CN116933042A - Water ecology evaluation method and system based on deep learning algorithm - Google Patents
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Abstract
The application discloses a water ecology evaluation method and a system based on a deep learning algorithm, wherein the water ecology evaluation method based on the deep learning algorithm comprises the following steps: acquiring aquatic organism monitoring data sets, and dividing the aquatic organism data sets to acquire three different data sets; adding optimal individuals and worst individuals into the three divided different data sets to obtain three new data sets; performing data standardization processing on three new data sets; establishing a variable self-encoder for three new data sets after data standardization is completed; obtaining the distance between each individual point and the optimal individual, and the distance between each individual point and the point corresponding to the worst individual; obtaining distance weights of different species; obtaining a projection distance; and obtaining comprehensive evaluation scores according to the distance weights and the projection distances of different species. The application takes the monitoring data of all biological groups into consideration, and considers the influence of different species, so that the obtained aquatic organism evaluation result is more accurate.
Description
Technical Field
The application relates to the field of data processing, in particular to a water ecology evaluation method and a water ecology evaluation system based on a deep learning algorithm.
Background
In the prior art, elements for evaluating the quality of the water ecological environment of a river mainly comprise physical and chemical parameters of a water body, physical habitat and biological groups. The water physical and chemical parameters and physical habitat can be scored by unified relevant standards or the same scoring system established by an environmental supervision organization, so that no deviation problem exists in calculating the final score. There are different index systems for biota groups, including BMWP scoring system, chandler biological index, shannon-Winner diversity index, hilsenhoff index, palmer algae pollution index, and biological integrity index. Different index systems are different for biological species, and the emphasis of index calculation is different, so that different index systems are selected for calculation for different watercourses. When the aquatic organism index is calculated, an index system adapting to the characteristic is selected according to the characteristics of the river basin, or the index system suitable for the river basin is selected purely according to the advice of a river basin environment responsible department or the experience of an environmental protection expert, and the index system with a higher score is promoted to be selected in the condition, so that unified, objective and reasonable evaluation and ranking of all the river basins in the whole country cannot be realized. Aquatic organisms in a body of water include phytoplankton, zooplankton, benthonic animals, fish and large aquatic weeds, but the above-mentioned index system does not perform comprehensive calculation on all aquatic organisms, for example, a BMWP integral system and a Chandler biological index are only suitable for evaluating large benthonic animals, but the types of data are different, the former is used for scoring and evaluating qualitative monitoring data, and the latter is used for quantitatively monitoring data. While Palmer evaluates the results of qualitative monitoring of algae. It is contemplated that the same scoring style will also tend to be different for several basins that select the same scoring style. And for the water body in the flow domain, the scoring mode also causes the phenomenon of unfair evaluation.
Therefore, how to provide a unified, reasonable and objective evaluation method for the quality of the water ecological environment in all the watercourses is an urgent problem to be solved in the field.
Disclosure of Invention
The application provides a water ecology evaluation method based on a deep learning algorithm, which comprises the following steps:
acquiring aquatic organism monitoring data sets, and dividing the aquatic organism data sets to acquire three different data sets; adding optimal individuals and worst individuals into the three divided different data sets to obtain three new data sets; performing data standardization processing on three new data sets; establishing a variable self-encoder for three new data sets after data standardization is completed; in response to completion of the establishment of the variation self-encoder, obtaining distances between the point of each individual in the new data set and the optimal individual, and between the point of each individual and the point corresponding to the worst individual; obtaining distance weights of different species according to the distances between each individual point in the new data set and the optimal individual and the distances between each individual point and the point corresponding to the worst individual; obtaining projection distances according to the distances between the point of each individual in the new data set and the optimal individual and the distances between the point of each individual and the point corresponding to the worst individual; and obtaining comprehensive evaluation scores according to the distance weights and the projection distances of different species.
