CN117171675B - Water environment microorganism detection method, system and medium based on multi-source data - Google Patents
Water environment microorganism detection method, system and medium based on multi-source data Download PDFInfo
- Publication number
- CN117171675B CN117171675B CN202311445525.1A CN202311445525A CN117171675B CN 117171675 B CN117171675 B CN 117171675B CN 202311445525 A CN202311445525 A CN 202311445525A CN 117171675 B CN117171675 B CN 117171675B
- Authority
- CN
- China
- Prior art keywords
- species
- data information
- data
- water environment
- acquiring
- 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.)
- Active
Links
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 154
- 244000005700 microbiome Species 0.000 title claims abstract description 132
- 238000001514 detection method Methods 0.000 title claims abstract description 52
- 241000894007 species Species 0.000 claims abstract description 296
- 238000003911 water pollution Methods 0.000 claims abstract description 14
- 230000000813 microbial effect Effects 0.000 claims description 48
- 238000000034 method Methods 0.000 claims description 36
- 238000009395 breeding Methods 0.000 claims description 22
- 230000001488 breeding effect Effects 0.000 claims description 22
- 239000011159 matrix material Substances 0.000 claims description 12
- 238000007781 pre-processing Methods 0.000 claims description 8
- 238000003066 decision tree Methods 0.000 claims description 6
- 238000013135 deep learning Methods 0.000 claims description 6
- 238000000605 extraction Methods 0.000 claims description 6
- 238000012847 principal component analysis method Methods 0.000 claims description 6
- 238000007619 statistical method Methods 0.000 claims description 5
- 238000001914 filtration Methods 0.000 claims description 3
- 230000004927 fusion Effects 0.000 claims description 3
- 238000001228 spectrum Methods 0.000 claims description 3
- 230000001105 regulatory effect Effects 0.000 claims 1
- 239000010865 sewage Substances 0.000 description 8
- 230000007613 environmental effect Effects 0.000 description 6
- 239000003344 environmental pollutant Substances 0.000 description 5
- 231100000719 pollutant Toxicity 0.000 description 5
- 238000004458 analytical method Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 238000012163 sequencing technique Methods 0.000 description 3
- 230000004083 survival effect Effects 0.000 description 3
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000010219 correlation analysis Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 229910001385 heavy metal Inorganic materials 0.000 description 1
- 239000002957 persistent organic pollutant Substances 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Abstract
The invention relates to a water environment microorganism detection method, a system and a medium based on multi-source data, which belong to the technical field of microorganism detection. According to the invention, the pollution condition in the current water environment is predicted according to the propagation quantity of the microorganism type symbiotic species detected by the target by detecting the microorganism type symbiotic species information, so that the pollution condition is predicted by a microorganism detection technology, the water pollution program is simplified, and the detection efficiency of the water pollution is improved.
Description
Technical Field
The invention relates to the technical field of microorganism detection, in particular to a water environment microorganism detection method, a system and a medium based on multi-source data.
Background
The microorganism detection technology used in the sewage detection mainly utilizes different reactions of microorganisms on pollutants in water to know the pollution condition of sewage quality. Microbial detection techniques involve biological, physical and environmental detection analytics knowledge. By analyzing different reactions of microorganisms in the sewage, analysis and research can be conducted, so that the harmful microorganisms in the water can be continuously bred and developed in the sewage and can be expressed in the form of molecules, and the pollutants contained in the sewage, namely the reason for sewage formation, can be clarified by analyzing and researching the molecules. Therefore, it can be seen that the microorganism detection technology has very important research opinion and function in chemical industry and urban sewage detection. However, in reality, the water is often sampled, and the sample is sampled into a laboratory, so that a great deal of time and detection cost are consumed in the process of detecting the sample by a microorganism detection technology, and the detection efficiency is low.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides a water environment microorganism detection method, a system and a medium based on multi-source data.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the invention provides a water environment microorganism detection method based on multi-source data, which comprises the following steps:
acquiring multi-source data information in a target area, acquiring image data information of a water environment in the target area according to the multi-source data information, and preprocessing the image data information of the water environment in the target area to acquire preprocessed image data information;
acquiring the microbial type detected by the target according to the multi-source data information, acquiring symbiotic species data information of the microbial type detected by the target through big data, and calculating the propagation correlation between the symbiotic species data information and the microbial type detected by the target;
constructing a species identification model, acquiring a large amount of historical species image data information, and predicting species data information in the preprocessed image data information according to the large amount of historical species image data information and the species identification model to obtain predicted species data;
generating microbial reproduction data of the current water environment area according to the reproduction correlation of the symbiotic species data information and the microbial type detected by the target and the predicted species data, and generating related regulation measures based on the microbial reproduction data of the current water environment area.
Further, in the method, the image data information of the water environment in the target area is preprocessed to obtain preprocessed image data information, which specifically includes:
dividing image data information of a water environment in a target area into a plurality of equal subsets along a spectrum dimension, and reducing redundant information of each subset by adopting an average fusion-based method in each subset to obtain dimension-reduced image data information;
dividing the dimension-reduced image data information into a plurality of groups of subsets with equal size, and carrying out de-colorization processing on a plurality of adjacent wave bands to obtain enhanced image data information;
decomposing the enhanced image data information by adopting edge preserving filtering to obtain a base layer image, subtracting the base layer image from the enhanced image data information to obtain a detail layer image, and introducing a principal component analysis method;
reconstructing the base layer image and the detail layer image by using a principal component analysis method to obtain an enhanced image of the image data of the water environment in the target area, and taking the enhanced image of the image data of the water environment in the target area as the preprocessed image data information.
