Water quality microorganism and heavy metal integrated big data analysis detection method

Abstract

The application discloses a water quality microorganism and heavy metal integrated big data analysis and detection method, and belongs to the technical field of big data analysis. The method comprises the following steps: obtaining an initial water quality multi-dimensional data set of a target water body; according to the spatial structure of each heavy metal form and the spatial structure of a specific metabolic enzyme, performing spatial matching simulation to determine the form-specific inhibition coefficient of each heavy metal form on the specific metabolic enzyme; calculating the biochemical reaction flux rate of the heavy metal form conversion; setting a fixed time step to obtain the predicted value of the concentration of each heavy metal form; and combining the total abundance of the microbial community to output a biological effective toxicity index. The application determines the form-specific inhibition coefficient by performing spatial matching simulation on each heavy metal form and the specific metabolic enzyme, thereby solving the problem that the coupling correlation between the heavy metal form evolution and the microbial metabolic conversion is fragmented in the prior art, which leads to distortion of toxicity evaluation, false alarm and missed alarm.

Description

Technical Field

[0001] This invention relates to the field of big data analysis technology, and in particular to an integrated big data analysis and detection method for water quality microorganisms and heavy metals. Background Technology

[0002] With the acceleration of industrialization and urbanization, heavy metal pollution in water bodies is becoming increasingly complex. In actual aquatic environments, heavy metals do not exist in a single form, and there are complex interactions between heavy metals and aquatic microbial communities. Microbial metabolic activities can alter the chemical form and bioavailability of heavy metals, thereby directly affecting their ecotoxicity. Therefore, accurate assessment of the synergistic effects between heavy metals and microorganisms in water quality has become a crucial aspect of water environment monitoring and management.

[0003] Existing water quality big data analysis and detection systems typically collect heavy metal total concentration data and microbial community abundance data from water samples independently, treat the two as independent variables, perform simple correlation analysis or weighted calculations, thereby assessing water quality ecological risks and outputting early warning information.

[0004] In the existing technology, the above-mentioned independent variable analysis method severs the coupling relationship between the evolution of heavy metal speciation and microbial metabolic transformation. It fails to effectively distinguish the toxicity differences of different chemical speciations of heavy metals and the role of microbial transformation, resulting in a disconnect between toxicity assessment and actual ecological hazards. This leads to frequent false alarms and missed alarms in the early warning system in complex pollution scenarios, ultimately causing water treatment processes to lose precise guidance and resulting in overtreatment or undertreatment. Summary of the Invention

[0005] This application provides an integrated big data analysis and detection method for water quality microorganisms and heavy metals, which solves the problems in the prior art that lead to distorted toxicity assessment, false alarms and missed alarms, and inaccurate guidance for water treatment processes by severing the coupling relationship between the evolution of heavy metal speciation and microbial metabolic transformation. It achieves the technical effect of accurately quantifying the coupled toxicity of heavy metal speciation and microbial metabolism, and dynamically outputting a bioeffective toxicity index that reflects the actual ecological harm.

[0006] This application provides a method for integrated big data analysis and detection of water quality microorganisms and heavy metals, including: acquiring an initial water quality multidimensional dataset of the target water body, wherein the initial water quality multidimensional dataset includes total heavy metal concentration, concentration of each heavy metal form, abundance of microbial community, physicochemical parameters of water body, spatial structure of specific metabolic enzymes of microorganisms, and spatial structure of each heavy metal form; Based on the spatial structure of each heavy metal morphology and the spatial structure of the specific metabolic enzyme, spatial matching simulation is performed to determine the morphology-specific inhibition coefficient of each heavy metal morphology on the specific metabolic enzyme. Based on the abundance of the microbial community and the physicochemical parameters of the water body, combined with the biochemical reaction network of heavy metal speciation, the biochemical reaction flux rate of heavy metal speciation was calculated. A fixed time step is set, and the concentration data in the initial water quality multidimensional dataset is used as the initial input for the current iteration cycle. The predicted concentration values ​​of each form of heavy metal are updated based on the biochemical reaction flux rate. The predicted concentrations of each heavy metal species are weighted based on the morphology-specific inhibition coefficient, and then combined with the total abundance of the microbial community to output the bioeffective toxicity index.