As above, wherein an aquatic organism monitoring dataset is obtained which is to be evaluatedDividing the aquatic organism monitoring data set into common species, contaminant-resistant species and invasive species data sets according to different attributes of aquatic organisms, respectivelyRepresentation of->For n evaluation of the water body->A data set of different aquatic organisms, and +.>。
As above, adding the optimal individual and the worst individual in the three different partitioned data sets to obtain three new data sets includes adding the optimal individual and the worst individual in the common species, contaminant-resistant species and invasive species data sets, respectively.
As above, three new data sets were each data normalized for aquatic organisms according to the following equation:
，
where d represents the old dataset element, s is the new dataset element,monitoring data for aquatic organism identification i for individual j identified as k for dataset,/>For normalized data, +.>A monitoring data sequence for all individuals identified as k for the dataset with aquatic organisms identified as i,/>The structured standardized dataset is defined as +.>A, b, c are symbols of species, a is common, b is stain resistant and c is intrusion.
As above, the variational self-encoder is built for three new data sets after data normalization is completed, and the point of each individual on the new two-dimensional space is acquired.
The water ecology evaluation system based on the deep learning algorithm comprises an original data set acquisition unit, a new data set acquisition unit, a standardized processing unit, a variation self-encoder establishment unit, a corresponding point distance acquisition unit, a distance weight acquisition unit, a projection distance acquisition unit and a comprehensive evaluation score acquisition unit; the original data set acquisition unit is used for acquiring aquatic organism monitoring data sets and dividing the aquatic organism data sets to acquire three different data sets; the new data set acquisition unit is used for adding an optimal individual and a worst individual into the three different divided data sets to obtain three new data sets; the standardized processing unit is used for carrying out data standardization processing on three new data sets; the variable self-encoder establishing unit is used for establishing variable self-encoders for three new data sets after data standardization is completed; the corresponding point distance acquisition unit is used for acquiring the distance between each individual point in the new data set and the optimal individual, and the distance between each individual point and the point corresponding to the worst individual; the distance weight acquisition unit is used for acquiring the distance weights of different species according to the distance between the point of each individual in the new data set and the point corresponding to the optimal individual and the point of each individual and the point corresponding to the worst individual; the projection distance acquisition unit is used for acquiring the projection distance according to the distance between the point of each individual in the new data set and the point corresponding to the optimal individual and the point of each individual and the point corresponding to the worst individual; the comprehensive evaluation score acquisition unit is used for acquiring comprehensive evaluation scores according to the distance weights and the projection distances of different species.
As above, in the raw data set acquisition unit, an aquatic organism monitoring data set to be evaluated is obtainedDividing the aquatic organism monitoring data set into common species, contaminant resistant species and invasive species data sets according to different attributes of aquatic organism, and using +.>Representation of->For n evaluation of the water body->A data set of different aquatic organisms, and +.>。
As described above, the new data set obtaining unit adds the optimal individual and the worst individual in the three different data sets after the division, and the obtaining of the three new data sets includes adding the optimal individual and the worst individual in the common species, the contamination resistant species, and the invasive species data sets, respectively.
As above, in the normalization processing unit, three new data sets are respectively normalized for aquatic organisms according to the following formula:
，
where d represents the old dataset element, s is the new dataset element,monitoring data for aquatic organism identification i for individual j identified as k for dataset,/>For normalized data, +.>A monitoring data sequence for all individuals identified as k for the dataset with aquatic organisms identified as i,/>The structured standardized dataset is defined as +.>A, b, c are symbols of species, a is common, b is stain resistant and c is intrusion.
As described above, the variation self-encoder creation unit creates the variation self-encoder for three new data sets after the data normalization is completed, and obtains the point of each individual on the new two-dimensional space.
The application has the following beneficial effects:
the application can realize unified and reasonable objective evaluation on all the drainage basins through the established evaluation method; meanwhile, the evaluation method provided by the application takes the monitoring data of all biological groups into account, and considers the influence of different species, so that the evaluation result of the obtained aquatic organisms is more accurate.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
FIG. 1 is a flow chart of a water ecology evaluation method based on a deep learning algorithm provided in accordance with an embodiment of the application;
fig. 2 is a schematic diagram of an internal structure of a water ecology evaluation system based on a deep learning algorithm according to an embodiment of the application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Example 1
As shown in fig. 1, the water ecology evaluation method based on the deep learning algorithm provided in this embodiment specifically includes the following steps:
step S110: the aquatic organism monitoring data set is acquired, and three different data sets are acquired by dividing the aquatic organism data set.
Wherein an aquatic monitoring dataset is obtained which is to be evaluatedDividing the aquatic organism monitoring data set into common species, contaminant-resistant species and invasive species data sets according to different attributes of aquatic organisms, respectivelyRepresentation of->For n evaluation of the water body->A data set of different aquatic organisms, and +.>。
Step S120: adding optimal individuals and worst individuals into the three different data sets after division to obtain three new data sets.
Wherein the optimal individual and the worst individual are added to the common species, contaminant-resistant species, and invasive species data sets (the common species, contaminant-resistant species, and invasive species data sets are collectively referred to as "old data sets"), respectively (the data sets after adding the optimal individual and the worst individual are collectively referred to as "new data sets").
In the common species data set, the monitoring number of one aquatic organism of the optimal individual is the maximum monitoring data of the same aquatic organism of other individuals; the monitoring number of certain aquatic organisms of the worst individuals is the minimum monitoring data of the same aquatic organisms of other individuals.
Adding optimal individuals and worst individuals in common speciation data set to form new common speciation data set。
The optimal individual aquatic organism monitoring number is the minimum monitoring data of the same aquatic organism of other individuals divided by the stain resistance value corresponding to each species according to the stain resistance value of the species in the stain resistance seed data set; the monitoring number of certain aquatic organisms of the worst individuals is the maximum monitoring data of the same aquatic organisms of other individuals multiplied by the stain resistance value corresponding to the species.
Adding optimal individuals and worst individuals into the soil-resistant seed data set to form a new soil-resistant seed data set。
In the invasive species data set, the monitoring number of a certain aquatic organism of the optimal individual is the minimum monitoring data of the same aquatic organism of other individuals; the monitoring number of the aquatic organisms of the worst individuals is the maximum monitoring data of the same aquatic organisms of other individuals.
Adding optimal individuals and worst individuals in invasive species dataset formation to form new invasive species dataset。
Step S130: data normalization processing is performed on the three new data sets.
Three new data sets were normalized separately for aquatic organisms according to the following equation:
，
where d represents the old dataset element, s is the new dataset element,the aquatic organisms identified as k in the old dataset are identified as i (since the species of the old three datasets are different for different regions, some species are common in region a, and may be invasive in region B, so when k is a, the common dataset, k is B the stain resistant dataset,k is c is the intrusion data set)>For normalized data, +.>A monitoring data sequence for all individuals identified as k for the dataset with aquatic organisms identified as i,/>The structured standardized dataset is defined as +.>。
The data normalization of the three new data sets is accomplished by the above formula.
Step S140: and establishing a variational self-encoder for three new data sets after data normalization is completed.
Wherein the variational self-encoder is built for each of the three new data sets. The variable self-encoder is a self-encoder in the prior art, and the specific structure is not described here.
Building and training different variational self-encoder models for three new data sets to obtain points of each individual on a new two-dimensional spaceThe method is specifically expressed as follows:
，
wherein the method comprises the steps ofFor the common species (or contaminant resistant species), the invasive species data set is identified as +.>Is self-contained in the encoder, +.>Representative objectIndividual whose species is identified as h->Data of->Mapped to points in two dimensions.
And establishing three new variable self-encoders of the data sets through the formula.
Step S150: and in response to completion of the establishment of the variation self-encoder, obtaining the distances between the point of each individual in the new data set and the optimal individual, and between the point of each individual and the point corresponding to the worst individual.
Each new dataset has an optimum, worst individuals, definitionsThe mapping values obtained by the variational self-encoder are respectively (/ for the worst, optimal individuals in the new common species (or stain-resistant species, invasive species) dataset, respectively, identified as k>) And (/ -and)>）。
The Wasserstein distance between any individual in the new dataset and the optimal, worst individual is calculated as follows:
；
wherein the method comprises the steps of
；
Wherein the method comprises the steps of，/>Respectively are provided withThe distance between the individual identified as i and the worst, optimal individual in the new dataset.