Further, in the method, symbiotic species data information of the microorganism type detected by the target is obtained through big data, and the propagation correlation between the symbiotic species data information and the microorganism type detected by the target is calculated, specifically including:
Acquiring symbiotic species data information of the microorganism type detected by the target through the big data, constructing a retrieval tag according to the symbiotic species data information, and retrieving through the big data based on the retrieval tag;
acquiring co-existence statistic data of the symbiotic species data information and the microorganism type detected by the target through retrieval, and carrying out statistic analysis on the co-existence statistic data of the symbiotic species data information and the microorganism type detected by the target;
and acquiring the breeding correlation data of the symbiotic species data information and the microorganism type detected by the target through statistical analysis, and outputting the symbiotic species data information and the microorganism type detected by the target.
Further, in the method, a species identification model is constructed, a large amount of historical species image data information is obtained, and predicted species data is obtained according to the large amount of historical species image data information and species data information in the image data information after the species identification model is predicted and preprocessed, specifically comprising:
constructing a species identification model based on a deep learning network, acquiring a large amount of historical species image data information through big data, classifying the large amount of historical species image data through a decision tree model, and acquiring the historical image data information of each species;
Introducing a feature pyramid network, carrying out feature extraction on the historical image data information of the species through the feature pyramid network, obtaining the feature data information of each species, and taking the feature data information of the species as a graph node;
constructing a characteristic topological structure of each species according to undirected edge description and graph nodes through undirected edge description, acquiring an adjacency matrix of characteristic data information of the species, inputting the adjacency matrix into a species identification model for training, and acquiring a species identification model after training;
predicting species data information in the preprocessed image data information according to the trained species identification model to obtain predicted species data, and outputting the predicted species data.
Further, in the method, microorganism reproduction data of the current water environment area is generated according to the reproduction correlation of symbiotic species data information and the microorganism type detected by the target and the predicted species data, and the method specifically comprises the following steps:
acquiring predicted species data in the current water environment, counting quantity data information in the predicted species data in the current water environment, and constructing a time stamp;
acquiring the quantity data information in the predicted species data in the current water environment based on the time sequence by combining the time stamp and the quantity data information in the predicted species data in the current water environment;
Constructing propagation curve data information of predicted species data according to quantity data information in the predicted species data in the current water environment based on the time sequence, and acquiring propagation quantity information within preset time according to the propagation curve data information of the predicted species data;
and predicting the breeding data information within the preset time based on the breeding correlation of the symbiotic species data information and the microorganism type detected by the target and the breeding quantity information within the preset time.
Further, in the method, the generation of the relevant regulation and control measures based on the microorganism reproduction data of the current water environment area specifically comprises the following steps:
presetting microorganism threshold data information, acquiring microorganism reproduction data of a current water environment area, and judging whether the microorganism reproduction data of the current water environment area is larger than the microorganism threshold data information;
when the microorganism reproduction data of the current water environment area is larger than the microorganism threshold data information, presetting a plurality of water pollution level ranges, and carrying out pollution level division on the microorganism reproduction data of the current water environment area according to the water pollution level ranges to generate the pollution level of the current water environment;
acquiring a pollution type corresponding to a microorganism type which promotes current propagation in a current water body environment through big data, and acquiring a treatment scheme corresponding to the pollution type through big data;
When the pollution level of the water body is higher than the preset pollution level, generating relevant regulation measures according to the pollution level of the current water body environment and the treatment scheme corresponding to the pollution type.
The invention provides a water environment microorganism detection system based on multi-source data, which comprises a memory and a processor, wherein the memory comprises a water environment microorganism detection system method program based on the multi-source data, and when the water environment microorganism detection system method program based on the multi-source data is executed by the processor, the following steps are realized:
acquiring multi-source data information in a target area, acquiring image data information of a water environment in the target area according to the multi-source data information, and preprocessing the image data information of the water environment in the target area to acquire preprocessed image data information;
acquiring the microbial type detected by the target according to the multi-source data information, acquiring symbiotic species data information of the microbial type detected by the target through big data, and calculating the propagation correlation between the symbiotic species data information and the microbial type detected by the target;
constructing a species identification model, acquiring a large amount of historical species image data information, and predicting species data information in the preprocessed image data information according to the large amount of historical species image data information and the species identification model to obtain predicted species data;
Generating microbial reproduction data of the current water environment area according to the reproduction correlation of the symbiotic species data information and the microbial type detected by the target and the predicted species data, and generating related regulation measures based on the microbial reproduction data of the current water environment area.
Further, in the system, a species identification model is built, a large amount of historical species image data information is obtained, and predicted species data is obtained according to the large amount of historical species image data information and species data information in the image data information after the species identification model is predicted and preprocessed, specifically comprising:
constructing a species identification model based on a deep learning network, acquiring a large amount of historical species image data information through big data, classifying the large amount of historical species image data through a decision tree model, and acquiring the historical image data information of each species;
introducing a feature pyramid network, carrying out feature extraction on the historical image data information of the species through the feature pyramid network, obtaining the feature data information of each species, and taking the feature data information of the species as a graph node;
constructing a characteristic topological structure of each species according to undirected edge description and graph nodes through undirected edge description, acquiring an adjacency matrix of characteristic data information of the species, inputting the adjacency matrix into a species identification model for training, and acquiring a species identification model after training;
Predicting species data information in the preprocessed image data information according to the trained species identification model to obtain predicted species data, and outputting the predicted species data.