[0007] Furthermore, the steps for obtaining the initial water quality cube dataset of the target water body include: Receive data on the total concentration of heavy metals and the concentration of each form of heavy metal in the target water body, as well as the physicochemical parameters of the water body; Acquire microbial community abundance data of the target water body and retrieve spatial structure data of specific metabolic enzymes from a preset database; Retrieve spatial structure data for various heavy metal morphologies; The acquired heavy metal concentration data, water body physicochemical parameter data, microbial community abundance data, specific metabolic enzyme spatial structure data, and heavy metal spatial structure data of various forms are time-stamped and aligned to form an initial water quality multidimensional dataset.

[0008] Further steps, including spatial matching simulations to determine the form-specific inhibitory coefficients of various heavy metal speciations on specific metabolic enzymes, include: The active site region is defined on the spatial structure of the specific metabolic enzyme, and the spatial structure data of each form of heavy metal is located within the active site region. A flexible matching strategy that allows for conformational deformation is adopted to enable heavy metal ions to search for spatial positions and optimize energy around the active sites. Calculate the spatial binding energy between each heavy metal species and the active site of each specific metabolic enzyme. The inherent physicochemical properties of heavy metal ions are retrieved, and the spatial binding energy value is fused with the physicochemical properties parameters for calculation. The fusion calculation results were processed using an absolute value standardization conversion method to calculate the morphological specific inhibition coefficient of each heavy metal form on specific metabolic enzymes.

[0009] Furthermore, the steps for calculating the biochemical reaction flux rate of heavy metal speciation, in conjunction with the biochemical reaction network of heavy metal speciation, include: The microbial community abundance data is compared with a pre-stored functional gene mapping database to screen and identify functional microbial groups with heavy metal speciation capabilities. Based on the microbial metabolic pathway database, the transformation pathways of the aforementioned functional microbial groups in heavy metal reduction, oxidation, methylation, and extracellular precipitation were identified. Using different chemical forms of heavy metals as network nodes and the transformation paths as edges connecting the nodes, a biochemical reaction chain network structure reflecting the transformation of heavy metal forms is constructed.

[0010] Furthermore, calculating the biochemical reaction flux rate of heavy metal speciation also includes: The microbial community abundance data is used as a reaction weight parameter and assigned to the corresponding biochemical reaction branch in the biochemical reaction chain network structure; The physicochemical parameters of the water body are used as reaction rate limiting conditions and substituted into the biochemical reaction chain network structure to set the reaction boundary. Using a grid flow analysis method based on mass conservation, the maximum possible flow rate of each heavy metal speciation biochemical reaction branch is calculated under the constraints of the reaction boundary. Multiplying the maximum possible flow rate by the corresponding reaction weight parameter yields the actual reaction rate under the combined constraints of the current microbial community size and aquatic environmental factors, which is then used as the biochemical reaction flux rate.

[0011] Furthermore, the step of updating the predicted concentrations of various heavy metal forms based on the biochemical reaction flux rate includes: A fixed time step is configured as the iteration period. At the beginning of the current iteration period, the total concentration data of heavy metals and the concentration data of each form in the initial water quality multidimensional dataset are retrieved as the initial input stream. Multiply the biochemical reaction flux rate by the time step to calculate the change in concentration of each form of heavy metal caused by microbial transformation within the current time step. Based on the properties of heavy metal forms as consumables or products in biochemical reactions, the changes in the concentration of each heavy metal form are added to or subtracted from the initial concentration data of each form, and the predicted concentration values ​​of each heavy metal form at the end of the current time step are updated.

[0012] Furthermore, the steps for weighted calculation of the predicted concentrations of various heavy metal speciations based on the speciation-specific inhibition coefficient include: The sum of the predicted concentrations of all forms of the same heavy metal element is calculated to obtain the total predicted concentration of that heavy metal element. Calculate the ratio of the predicted concentration of each heavy metal form to the total predicted concentration to obtain the real-time proportion weight of the corresponding form; The toxicity contribution of each heavy metal form is obtained by multiplying its real-time weighting with the corresponding form-specific inhibition coefficient. The toxicity contribution components of all forms of the same heavy metal element are summed to obtain a composite toxicity component that reflects the combined toxic effects of multiple heavy metal forms.