Step S160: and obtaining the distance weights of different species according to the distance between each individual point in the new data set and the optimal individual and the distance between each individual point and the point corresponding to the worst individual.
Before the distance weights of different species are obtained, a distance set between each sample except the optimal sample and the worst sample and the optimal sample and the worst sample is obtained according to the distance between the point of each individual and the optimal individual and the distance between the point of each individual and the point corresponding to the worst individual.
Wherein each sample except the optimal and the worst samples is combined with the optimal, and the distance set between the worst samples is defined as,
，
Wherein the method comprises the steps ofIs->Average value of>Is->A, b, c are the sign of the species, a is common, b is stain resistant c is intrusion;
weights for sequences identified as k for the datasetThe calculation formula is as follows:
。
step S170: and obtaining projection distances according to the distances between the point of each individual and the optimal individual and the distances between the point of each individual and the point corresponding to the worst individual.
Calculating, for each individual, a projection distance between it and the optimal individual; depending on the characteristics of the distance, for any individual i:
；
wherein the method comprises the steps ofThe projection distance of sample i from sample g, identified as k for the dataset.
Step S180: and obtaining comprehensive evaluation scores according to the distance weights and the projection distances of different species.
Based on the obtained weights and the corresponding projection distances, a final score is calculated for each individualI.e., the composite score of the individual sample numbered i.
。
Example two
As shown in fig. 2, the present application provides a water ecology evaluation system based on a deep learning algorithm, wherein the system specifically comprises: an original dataset acquisition unit 210, a new dataset acquisition unit 220, a normalization processing unit 230, a variation self-encoder creation unit 240, a corresponding point distance acquisition unit 250, a distance weight acquisition unit 260, a projection distance acquisition unit 270, and a comprehensive evaluation score acquisition unit 280.
The raw dataset acquisition unit 210 is configured to acquire aquatic organism monitoring datasets, and divide the aquatic organism datasets to acquire three different datasets.
Wherein an aquatic monitoring dataset is obtained which is to be evaluatedAnd generating waterThe object monitoring data set is divided into common species, contaminant resistant species and invasive species data sets according to different attributes of aquatic organisms, respectivelyRepresentation of->For n evaluation of the water body->A data set of different aquatic organisms, and +.>。
The new data set obtaining unit 220 is configured to add the optimal individual and the worst individual to the three different data sets after the division, so as to obtain three new data sets.
Wherein optimal individuals and worst individuals are added to the common species, contaminant resistant species, and invasive species data sets, respectively.
In the common species data set, the monitoring number of one aquatic organism of the optimal individual is the maximum monitoring data of the same aquatic organism of other individuals; the monitoring number of certain aquatic organisms of the worst individuals is the minimum monitoring data of the same aquatic organisms of other individuals.
Adding optimal individuals and worst individuals in common speciation data set to form new common speciation data set。
The optimal individual aquatic organism monitoring number is the minimum monitoring data of the same aquatic organism of other individuals divided by the stain resistance value corresponding to each species according to the stain resistance value of the species in the stain resistance seed data set; the monitoring number of certain aquatic organisms of the worst individuals is the maximum monitoring data of the same aquatic organisms of other individuals multiplied by the stain resistance value corresponding to the species.
Adding optimal individuals and worst individuals into the soil-resistant seed data set to form a new soil-resistant seed data set。
In the invasive species data set, the monitoring number of a certain aquatic organism of the optimal individual is the minimum monitoring data of the same aquatic organism of other individuals; the monitoring number of the aquatic organisms of the worst individuals is the maximum monitoring data of the same aquatic organisms of other individuals.
Adding optimal individuals and worst individuals in invasive species dataset formation to form new invasive species dataset。
The normalization processing unit 230 is configured to perform data normalization processing on three new data sets.
