology |
CN114935637A (en) * | 2022-06-06 | 2022-08-23 | 倪文兵 | Environmental pollution monitoring system based on big data |
CN115187135A (en) * | 2022-08-08 | 2022-10-14 | 天津市引滦工程黎河管理中心 | Hydraulic engineering risk early warning analysis system and method based on big data scene |
CN115237972A (en) * | 2021-04-23 | 2022-10-25 | 中国石油化工股份有限公司 | System and method for monitoring underground environment of risk site in real time |
CN115640178A (en) * | 2022-10-18 | 2023-01-24 | 苏布道 | Computer resource management system and method based on encryption of Internet of things |
CN115684523A (en) * | 2022-09-27 | 2023-02-03 | 华艺生态园林股份有限公司 | Smart urban water environment monitoring system |
CN115685853A (en) * | 2022-11-08 | 2023-02-03 | 山东省生态环境监测中心 | Water environment pollution analysis management system and method based on big data |
CN115828508A (en) * | 2022-10-25 | 2023-03-21 | 吉林大学 | Underground water environmental assessment automatic prediction method based on GIS platform |
-
2023
- 2023-04-04 CN CN202310349883.6A patent/CN116340980B/en active Active
Patent Citations (16)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
KR20130104661A (en) * | 2012-03-15 | 2013-09-25 | 김준현 | Mmultidimensional system for modeling water quality |
JP6158967B1 (en) * | 2016-02-05 | 2017-07-05 | インダストリー アカデミック コーポレーション ファウンデーション ケミョン ユニバーシティIndustry Academic Cooperation Foundation Keimyung University | Environmental pollution prediction system and method |
CN106830353A (en) * | 2017-01-18 | 2017-06-13 | 亿利生态修复股份有限公司 | Water pollution processing method and equipment |
CN108492007A (en) * | 2018-03-02 | 2018-09-04 | 交通运输部水运科学研究所 | A kind of marine eco-environment damage causality determination method |
CN110085281A (en) * | 2019-04-26 | 2019-08-02 | 成都之维安科技股份有限公司 | A kind of environmental pollution traceability system and method based on feature pollution factor source resolution |
US20210389293A1 (en) * | 2020-06-12 | 2021-12-16 | Chinese Research Academy Of Environmental Sciences | Methods and Systems for Water Area Pollution Intelligent Monitoring and Analysis |
CN112417788A (en) * | 2020-11-30 | 2021-02-26 | 重庆市生态环境大数据应用中心 | Water environment pollution analysis system and method based on big data |
CN115237972A (en) * | 2021-04-23 | 2022-10-25 | 中国石油化工股份有限公司 | System and method for monitoring underground environment of risk site in real time |
CN114527206A (en) * | 2022-01-25 | 2022-05-24 | 长安大学 | Method and system for tracing groundwater pollution by sulfonamides antibiotics |
CN114757687A (en) * | 2022-05-07 | 2022-07-15 | 合肥先进产业研究院 | Atmospheric pollutant tracing system and method based on big data technology |
CN114935637A (en) * | 2022-06-06 | 2022-08-23 | 倪文兵 | Environmental pollution monitoring system based on big data |
CN115187135A (en) * | 2022-08-08 | 2022-10-14 | 天津市引滦工程黎河管理中心 | Hydraulic engineering risk early warning analysis system and method based on big data scene |
CN115684523A (en) * | 2022-09-27 | 2023-02-03 | 华艺生态园林股份有限公司 | Smart urban water environment monitoring system |
CN115640178A (en) * | 2022-10-18 | 2023-01-24 | 苏布道 | Computer resource management system and method based on encryption of Internet of things |
CN115828508A (en) * | 2022-10-25 | 2023-03-21 | 吉林大学 | Underground water environmental assessment automatic prediction method based on GIS platform |
CN115685853A (en) * | 2022-11-08 | 2023-02-03 | 山东省生态环境监测中心 | Water environment pollution analysis management system and method based on big data |
Non-Patent Citations (4)
Title |
---|
倪健等: "基于高斯模型的城市大气污染物溯源模拟", 《电脑知识与技术》 * |
石剑荣: "水体扩散衍生公式在环境风险评价中的应用", 水科学进展, no. 01 * |
袁博宇等: "一种考虑水生态修复措施对污染物降解影响的水质模型", 《环境科学学报》 * |
许志伟;易加仁;: "基于WebGIS的环境污染物扩散预测分析系统设计与实现", 科技风, no. 24 * |
Also Published As
Publication number | Publication date |
---|---|
CN116340980B (en) | 2023-09-05 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
WO2021174751A1 (en) | Method, apparatus and device for locating pollution source on basis of big data, and storage medium | |
Huang et al. | A crowdsource-based sensing system for monitoring fine-grained air quality in urban environments | |
Li et al. | On discovering co-location patterns in datasets: a case study of pollutants and child cancers | |
Chakraborty et al. | Disproportionate proximity to environmental health hazards: methods, models, and measurement | |
Schwarz et al. | Spatial variation in the joint effect of extreme heat events and ozone on respiratory hospitalizations in California | |
Adamala | An overview of big data applications in water resources engineering | |
Mubea et al. | Assessing application of Markov chain analysis in predicting land cover change: a case study of Nakuru municipality | |
Wang et al. | Inferring urban air quality based on social media | |
Maantay et al. | Proximity to environmental hazards: Environmental justice and adverse health outcomes | |
CN113706127B (en) | Water area analysis report generation method and electronic equipment | |
CN114329245A (en) | Air pollution tracing method and device, server and storage medium | |
CN107563597A (en) | The intelligent early-warning method of gross contamination emission | |
Banks et al. | Bayesian CAR models for syndromic surveillance on multiple data streams: theory and practice | |
Pantelic et al. | Transformational IoT sensing for air pollution and thermal exposures | |
Zhang et al. | Issues in the aggregation and spatial analysis of neighborhood crime | |
Zhao et al. | Innovative spatial-temporal network modeling and analysis method of air quality | |
Wang et al. | Prediction of daily pm 2.5 concentration in china using partial differential equations | |
CN112418571A (en) | Method and device for enterprise environmental protection comprehensive evaluation | |
Alemayehu et al. | Spatiotemporal and hotspot detection of U5-children diarrhea in resource-limited areas of Ethiopia | |
Tawsif et al. | A review on complex event processing systems for big data | |
CN116340980B (en) | Water environment pollution analysis management system and method based on big data | |
Mahamuni et al. | Machine learning for smart cities: A survey | |
Furey et al. | Evaluating water quality impacts on visitation to coastal recreation areas using data derived from cell phone locations | |
Pandey et al. | Geomatics approach for assessment of respiratory disease mapping | |
Wang et al. | Clean manufacturing structure and its impact on water quality: A case study of Northeast China |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant |