A method for evaluating ecological risk of heavy metals in whole cycle of thermal stratification of reservoir

Abstract

The application discloses a reservoir thermal stratification full-cycle heavy metal ecological risk evaluation method, and the method comprises the following steps: obtaining environmental factor data of a target reservoir at different thermal stratification stages and effective concentration values of target heavy metals at sampling points of each reservoir position; constructing a three-dimensional effective concentration field of each target heavy metal at each stratification stage based on the effective concentration values, and simultaneously constructing a dynamic environmental weight coefficient field varying with water depth and period; combining a preset risk benchmark to calculate a three-dimensional weighted risk quotient field of each target heavy metal at each stratification stage; and finally, through accumulation and summation of the three-dimensional weighted risk quotient field of each target heavy metal at each stratification stage in terms of heavy metal types and thermal stratification stages, a full-cycle comprehensive ecological three-dimensional risk field is generated as a heavy metal ecological risk evaluation result of the target reservoir, and the technical scheme of the embodiment of the application can realize full-cycle and three-dimensional fine dynamic evaluation of the ecological risk of the reservoir heavy metals.

Description

[0001] This invention relates to the field of environmental monitoring technology, and in particular to a method for full-cycle heavy metal ecological risk assessment of reservoir thermal stratification. Background Technology

[0002] With the acceleration of urbanization and the continuous improvement of drinking water safety requirements, the risk assessment of heavy metal pollution in reservoir-type water sources has become an increasingly important issue in the field of environmental monitoring. Unlike shallow lakes, deep reservoirs exhibit significant seasonal thermal stratification, leading to high heterogeneity in the vertical distribution of heavy metals in the water body. Accurately characterizing the dynamic risks of heavy metal migration and transformation within the thermal stratification cycle has become one of the most critical scientific challenges in ensuring the safety of water quality in water sources.

[0003] In existing technologies, heavy metal concentrations in reservoirs are mainly monitored and assessed through fixed-frequency sampling or single-depth sampling. However, fixed-frequency sampling is difficult to capture the heavy metal pollution characteristics at each key stage of thermal stratification, while single-depth sampling completely ignores vertical spatial heterogeneity, making it impossible to accurately assess key risk processes such as heavy metal release from the bottom anoxic zone and the upwelling and diffusion of pollutants during the recession phase. Summary of the Invention

[0004] This invention provides a method for assessing the ecological risk of heavy metals throughout the entire thermal stratification cycle of a reservoir, in order to solve the technical problem of accurately assessing the ecological risk of heavy metals within the thermal stratification cycle of a reservoir.

[0005] According to one aspect of the present invention, a method for assessing the ecological risk of heavy metals in a reservoir is provided, the method comprising: Environmental factor data of the target reservoir under different thermal stratification stages were obtained, and the effective concentration values ​​of each target heavy metal at each sampling point in the target reservoir under each thermal stratification stage were obtained. Based on the effective concentration values ​​of each target heavy metal at each reservoir location sampling point under each thermal stratification stage, a three-dimensional effective concentration field of each target heavy metal under each stratification stage is constructed; wherein, the set data point in the three-dimensional effective concentration field of the target heavy metal under the set thermal stratification stage represents the effective concentration value of the set target heavy metal at the set reservoir location coordinate point under the set thermal stratification stage. Based on the environmental factor data of the target reservoir under different thermal stratification stages, a dynamic environmental weight coefficient field is constructed. The set data points in the dynamic environmental weight coefficient field represent the environmental weights at a set reservoir water level depth under a set thermal stratification stage. Based on the three-dimensional effective concentration field of each target heavy metal in each thermal stratification stage, the preset risk benchmark and dynamic environmental weight coefficient field, the three-dimensional weighted risk market of each target heavy metal in each thermal stratification stage is calculated. The three-dimensional weighted risk market for each target heavy metal at each thermal stratification stage is calculated by summing the heavy metal dimension and the thermal stratification dimension to obtain the full-cycle comprehensive ecological three-dimensional risk field, which serves as the heavy metal ecological risk assessment result for the target reservoir.

[0006] According to another aspect of the present invention, a heavy metal ecological risk assessment device for reservoirs is provided, the device comprising: The data acquisition module is used to acquire environmental factor data of the target reservoir under different thermal stratification stages, and to acquire the effective concentration values ​​of each target heavy metal at each sampling point in the target reservoir under each thermal stratification stage. The three-dimensional effective concentration field construction module is used to construct the three-dimensional effective concentration field of each target heavy metal at each reservoir location sampling point under each thermal stratification stage based on the effective concentration values ​​of each target heavy metal at each reservoir location sampling point under each thermal stratification stage; wherein, the set data point in the three-dimensional effective concentration field of the target heavy metal under the set thermal stratification stage represents the effective concentration value of the set target heavy metal at the set reservoir location coordinate point under the set thermal stratification stage. The dynamic weight coefficient field construction module is used to construct a dynamic environmental weight coefficient field based on the environmental factor data of the target reservoir under different thermal stratification stages. The set data points in the dynamic environmental weight coefficient field represent the environmental weights at a set water level depth in the set thermal stratification stage. The weighted risk field calculation module is used to calculate the three-dimensional weighted risk field of each target heavy metal in each thermal stratification stage based on the three-dimensional effective concentration field of each target heavy metal in each thermal stratification stage, the preset risk benchmark and the dynamic environmental weight coefficient field. The full-cycle risk fusion module is used to perform cumulative calculation of the heavy metal dimension and the thermal stratification dimension of the three-dimensional weighted risk market of each target heavy metal at each thermal stratification stage, so as to obtain the full-cycle comprehensive ecological three-dimensional risk field, which serves as the heavy metal ecological risk assessment result of the target reservoir.

[0007] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the heavy metal ecological risk assessment method for reservoirs according to any embodiment of the present invention.

[0008] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute and implement the heavy metal ecological risk assessment method for reservoirs according to any embodiment of the present invention.

[0009] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps of the method as described in any embodiment of the present invention.