Three new data sets were normalized separately for aquatic organisms according to the following equation:
，
where d represents the old dataset element, s is the new dataset element,the aquatic organisms identified as k in the old dataset are identified as i (since the species of the old three datasets are different for different regions, some species are common in region a and may be invasive in region B, thus when k is a common dataset, k is B is a stain resistant dataset, k is c is an invasive dataset, a, B, c is the sign of the species, a is common, B is stain resistant c is invasive).
The data normalization of the three new data sets is accomplished by the above formula.
The variable self-encoder creation unit 240 is used to create a variable self-encoder for three new data sets after data normalization is completed.
The corresponding point distance acquiring unit 250 is configured to acquire a distance between a point of each individual and a point corresponding to an optimal individual, and a point of each individual and a point corresponding to a worst individual.
The distance weight obtaining unit 260 is configured to obtain distance weights of different species according to distances between the point of each individual and the optimal individual, and between the point of each individual and the point corresponding to the worst individual.
Before the distance weights of different species are obtained, a distance set between each sample except the optimal sample and the worst sample and the optimal sample and the worst sample is obtained according to the distance between the point of each individual and the optimal individual and the distance between the point of each individual and the point corresponding to the worst individual.
Wherein each sample except the optimal and the worst samples is combined with the optimal, and the distance set between the worst samples is defined as,
，
Wherein the method comprises the steps ofIs->Average value of>Is->A, b, c are the sign of the species, a is common, b is stain resistant c is intrusion;
the weight calculation formula for the sequence identified as k for the dataset is:
，
the projection distance acquiring unit 270 is configured to acquire a projection distance according to a distance between a point of each individual and a point corresponding to an optimal individual, and a point of each individual and a point corresponding to a worst individual.
Calculating, for each individual, a projection distance between it and the optimal individual; depending on the characteristics of the distance, for any individual i:
，
wherein the method comprises the steps ofThe projection distance of sample i to sample g identified as k for the dataset,
the comprehensive evaluation score acquisition unit 280 is configured to acquire a comprehensive evaluation score according to the distance weights and the projection distances of different species.
Based on the obtained weights and the corresponding projection distances, a final score for each individual, i.e. a comprehensive evaluation score for the sample individual numbered i, is calculated.
。
The application has the following beneficial effects:
the application can realize unified and reasonable objective evaluation on all the drainage basins through the established evaluation method; meanwhile, the evaluation method provided by the application takes the monitoring data of all biological groups into account, and considers the influence of different species, so that the evaluation result of the obtained aquatic organisms is more accurate.
Although the examples referred to in the present application are described for illustrative purposes only and not to be limiting of the application, modifications, additions and/or deletions to the embodiments may be made without departing from the scope of the application.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. The water ecology evaluation method based on the deep learning algorithm is characterized by comprising the following steps of:
acquiring aquatic organism monitoring data sets, and dividing the aquatic organism data sets to acquire three different data sets;
adding optimal individuals and worst individuals into the three divided different data sets to obtain three new data sets;
performing data standardization processing on three new data sets;
establishing a variable self-encoder for three new data sets after data standardization is completed;
in response to completion of the establishment of the variation self-encoder, obtaining distances between the point of each individual in the new data set and the optimal individual, and between the point of each individual and the point corresponding to the worst individual;
obtaining distance weights of different species according to the distances between each individual point in the new data set and the optimal individual and the distances between each individual point and the point corresponding to the worst individual;
obtaining projection distances according to the distances between the point of each individual in the new data set and the optimal individual and the distances between the point of each individual and the point corresponding to the worst individual;
and obtaining comprehensive evaluation scores according to the distance weights and the projection distances of different species.
2. A deep learning algorithm-based water ecology assessment method as recited in claim 1 wherein an aquatic organism monitoring dataset is obtained for assessmentDividing the aquatic organism monitoring data set into common species, contaminant resistant species and invasive species data sets according to different attributes of aquatic organism, and using +.>Representation of whereinFor n evaluation of the water body->A data set of different aquatic organisms, and +.>A, b, c are symbols of species, a is common, b is stain resistant and c is intrusion.