[0013] Furthermore, the steps for deriving a bioeffective toxicity index by combining the total abundance of the microbial community include: The abundance values ​​of all microbial species in the target water body are summed to obtain the total abundance data of the microbial community. The composite toxicity component and the total abundance data of the microbial community are input into a preset normalized mapping model; By standardizing the data to eliminate dimensional differences, the bioeffective toxicity index at the end of the current time step is calculated, and the bioeffective toxicity index is output through the data output port.

[0014] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: Considering that the ecotoxicity of heavy metals in water bodies is highly dependent on their chemical forms and that microbial metabolism can significantly alter their forms, this study uses spatial matching simulations of different heavy metal forms with metabolic enzymes in microorganisms to determine form-specific inhibition coefficients. By combining this with the biochemical reaction network of heavy metal transformation in microbial communities, the predicted concentration values ​​of each form are dynamically updated. This approach accurately quantifies the coupling effect between heavy metal form evolution and microbial metabolism, effectively distinguishing between scenarios of "high concentration but low toxicity" and "low concentration but high toxicity." It overcomes the problem of toxicity assessment distortion caused by existing methods that rely solely on total amount and simple weighting, thus improving the accuracy of water quality toxicity assessment.

[0015] Furthermore, based on the improved accuracy of toxicity assessment, the dynamically updated concentration prediction values ​​are weighted according to the morphology-specific inhibition coefficient, and the bioeffective toxicity index is output by combining the total abundance of the microbial community. This allows the warning threshold to directly reflect the actual level of harm of heavy metals to aquatic microorganisms, effectively avoiding false alarms when the total amount of heavy metals exceeds the standard but has been transformed into a low-toxic form by microorganisms, as well as underreporting when the concentration of heavy metals is low but has been transformed into a highly toxic form by microorganisms. This improves the reliability and accuracy of warnings in complex pollution scenarios and prevents unnecessary emergency responses or pollution spread.

[0016] Furthermore, in the process of achieving precise early warning, the use of fixed time steps for dynamic iterative updates continuously outputs the bioeffective toxicity index values ​​at each moment, providing precise parameter guidance for water treatment processes to be adjusted in real time according to water quality changes. This avoids increased costs due to excessive dosing and suppression of beneficial microbial activity caused by false alarms, and also prevents inadequate treatment processes and discharge of highly toxic substances due to missed alarms. It truly achieves deep linkage of "chemical regulation-biodegradation" synergistic treatment, significantly improving the accuracy and operational economy of water treatment processes and ensuring the ecological safety of water bodies. Attached Figure Description

[0017] Figure 1 A flowchart of an integrated big data analysis and detection method for water quality microorganisms and heavy metals provided in this application embodiment. Detailed Implementation

[0018] This application provides an integrated big data analysis and detection method for water quality microorganisms and heavy metals. It solves the problems in the prior art where the coupling relationship between the evolution of heavy metal speciation and microbial metabolic transformation is severed, leading to distorted toxicity assessment, false alarms and missed alarms, and inaccurate guidance for water treatment processes. By spatially matching and simulating the spatial structure of various heavy metal speciations with the spatial structure of specific metabolic enzymes, the speciation-specific inhibition coefficient is determined. Combined with the biochemical reaction flux rate of heavy metal speciation transformation, the dynamically updated concentration prediction value is weighted and calculated. This achieves accurate quantification of the coupled toxicity of heavy metal speciation and microbial metabolism, dynamically outputting a bioeffective toxicity index that reflects the actual ecological harm, and guiding the technical effect of water treatment process regulation.

[0019] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0020] like Figure 1 The diagram shown is a flowchart of an integrated big data analysis and detection method for water quality microorganisms and heavy metals provided in an embodiment of this application. The method includes the following steps: obtaining an initial water quality multidimensional dataset of the target water body, wherein the initial water quality multidimensional dataset includes total heavy metal concentration, concentration of each heavy metal form, abundance of microbial community, physicochemical parameters of water body, spatial structure of specific metabolic enzymes of microorganisms, and spatial structure of each heavy metal form; Based on the spatial structure of each heavy metal morphology and the spatial structure of the specific metabolic enzyme, spatial matching simulation is performed to determine the morphology-specific inhibition coefficient of each heavy metal morphology on the specific metabolic enzyme. Based on the abundance of the microbial community and the physicochemical parameters of the water body, combined with the biochemical reaction network of heavy metal speciation, the biochemical reaction flux rate of heavy metal speciation was calculated. A fixed time step is set, and the concentration data in the initial water quality multidimensional dataset is used as the initial input for the current iteration cycle. The predicted concentration values ​​of each form of heavy metal are updated based on the biochemical reaction flux rate. The predicted concentrations of each heavy metal species are weighted based on the morphology-specific inhibition coefficient, and then combined with the total abundance of the microbial community to output the bioeffective toxicity index.

[0021] Furthermore, the steps for obtaining the initial water quality cube dataset of the target water body include: Receive data on the total concentration of heavy metals and the concentration of each form of heavy metal in the target water body, as well as the physicochemical parameters of the water body; The heavy metals in various forms include hexavalent chromium ions, trivalent chromium ions, pentavalent arsenic ions, trivalent arsenic ions, divalent mercury ions, and methylmercury ions; The physicochemical parameters of the water body include pH, dissolved oxygen concentration, temperature, and redox potential; Acquire microbial community abundance data of the target water body and retrieve spatial structure data of specific metabolic enzymes from a preset database; The specific metabolic enzymes include dehydrogenases, adenosine triphosphate enzymes, alkaline phosphatases, and desulfurases; Retrieve spatial structure data for various heavy metal morphologies; The acquired heavy metal concentration data, water body physicochemical parameter data, microbial community abundance data, specific metabolic enzyme spatial structure data, and heavy metal spatial structure data of various forms are time-stamped and aligned to form an initial water quality multidimensional dataset.

[0022] In this embodiment, data acquisition is performed first. The total heavy metal concentration data and the concentration data of each heavy metal species in the target water body are obtained by receiving detection results sent by an external detection device. The specific heavy metal species include hexavalent chromium ions, trivalent chromium ions, pentavalent arsenic ions, trivalent arsenic ions, divalent mercury ions, and methylmercury ions. The external detection device can use inductively coupled plasma mass spectrometry (ICP-MS) coupled with high-performance liquid chromatography (HPLC) to measure the water sample and output concentration data.

[0023] Simultaneously, it receives water physicochemical parameter data, including pH, dissolved oxygen concentration, temperature, and redox potential. This data can be acquired in real time using a multi-parameter water quality sensor.

[0024] Microbial community abundance data for the target water body was acquired through high-throughput sequencing analysis of water samples. Spatial structure data of specific metabolic enzymes, including dehydrogenases, adenosine triphosphate (ATP) enzymes, alkaline phosphatases, and desulfurases, were retrieved from a pre-defined database. These enzymes were three-dimensional coordinate files downloaded from a protein structure database. Spatial structure data for various heavy metal speciations were also retrieved, constructed based on the atomic coordinates and chemical bond parameters of the heavy metal elements.

[0025] Because of the differences in the collection frequency and timestamps of the aforementioned data, the acquired heavy metal concentration data, water physicochemical parameter data, microbial community abundance data, specific metabolic enzyme spatial structure data, and heavy metal spatial structure data of various forms are timestamped and aligned. Specifically, the data with the longest timestamp interval is used as the baseline time point, and other high-frequency data are averaged using a sliding window to align all data to the same time slice, thus constructing the initial water quality multidimensional dataset.

[0026] Further steps, including spatial matching simulations to determine the form-specific inhibitory coefficients of various heavy metal speciations on specific metabolic enzymes, include: The active site region is defined on the spatial structure of the specific metabolic enzyme, and the spatial structure data of each form of heavy metal is located within the active site region. A flexible matching strategy that allows for conformational deformation is adopted to enable heavy metal ions to search for spatial positions and optimize energy around the active sites. Calculate the spatial binding energy between each heavy metal species and the active site of each specific metabolic enzyme. The inherent physicochemical properties of heavy metal ions are retrieved, and the spatial binding energy value is fused with the physicochemical properties parameters for calculation. The fusion calculation results were processed using an absolute value standardization conversion method to calculate the morphological specific inhibition coefficient of each heavy metal form on specific metabolic enzymes.

[0027] In this embodiment, spatial matching simulation is performed based on the spatial structures of various heavy metal forms and the spatial structures of specific metabolic enzymes. Specifically, an active site region is defined on the spatial structure of a specific metabolic enzyme, and this region is obtained by parsing the active pocket annotation information in the spatial structure file. The spatial structure data of each heavy metal form is then located within the active site region.

[0028] Subsequently, a flexible matching strategy that allows for conformational deformation is employed to enable heavy metal ions to search for spatial positions and optimize energy around the active site. This process is achieved by invoking a conventional molecular docking algorithm, adjusting the spatial orientation of the heavy metal ions and the conformation of the side chains at the enzyme active site within the search space to find the stable binding state with the lowest energy.

[0029] In this state, the spatial binding energy between each heavy metal form and the active site of each specific metabolic enzyme is calculated. The inherent physicochemical properties of the heavy metal ions are retrieved, including the ion's charge and atomic radius.

[0030] The space binding energy value and the physicochemical property parameters are fused together, and the fusion calculation result is processed using an absolute value standardization conversion method. The fusion calculation formula is as follows: ; in, For intermediate parameters, For the first The first heavy metal form and the second The spatial binding energy value of a specific metabolic enzyme. For the first The amount of charge in each form of heavy metal. denoted as , where is the atomic radius of the heavy metal ion.

[0031] Then, absolute value standardization is performed, calculated using the following formula: ; in, For the first The first heavy metal form on the The morphological specific inhibition coefficient of a specific metabolic enzyme. For all heavy metal forms, the first The maximum value among the intermediate parameters of a specific metabolic enzyme. This coefficient ranges from 0 to 1, with a larger value indicating a stronger degree of inhibition. In alternative embodiments, the physicochemical property parameter can also incorporate the electron affinity of ions to more precisely characterize the interference ability of heavy metals on the enzyme.

[0032] Furthermore, the steps for calculating the biochemical reaction flux rate of heavy metal speciation, in conjunction with the biochemical reaction network of heavy metal speciation, include: The microbial community abundance data is compared with a pre-stored functional gene mapping database to screen and identify functional microbial groups with heavy metal speciation capabilities. The functional microbial groups include sulfate-reducing bacteria, iron-reducing bacteria, mercury-resistant microorganisms, and specific methylating bacteria; Based on the microbial metabolic pathway database, the transformation pathways of the aforementioned functional microbial groups in heavy metal reduction, oxidation, methylation, and extracellular precipitation were identified. Using different chemical forms of heavy metals as network nodes and the transformation paths as edges connecting the nodes, a biochemical reaction chain network structure reflecting the transformation of heavy metal forms is constructed.

[0033] In this embodiment, a network is constructed based on the microbial community abundance and water physicochemical parameters, combined with a biochemical reaction network for heavy metal speciation. The microbial community abundance data is compared with a pre-stored functional gene mapping database. The pre-stored functional gene mapping database stores the correspondence between functional microbial groups and functional gene identifiers. Through comparison, functional microbial groups with heavy metal speciation capabilities are screened and identified. These functional microbial groups include sulfate-reducing bacteria, iron-reducing bacteria, mercury-resistant microorganisms, and specific methylating bacteria.

[0034] Based on the microbial metabolic pathway database, the transformation pathways of the aforementioned functional microbial groups in heavy metals through reduction, oxidation, methylation, and extracellular precipitation reactions were identified. For example, sulfate-reducing bacteria reduce hexavalent chromium ions to trivalent chromium ions, and certain methylating bacteria convert divalent mercury ions into methylmercury ions.

[0035] Using different chemical forms of heavy metals as network nodes and the transformation paths as edges connecting the nodes, a biochemical reaction chain network structure reflecting the transformation of heavy metal forms is constructed. This network structure is stored in memory as a directed graph.

[0036] Furthermore, calculating the biochemical reaction flux rate of heavy metal speciation also includes: The microbial community abundance data is used as a reaction weight parameter and assigned to the corresponding biochemical reaction branch in the biochemical reaction chain network structure; The physicochemical parameters of the water body are used as reaction rate limiting conditions and substituted into the biochemical reaction chain network structure to set the reaction boundary. Using a grid flow analysis method based on mass conservation, the maximum possible flow rate of each heavy metal speciation biochemical reaction branch is calculated under the constraints of the reaction boundary. Multiplying the maximum possible flow rate by the corresponding reaction weight parameter yields the actual reaction rate under the combined constraints of the current microbial community size and aquatic environmental factors, which is then used as the biochemical reaction flux rate.

