CN113706048B - River ecosystem health monitoring and evaluating method and system - Google Patents
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Abstract
The invention discloses a method and a system for monitoring and evaluating the health of a river ecosystem, wherein the method comprises the following steps: collecting river ecological indexes according to preset time, and constructing an ecological index matrix; constructing a river health evaluation module based on a river biological environment adaptability curve; constructing a pressure-state-response model; and calculating a river ecosystem health value based on the ecological index matrix through a river health evaluation module and a pressure-state-response model. The technical scheme of the invention can solve the problems of large acquisition workload and difficult acquisition in partial areas at present. By the intelligent acquisition system and method, the acquisition workload can be greatly reduced, and the acquisition, monitoring and evaluation efficiency can be improved.
Description
Technical Field
The invention relates to an environment intelligent supervision system, which is used for environmental pollution monitoring and environmental protection, in particular to a method for health monitoring and evaluation of a river ecosystem.
Background
The medium and small rivers are sources of material exchange and energy transmission of the large rivers, are life lines of drinking water safety, monitor and evaluate the quality of the medium and small river ecological systems, establish a scientific monitoring and evaluating system, and are beneficial to the river environment protection.
In the existing research process, technical personnel provide various schemes, for example, CN107436346 provides a river water ecological health assessment technical method, which constructs a model by using fewer indexes and has the advantages of low index acquisition difficulty and low requirement on the specialty. The present inventors have also proposed a series of methods in recent years.
However, these methods have many problems and further improvements are required.
Disclosure of Invention
The purpose of the invention is as follows: provides a method for monitoring and evaluating the health of a river ecosystem, and aims to solve the problems in the prior art. And a river ecosystem health evaluation system is constructed based on the method.
The technical scheme is as follows: the river ecosystem health monitoring and evaluating method comprises the following steps:
step 1, collecting river ecological indexes according to preset time, and constructing an ecological index matrix;
step 2, constructing a river health evaluation module based on a river biological environment adaptability curve;
and 4, calculating the health value of the river ecosystem through a river health evaluation module and a pressure-state-response model based on the ecological index matrix.
According to an aspect of the embodiment of the present invention, the step 1 includes: collecting a biological population and environmental factors, wherein the biological population comprises arthropods, mollusks, annelids and plankton, and the environmental factors comprise chemical factors and physical factors.
According to an aspect of the embodiment of the present invention, the step 2 includes:
a CCA module is constructed and used for calculating a group of sample ordering values and category ordering values for river ecological indexes, then combining the sample ordering values with environmental factors by a regression analysis method, and then carrying out weighted average on the sample ordering values to obtain the category ordering values;
constructing a correlation analysis module, and calculating the correlation between the environmental factors and the biological population through the Pearson correlation and the Spireman grade correlation coefficient to obtain key environmental factors and dominant organisms;
and (3) constructing a membership model by using the key environmental factors as dependent variables and the number of dominant organisms as independent variables and utilizing polynomial fitting and a generalized additive model, and comparing the degree of contact of results.
According to an aspect of an embodiment of the present invention, in the step 4, the health status of the river is evaluated using the ecosystem health comprehensive index,
wherein, the EHCI is a ecological health comprehensive index value; wiThe weighted value of the evaluation index is in the range of 0-1; i isiThe range of the evaluation index actual value is 0-1.
According to an aspect of the embodiment of the present invention, further comprising step 5:
selecting downstream acquisition points as reference points, constructing ecological index matrixes of the upstream acquisition points at different moments, constructing a transmission matrix based on the river ecological indexes, and solving an inverse matrix of the transmission matrix;
in the next monitoring period, based on the monitoring data of the reference point and the inverse matrix of the transmission matrix, calculating the predicted data of each acquisition point at the upstream of the reference point, and comparing the predicted data with the actual monitoring data of each acquisition point; if the error is less than the threshold value, the transmission matrix is adopted; otherwise, the transmission matrix is recalculated according to the data acquired in each period.
According to an aspect of the embodiment of the present invention, further comprising step 6:
in a preset time period, according to a preset frequency, calculating an inverse matrix of a transmission matrix, calculating a modulus of each inverse matrix, and solving an average value of the inverse matrices; calculating the difference between the modulus of the inverse matrix of each transmission matrix and the average value of the inverse matrix, and constructing a difference matrix;
calculating the Spireman coefficient of each transmission matrix, and constructing a Spireman coefficient matrix;
calculating the modulus of an inverse matrix of the transmission matrix obtained in the subsequent acquisition period, and obtaining the difference value between the inverse matrix and the average value of the inverse matrix; calculating the variance I of each numerical value in the determinant corresponding to the difference value and the difference value matrix;
calculating a spearman coefficient of a matrix formed by the inverse matrix average value and calculating the variance II of each numerical value in a determinant corresponding to the spearman coefficient matrix;
if both variance I and variance II are greater than the threshold, a flag is made.
According to one aspect of the embodiment of the invention, for each downstream acquisition point, a transmission matrix is calculated, and a system transmission matrix is constructed on the basis of each transmission matrix; and establishing a transmission relation between each acquisition point and other acquisition points so as to analyze the transmission relation between a certain acquisition point and other acquisition points.
According to an aspect of an embodiment of the present invention, the strongly coupled acquisition points are obtained according to the transmission relationship of the acquisition points of the reaction of the system transmission matrix.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of any of the above methods when executing the computer program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any of the above.
Has the advantages that: the technical scheme of the invention can solve the problems of large acquisition workload and difficult acquisition in partial areas at present. By the intelligent acquisition system and method, the acquisition workload can be greatly reduced, and the acquisition, monitoring and evaluation efficiency can be improved.
Drawings
Fig. 1 is a schematic diagram of a first embodiment of the present invention.
Figure 2 is a graph of the sequence for the chemokine CCA of the present invention.
Fig. 3 is a diagram of the physical sub-CCA ordering of the present invention.
Fig. 4 is a schematic diagram of another embodiment of the present invention.
Detailed Description
In order to solve the problems of the prior art, the applicant has conducted a great deal of practical investigation and literature research, resulting in a series of achievements. In the prior art, data and environmental factors of a biological population are generally acquired by manually determining a collection point and regularly monitoring, a mapping relation between some key species and part or all of the environmental factors in the biological population is calculated through a corresponding algorithm, an indication species for judging the ecological health of a river is established, a river evaluation system is established through indexes such as the indication species, the environmental factors and the physical structure of the river, and the ecological system is evaluated through establishing a relatively reasonable evaluation system. The idea is the mainstream research path at present, and a plurality of articles or patents issued by the inventor adopt similar ideas.
However, in practice, the above-mentioned idea has many problems, for example, the same evaluation index has different effects in different river systems, which results in the objective performance of the evaluation index being misaligned. In addition, due to the fact that a large number of evaluation indexes including biological populations, environmental factors and the like need to be collected through manual or manual installation equipment, the workload is large, and the collection work is very difficult for remote and mountain areas. Moreover, regular collection is required, the collection difficulty is further increased, and the time cost is very high. In short, the existing research methods and means have insufficient mobility and are too heavy.
In order to solve the above problems, and improve monitoring efficiency and evaluation quality, the following solutions are provided. First, a river health monitoring and evaluation method is provided.
The method specifically comprises the following steps:
step 1, collecting river ecological indexes according to preset time, and constructing an ecological index matrix;
as shown in fig. 1, a sampling device is manually arranged at each point location to perform periodic sampling, or a manual sampling manner is periodically adopted to obtain sampling data of each point location. And obtaining enough data such as biological population, physical and chemical indexes and the like. And preprocessing the acquired data, and removing data obviously deviating from the mean value or data obviously having errors. Calculating the weight of each index by a CCA or PCA analysis method to obtain the weight of each index, constructing a weight matrix, and screening out the main components of which the weight is greater than a threshold value. It should be noted that fig. 1 only shows that sampling points are arranged on the main trunk of a river, and in actual operation, because of more branches of the river, a plurality of sampling points are generally arranged on each branch.
If CCA is adopted, then:
assuming that there are N samples of both the biological population and the environmental factors, the N samples were linearly varied to obtain the following two sets of data:
Sxwx＝(wx Tx1,...,wx TxN)，Sywy＝(wy Tx1,...,wy TxN)
the CCA canonical correspondence analysis maximizes the correlation between the two sets of data, and the specific form is:
by mathematical derivation, the following formula is obtained:
obtaining S by numerical solutionxwx，SywyAnd plotting to observe the correlation.