3. The method of claim 2, wherein adding optimal individuals and worst individuals to the three partitioned different data sets to obtain three new data sets comprises adding optimal individuals and worst individuals to the common species, contaminant-resistant species and invasive species data sets, respectively.
4. A deep learning algorithm-based water ecology assessment method according to claim 3 wherein three new data sets are data normalized for aquatic organisms respectively according to the following equation:
，
where d represents the old dataset element, s is the new dataset element,monitoring data for aquatic organism identification i for individual j identified as k for dataset,/>For normalized data, +.>A monitoring data sequence for all individuals identified as k for the dataset with aquatic organisms identified as i,/>Is composed ofThe normalized dataset is defined as +.>A, b, c are symbols of species, a is common, b is stain resistant and c is intrusion.
5. The method of water ecology evaluation based on a deep learning algorithm of claim 4 wherein a variational self-encoder is built for three new data sets after data normalization is completed to obtain points of each individual on a new two-dimensional space.
6. The water ecology evaluation system based on the deep learning algorithm is characterized by comprising an original data set acquisition unit, a new data set acquisition unit, a standardized processing unit, a variation self-encoder establishment unit, a corresponding point distance acquisition unit, a distance weight acquisition unit, a projection distance acquisition unit and a comprehensive evaluation score acquisition unit;
the original data set acquisition unit is used for acquiring aquatic organism monitoring data sets and dividing the aquatic organism data sets to acquire three different data sets;
the new data set acquisition unit is used for adding an optimal individual and a worst individual into the three different divided data sets to obtain three new data sets;
the standardized processing unit is used for carrying out data standardization processing on three new data sets;
the variable self-encoder establishing unit is used for establishing variable self-encoders for three new data sets after data standardization is completed;
the corresponding point distance acquisition unit is used for acquiring the distance between each individual point in the new data set and the optimal individual, and the distance between each individual point and the point corresponding to the worst individual;
the distance weight acquisition unit is used for acquiring the distance weights of different species according to the distance between the point of each individual in the new data set and the point corresponding to the optimal individual and the point of each individual and the point corresponding to the worst individual;
the projection distance acquisition unit is used for acquiring the projection distance according to the distance between the point of each individual in the new data set and the point corresponding to the optimal individual and the point of each individual and the point corresponding to the worst individual;
the comprehensive evaluation score acquisition unit is used for acquiring comprehensive evaluation scores according to the distance weights and the projection distances of different species.
7. A deep learning algorithm-based water ecology assessment system as recited in claim 6 wherein the raw dataset acquisition unit obtains an aquatic organism monitoring dataset to be assessedDividing the aquatic organism monitoring data set into common species, contaminant-resistant species and invasive species data sets according to different attributes of aquatic organisms, respectivelyRepresentation of->For n evaluation of the water body->A data set of different aquatic organisms, and +.>。
8. The water ecology evaluation system based on a deep learning algorithm of claim 7, wherein the new data set obtaining unit adds an optimal individual and a worst individual in the three different data sets after division, and the obtaining of three new data sets includes adding the optimal individual and the worst individual in the common species, the soil-resistant species, and the invasive species data sets, respectively.
9. A deep learning algorithm based water ecology assessment system according to claim 8 wherein in the normalization processing unit, three new data sets are data normalized for aquatic organisms respectively according to the following formula:
，
where d represents the old dataset element, s is the new dataset element,monitoring data for aquatic organism identification i for individual j identified as k for dataset,/>For normalized data, +.>A monitoring data sequence for all individuals identified as k for the dataset with aquatic organisms identified as i,/>The structured standardized dataset is defined as +.>A, b, c are symbols of species, a is common, b is stain resistant and c is intrusion.
10. The water ecology evaluation system based on a deep learning algorithm as recited in claim 9, wherein the variation self-encoder creation unit creates a variation self-encoder for three new data sets after data normalization is completed, and obtains a point of each individual on a new two-dimensional space.
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