[0010] The technical solution of this invention involves acquiring environmental factor data of a target reservoir at different thermal stratification stages, and simultaneously acquiring the effective concentration values ​​of target heavy metals at sampling points at each reservoir location during each stage; constructing a three-dimensional effective concentration field for each target heavy metal at each stratification stage based on the effective concentration values ​​of target heavy metals at each sampling point during each stage; constructing a dynamic environmental weight coefficient field that varies with water depth and time by combining the environmental factor data; and then combining the three-dimensional effective concentration field of each target heavy metal at each stratification stage with a preset risk benchmark and the dynamic environmental weight coefficient field to calculate the effective concentration of each target heavy metal. The three-dimensional weighted risk market for heavy metals at each stratification stage is used. Finally, the three-dimensional weighted risk market for each target heavy metal at each stratification stage is summed by the heavy metal type and the thermal stratification stage to generate a comprehensive three-dimensional ecological risk field for the entire cycle. This serves as the result of the heavy metal ecological risk assessment for the target reservoir, thereby achieving a three-dimensional, dynamic, and refined assessment of the heavy metal ecological risk of the reservoir throughout the entire thermal stratification cycle. This effectively solves the problem that traditional methods are difficult to accurately depict the migration and transformation patterns and dynamic ecological risks of heavy metals during the seasonal thermal stratification process of reservoirs, and improves the accuracy and timeliness of water quality risk early warning for water sources.

[0011] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart of a method for assessing the ecological risk of heavy metals in a reservoir according to Embodiment 1 of the present invention; Figure 2This is a flowchart of another method for assessing the ecological risk of heavy metals in a reservoir according to Embodiment 2 of the present invention; Figure 3 This is a flowchart illustrating the technical route for heavy metal ecological risk assessment of a reservoir in a specific scenario applicable to the embodiments of the present invention. Figure 4 This is a schematic diagram of the structure of a heavy metal ecological risk assessment device for a reservoir provided according to Embodiment 3 of the present invention; Figure 5 This is a schematic diagram of the structure of an electronic device for implementing a method for assessing the ecological risk of heavy metals in a reservoir according to an embodiment of the present invention. Detailed Implementation

[0014] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0015] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0016] Example 1 Figure 1 This is a flowchart of a method for assessing the ecological risk of heavy metals in a reservoir, provided in Embodiment 1 of the present invention. This embodiment is applicable to the situation of conducting a refined assessment of the ecological risk of heavy metals in deep-water reservoirs with thermal stratification. The method can be executed by a heavy metal ecological risk assessment device for the reservoir. The device can be implemented in hardware and / or software and is generally configured in a point device.

[0017] Correspondingly, such as Figure 1 As shown, the method includes: S110. Obtain environmental factor data of the target reservoir under different thermal stratification stages, and obtain the effective concentration values ​​of each target heavy metal at each sampling point in the target reservoir under each thermal stratification stage.

[0018] The thermal stratification stage can be understood as the three phases a reservoir experiences throughout the year due to changes in the vertical distribution of its water temperature. The formation phase typically occurs in spring, when the surface water temperature rises, creating a significant temperature difference with the colder bottom water, leading to stable stratification. The stable phase occurs in summer, when the stratification structure is most robust, making mixing difficult and causing oxygen deficiency in the bottom layer. The regression phase occurs in autumn, when the surface water temperature drops, triggering vertical convection and gradually weakening and eventually eliminating the stratification structure. These three phases constitute a complete thermal stratification cycle, each with its unique hydrological and chemical conditions that directly influence the behavior and distribution of heavy metals.

[0019] Environmental factors can be understood as physicochemical parameters measured at different depths in a reservoir during various stages of thermal stratification, which can influence the speciation, migration, and toxicity of heavy metals. These key data primarily include dissolved oxygen concentration, which directly reflects the redox state of the water body; low oxygen conditions promote the release of heavy metals from sediments; pH, whose fluctuations affect the solubility and bioavailability of heavy metals; and water temperature, which not only serves as the basis for delineating thermal stratification stages but also directly influences other environmental factors and chemical reaction rates. The effective concentration value of the target heavy metal can be understood as specifically referring to the concentration of heavy metal speciation that can be directly absorbed and utilized by organisms, measured in situ using gradient diffusion thin-film technology.

[0020] In this embodiment, by monitoring water temperature and dissolved oxygen profile data, the three key stages of reservoir thermal stratification—formation, stability, and decline—are accurately identified. Sampling devices are simultaneously deployed at key characteristic layers in each stage to obtain the effective concentration of target heavy metals reflecting bioavailability. At the same time, an analyzer is used to collect environmental factor data such as dissolved oxygen and pH, providing a complete dataset with time-series matching and vertical coverage for subsequent analysis.

[0021] Optionally, based on the above embodiments, obtaining environmental factor data of the target reservoir at different thermal stratification stages, and obtaining the effective concentration values ​​of each target heavy metal at each sampling point in the target reservoir at each thermal stratification stage, may include: The current thermal stratification stage of the target reservoir is identified by detecting water sample data from the target reservoir. When the target reservoir is identified as being in the target thermal stratification stage, a gradient diffusion film device is deployed at sampling points at each reservoir location that matches the target thermal stratification stage to collect heavy metals from each target reservoir within a preset exposure time. During the exposure time, environmental factor data of the target reservoir under the target thermal stratification stage are collected simultaneously by a multi-parameter water quality analyzer. After the preset exposure time is reached, the gradient diffusion film recovery device performs data analysis to obtain the effective concentration values ​​of each target heavy metal at each sampling point in the target reservoir during the target thermal stratification stage.

[0022] Generally, the first step is to continuously monitor the vertical profile data of key parameters such as water temperature and dissolved oxygen, and then accurately determine the current thermal stratification stage of the reservoir based on the characteristics of its gradient changes. For example, the formation period, stable period, or receding period can be identified by whether the temperature gradient has formed, stabilized, or weakened.