Further, in the system, the microbial reproduction data of the current water environment area is generated according to the reproduction correlation between the symbiotic species data information and the microbial type detected by the target and the predicted species data, and specifically includes:
acquiring predicted species data in the current water environment, counting quantity data information in the predicted species data in the current water environment, and constructing a time stamp;
acquiring the quantity data information in the predicted species data in the current water environment based on the time sequence by combining the time stamp and the quantity data information in the predicted species data in the current water environment;
constructing propagation curve data information of predicted species data according to quantity data information in the predicted species data in the current water environment based on the time sequence, and acquiring propagation quantity information within preset time according to the propagation curve data information of the predicted species data;
and predicting the breeding data information within the preset time based on the breeding correlation of the symbiotic species data information and the microorganism type detected by the target and the breeding quantity information within the preset time.
The third aspect of the present invention provides a computer readable storage medium, where the computer readable storage medium includes a multi-source data-based water environment microorganism detection method program, and when the multi-source data-based water environment microorganism detection method program is executed by a processor, the steps of any one of the multi-source data-based water environment microorganism detection methods are implemented.
The invention solves the defects existing in the background technology, and has the following beneficial effects:
according to the method, multi-source data information in a target area is obtained, image data information of a water environment in the target area is obtained according to the multi-source data information, the image data information of the water environment in the target area is preprocessed, preprocessed image data information is obtained, then a microbial species data of a target detection is obtained according to the multi-source data information, a symbiotic species data of the microbial species of the target detection is obtained through big data, propagation correlation between the symbiotic species data information and the microbial species of the target detection is calculated, so that a species identification model is constructed, a large number of historical species image data information is obtained, species data information in the preprocessed image data information is predicted according to the large number of historical species image data information and the species identification model, predicted species data is obtained, finally microbial species reproduction data of a current water environment area is generated according to the propagation correlation between the symbiotic species data information and the microbial species of the target detection and the predicted species data, and relevant regulation measures are generated based on the microbial species reproduction data of the current water environment area. According to the invention, the pollution condition in the current water environment is predicted according to the propagation quantity of the microorganism type symbiotic species detected by the target by detecting the microorganism type symbiotic species information, so that the pollution condition is predicted by a microorganism detection technology, the water pollution program is simplified, and the detection efficiency of the water pollution is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other embodiments of the drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 shows an overall method flow diagram of a water environment microorganism detection method based on multi-source data;
FIG. 2 shows a first method flow diagram of a method of aquatic environment microorganism detection based on multi-source data;
FIG. 3 shows a second method flow diagram of a method of aquatic environment microorganism detection based on multi-source data;
fig. 4 shows a system block diagram of a multi-source data based aqueous environment microorganism detection system.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
As shown in fig. 1, the first aspect of the present invention provides a method for detecting microorganisms in a water environment based on multi-source data, comprising the steps of:
s102, acquiring multi-source data information in a target area, acquiring image data information of a water environment in the target area according to the multi-source data information, and preprocessing the image data information of the water environment in the target area to acquire preprocessed image data information;
s104, acquiring the microbial type detected by the target according to the multi-source data information, acquiring symbiotic species data information of the microbial type detected by the target through big data, and calculating the propagation correlation between the symbiotic species data information and the microbial type detected by the target;
s106, constructing a species identification model, acquiring a large amount of historical species image data information, and predicting species data information in the preprocessed image data information according to the large amount of historical species image data information and the species identification model to obtain predicted species data;
S108, generating microorganism reproduction data of the current water environment area according to the reproduction correlation of the symbiotic species data information and the microorganism type detected by the target and the predicted species data, and generating related regulation measures based on the microorganism reproduction data of the current water environment area.
It is noted that the invention predicts the pollution condition in the current water environment according to the propagation quantity of the microorganism type symbiotic species detected by the target by detecting the microorganism type symbiotic species information, thereby avoiding the pollution condition predicted by the microorganism detection technology, simplifying the procedure of water pollution and improving the detection efficiency of water pollution.
Further, in the method, the image data information of the water environment in the target area is preprocessed to obtain preprocessed image data information, which specifically includes:
dividing image data information of a water environment in a target area into a plurality of equal subsets along a spectrum dimension, and reducing redundant information of each subset by adopting an average fusion-based method in each subset to obtain dimension-reduced image data information;
dividing the dimension-reduced image data information into a plurality of groups of subsets with equal size, and carrying out de-colorization processing on a plurality of adjacent wave bands to obtain enhanced image data information;
Decomposing the enhanced image data information by adopting edge preserving filtering to obtain a base layer image, subtracting the base layer image from the enhanced image data information to obtain a detail layer image, and introducing a principal component analysis method;
reconstructing the base layer image and the detail layer image by using a principal component analysis method to obtain an enhanced image of the image data of the water environment in the target area, and taking the enhanced image of the image data of the water environment in the target area as the preprocessed image data information.
The main analysis method is introduced to reduce noise and redundant information of the hyperspectral image summary, so that characteristic detail information in image data of the water environment in the target area can be enhanced.
Further, in the method, symbiotic species data information of the microorganism type detected by the target is obtained through big data, and the propagation correlation between the symbiotic species data information and the microorganism type detected by the target is calculated, specifically including:
acquiring symbiotic species data information of the microorganism type detected by the target through the big data, constructing a retrieval tag according to the symbiotic species data information, and retrieving through the big data based on the retrieval tag;
Acquiring co-existence statistic data of the symbiotic species data information and the microorganism type detected by the target through retrieval, and carrying out statistic analysis on the co-existence statistic data of the symbiotic species data information and the microorganism type detected by the target;
and acquiring the breeding correlation data of the symbiotic species data information and the microorganism type detected by the target through statistical analysis, and outputting the symbiotic species data information and the microorganism type detected by the target.