Step 2, constructing a river health evaluation module based on a river biological environment adaptability curve;
the middle and small rivers are mostly distributed in remote areas with rare people, the health conditions of the rivers are often ignored, conventional hydrology and water quality monitoring sites are lacked in the watershed, and the data volume requirements of intelligent algorithms such as an artificial neural network and the like in model training cannot be met. Although the expert experience method is simple and convenient to operate, the subjectivity is large, and the popularization of the result is not facilitated. Therefore, the optimal choice of the medium and small rivers is to construct the aquatic habitat suitability curve through mathematical statistics. Polynomial fitting is a common biological suitability curve fitting method, and the principle is that a polynomial function is utilized, and the function is expanded into Lagrange series and Meglaolin series through a Taylor formula, so that an objective function is approximated. The GAM model is a new biological habitat applicability research model, and the principle of the GAM model is to fit variables through a smooth spline function, a kernel function or a local regression smooth function.
The number of dominant species appearing at the point positions is used as an independent variable, the key habitat factor is used as a dependent variable, a membership model of the large benthic invertebrates and the habitat factor is constructed by utilizing polynomial fitting and a generalized additive model, and the result degree of cut is compared, so that the fitting result accuracy can be maximized, and the reliability of the evaluation result is increased.
The general formula of the Generalized Additive Model (GAM) is: g (μ (Y)) ═ β0+f1(x1)+...+fm(xm)；
Where the g () function is the join function, μ (Y) is the expectation of Y, β0Is intercept, fi() Is a non-parametric smooth function.
compared with other index system methods, the stress-state-response model (PSR) has the greatest advantage of using human activity factors as an index layer for environmental quality evaluation.
And 4, calculating the health value of the river ecosystem through a river health evaluation module and a pressure-state-response model based on the ecological index matrix.
The construction process of the index system is as follows:
firstly, defining a compromise coefficient beta (1 is more than or equal to beta and more than or equal to 0), and constructing a comprehensive decision matrix Q through a least square method subjective decision matrix F and an entropy coefficient objective decision matrix C, wherein Q is beta F + (1-beta) C
qii＝βfii+(1-β)cii，i∈[1,n]；qij＝βfijI ≠ j and i, j ∈ [1, n ]],
The integrated weight model may be represented as
Solving to obtain w ═ Q-1e/eTQ-1e。
According to an aspect of the embodiment of the present invention, the step 1 includes: collecting a biological population and environmental factors, wherein the biological population comprises arthropods, mollusks, annelids and plankton, and the environmental factors comprise chemical factors and physical factors.
In one embodiment, the implementation is as follows:
the ecological field investigation of river water at the side of the bridge is carried out in the dry season (1 month) and the rich season (8 months) in 2019. The two samplings together identified a large benthonic animal of phylum 3 (Arthropoda; Mollusca, Mollusca Annelida); class 5 (Insecta, Isecta; Crustacea, Crustacea; Gastropoda; Bivalvia; Oligochaeta); 13 order, 19 family, 1152 total benthos species, detailed bridgeriver large benthos species composition is shown in the table below.
Large benthic animal species composition
The structure distribution of the large benthonic animal community of the bridge side river is as follows: the arthropods are most abundant in species, accounting for 68.4% of the total number of the animals, the mollusks are secondly accounted for 26.3% of the total number of the animals, and the annelids are least and account for 5.3% of the total number of the animals. Although the species of arthropods are significantly different in number from those of other phyla, the statistical results of the number of individuals are different. The statistical result of the individual number of the large benthonic animal species shows that the individual number of the mollusk species accounts for 54.8 percent of the total number, the individual number of the arthropod species accounts for 38.8 percent of the total number of the individuals, and the individual number of the annelids still occupies little amount in each classification and only accounts for 6.4 percent of the total number of the individuals.