[0023] Generally, after determining the current thermal stratification stage, the most representative vertical sampling points are dynamically selected based on the typical hydrological characteristics of this stage (such as thermocline depth and mixing layer thickness). Two types of equipment are simultaneously deployed at these points: gradient diffusion film devices are used to passively enrich bioavailable target heavy metals in the water, while multi-parameter water quality analyzers are used to record environmental parameters such as dissolved oxygen and pH in real time (i.e., environmental factor data of the target reservoir), ensuring that the two types of data are completely synchronized in time and space.

[0024] Generally, after the preset exposure time is completed, the gradient diffusion film device is recovered and the data is analyzed. By measuring the content of each target heavy metal enriched in the device, and combining the exposure time and diffusion coefficient, the effective state concentration value of each target heavy metal at each sampling point is finally calculated, thereby obtaining effective state concentration value data that accurately matches the hydrological conditions of a specific thermal stratification stage.

[0025] S120. Based on the effective concentration values ​​of each target heavy metal at each reservoir location sampling point under each thermal stratification stage, construct a three-dimensional effective concentration field of each target heavy metal under each stratification stage; wherein, the set data point in the three-dimensional effective concentration field of the target heavy metal under the set thermal stratification stage represents the effective concentration value of the set target heavy metal at the set reservoir location coordinate point under the set thermal stratification stage.

[0026] The three-dimensional effective concentration field can be understood as a digital model that can completely reflect the spatial distribution characteristics of heavy metals at any point in the reservoir water. Based on measured data from a limited number of sampling points, it uses three-dimensional spatial interpolation technology to transform discrete sampling point information into continuous spatial distribution data.

[0027] In this embodiment, the effective concentration values ​​of discrete point-like heavy metals collected at each stage are combined with the underwater topographic digital elevation model as spatial constraints, and spatial interpolation calculation is performed using a three-dimensional kriging interpolation algorithm. By analyzing the spatial correlation between the three-dimensional spatial coordinates of each sampling point and the effective concentration values ​​of each target heavy metal, an effective concentration field that can continuously reflect the three-dimensional spatial distribution of each target heavy metal in the entire reservoir is generated, so that the concentration value on each grid node represents the effective concentration of each target heavy metal at that location.

[0028] Optionally, based on the above embodiments, a three-dimensional effective concentration field of each target heavy metal at each reservoir location sampling point is constructed according to the effective concentration values ​​of each target heavy metal at each thermal stratification stage. This can include: The effective concentration values ​​of each target heavy metal at each reservoir sampling point are obtained under each thermal stratification stage, wherein each reservoir sampling point corresponds to a set three-dimensional coordinate value in three-dimensional space. The underwater topographic digital elevation model of the target reservoir is used as a three-dimensional boundary constraint for spatial interpolation. Based on the effective concentration values ​​of each target heavy metal at each reservoir sampling point under each thermal stratification stage, and the three-dimensional boundary constraints, a preset three-dimensional kriging interpolation algorithm is used to calculate and generate the three-dimensional effective concentration field of each target heavy metal under each stratification stage.

[0029] The underwater topographic digital elevation model (DEM) can be understood as a digitized three-dimensional mathematical model used to accurately describe the undulating shape of the reservoir bottom. By collecting a large amount of spatial coordinates and elevation data from underwater sounding points, a continuous underwater topographic surface is constructed. The three-dimensional kriging interpolation algorithm can be understood as a spatial data prediction method based on geostatistics. Its core idea is that spatially adjacent points have similarity (i.e., spatial autocorrelation), and this correlation weakens as the distance between points increases. This algorithm analyzes the spatial distribution structure and statistical characteristics of existing sampled point data to fit an optimal mathematical model, thereby scientifically predicting values ​​for unknown spatial locations.

[0030] Generally, the first step is to obtain the effective concentration values ​​of each target heavy metal at different locations and depths in the reservoir during different thermal stratification periods. Each sampling point corresponds to a specific location with three-dimensional spatial coordinates (i.e., three-dimensional coordinate values).

[0031] Generally, in order to improve the accuracy of spatial interpolation, the topographic elevation data of the bottom of the reservoir needs to be used as an important boundary constraint. This can ensure that the subsequently generated three-dimensional concentration field is completely consistent with the actual spatial morphology of the water body and avoid false values ​​that do not conform to the actual terrain.

[0032] Generally, after obtaining spatially distributed sampling point data and considering topographic constraints, an interpolation algorithm that considers spatial correlation is used for processing. This algorithm can calculate the concentration value of each unknown point in the three-dimensional space of the entire water body based on the known effective concentration values ​​of each target heavy metal and their spatial location relationships at the sampling points, ultimately generating a three-dimensional effective concentration field that fully reflects the effective concentration of each target heavy metal at each stratification stage. The calculation formula for the three-dimensional effective concentration field is as follows: in, The environmental risk correction weight at depth z in the thermal stratification stage t is dimensionless and has a baseline value of 1. The measured dissolved oxygen concentration at depth z (t) during the thermal stratification stage; This represents the saturated dissolved oxygen concentration at a depth z and water temperature T during the thermal stratification stage. This represents the average temperature across the temperature range corresponding to stage t in the thermal stratification process. It's worth noting that during the stable stratification period, the bottom layer... Extremely low; this weight will increase significantly, reflecting the risk of heavy metal reducing release and enhanced toxicity caused by hypoxia. This represents the measured pH value at depth z during the thermal stratification stage. and This is the environmental impact adjustment coefficient, typically ranging from 0.1 to 1.0. It is determined based on the redox potential (affected by the redox potential) of each target heavy metal. The sensitivity of pH (affected by pH) and pH level was determined.

[0033] S130. Based on the environmental factor data of the target reservoir under different thermal stratification stages, construct a dynamic environmental weight coefficient field, where the set data points in the dynamic environmental weight coefficient field represent the environmental weights at the set reservoir water level depth under the set thermal stratification stage.

[0034] In this embodiment, based on synchronously collected environmental factor data, the ratio of dissolved oxygen concentration to saturated dissolved oxygen at different water depths and the degree of pH deviation from neutrality are considered. A weighted coefficient field is established to quantify the corrective effect of environmental conditions on the toxicity of target heavy metals. The weighted coefficient changes dynamically with the target reservoir at different thermal stratification stages and water depth, paying particular attention to the amplification effect of the hypoxic zone at the bottom of the stratification stable period on the toxicity of heavy metals, thus forming an environmental weighted field that reflects the spatiotemporal variation characteristics.