It should be noted that some microorganisms and plants are symbiotic, and that specific plants have coexisting microorganism species in different regions. While some harmful microorganisms in the polluted water environment can be continuously bred in the sewage, so that an ecological chain is formed, and the stronger the plant which is symbiotic with the related microorganism type is bred, the more the corresponding microorganism is bred. Through statistical analysis, the correlation data of the plant propagation quantity and the microorganism propagation quantity can be obtained. According to the invention, the pollution condition in the current water environment is predicted according to the propagation quantity of the microorganism type symbiotic species detected by the target by detecting the microorganism type symbiotic species information, so that the pollution condition is predicted by a microorganism detection technology, the water pollution program is simplified, and the detection efficiency of the water pollution is improved.
As shown in fig. 2, further, in the method, a species identification model is constructed, a large amount of historical species image data information is obtained, and predicted species data is obtained according to the large amount of historical species image data information and species data information in the image data information after the species identification model prediction preprocessing, which specifically includes:
s202, constructing a species identification model based on a deep learning network, acquiring a large amount of historical species image data information through big data, classifying the large amount of historical species image data through a decision tree model, and acquiring the historical image data information of each species;
s204, introducing a feature pyramid network, carrying out feature extraction on the historical image data information of the species through the feature pyramid network, obtaining the feature data information of each species, and taking the feature data information of the species as a graph node;
s206, constructing a characteristic topological structure of each species according to the undirected edge description and the graph nodes through undirected edge description, acquiring an adjacent matrix of characteristic data information of the species, inputting the adjacent matrix into the species identification model for training, and acquiring a trained species identification model;
s208, predicting species data information in the preprocessed image data information according to the trained species identification model to obtain predicted species data, and outputting the predicted species data.
The species recognition model can be constructed by the method to recognize the photographed image, wherein the photographing process can be performed by remote sensing technology, unmanned aerial vehicle remote sensing technology, cameras and the like.
As shown in fig. 3, in the method, further, the generating the microorganism reproduction data of the current water environment area according to the reproduction correlation between the symbiotic species data information and the microorganism type detected by the target and the predicted species data specifically includes:
s302, acquiring predicted species data in the current water environment, counting quantity data information in the predicted species data in the current water environment, and constructing a time stamp;
s304, acquiring the quantity data information in the predicted species data in the current water environment based on the time sequence by combining the time stamp and the quantity data information in the predicted species data in the current water environment;
s306, constructing propagation curve data information of predicted species data according to quantity data information in the predicted species data in the current water environment based on the time sequence, and acquiring propagation quantity information within preset time according to the propagation curve data information of the predicted species data;
and S308, predicting the breeding data information within the preset time based on the breeding correlation of the symbiotic species data information and the microorganism type detected by the target and the breeding quantity information within the preset time.
The invention predicts the pollution condition in the current water environment by the breeding quantity of the symbiotic species of the microorganism type detected by the target, avoids the situation of predicting pollution by a microorganism detection technology, simplifies the procedure of water pollution and improves the detection efficiency of water pollution. And predicting the propagation data information within the preset time according to the propagation correlation between the symbiotic species data information and the microorganism type detected by the target and the propagation quantity information within the preset time, wherein the propagation data information within the preset time can be the propagation quantity of microorganisms within a unit volume and can also be the estimated total quantity in the current water environment.
Further, in the method, the generation of the relevant regulation and control measures based on the microorganism reproduction data of the current water environment area specifically comprises the following steps:
presetting microorganism threshold data information, acquiring microorganism reproduction data of a current water environment area, and judging whether the microorganism reproduction data of the current water environment area is larger than the microorganism threshold data information;
when the microorganism reproduction data of the current water environment area is larger than the microorganism threshold data information, presetting a plurality of water pollution level ranges, and carrying out pollution level division on the microorganism reproduction data of the current water environment area according to the water pollution level ranges to generate the pollution level of the current water environment;
Acquiring a pollution type corresponding to a microorganism type which promotes current propagation in a current water body environment through big data, and acquiring a treatment scheme corresponding to the pollution type through big data;
when the pollution level of the water body is higher than the preset pollution level, generating relevant regulation measures according to the pollution level of the current water body environment and the treatment scheme corresponding to the pollution type.
When the microbial data in a certain area is predicted to be greater than the microbial threshold data information, the water environment is indicated to exist a certain type of pollutants such as organic pollutants, heavy metal pollutants and the like, and the water pollution grade range comprises no pollution, low grade pollution, medium grade pollution and high grade pollution.
In addition, the invention can also comprise the following steps:
acquiring influence factor data information influencing the survival of the symbiotic species data of the microorganism type detected by the target through big data, introducing an analytic hierarchy process, and calculating influence weight information of the influence factor data information on the survival of the symbiotic species data through the analytic hierarchy process; introducing a gray correlation analysis method, acquiring the correlation of the influence factor data information and the survival of the symbiotic species data according to the influence weight information by the gray correlation analysis method, and generating the symbiotic correlation of the influence factor data and the symbiotic species; acquiring parameter information of influence factor data of the water environment within a preset time through big data, and calculating influence weight information according to the parameter information of the influence factor data of the water environment within the preset time and symbiotic correlation of the influence factor data and symbiotic species; and correcting the microorganism reproduction data of the current water environment area according to the influence weight information, so as to improve the prediction accuracy of the microorganism reproduction data. The influence factor data information includes data such as temperature and humidity.