The number of large animal species along the river course at the bridge edge is space-time: during the dry season, the average number of benthic animal species from upstream to downstream is: the number of species of 7.0, 4.3 and 4.7 shows a trend of decreasing and then increasing from upstream to downstream. The survey results of the rich water period show that the average numbers of species in the three regions of the upper, middle and lower parts are respectively: 5.5, 3.3 and 6.0, the variation trend of the species number is consistent with the dry season, the variation of the average species number is not obvious, and the average species number in two seasons is approximately equivalent. The upstream and midstream survey results show that the average number of benthic animal species in the dry season is higher than that in the rich season, and the trend is more obvious towards the upstream. In contrast, the downstream case shows a higher average number of species in the rich water phase than in the dry water phase. According to two-factor analysis of variance (two-way ANOVA), the average number of species of sampling points in different river sections (upstream, middle and downstream) and different seasons (a dry water period and a rich water period) is analyzed, the difference of the species number in each river section (P <0.01) is obvious, but the difference of the species number in different seasons (P ═ 0.121) is not obvious.
The density of the large benthonic animals in three areas of the river, the middle and the downstream of the bridge edge is changed: the density of the benthic animals in the dry period from upstream to downstream is respectively as follows: 35.0 pieces/m270.7 pieces/m278.0 pieces/m2The density tends to increase from upstream to downstream. The survey result of the rich water period shows that the densities of the three areas in the upper, middle and lower streams are respectively: 41.0 pieces/m267.3 pieces/m287.3 pieces/m2The density variation trend is consistent with the dry season, and the densities in two seasons are approximately equivalent. The upstream and downstream surveys show that the mean number of benthonic species is higher during the flood period than during the flood period. In contrast, in the midstream, the density is higher in the low water period than in the rich water period. According to two-factor analysis of variance (two-way ANOVA), the average number of species at sampling points of different river sections (upstream, middle and downstream) and different seasons (dry water period and rich water period) is analyzed, and the species number is analyzed in each river section (P)<0.01), but not in different seasons (P ═ 0.146).
Biomass variation of large benthonic animals in three areas, namely, upstream, middle and downstream of the river beside the bridge: the density of the benthic animals in the dry period from upstream to downstream is respectively as follows: 19.03g/m2，35.17g/m2，41.26g/m2The biomass shows a gradual increasing trend from upstream to downstream. The survey result of the rich water period shows that the densities of the three areas in the upper, middle and lower streams are respectively: 21.34g/m2， 48.48g/m2，30.4g/m2The biomass trend shows a trend of increasing first and then decreasing, and the densities in the two seasons are approximately equivalent. The results of the upstream and midstream surveys show that the mean number of benthic animal species is higher in the flooded stage than in the flooded stage. In contrast, the density in the low water period is higher than that in the rich water period. According to two-factor analysis of variance (two-way ANOVA), the average number of species at sampling points of different river sections (upstream, middle and downstream) and different seasons (dry water period and rich water period) is analyzed, and the species number is analyzed in each river section (P)<0.01), but not in different seasons (P ═ 0.067).
The biodiversity index distribution of the large benthonic animals in the river basin beside the bridge in the dry period and the rich period is as follows: the distribution ranges of the Shannon-Wiener index, Simpson index, richness index and uniformity index in the dry season are 0.42-1.84, 0.180-0.083, 0.62-1.73 and 0.30-0.96; the distribution ranges of the Shannon-Wiener index, the Simpson index, the richness index and the uniformity index in the water-rich period are 0.42-1.58, 0.255-0.763, 0.228-1.401 and 0.45-0.93. The statistical result shows that the Shannon-Wiener index (H) and the abundance index (dM) have larger variation range, and the biodiversity of the large benthonic animals in the river basin beside the bridge is in a common level according to the biodiversity threshold evaluation standard.
The calculation result of the dominance index shows that the main dominant species of the Qiaobian river comprise yellow chironomid, creeper, croissant, calomel, otoplophora, pear-shaped cripple and corbicula fluminea. The yellow chironomid, the poncirus squared, the pearl mussel and the conch papulosus are mainly distributed at the army-bridge side, and the mud crab Tanshi is mainly distributed at the soil city-source head section. The corbicula fluminea appears in all areas of a river, so that the corbicula fluminea is taken as an indication species for research.