[0035] S140. Based on the three-dimensional effective concentration field of each target heavy metal in each thermal stratification stage, the preset risk benchmark and dynamic environmental weight coefficient field, calculate the three-dimensional weighted risk market of each target heavy metal in each thermal stratification stage.

[0036] The pre-defined risk benchmark can be understood as a multi-level concentration threshold system derived from toxicological data to protect aquatic ecosystems. This system constructs species sensitivity distribution curves by fitting a large amount of acute and chronic toxicity data of aquatic organisms to specific heavy metals, and calculates the corresponding protective concentration limits. Specifically, a short-term water quality benchmark derived from acute toxicity data is used to assess short-term, high-concentration exposure risks; a risk benchmark derived from chronic toxicity data is used to assess long-term, low-concentration exposure risks; and based on the risk benchmark, more stringent assessment factors are applied to derive the predicted no-effect concentration, which serves as the most conservative ecological protection threshold. This multi-level benchmark system collectively constitutes a quantitative scale for risk assessment.

[0037] In this embodiment, the heavy metal concentration values ​​at each location in the three-dimensional effective concentration field are compared with the risk benchmark derived from the species sensitivity distribution curve to obtain the initial risk quotient. Then, the initial risk quotient is multiplied by the weight coefficient at the corresponding location in the dynamic environmental weight coefficient field to achieve quantitative correction of the toxicity enhancement effect produced by environmental conditions, and to obtain a three-dimensional weighted risk market for each target heavy metal at each thermal stratification stage that is more consistent with the actual ecological risk situation.

[0038] Furthermore, based on the above embodiments, before calculating the three-dimensional weighted risk market of each target heavy metal at each thermal stratification stage according to the three-dimensional effective concentration field of each target heavy metal at each thermal stratification stage, the preset risk benchmark field, and the dynamic environmental weight coefficient field, it may further include: Chronic toxicity data of various target heavy metals on multiple aquatic organisms were screened from authoritative toxicology databases. Using the selected chronic toxicity dataset, a statistical distribution function was used to fit the species sensitivity distribution curve; The concentration values ​​of species that pose a preset percentage of harm are calculated from the species sensitivity distribution curve, and risk benchmarks corresponding to each target heavy metal are derived based on these concentration values.

[0039] Generally, based on the systematic collection of chronic toxicity test data of target heavy metals on various representative aquatic organisms from internationally recognized toxicology databases, these data mainly reflect the sub-lethal effects produced by organisms under long-term exposure to low concentrations of pollutants.

[0040] Generally, after obtaining sufficient toxicity datasets, statistical distribution functions are used to fit acute and chronic toxicity data separately. The fitting process first sorts the selected toxicity data by concentration value from smallest to largest, and calculates the cumulative probability for each data point using a formula. Then, the sorted concentration values ​​and cumulative probabilities are used as input data, and a specific statistical distribution function is applied for fitting. Through parameter optimization methods such as maximum likelihood estimation, the characteristic parameters of the distribution function are determined, ultimately resulting in a curve model that best describes the distribution pattern of species sensitivity: a curve based on acute SSD (Species Sensitivity Distribution) and a curve based on chronic SSD.

[0041] Generally, based on the fitted SSD distribution curve, the concentration of species that pose a predetermined percentage risk can be calculated as a key threshold. For acute SSD curves, short-term water quality benchmarks can be derived by combining assessment factors to evaluate the risk of short-term exposure to high concentrations. For chronic SSD curves, risk benchmarks are derived to evaluate the risk of long-term exposure to low concentrations. Based on these benchmarks, more stringent assessment factors are applied to further derive the predicted ineffective concentration, which serves as the most conservative ecological protection threshold, thereby establishing a multi-level risk assessment benchmark system.

[0042] S150. The three-dimensional weighted risk market for each target heavy metal at each thermal stratification stage is calculated by summing the heavy metal dimension and the thermal stratification dimension to obtain the full-cycle comprehensive ecological three-dimensional risk field, which serves as the heavy metal ecological risk assessment result for the target reservoir.

[0043] The heavy metal dimension can be understood as a comprehensive approach in risk assessment that treats various heavy metal pollutants as a whole. The thermal stratification dimension can be understood as a comprehensive approach in risk assessment that treats different stages of reservoir thermal stratification succession (formation period, stable period, and decline period) as a complete cycle.

[0044] In this embodiment, at the heavy metal dimension, the individual weighted risk quotients of all heavy metals at the same spatial location and within the same stage are summed to obtain a mixed ecological risk index reflecting the combined pollution effect. Subsequently, at the thermal stratification dimension, the mixed risk indices for different water periods are weighted according to the duration weights of the formation period, the stable period, and the decline period. Through the hierarchical aggregation of these two dimensions, a comprehensive three-dimensional risk field that can simultaneously reflect the combined toxic effects of multiple heavy metals and the dynamic changes in risk throughout the year is finally generated, thereby achieving a panoramic and quantitative evaluation of the reservoir's ecological risk.

[0045] The technical solution of this invention involves acquiring environmental factor data of a target reservoir at different thermal stratification stages, and simultaneously acquiring the effective concentration values ​​of target heavy metals at sampling points at each reservoir location during each stage; constructing a three-dimensional effective concentration field for each target heavy metal at each stratification stage based on the effective concentration values ​​of target heavy metals at each sampling point during each stage; constructing a dynamic environmental weight coefficient field that varies with water depth and time by combining the environmental factor data; and then combining the three-dimensional effective concentration field of each target heavy metal at each stratification stage with a preset risk benchmark and the dynamic environmental weight coefficient field to calculate the effective concentration of each target heavy metal. The three-dimensional weighted risk market for heavy metals at each stratification stage is used. Finally, the three-dimensional weighted risk market for each target heavy metal at each stratification stage is summed by the heavy metal type and the thermal stratification stage to generate a comprehensive three-dimensional ecological risk field for the entire cycle. This serves as the result of the heavy metal ecological risk assessment for the target reservoir, thereby achieving a three-dimensional, dynamic, and refined assessment of the heavy metal ecological risk of the reservoir throughout the entire thermal stratification cycle. This effectively solves the problem that traditional methods are difficult to accurately depict the migration and transformation patterns and dynamic ecological risks of heavy metals during the seasonal thermal stratification process of reservoirs, and improves the accuracy and timeliness of water quality risk early warning for water sources.