In addition, the invention can also comprise the following steps:
acquiring pollution characteristic data information of each water environment in a target area, classifying the pollution characteristic data information of the water environments to acquire a classification result, and judging whether volatile pollution characteristic data exist in the classification result;
when volatile pollution characteristic data exist, acquiring environmental factor data in each water environment in a target area, acquiring the volatile characteristic information of each pollution characteristic data under each environmental factor data through big data, and constructing a database;
the volatilization characteristic information of the pollution characteristic data under the environmental factor data is input into the database for storage, the environmental factor data in each water environment in the target area is input into the database for data matching, and the volatilization characteristic information of the pollution of each water environment area is obtained;
sequencing from large to small according to the volatilization characteristic information of the water environment area pollution, generating a sequencing result, generating a related treatment priority according to the sequencing result, and treating the water environment based on the treatment priority.
It should be noted that, the volatile characteristic of the volatile pollution has a certain correlation with the environmental factor, for example, the higher the volatile characteristic of the volatile pollution is affected by the too high temperature, the higher the volatile amount in unit time is, the volatile amount of the volatile pollutant is in unit time, the higher the volatile characteristic information is, the more easily the volatile characteristic is, the pollution volatilizes into the air, thus the more difficult to treat.
As shown in fig. 4, the second aspect of the present invention provides a multi-source data-based water environment microorganism detection system 4, where the multi-source data-based water environment microorganism detection system 4 includes a memory 41 and a processor 42, the memory 41 includes a multi-source data-based water environment microorganism detection system method program, and when the multi-source data-based water environment microorganism detection system method program is executed by the processor 42, the following steps are implemented:
acquiring multi-source data information in a target area, acquiring image data information of a water environment in the target area according to the multi-source data information, and preprocessing the image data information of the water environment in the target area to acquire preprocessed image data information;
acquiring the microbial type detected by the target according to the multi-source data information, acquiring symbiotic species data information of the microbial type detected by the target through big data, and calculating the propagation correlation between the symbiotic species data information and the microbial type detected by the target;
constructing a species identification model, acquiring a large amount of historical species image data information, and predicting species data information in the preprocessed image data information according to the large amount of historical species image data information and the species identification model to obtain predicted species data;
Generating microbial reproduction data of the current water environment area according to the reproduction correlation of the symbiotic species data information and the microbial type detected by the target and the predicted species data, and generating related regulation measures based on the microbial reproduction data of the current water environment area.
Further, in the system, a species identification model is built, a large amount of historical species image data information is obtained, and predicted species data is obtained according to the large amount of historical species image data information and species data information in the image data information after the species identification model is predicted and preprocessed, specifically comprising:
constructing a species identification model based on a deep learning network, acquiring a large amount of historical species image data information through big data, classifying the large amount of historical species image data through a decision tree model, and acquiring the historical image data information of each species;
introducing a feature pyramid network, carrying out feature extraction on the historical image data information of the species through the feature pyramid network, obtaining the feature data information of each species, and taking the feature data information of the species as a graph node;
constructing a characteristic topological structure of each species according to undirected edge description and graph nodes through undirected edge description, acquiring an adjacency matrix of characteristic data information of the species, inputting the adjacency matrix into a species identification model for training, and acquiring a species identification model after training;
Predicting species data information in the preprocessed image data information according to the trained species identification model to obtain predicted species data, and outputting the predicted species data.
Further, in the system, the microbial reproduction data of the current water environment area is generated according to the reproduction correlation between the symbiotic species data information and the microbial type detected by the target and the predicted species data, and specifically includes:
acquiring predicted species data in the current water environment, counting quantity data information in the predicted species data in the current water environment, and constructing a time stamp;
acquiring the quantity data information in the predicted species data in the current water environment based on the time sequence by combining the time stamp and the quantity data information in the predicted species data in the current water environment;
constructing propagation curve data information of predicted species data according to quantity data information in the predicted species data in the current water environment based on the time sequence, and acquiring propagation quantity information within preset time according to the propagation curve data information of the predicted species data;
and predicting the breeding data information within the preset time based on the breeding correlation of the symbiotic species data information and the microorganism type detected by the target and the breeding quantity information within the preset time.