[0037] In this embodiment, after the biochemical reaction chain network structure is constructed, the biochemical reaction flux rate is calculated. The microbial community abundance data is used as a reaction weight parameter and assigned to the corresponding biochemical reaction branches in the biochemical reaction chain network structure. Specifically, the higher the abundance of the microbial community corresponding to a certain biochemical reaction branch, the larger the weight parameter assigned to that branch.

[0038] The physicochemical parameters of the water body are used as reaction rate limiting conditions and substituted into the biochemical reaction chain network structure to set the reaction boundary. The reaction rate limiting conditions restrict the theoretical maximum reaction rate through a correction function. The formula for the correction function is: ; in, For the first The maximum possible flow rate of each biochemical reaction branch under environmental constraints. The preset theoretical reference rate, , , , These are the limiting coefficients for pH, dissolved oxygen, temperature, and redox potential, respectively, with values ​​ranging from zero to one. When a certain physicochemical parameter of the water body deviates from the optimal metabolic range for microorganisms, the corresponding limiting coefficient decreases.

[0039] Using a grid flow analysis method based on mass conservation, the maximum possible flow rate of each heavy metal speciation biochemical reaction branch is calculated under the constraints of the reaction boundary.

[0040] Multiplying the maximum possible flow rate by the corresponding reaction weight parameter yields the actual reaction rate under the combined constraints of the current microbial community size and aquatic environmental factors. The calculation formula is as follows: ; in, For the first The actual reaction rate of each biochemical reaction branch, i.e., the biochemical reaction flux rate. This is the reaction weighting parameter for this branch. This flux rate quantification describes the dynamic velocity distribution of heavy metals flowing progressively between different chemical forms. In alternative embodiments, the constraint coefficient... Linear or nonlinear functions can be used, as long as they can reflect the suppression trend of deviation from the optimal range.

[0041] Furthermore, the step of updating the predicted concentrations of various heavy metal forms based on the biochemical reaction flux rate includes: A fixed time step is configured as the iteration period. At the beginning of the current iteration period, the total concentration data of heavy metals and the concentration data of each form in the initial water quality multidimensional dataset are retrieved as the initial input stream. Multiply the biochemical reaction flux rate by the time step to calculate the change in concentration of each form of heavy metal caused by microbial transformation within the current time step. Based on the properties of heavy metal forms as consumables or products in biochemical reactions, the changes in the concentration of each heavy metal form are added to or subtracted from the initial concentration data of each form, and the predicted concentration values ​​of each heavy metal form at the end of the current time step are updated.

[0042] In this embodiment, a fixed time step is configured as the iteration period, for example, set to one hour. At the start of the current iteration period, the total heavy metal concentration data and the concentration data of each form in the initial water quality multidimensional dataset are retrieved as the initial input stream.

[0043] Multiply the biochemical reaction flux rate by the time step to calculate the change in concentration of each heavy metal form caused by microbial transformation within the current time step. The calculation formula is as follows: ; in, For the first Changes in the concentration of various heavy metal forms. To generate the first Biochemical reaction flux rates of various heavy metal forms. To consume the first Biochemical reaction flux rates of various heavy metal forms. For time step.

[0044] Based on the properties of heavy metal speciations as consumables or products in biochemical reactions, the corresponding concentration changes are added to or subtracted from the initial concentration data of each speciation, thus updating the predicted concentration values ​​of each heavy metal speciation at the end of the current time step. The calculation formula is as follows: ; in, This is the predicted concentration value at the end of the current time step. This is the initial concentration data for each form.

[0045] Furthermore, the steps for weighted calculation of the predicted concentrations of various heavy metal speciations based on the speciation-specific inhibition coefficient include: The sum of the predicted concentrations of all forms of the same heavy metal element is calculated to obtain the total predicted concentration of that heavy metal element. Calculate the ratio of the predicted concentration of each heavy metal form to the total predicted concentration to obtain the real-time proportion weight of the corresponding form; The toxicity contribution of each heavy metal form is obtained by multiplying its real-time weighting with the corresponding form-specific inhibition coefficient. The toxicity contribution components of all forms of the same heavy metal element are summed to obtain a composite toxicity component that reflects the combined toxic effects of multiple heavy metal forms.

[0046] In this embodiment, the predicted concentration values ​​of each form of the heavy metal are weighted and calculated based on the morphology-specific inhibition coefficient. First, the sum of the predicted concentration values ​​of all forms of the same heavy metal element is calculated to obtain the total predicted concentration value of that heavy metal element.