According to an aspect of the embodiment of the present invention, the step 2 includes:
a CCA module is constructed and used for calculating a group of sample ordering values and category ordering values for river ecological indexes, then combining the sample ordering values with environmental factors by a regression analysis method, and then carrying out weighted average on the sample ordering values to obtain the category ordering values;
constructing a correlation analysis module, and calculating the correlation between the environmental factors and the biological population through the Pearson correlation and the Spireman grade correlation coefficient to obtain key environmental factors and dominant organisms;
and (3) constructing a membership model by using the key environmental factors as dependent variables and the number of dominant organisms as independent variables and utilizing polynomial fitting and a generalized additive model, and comparing the degree of contact of results.
According to an aspect of an embodiment of the present invention, in the step 4, the health status of the river is evaluated using the ecosystem health comprehensive index,
wherein, the EHCI is a ecological health comprehensive index value; wiThe weight value of the evaluation index is in the range of 0-1; i isiThe range of the evaluation index actual value is 0-1.
In one example, CCA analysis of benthic animal species with chemical factors is shown in table 2 below, with a 47.72% cumulative interpretation of variation for the first four axes, monte carlo test (n ═ c)499) Has significance for all axes (P)<0.05). Chemical factors with high correlation with the second sequencing axis include TN and NO3-N、 NH4-N、CODMn、pH、DO。TN、NO3-N、NH4-N、CODMnSignificantly negatively correlated with the second axis of ordering, and significantly positively correlated with the second axis of ordering, pH, DO. The included angle between the corbicula fluminea and DO and the pH value is an acute angle, and the corbicula fluminea and DO are in positive correlation; corbicula fluminea with TN and NO3-N、NH4-N、CODMnThe included angle of (a) is obtuse, and corbicula fluminea is inversely related to these factors.
Correlation of the chemical factor CCA
The canonical correspondence analysis of benthic animal species and physical factors is shown in the following table and fig. 3, where the interpretation rate of the variation of the first four axes is accumulated to 67.4%, and the monte carlo test (n 499) has significance for all axes (P ═ P)<0.05). The factor having higher correlation with the second sorting axis has D50Dep, Tur. Tem has a higher correlation with the first axis, but the contribution of the first axis to explain the cluster changes is smaller than the second axis, so the contribution of Tem is smaller. Dominant species corbicula fluminea and D50The included angle between Dep and Tur is obtuse, which indicates that corbicula fluminea is in negative correlation with the environmental factors.
Physical factor CCA correlation
According to one aspect of an embodiment of the invention, as shown in fig. 4, a method for intelligently monitoring and evaluating river health is provided. According to the method, the biological population and environment factor transmission matrix is constructed based on the acquired data, and the inconvenient acquisition point data is reversely deduced through the more convenient acquisition point data, so that the data acquisition difficulty and workload can be reduced, the coupling relation and the variation trend between the biological population and the ecological factor can be more clearly obtained, and the method has better accuracy and applicability in time and space dimensions.
On the basis of the above embodiment, the embodiment further includes step 5:
selecting downstream acquisition points as reference points, constructing ecological index matrixes of the upstream acquisition points at different moments, constructing a transmission matrix based on the river ecological indexes, and solving an inverse matrix of the transmission matrix;
as shown in FIG. 4, in this figure, P02 is the downstream acquisition point of P01, P03 is the downstream acquisition point of P02, and so on, among P01-P07. In the side stream, P12 is the downstream collection point of P11 and P34 is the downstream collection point of P31.
P43 is the downstream acquisition point of P41, P64 is the downstream acquisition point of P61, and so on.
During a relatively steady period, generally speaking, as the water flows, a portion of the biological population and environmental factors flow downward, creating a dynamic transport equilibrium in which the environment at the downstream collection point is affected by the upstream collection points in addition to the ambient influences. For example, at point P05, the various acquisition indicators of its acquisition points are influenced by the environment of the respective acquisition points P01 to P04, P11 to P12, P21 to P23, P41 to P43, P51 to P53, and so on. Therefore, the data of the above-mentioned each acquisition point are correlated in a certain space-time range. However, due to different factors, the coupling relationship between different acquisition points and a downstream acquisition point, and the migration rate are different, so a transmission equation needs to be constructed in a certain period. As for the same river ecosystem, researches show that the transmission relationship is basically consistent in the rich water period and the dry water period of each year, and a relatively stable coupling relationship appears. Thus, monitoring and evaluation can be performed by the above-described method.