[0046] Example 2 Figure 2 This is a flowchart of a method for assessing the ecological risk of heavy metals in a reservoir, provided in Embodiment 2 of the present invention. This embodiment is an optimization based on the above embodiments. Specifically, the operation of "calculating the three-dimensional weighted risk market of each target heavy metal in each thermal stratification stage based on the three-dimensional effective concentration field of each target heavy metal in each thermal stratification stage, the preset risk benchmark and the dynamic environmental weight coefficient field" has been refined.

[0047] Correspondingly, such as Figure 2 As shown, the method includes: S210. Obtain environmental factor data of the target reservoir under different thermal stratification stages, and obtain the effective concentration values ​​of each target heavy metal at each sampling point in the target reservoir under each thermal stratification stage.

[0048] S220. Based on the effective concentration values ​​of each target heavy metal at each reservoir location sampling point under each thermal stratification stage, construct a three-dimensional effective concentration field of each target heavy metal under each stratification stage; wherein, the set data point in the three-dimensional effective concentration field of the target heavy metal under the set thermal stratification stage represents the effective concentration value of the set target heavy metal at the set reservoir location coordinate point under the set thermal stratification stage.

[0049] S230. Based on the environmental factor data of the target reservoir under different thermal stratification stages, construct a dynamic environmental weight coefficient field, where the set data points in the dynamic environmental weight coefficient field represent the environmental weights at the set reservoir water level depth under the set thermal stratification stage.

[0050] S240. Obtain the current three-dimensional effective concentration field of the target heavy metal in the current thermal stratification stage, and obtain the current risk benchmark that matches the target heavy metal.

[0051] In this embodiment, the three-dimensional effective concentration field data generated in the previous work is first called, and the corresponding target heavy metal type and its specific thermal stratification stage are associated. At the same time, the risk benchmark value matching the heavy metal is extracted from the preset risk benchmark library.

[0052] S250. Calculate the current effective concentration value at each current reservoir location coordinate point in the current three-dimensional effective concentration field and divide it by the current risk benchmark to obtain the current initial risk quotient.

[0053] In this embodiment, for each location point in three-dimensional space, the effective concentration value of each target heavy metal at the current location point is divided by the corresponding long-term baseline value to obtain the initial risk quotient. This calculation essentially compares the measured environmental exposure level with the ecological safety threshold, quantifying the basic risk level without considering the impact of environmental conditions.

[0054] S260. Multiply the current initial risk quotient value of each current reservoir location coordinate point with the current environmental weight selected in the dynamic environmental weight coefficient field that matches the current reservoir location coordinate point and the current thermal stratification stage to obtain the three-dimensional weighted risk market of the current target heavy metal in the current thermal stratification stage; wherein, the set data point in the three-dimensional weighted risk market represents the weighted risk quotient value of the set reservoir location coordinate point.

[0055] In this embodiment, a dynamic environmental weighting coefficient is introduced to correct the initial risk: based on the spatial coordinates and time stage of the current location, the corresponding weight value is extracted from the environmental weighting field, and this weight value is multiplied by the initial risk quotient. This calculation quantifies the enhancing effect of environmental factors such as dissolved oxygen and pH on the biotoxicity of heavy metals, especially strengthening the risk characterization of hypoxic areas and periods of high pollution, ultimately generating a three-dimensional weighted risk field that more closely reflects the actual ecological impact. The calculation formula for the three-dimensional weighted risk field is shown below: in, For the first The three-dimensional weighted risk value of a target heavy metal at coordinate points in three-dimensional space. For coordinates of a point in three-dimensional space, Represented as the first Target heavy metals, Represented as the first The effective concentration value of the target heavy metal at the coordinates of the target heavy metal in three-dimensional space. Represented as the first The risk benchmarks for the target heavy metals include short-term water quality benchmarks for assessing the risk of short-term high-concentration exposure, long-term water quality benchmarks for assessing the risk of long-term low-concentration exposure, and a predicted ineffective concentration as the most conservative ecological protection threshold. It is represented as the environmental weight coefficient at depth z in three-dimensional space.

[0056] S270. The three-dimensional weighted risk market for each target heavy metal at each thermal stratification stage is calculated by summing the heavy metal dimension and the thermal stratification dimension to obtain the full-cycle comprehensive ecological three-dimensional risk field, which serves as the heavy metal ecological risk assessment result for the target reservoir.

[0057] Optionally, based on the above embodiments, the three-dimensional weighted risk quotients of each target heavy metal at each thermal stratification stage are summed by the heavy metal dimension and the thermal stratification dimension to obtain a comprehensive ecological three-dimensional risk field for the entire cycle, which may include: The weighted risk quotients of all target heavy metals at each reservoir location coordinate point under each thermal stratification stage are summed to obtain the three-dimensional mixed ecological risk index field corresponding to each thermal stratification stage. Among them, the set data points in the three-dimensional hybrid ecological risk index field represent the hybrid ecological risk index values ​​under the set reservoir location coordinates; Based on the duration weight of each thermal stratification stage, the three-dimensional hybrid ecological risk index fields corresponding to each thermal stratification stage are weighted and summed to generate a full-cycle comprehensive three-dimensional ecological risk field. Among them, the set data points of the full-cycle integrated ecological three-dimensional risk field represent the full-cycle integrated ecological risk value under the set reservoir location coordinates.