The third aspect of the present invention provides a computer readable storage medium, where the computer readable storage medium includes a multi-source data-based water environment microorganism detection method program, and when the multi-source data-based water environment microorganism detection method program is executed by a processor, the steps of any one of the multi-source data-based water environment microorganism detection methods are implemented.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the above-described integrated units of the present invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
The foregoing is merely illustrative embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think about variations or substitutions within the technical scope of the present invention, and the invention should be covered. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (6)
1. The water environment microorganism detection method based on the multi-source data is characterized by comprising the following steps of:
acquiring multi-source data information in a target area, acquiring image data information of a water environment in the target area according to the multi-source data information, and preprocessing the image data information of the water environment in the target area to acquire preprocessed image data information;
acquiring the microbial type detected by the target according to the multi-source data information, acquiring symbiotic species data information of the microbial type detected by the target through big data, and calculating the propagation correlation between the symbiotic species data information and the microbial type detected by the target;
constructing a species identification model, acquiring a large amount of historical species image data information, and predicting species data information in the preprocessed image data information according to the large amount of historical species image data information and the species identification model to obtain predicted species data;
Generating microbial reproduction data of the current water environment area according to the reproduction correlation of the symbiotic species data information and the microbial type detected by the target and the predicted species data, and generating related regulation measures based on the microbial reproduction data of the current water environment area;
constructing a species identification model, acquiring a large amount of historical species image data information, and predicting species data information in the preprocessed image data information according to the large amount of historical species image data information and the species identification model to obtain predicted species data, wherein the method specifically comprises the following steps of:
constructing a species identification model based on a deep learning network, acquiring a large amount of historical species image data information through big data, classifying the large amount of historical species image data through a decision tree model, and acquiring the historical image data information of each species;
introducing a feature pyramid network, carrying out feature extraction on historical image data information of the species through the feature pyramid network, obtaining feature data information of each species, and taking the feature data information of the species as a graph node;
constructing a characteristic topological structure of each species according to the undirected edge description and graph nodes through undirected edge description, acquiring an adjacency matrix of characteristic data information of the species, inputting the adjacency matrix into the species identification model for training, and acquiring a trained species identification model;
Predicting species data information in the preprocessed image data information according to the trained species identification model to obtain predicted species data, and outputting the predicted species data;
generating microbial reproduction data of the current water environment area according to the reproduction correlation of the symbiotic species data information and the microbial type detected by the target and the predicted species data, wherein the microbial reproduction data specifically comprises the following steps:
acquiring predicted species data in the current water environment, counting quantity data information in the predicted species data in the current water environment, and constructing a time stamp;
acquiring the quantity data information in the predicted species data in the current water environment based on the time sequence by combining the time stamp and the quantity data information in the predicted species data in the current water environment;
constructing propagation curve data information of predicted species data according to the quantity data information in the predicted species data in the current water environment based on the time sequence, and acquiring propagation quantity information within preset time according to the propagation curve data information of the predicted species data;
and predicting the breeding data information within the preset time based on the breeding correlation of the symbiotic species data information and the microbial type detected by the target and the breeding quantity information within the preset time.
2. The multi-source data-based water environment microorganism detection method according to claim 1, wherein the preprocessing is performed on the image data information of the water environment in the target area to obtain the preprocessed image data information, and specifically comprises the following steps:
dividing the image data information of the water environment in the target area into a plurality of equal subsets along a spectrum dimension, and reducing redundant information of each subset by adopting an average fusion-based method in each subset to obtain the image data information after dimension reduction;
dividing the dimension-reduced image data information into a plurality of groups of subsets with equal size, and carrying out de-colorization processing on a plurality of adjacent wave bands to obtain enhanced image data information;
decomposing the enhanced image data information by adopting edge preserving filtering to obtain a base layer image, subtracting the base layer image from the enhanced image data information to obtain a detail layer image, and introducing a principal component analysis method;
reconstructing the base layer image and the detail layer image through the principal component analysis method to obtain an enhanced image of image data of the water environment in the target area, and taking the enhanced image of the image data of the water environment in the target area as preprocessed image data information.
3. The multi-source data-based water environment microorganism detection method according to claim 1, wherein the symbiotic species data information of the microorganism type detected by the target is obtained through big data, and the propagation correlation between the symbiotic species data information and the microorganism type detected by the target is calculated, specifically comprising:
acquiring symbiotic species data information of the microorganism type detected by the target through big data, constructing a retrieval tag according to the symbiotic species data information, and retrieving through big data based on the retrieval tag;
acquiring the symbiotic species data information and the coexisting statistical data of the microorganism type detected by the target through retrieval, and carrying out statistical analysis on the symbiotic species data information and the coexisting statistical data of the microorganism type detected by the target;
and acquiring the breeding correlation data of the symbiotic species data information and the microorganism type detected by the target through statistical analysis, and outputting the symbiotic species data information and the microorganism type detected by the target.
4. The multi-source data-based aqueous environment microorganism detection method of claim 1, wherein generating relevant regulatory measures based on microorganism reproduction data of the current aqueous environment region specifically comprises:
Presetting microorganism threshold data information, acquiring microorganism reproduction data of a current water environment area, and judging whether the microorganism reproduction data of the current water environment area is larger than the microorganism threshold data information;
when the microorganism reproduction data of the current water environment area is larger than the microorganism threshold data information, presetting a plurality of water pollution level ranges, and carrying out pollution level division on the microorganism reproduction data of the current water environment area according to the water pollution level ranges to generate the pollution level of the current water environment;
acquiring a pollution type corresponding to a microorganism type which promotes current propagation in a current water body environment through big data, and acquiring a treatment scheme corresponding to the pollution type through big data;
when the pollution level of the water body is higher than a preset pollution level, generating relevant regulation measures according to the pollution level of the current water body environment and a treatment scheme corresponding to the pollution type.