In the next monitoring period, calculating the predicted data of each acquisition point at the upstream of the reference point based on the monitoring data of the reference point and the inverse matrix of the transmission matrix, and comparing the predicted data with the actual monitoring data of each acquisition point; if the error is less than the threshold value, the transmission matrix is adopted; otherwise, the transmission matrix is recalculated according to the data acquired in each period.
Based on data accumulated in a certain period, a transmission matrix is preliminarily constructed, after the mutual relation is described, in order to improve the monitoring accuracy, the transmission relation is reversely deduced, new collected data is adopted for verification, the accuracy is further improved, and data meeting engineering requirements are obtained through data verification. In a specific case, the transmission matrix can be respectively constructed in a rich water period and a dry water period according to the actual distribution condition of the river.
Therefore, by the method, the arrangement of the acquisition can be optimized, repeated or unnecessary work is reduced, the difficulty of the acquisition work is reduced, the acquisition efficiency is improved, the accuracy of the data can be improved based on a data mining mode, and data errors caused by manual acquisition are reduced.
In practice it has been found that if a new source of pollution is found upstream or between collection points, or if the river ecology has significantly changed significantly, it will result in a decrease in the accuracy of the transmission matrix, resulting in a large error in the measurement. On the contrary, if the actual monitoring data is compared with the data calculated by artificial intelligence, the transmission matrix can be presumed to change after the error is found to be large, and further the river ecology is deduced to have great change. Therefore, a method for determining whether the above-mentioned situation really occurs is needed.
According to an aspect of the embodiment of the present invention, further comprising step 6:
in a preset time period, according to a preset frequency, calculating an inverse matrix of a transmission matrix, calculating a modulus of each inverse matrix, and solving an average value of the inverse matrices; calculating the difference between the modulus of the inverse matrix of each transmission matrix and the average value of the inverse matrix, and constructing a difference matrix;
calculating the Spireman coefficient of each transmission matrix, and constructing a Spireman coefficient matrix;
calculating the modulus of an inverse matrix of the transmission matrix obtained in the subsequent acquisition period, and obtaining the difference value between the inverse matrix and the average value of the inverse matrix; calculating the variance I of each numerical value in the determinant corresponding to the difference value and the difference value matrix;
calculating a spearman coefficient of a matrix formed by the inverse matrix average value and calculating the variance II of each numerical value in a determinant corresponding to the spearman coefficient matrix;
if both variance I and variance II are greater than the threshold, a flag is made.
Whether the transmission matrix is stable or not is judged through comparing the change degree of the transmission matrix and the absolute change amount and the relative similarity, if the transmission matrix is unstable, the transmission matrix is judged to be changed, and the position where the change occurs is deduced, so that the transmission matrix is corrected according to re-measurement and investigation of the river section, and the accuracy of follow-up monitoring and evaluation is ensured. By calculating the stability of the transmission matrix, whether the river ecology changes or not can be judged quickly, and data support is provided for efficient monitoring.
According to one aspect of the embodiment of the invention, for each downstream acquisition point, a transmission matrix is calculated, and a system transmission matrix is constructed on the basis of each transmission matrix; and establishing a transmission relation between each acquisition point and other acquisition points so as to analyze the transmission relation between a certain acquisition point and other acquisition points.
In a further embodiment, in order to improve the overall controllability and accuracy of monitoring, in an important river ecology, a system transmission matrix is constructed, so that a passive monitoring network and a system are strongly coupled.
For example, if the transmission relationship between P02 and P01, P11, P12 is constructed in the above method, P02 has transmission relationship with each point downstream. Furthermore, for any acquisition point, the transmission relationship is theoretically established between the acquisition point and other points, so that each point can establish a transmission matrix with other points, and for the whole system, the transmission matrixes can be combined into a system transmission matrix, so that the whole system is analyzed. In practical circumstances, part of the biological population may flow upstream and part of the environmental factors may for some reason flow to the collection points of two different rivers via terminal branches. But this relationship is statistically weak coupling.
According to an aspect of the embodiment of the present invention, acquisition points with strong coupling (coupling coefficient greater than threshold) are obtained according to the transmission relationship of each acquisition point reflected by the system transmission matrix.