[0058] Generally, for each specific thermal stratification stage, the weighted risk quotients of all different target heavy metals at the same coordinate point in the water body are summed. This operation assumes that the ecotoxicological effects of multiple heavy metals are additive, thus obtaining a mixed ecological risk index value reflecting the combined effect of all heavy metals at that thermal stratification stage, and forming a three-dimensional mixed ecological risk index field for that thermal stratification stage. The formula for calculating the mixed ecological risk index value is as follows: in, It is represented as a mixed ecological risk index at coordinates (x, y, z) in three-dimensional space. This represents the total number of target heavy metal species.

[0059] For the formation stage, the formula for calculating the mixed ecological risk index is as follows: For the stable period, the formula for calculating the mixed ecological risk index is as follows: For the stable period, the formula for calculating the mixed ecological risk index is as follows: Generally, after obtaining the three-dimensional hybrid ecological risk index field for each thermal stratification stage, a corresponding time weight coefficient is assigned to each stage based on the duration proportion of the formation, stabilization, and decline stages in the actual hydrological cycle. Subsequently, for each location point in the three-dimensional space, its risk index at different periods is multiplied by its corresponding time weight, and the weighted results are accumulated to finally generate a full-cycle three-dimensional risk field that can comprehensively reflect the spatiotemporal distribution characteristics of heavy metal ecological risk throughout the entire thermal stratification cycle.

[0060] Optionally, based on the above embodiments, according to the duration weight of each thermal stratification stage, the three-dimensional hybrid ecological risk index field corresponding to each thermal stratification stage is weighted and summed to generate a full-cycle comprehensive three-dimensional ecological risk field, which may include: Calculate the proportion of time each thermal stratification stage represents in the entire cycle based on the duration of each thermal stratification stage. Based on the time proportions, determine the duration weights corresponding to each thermal stratification stage; Based on the duration weights corresponding to each thermal stratification stage, the full-cycle comprehensive ecological risk values ​​at each reservoir location coordinate point in the three-dimensional hybrid ecological risk index field corresponding to each thermal stratification stage are weighted and summed to generate a three-dimensional risk field of full-cycle comprehensive ecological risk.

[0061] Generally, it is necessary to statistically analyze the duration of the three thermal stratification stages—formation, stabilization, and regression—within a complete thermal stratification cycle, and calculate the proportion of each stage's duration to the total cycle time. Based on the time proportion of each thermal stratification stage, a corresponding duration weight is assigned to each stage, and this weight coefficient is proportional to the duration of each stage.

[0062] Generally, based on the duration weights of each thermal stratification stage, the weighted summation of the three-dimensional hybrid ecological risk index for each coordinate point in three-dimensional space at different thermal stratification stages is performed to ultimately generate a three-dimensional risk field reflecting the comprehensive ecological risk level throughout the entire thermal stratification cycle. The calculation formula for the three-dimensional risk field of the cycled comprehensive ecological risk is shown below: in, This is represented by the time-weighted coefficient for the duration of the formation period. This is represented by the time-weighted coefficient indicating the duration of the stable period. This represents the time weighting coefficient for the duration of the decay period.

[0063] Furthermore, by using 3D visualization technology, the 3D hybrid ecological risk index field of "formation period - stable period - decline period" and the 3D risk field of comprehensive ecological risk throughout the entire cycle are rendered, generating a spatial heat map that intuitively expresses the level of risk with color gradients. The map uses warm colors (such as red and orange) to mark high-risk areas and cool colors (such as blue and green) to mark low-risk areas, thus clearly presenting the distribution characteristics of heavy metal ecological risk in vertical and horizontal space under different water periods and throughout the entire cycle.

[0064] The technical solution of this invention involves acquiring environmental factor data of a target reservoir at different thermal stratification stages, and simultaneously acquiring the effective concentration values ​​of target heavy metals at sampling points at each reservoir location during each stage. Then, based on the effective concentration values, a three-dimensional effective concentration field for each target heavy metal at each stratification stage is constructed. Simultaneously, a dynamic environmental weighting coefficient field that varies with water depth and time is constructed based on the environmental factor data. Subsequently, the concentration values ​​at each location point in the three-dimensional effective concentration field are compared with a preset risk benchmark to obtain an initial risk quotient, which is then corrected using the weighting coefficients corresponding to the spatiotemporal location in the dynamic environmental weighting coefficient field. This process calculates the three-dimensional weighted risk market for each target heavy metal at each thermal stratification stage. Finally, by summing the three-dimensional weighted risk markets for each target heavy metal at each thermal stratification stage based on the heavy metal type and thermal stratification stage, a comprehensive three-dimensional ecological risk field is generated, serving as the heavy metal ecological risk assessment result for the target reservoir. This scheme solves the problem that traditional static assessment methods are difficult to quantify the spatiotemporal dynamic evolution of heavy metal risk within the thermal stratification cycle. It achieves the beneficial effect of realizing full-cycle, three-dimensional, refined dynamic assessment of heavy metal ecological risk in reservoirs. In particular, by introducing dynamic environmental weights to correct the basic risk, the assessment results more realistically reflect the ecotoxicological effects under actual environmental stress.

[0065] To facilitate understanding, the specific application scenarios applicable to each embodiment of the present invention are described below. In this specific embodiment, in order to conduct a refined and dynamic assessment of the heavy metal ecological risk of deep-water reservoirs exhibiting seasonal thermal stratification, the present invention designs a complete reservoir heavy metal ecological risk assessment scheme based on full-cycle three-dimensional dynamic simulation of thermal stratification.

[0066] Specifically, Figure 3 A flowchart illustrating the technical approach for heavy metal ecological risk assessment in a reservoir, such as... Figure 3 As shown, this process clearly illustrates the complete technical chain from on-site in-situ sampling to the final risk visualization output.

[0067] The process begins with the core data acquisition phase, namely "in-situ sampling and data acquisition based on thermal stratification (DGT, or Diffusion Gradients in Thin-films)." This phase is specifically broken down into three concurrent sub-tasks: "Vertical Stratification," which dynamically identifies and determines key characteristic layers and sampling depths for each period based on water temperature and dissolved oxygen profiles; "Bioavailable Content Determination," which uses gradient diffusion thin-film devices to enrich and subsequently analyze heavy metals in situ to obtain their bioavailable concentrations; and "Physicochemical Parameter Acquisition," which uses a multi-parameter water quality analyzer to simultaneously record key environmental factors such as dissolved oxygen and pH. These three elements together form the original data foundation for all subsequent model construction.