5. The water environment microorganism detection system based on the multi-source data is characterized by comprising a memory and a processor, wherein the memory comprises a water environment microorganism detection system method program based on the multi-source data, and when the water environment microorganism detection system method program based on the multi-source data is executed by the processor, the following steps are realized:
Acquiring multi-source data information in a target area, acquiring image data information of a water environment in the target area according to the multi-source data information, and preprocessing the image data information of the water environment in the target area to acquire preprocessed image data information;
acquiring the microbial type detected by the target according to the multi-source data information, acquiring symbiotic species data information of the microbial type detected by the target through big data, and calculating the propagation correlation between the symbiotic species data information and the microbial type detected by the target;
constructing a species identification model, acquiring a large amount of historical species image data information, and predicting species data information in the preprocessed image data information according to the large amount of historical species image data information and the species identification model to obtain predicted species data;
generating microbial reproduction data of the current water environment area according to the reproduction correlation of the symbiotic species data information and the microbial type detected by the target and the predicted species data, and generating related regulation measures based on the microbial reproduction data of the current water environment area;
constructing a species identification model, acquiring a large amount of historical species image data information, and predicting species data information in the preprocessed image data information according to the large amount of historical species image data information and the species identification model to obtain predicted species data, wherein the method specifically comprises the following steps of:
Constructing a species identification model based on a deep learning network, acquiring a large amount of historical species image data information through big data, classifying the large amount of historical species image data through a decision tree model, and acquiring the historical image data information of each species;
introducing a feature pyramid network, carrying out feature extraction on historical image data information of the species through the feature pyramid network, obtaining feature data information of each species, and taking the feature data information of the species as a graph node;
constructing a characteristic topological structure of each species according to the undirected edge description and graph nodes through undirected edge description, acquiring an adjacency matrix of characteristic data information of the species, inputting the adjacency matrix into the species identification model for training, and acquiring a trained species identification model;
predicting species data information in the preprocessed image data information according to the trained species identification model to obtain predicted species data, and outputting the predicted species data;
generating microbial reproduction data of the current water environment area according to the reproduction correlation of the symbiotic species data information and the microbial type detected by the target and the predicted species data, wherein the microbial reproduction data specifically comprises the following steps:
Acquiring predicted species data in the current water environment, counting quantity data information in the predicted species data in the current water environment, and constructing a time stamp;
acquiring the quantity data information in the predicted species data in the current water environment based on the time sequence by combining the time stamp and the quantity data information in the predicted species data in the current water environment;
constructing propagation curve data information of predicted species data according to the quantity data information in the predicted species data in the current water environment based on the time sequence, and acquiring propagation quantity information within preset time according to the propagation curve data information of the predicted species data;
and predicting the breeding data information within the preset time based on the breeding correlation of the symbiotic species data information and the microbial type detected by the target and the breeding quantity information within the preset time.
6. A computer readable storage medium, wherein the computer readable storage medium includes a multi-source data based water environment microorganism detection method program, and when the multi-source data based water environment microorganism detection method program is executed by a processor, the steps of the multi-source data based water environment microorganism detection method according to any one of claims 1-4 are implemented.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311445525.1A CN117171675B (en) | 2023-11-02 | 2023-11-02 | Water environment microorganism detection method, system and medium based on multi-source data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311445525.1A CN117171675B (en) | 2023-11-02 | 2023-11-02 | Water environment microorganism detection method, system and medium based on multi-source data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117171675A CN117171675A (en) | 2023-12-05 |
| CN117171675B true CN117171675B (en) | 2024-01-12 |
Family
ID=88939791
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311445525.1A Active CN117171675B (en) | 2023-11-02 | 2023-11-02 | Water environment microorganism detection method, system and medium based on multi-source data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117171675B (en) |
Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006325529A (en) * | 2005-05-27 | 2006-12-07 | Gifu Univ | Method for detecting organism species, microarray used therefor and kit for detecting organism species |
| JP2012208046A (en) * | 2011-03-30 | 2012-10-25 | Fujitsu Ltd | Plant species identification method and plant species identification device |
| CN109182443A (en) * | 2018-08-27 | 2019-01-11 | 江苏大学 | A method of the transducer production method of monitoring spoilage organisms breeding and quickly detection spoilage organisms |
| CN109559096A (en) * | 2018-11-22 | 2019-04-02 | 贵州省食品安全检测应用工程技术研究中心有限公司 | A kind of ecology cloud platform data analysis application system |
| CN110376343A (en) * | 2019-06-04 | 2019-10-25 | 中国环境科学研究院 | The accurate diagnostic method of pollution sources, device and electronic device |
| CN110717533A (en) * | 2019-09-27 | 2020-01-21 | 广东聚源管业实业有限公司 | Water body purification method based on image recognition pipeline device |
| WO2021051609A1 (en) * | 2019-09-20 | 2021-03-25 | 平安科技（深圳）有限公司 | Method and apparatus for predicting fine particulate matter pollution level, and computer device |