[0047] Calculate the ratio of the predicted concentration of each heavy metal species to the total predicted concentration to obtain the real-time weight of the corresponding species. The calculation formula is as follows: ; in, For the first Real-time weighting of different heavy metal forms This is the predicted value for total concentration.

[0048] The real-time weighted proportion of each heavy metal speciation is multiplied by its corresponding speciation-specific inhibition coefficient to obtain the toxicity contribution component of that speciation. The toxicity contribution components of all speciations of the same heavy metal element are then summed to obtain the composite toxicity component, reflecting the combined toxic effects of multiple heavy metal speciations. The calculation formula is as follows: ; in, As a complex toxic component, This represents the corresponding morphology-specific inhibition coefficient.

[0049] Furthermore, the steps for deriving a bioeffective toxicity index by combining the total abundance of the microbial community include: The abundance values ​​of all microbial species in the target water body are summed to obtain the total abundance data of the microbial community. The composite toxicity component and the total abundance data of the microbial community are input into a preset normalized mapping model; By standardizing the data to eliminate dimensional differences, the bioeffective toxicity index at the end of the current time step is calculated, and the bioeffective toxicity index is output through the data output port.

[0050] In this embodiment, the bioeffective toxicity index is output by combining the total abundance of the microbial community. The total abundance data of the microbial community is obtained by summing the abundance values ​​of all microbial species in the target water body.

[0051] The composite toxicity component and the total abundance data of the microbial community are input into a preset normalized mapping model. The normalized mapping model uses a logarithmic transformation function and, through standardization to eliminate dimensional differences, calculates the bioeffective toxicity index at the end of the current time step. The calculation formula is: ; BTI stands for Bioeffective Toxicity Index. The formula for total abundance data of microbial communities includes a 1 to avoid logarithmic calculation errors caused by zero values.

[0052] The bioeffective toxicity index is output through the data output port. In specific implementations, the output operation may include writing the data to a local storage file, sending it to a remote monitoring terminal, or triggering the generation of a visualization chart.

[0053] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0054] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0055] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0056] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0057] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0058] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A big data analysis and detection method for water quality microorganisms and heavy metals, characterized in that, Includes the following steps: Obtain the initial water quality multidimensional dataset of the target water body. The initial water quality multidimensional dataset includes the total concentration of heavy metals, the concentration of each form of heavy metals, the abundance of microbial communities, the physicochemical parameters of the water body, as well as the spatial structure of specific metabolic enzymes of microorganisms and the spatial structure of each form of heavy metals. Based on the spatial structure of each heavy metal morphology and the spatial structure of the specific metabolic enzyme, spatial matching simulation is performed to determine the morphology-specific inhibition coefficient of each heavy metal morphology on the specific metabolic enzyme. Based on the abundance of the microbial community and the physicochemical parameters of the water body, combined with the biochemical reaction network of heavy metal speciation, the biochemical reaction flux rate of heavy metal speciation was calculated. A fixed time step is set, and the concentration data in the initial water quality multidimensional dataset is used as the initial input for the current iteration cycle. The predicted concentration values ​​of each form of heavy metal are updated based on the biochemical reaction flux rate. The predicted concentrations of each heavy metal species are weighted based on the morphology-specific inhibition coefficient, and then combined with the total abundance of the microbial community to output the bioeffective toxicity index.

2. The integrated big data analysis and detection method for water quality microorganisms and heavy metals as described in claim 1, characterized in that, The steps to obtain the initial water quality cube dataset for the target water body include: Receive data on the total concentration of heavy metals and the concentration of each form of heavy metal in the target water body, as well as the physicochemical parameters of the water body; Acquire microbial community abundance data of the target water body and retrieve spatial structure data of specific metabolic enzymes from a preset database; Retrieve spatial structure data of various heavy metal morphologies; The acquired heavy metal concentration data, water body physicochemical parameter data, microbial community abundance data, specific metabolic enzyme spatial structure data, and heavy metal spatial structure data of various forms are time-stamped and aligned to form an initial water quality multidimensional dataset.