[0068] The process then moves on to the "construction of a multi-level risk benchmark system." This system begins with the collected toxicological data, first by "screening toxicity data" to obtain chronic toxicity data of the target heavy metals on various aquatic organisms from authoritative databases. Next, the screened dataset is used to "construct an SSD model," which involves fitting a species sensitivity distribution curve using a statistical distribution function. Finally, based on this curve, "multi-level risk benchmarks are derived," calculating key risk assessment thresholds such as the risk benchmark, providing a scientific quantitative standard for risk assessment.

[0069] After obtaining the basic data and evaluation benchmarks, the process begins to "construct a full-cycle three-dimensional effective concentration field". This step first performs "spatiotemporal discretization" on the reservoir water body, dividing it into grid cells with three-dimensional coordinates; then, a "staged three-dimensional interpolation" algorithm is used to spatially interpolate the effective state concentration values ​​of each period and each sampling point to each grid cell, thereby generating independent three-dimensional concentration distribution fields for the formation period, the stable period, and the decline period.

[0070] Subsequently, the process transforms synchronously collected environmental factor data into quantifiable correction coefficients for environmental stress effects by "constructing a dynamic vertically differentiated weighting model." The core of this model lies in quantifying the enhancing effects of dissolved oxygen, pH, and other conditions on the ecotoxicity of heavy metals at different water depths and time periods. Finally, all intermediate results converge in the risk assessment module: by integrating the three-dimensional concentration field, risk benchmark, and dynamic weights, the "single-factor weighted risk quotient" and "mixed ecological risk" are calculated sequentially. Finally, by weighted fusion of risks from the three periods, the final "full-cycle comprehensive ecological risk" result is output, completing the entire assessment process.

[0071] Example 3 Figure 4 This is a heavy metal ecological risk assessment device for a reservoir provided in Embodiment 3 of the present invention. Figure 4 As shown, the device includes: The data acquisition module 410 is used to acquire environmental factor data of the target reservoir under different thermal stratification stages, and to acquire the effective concentration values ​​of each target heavy metal at each sampling point in the target reservoir under each thermal stratification stage. The three-dimensional effective concentration field construction module 420 is used to construct the three-dimensional effective concentration field of each target heavy metal in each stratification stage based on the effective concentration values ​​of each target heavy metal at each reservoir location sampling point under each thermal stratification stage; wherein, the set data point in the three-dimensional effective concentration field of the target heavy metal under the set thermal stratification stage represents the effective concentration value of the set target heavy metal at the set reservoir location coordinate point under the set thermal stratification stage. The dynamic weight coefficient field construction module 430 is used to construct a dynamic environmental weight coefficient field based on the environmental factor data of the target reservoir under different thermal stratification stages. The set data points in the dynamic environmental weight coefficient field represent the environmental weights at a set reservoir water level depth under a set thermal stratification stage. The weighted risk field calculation module 440 is used to calculate the three-dimensional weighted risk field of each target heavy metal in each thermal stratification stage based on the three-dimensional effective concentration field of each target heavy metal in each thermal stratification stage, the preset risk benchmark and the dynamic environmental weight coefficient field. The full-cycle risk fusion module 450 is used to perform cumulative calculation of the heavy metal dimension and the thermal stratification dimension of the three-dimensional weighted risk market of each target heavy metal at each thermal stratification stage, so as to obtain the full-cycle comprehensive ecological three-dimensional risk field, which serves as the heavy metal ecological risk assessment result of the target reservoir.

[0072] The technical solution of this invention involves acquiring environmental factor data of a target reservoir at different thermal stratification stages, and simultaneously acquiring the effective concentration values ​​of target heavy metals at sampling points at each reservoir location during each stage; constructing a three-dimensional effective concentration field for each target heavy metal at each stratification stage based on the effective concentration values ​​of target heavy metals at each sampling point during each stage; constructing a dynamic environmental weight coefficient field that varies with water depth and time by combining the environmental factor data; and then combining the three-dimensional effective concentration field of each target heavy metal at each stratification stage with a preset risk benchmark and the dynamic environmental weight coefficient field to calculate the effective concentration of each target heavy metal. The three-dimensional weighted risk market for heavy metals at each stratification stage is used. Finally, the three-dimensional weighted risk market for each target heavy metal at each stratification stage is summed by the heavy metal type and the thermal stratification stage to generate a comprehensive three-dimensional ecological risk field for the entire cycle. This serves as the result of the heavy metal ecological risk assessment for the target reservoir, thereby achieving a three-dimensional, dynamic, and refined assessment of the heavy metal ecological risk of the reservoir throughout the entire thermal stratification cycle. This effectively solves the problem that traditional methods are difficult to accurately depict the migration and transformation patterns and dynamic ecological risks of heavy metals during the seasonal thermal stratification process of reservoirs, and improves the accuracy and timeliness of water quality risk early warning for water sources.

[0073] Based on the above embodiments, the data acquisition module 410 is specifically used for: The current thermal stratification stage of the target reservoir is identified by detecting water sample data from the target reservoir. When the target reservoir is identified as being in the target thermal stratification stage, a gradient diffusion film device is deployed at sampling points at each reservoir location that matches the target thermal stratification stage to collect heavy metals from each target reservoir within a preset exposure time. During the exposure time, environmental factor data of the target reservoir under the target thermal stratification stage are collected simultaneously by a multi-parameter water quality analyzer. After the preset exposure time is reached, the gradient diffusion film recovery device performs data analysis to obtain the effective concentration values ​​of each target heavy metal at each sampling point in the target reservoir during the target thermal stratification stage.