| CN114997536A (en) * | 2022-08-01 | 2022-09-02 | 中国水产科学研究院南海水产研究所 | Culture tail water pollution detection method, system and medium |
| CN115100512A (en) * | 2022-05-16 | 2022-09-23 | 中国水产科学研究院南海水产研究所 | Monitoring, identifying and catching method and system for marine economic species and storage medium |
| CN115315526A (en) * | 2019-12-02 | 2022-11-08 | 佰优米利斯卡皮塔尔格鲁普有限公司 | Method for identifying microorganisms in clinical and non-clinical settings |
| CN115424143A (en) * | 2022-08-29 | 2022-12-02 | 南方海洋科学与工程广东省实验室(广州) | Water source pollution tracing method and device, storage medium and computer equipment |
| CN115494047A (en) * | 2022-11-17 | 2022-12-20 | 广东博创佳禾科技有限公司 | Detection method and system for water environment agricultural pollutants |
| CN116384754A (en) * | 2023-06-02 | 2023-07-04 | 北京建工环境修复股份有限公司 | Deep learning-based environmental pollution risk assessment method in chemical industry park |
| CN116385689A (en) * | 2023-06-02 | 2023-07-04 | 北京建工环境修复股份有限公司 | Visual information management method, system and medium for site pollution data |
| CN116947220A (en) * | 2023-07-13 | 2023-10-27 | 河海大学 | Method for restoring ecological environment of water body based on subsurface flow microbial community |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11361547B2 (en) * | 2017-12-08 | 2022-06-14 | Nec Communication Systems, Ltd. | Object detection apparatus, prediction model generation apparatus, object detection method, and program |
| TWI694344B (en) * | 2018-10-26 | 2020-05-21 | 財團法人資訊工業策進會 | Apparatus and method for detecting impact factor for an operating environment |
| US20230007082A1 (en) * | 2021-06-30 | 2023-01-05 | Microsoft Technology Licensing, Llc | Pollutant sensor placement |
-
2023
- 2023-11-02 CN CN202311445525.1A patent/CN117171675B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006325529A (en) * | 2005-05-27 | 2006-12-07 | Gifu Univ | Method for detecting organism species, microarray used therefor and kit for detecting organism species |
| JP2012208046A (en) * | 2011-03-30 | 2012-10-25 | Fujitsu Ltd | Plant species identification method and plant species identification device |
| CN109182443A (en) * | 2018-08-27 | 2019-01-11 | 江苏大学 | A method of the transducer production method of monitoring spoilage organisms breeding and quickly detection spoilage organisms |
| CN109559096A (en) * | 2018-11-22 | 2019-04-02 | 贵州省食品安全检测应用工程技术研究中心有限公司 | A kind of ecology cloud platform data analysis application system |
| CN110376343A (en) * | 2019-06-04 | 2019-10-25 | 中国环境科学研究院 | The accurate diagnostic method of pollution sources, device and electronic device |
| WO2021051609A1 (en) * | 2019-09-20 | 2021-03-25 | 平安科技（深圳）有限公司 | Method and apparatus for predicting fine particulate matter pollution level, and computer device |
| CN110717533A (en) * | 2019-09-27 | 2020-01-21 | 广东聚源管业实业有限公司 | Water body purification method based on image recognition pipeline device |
| CN115315526A (en) * | 2019-12-02 | 2022-11-08 | 佰优米利斯卡皮塔尔格鲁普有限公司 | Method for identifying microorganisms in clinical and non-clinical settings |
| CN115100512A (en) * | 2022-05-16 | 2022-09-23 | 中国水产科学研究院南海水产研究所 | Monitoring, identifying and catching method and system for marine economic species and storage medium |
| CN114997536A (en) * | 2022-08-01 | 2022-09-02 | 中国水产科学研究院南海水产研究所 | Culture tail water pollution detection method, system and medium |
| CN115424143A (en) * | 2022-08-29 | 2022-12-02 | 南方海洋科学与工程广东省实验室(广州) | Water source pollution tracing method and device, storage medium and computer equipment |
| CN115494047A (en) * | 2022-11-17 | 2022-12-20 | 广东博创佳禾科技有限公司 | Detection method and system for water environment agricultural pollutants |
| CN116384754A (en) * | 2023-06-02 | 2023-07-04 | 北京建工环境修复股份有限公司 | Deep learning-based environmental pollution risk assessment method in chemical industry park |
| CN116385689A (en) * | 2023-06-02 | 2023-07-04 | 北京建工环境修复股份有限公司 | Visual information management method, system and medium for site pollution data |
| CN116947220A (en) * | 2023-07-13 | 2023-10-27 | 河海大学 | Method for restoring ecological environment of water body based on subsurface flow microbial community |
Non-Patent Citations (1)
| Title |
|---|
| 流域水环境与生态学研究回顾与展望;彭文启等;水利学报;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117171675A (en) | 2023-12-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Sivapragasam et al. | Prediction of algal blooms using genetic programming | |
| CN116186566A (en) | Diffusion prediction method and system based on deep learning | |
| Zhao et al. | Comparison between optimized MaxEnt and random forest modeling in predicting potential distribution: A case study with Quasipaa boulengeri in China | |
| CN116363440B (en) | Deep learning-based identification and detection method and system for colored microplastic in soil | |
| CN116384776B (en) | Site pollution evaluation method, system and storage medium based on fuzzy evaluation | |
| CN115494047B (en) | Detection method and system for agricultural pollutants in water environment | |
| CN116168771B (en) | Method and system for monitoring pollution of perfluorinated compounds in river based on Internet of things | |
| CN115424143A (en) | Water source pollution tracing method and device, storage medium and computer equipment | |
| Shi et al. | Amur tiger stripes: Individual identification based on deep convolutional neural network | |
| CN116385863B (en) | Deep learning-based method and system for identifying microplastic in water body | |
| CN117172992B (en) | Microecological characteristic investigation method and system for polluted farmland soil | |
| CN116629619A (en) | Method and system for predicting occurrence condition of new soil pollutants | |
| CN115602337A (en) | Cryptocaryon irritans disease early warning method and system based on machine learning | |
| CN115308101A (en) | Water quality monitoring management method and system | |
| CN115170926A (en) | Lightweight target image recognition method, device and storage medium | |
| CN117171675B (en) | Water environment microorganism detection method, system and medium based on multi-source data | |
| CN115937692B (en) | Coastal wetland carbon sink effect evaluation method and system | |
| CN116384775A (en) | Management method, system and storage medium of pollution site monitoring equipment | |
| CN117172430B (en) | Deep learning-based water body environment assessment and prediction method and system | |
| CN117172578B (en) | Soil ecological environment restoration method and system based on microorganism identification | |
| CN117172990B (en) | Method and system for predicting migration of antibiotic pollution in groundwater environment | |
| Ung et al. | Leverage samples with single positive labels to train cnn-based models for multi-label plant species prediction | |
| CN117172996B (en) | Microbial activity identification monitoring method and system for ecological environment restoration | |
| CN117172429B (en) | Sewage treatment method and system based on bioinformatics | |
| Si-Moussi et al. | Plant recommendation using the environment and biotic associations |
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 |