According to the relationship of the acquisition points with strong coupling, the layout of the acquisition points can be reduced, and the data with accuracy meeting the requirement can be obtained by using fewer acquisition points.
Although the preferred embodiments of the present invention have been described in detail, the present invention is not limited to the details of the embodiments, and various equivalent modifications can be made within the technical spirit of the present invention, and the scope of the present invention is also within the scope of the present invention.
Claims (5)
1. The method for monitoring and evaluating the health of the river ecosystem is characterized by comprising the following steps:
step 1, collecting river ecological indexes according to preset time, and constructing an ecological index matrix;
step 2, constructing a river health evaluation module based on a river biological environment adaptability curve;
step 3, constructing a pressure-state-response model;
step 4, calculating a health value of a river ecosystem through a river health evaluation module and a pressure-state-response model based on the ecological index matrix; the step 1 comprises the following steps: collecting a biological population and environmental factors, wherein the biological population comprises arthropods, mollusks, annelids and plankton, and the environmental factors comprise chemical factors and physical factors;
the step 2 comprises the following steps: constructing a CCA module for calculating a group of sample ordering values and category ordering values for river ecological indexes, combining the sample ordering values with environmental factors by using a regression analysis method, and carrying out weighted average on the sample ordering values to obtain the category ordering values;
constructing a correlation analysis module, and calculating the correlation between the environmental factors and the biological population through the Pearson correlation and the Spireman grade correlation coefficient to obtain key environmental factors and dominant organisms;
constructing a membership model by using a polynomial fitting and a generalized additive model and taking the key environmental factor as a dependent variable and the number of dominant organisms as an independent variable, and comparing the degree of tangency of results;
in the step 4, the comprehensive index of the health of the ecosystem is used for evaluating the health condition of the river,
wherein, the EHCI is a ecological health comprehensive index value; wiThe weighted value of the evaluation index is in the range of 0-1; i isiThe range of the evaluation index actual value is 0-1;
further comprising step 5: in a certain period, selecting downstream acquisition points as reference points, constructing ecological index matrixes of the upstream acquisition points at different moments, constructing a transmission matrix based on the river ecological indexes, and solving an inverse matrix of the transmission matrix, wherein the transmission matrix is constructed in a rich water period and a dry water period respectively;
in the next monitoring period, based on the monitoring data of the reference point and the inverse matrix of the transmission matrix, calculating the predicted data of each acquisition point at the upstream of the reference point, and comparing the predicted data with the actual monitoring data of each acquisition point; if the error is less than the threshold value, the transmission matrix is adopted; otherwise, recalculating the transmission matrix according to the data acquired in each period;
further comprising the step 6: in a preset time period, according to a preset frequency, calculating an inverse matrix of a transmission matrix, calculating a modulus of each inverse matrix, and solving an average value of the inverse matrices; calculating the difference between the modulus of the inverse matrix of each transmission matrix and the average value of the inverse matrix, and constructing a difference matrix;
calculating the Spireman coefficient of each transmission matrix, and constructing a Spireman coefficient matrix;
calculating the modulus of an inverse matrix of the transmission matrix obtained in the subsequent acquisition period, and obtaining the difference value between the inverse matrix and the average value of the inverse matrix; calculating the variance I of each numerical value in the determinant corresponding to the difference value and the difference value matrix;
calculating a spearman coefficient of a matrix formed by the inverse matrix average value and calculating the variance II of each numerical value in a determinant corresponding to the spearman coefficient matrix;
and if the variance I and the variance II are both larger than the threshold value, marking, judging that the ecological system changes, and then detecting and investigating the river section again and correcting the transmission matrix.
2. The river ecosystem health monitoring and evaluation method according to claim 1, wherein for each downstream collection point, its transmission matrix is calculated, and a system transmission matrix is constructed on the basis of each transmission matrix; and establishing a transmission relation between each acquisition point and other acquisition points so as to analyze the transmission relation between a certain acquisition point and other acquisition points.
3. The river ecosystem health monitoring and evaluation method according to claim 2, wherein the strongly coupled acquisition points are obtained according to the transmission relationship of the acquisition points reflected by the system transmission matrix.
4. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of claims 1 to 3 are implemented when the computer program is executed by the processor.
5. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 3.
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