3. The integrated big data analysis and detection method for water quality microorganisms and heavy metals as described in claim 1, characterized in that, The steps for performing spatial matching simulations to determine the form-specific inhibitory coefficients of various heavy metal speciations on specific metabolic enzymes include: The active site region is defined on the spatial structure of the specific metabolic enzyme, and the spatial structure data of each form of heavy metal is located within the active site region. A flexible matching strategy that allows for conformational deformation is adopted to enable heavy metal ions to search for spatial positions and optimize energy around the active sites. Calculate the spatial binding energy between each heavy metal species and the active site of each specific metabolic enzyme. The inherent physicochemical properties of heavy metal ions are retrieved, and the spatial binding energy value is fused with the physicochemical properties parameters for calculation. The fusion calculation results were processed using an absolute value standardization conversion method to calculate the morphological specific inhibition coefficient of each heavy metal form on specific metabolic enzymes.

4. The integrated big data analysis and detection method for water quality microorganisms and heavy metals as described in claim 1, characterized in that, The steps for calculating the biochemical reaction flux rate of heavy metal speciation, based on the biochemical reaction network, include: The microbial community abundance data is compared with a pre-stored functional gene mapping database to screen and identify functional microbial groups with heavy metal speciation capabilities. Based on the microbial metabolic pathway database, the transformation pathways of the aforementioned functional microbial groups in heavy metal reduction, oxidation, methylation, and extracellular precipitation were identified. Using different chemical forms of heavy metals as network nodes and the transformation paths as edges connecting the nodes, a biochemical reaction chain network structure reflecting the transformation of heavy metal forms is constructed.

5. The integrated big data analysis and detection method for water quality microorganisms and heavy metals as described in claim 1, characterized in that, Calculating the biochemical reaction flux rate for heavy metal speciation also includes: The microbial community abundance data is used as a reaction weight parameter and assigned to the corresponding biochemical reaction branch in the biochemical reaction chain network structure; The physicochemical parameters of the water body are used as reaction rate limiting conditions and substituted into the biochemical reaction chain network structure to set the reaction boundary. Using a grid flow analysis method based on mass conservation, the maximum possible flow rate of each heavy metal speciation biochemical reaction branch is calculated under the constraints of the reaction boundary. Multiplying the maximum possible flow rate by the corresponding reaction weight parameter yields the actual reaction rate under the combined constraints of the current microbial community size and aquatic environmental factors, which is then used as the biochemical reaction flux rate.

6. The integrated big data analysis and detection method for water quality microorganisms and heavy metals as described in claim 1, characterized in that, The steps for updating the predicted concentrations of various heavy metal forms based on the biochemical reaction flux rate include: A fixed time step is configured as the iteration period. At the beginning of the current iteration period, the total concentration data of heavy metals and the concentration data of each form in the initial water quality multidimensional dataset are retrieved as the initial input stream. Multiply the biochemical reaction flux rate by the time step to calculate the change in concentration of each form of heavy metal caused by microbial transformation within the current time step. Based on the properties of heavy metal forms as consumables or products in biochemical reactions, the changes in the concentration of each heavy metal form are added to or subtracted from the initial concentration data of each form, and the predicted concentration values ​​of each heavy metal form at the end of the current time step are updated.

7. The integrated big data analysis and detection method for water quality microorganisms and heavy metals as described in claim 1, characterized in that, The steps for weighted calculation of the predicted concentrations of various heavy metal speciations based on the speciation-specific inhibition coefficient include: The sum of the predicted concentrations of all forms of the same heavy metal element is calculated to obtain the total predicted concentration of that heavy metal element. Calculate the ratio of the predicted concentration of each heavy metal form to the total predicted concentration to obtain the real-time proportion weight of the corresponding form; The toxicity contribution of each heavy metal form is obtained by multiplying its real-time weighting with the corresponding form-specific inhibition coefficient. The toxicity contribution components of all forms of the same heavy metal element are summed to obtain a composite toxicity component that reflects the combined toxic effects of multiple heavy metal forms.

8. The integrated big data analysis and detection method for water quality microorganisms and heavy metals as described in claim 7, characterized in that, The steps for deriving a bioeffective toxicity index based on the total abundance of the microbial community include: The abundance values ​​of all microbial species in the target water body are summed to obtain the total abundance data of the microbial community. The composite toxicity component and the total abundance data of the microbial community are input into a preset normalized mapping model; By standardizing the data to eliminate dimensional differences, the bioeffective toxicity index at the end of the current time step is calculated, and the bioeffective toxicity index is output through the data output port.

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