[0074] Based on the above embodiments, the three-dimensional effective concentration field construction module 420 is specifically used for: The effective concentration values ​​of each target heavy metal at each reservoir sampling point are obtained under each thermal stratification stage, wherein each reservoir sampling point corresponds to a set three-dimensional coordinate value in three-dimensional space. The underwater topographic digital elevation model of the target reservoir is used as a three-dimensional boundary constraint for spatial interpolation. Based on the effective concentration values ​​of each target heavy metal at each reservoir sampling point under each thermal stratification stage, and the three-dimensional boundary constraints, a preset three-dimensional kriging interpolation algorithm is used to calculate and generate the three-dimensional effective concentration field of each target heavy metal under each stratification stage.

[0075] Furthermore, based on the above embodiments, a heavy metal ecological risk assessment device for a reservoir may further include: The data screening module is used to screen the chronic toxicity data of each target heavy metal to a variety of aquatic organisms from an authoritative toxicology database before calculating the three-dimensional weighted risk market of each target heavy metal at each thermal stratification stage based on the three-dimensional effective concentration field of each target heavy metal at each thermal stratification stage, the preset risk benchmark field and the dynamic environmental weight coefficient field. The curve fitting module is used to fit the species sensitivity distribution curve using a statistical distribution function based on the selected chronic toxicity dataset. The risk benchmark module is used to calculate the concentration value of species that pose a preset percentage of harm from the species sensitivity distribution curve, and derive the risk benchmark corresponding to each target heavy metal based on the concentration value.

[0076] Based on the above embodiments, the weighted risk field calculation module 440 is specifically used for: Obtain the current three-dimensional effective concentration field of the target heavy metal in the current thermal stratification stage, and obtain the current risk benchmark that matches the target heavy metal; The current initial risk quotient is obtained by dividing the current effective concentration value at each current reservoir location coordinate point in the current three-dimensional effective concentration field by the current risk benchmark. The initial risk quotient value at each current reservoir location coordinate point is multiplied by the current environmental weight selected from the dynamic environmental weight coefficient field that matches the current reservoir location coordinate point and the current thermal stratification stage to obtain the three-dimensional weighted risk market of the current target heavy metal at the current thermal stratification stage. In the three-dimensional weighted risk market, the set data points represent the weighted risk quotient value under the set reservoir location coordinates.

[0077] Furthermore, based on the above embodiments, the weighted risk field calculation module 440 may further include: The hybrid ecological risk index field calculation submodule is used to accumulate the weighted risk quotient values ​​of all target heavy metals under each thermal stratification stage at each reservoir location coordinate point to obtain the three-dimensional hybrid ecological risk index field corresponding to each thermal stratification stage; wherein, the set data points in the three-dimensional hybrid ecological risk index field represent the hybrid ecological risk index value under the set reservoir location coordinate point. The comprehensive risk field generation submodule is used to perform weighted summation of the three-dimensional hybrid ecological risk index fields corresponding to each thermal stratification stage according to the duration weight of each thermal stratification stage, so as to generate a full-cycle comprehensive ecological three-dimensional risk field; wherein, the set data points of the full-cycle comprehensive ecological three-dimensional risk field represent the full-cycle comprehensive ecological risk value under the set reservoir location coordinates.

[0078] Based on the above embodiments, the integrated risk field generation submodule is specifically used for: Calculate the proportion of time each thermal stratification stage represents in the entire cycle based on the duration of each thermal stratification stage. Based on the time proportions, determine the duration weights corresponding to each thermal stratification stage; Based on the duration weights corresponding to each thermal stratification stage, the full-cycle comprehensive ecological risk values ​​at each reservoir location coordinate point in the three-dimensional hybrid ecological risk index field corresponding to each thermal stratification stage are weighted and summed to generate a three-dimensional risk field of full-cycle comprehensive ecological risk.

[0079] The heavy metal ecological risk assessment device for reservoirs provided in this embodiment of the invention can execute the heavy metal ecological risk assessment method for reservoirs provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0080] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0081] Example 4 Figure 5 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0082] like Figure 5 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0083] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0084] Processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as performing a method for heavy metal ecological risk assessment of a reservoir as described in any embodiment of the present invention, namely: Environmental factor data of the target reservoir under different thermal stratification stages were obtained, and the effective concentration values ​​of each target heavy metal at each sampling point in the target reservoir under each thermal stratification stage were obtained. Based on the effective concentration values ​​of each target heavy metal at each reservoir location sampling point under each thermal stratification stage, a three-dimensional effective concentration field of each target heavy metal under each stratification stage is constructed; wherein, the set data point in the three-dimensional effective concentration field of the target heavy metal under the set thermal stratification stage represents the effective concentration value of the set target heavy metal at the set reservoir location coordinate point under the set thermal stratification stage. Based on the environmental factor data of the target reservoir under different thermal stratification stages, a dynamic environmental weight coefficient field is constructed. The set data points in the dynamic environmental weight coefficient field represent the environmental weights at a set reservoir water level depth under a set thermal stratification stage. Based on the three-dimensional effective concentration field of each target heavy metal in each thermal stratification stage, the preset risk benchmark and dynamic environmental weight coefficient field, the three-dimensional weighted risk market of each target heavy metal in each thermal stratification stage is calculated. The three-dimensional weighted risk market for each target heavy metal at each thermal stratification stage is calculated by summing the heavy metal dimension and the thermal stratification dimension to obtain the full-cycle comprehensive ecological three-dimensional risk field, which serves as the heavy metal ecological risk assessment result for the target reservoir.

[0085] In some embodiments, a method for assessing the ecological risk of heavy metals in a reservoir as described in any one of the embodiments of the present invention can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded into and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method for assessing the ecological risk of heavy metals in a reservoir as described above can be performed. Alternatively, in other embodiments, processor 11 can be configured by any other suitable means (e.g., by means of firmware) to perform the method for assessing the ecological risk of heavy metals in a reservoir as described in any one of the embodiments of the present invention.

[0086] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0087] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0088] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0089] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0090] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0091] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0092] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0093] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for assessing ecological risk of heavy metals in a reservoir, characterized by, The method includes: Environmental factor data of the target reservoir under different thermal stratification stages were obtained, and the effective concentration values ​​of each target heavy metal at each sampling point in the target reservoir under each thermal stratification stage were obtained. Based on the effective concentration values ​​of each target heavy metal at each reservoir location sampling point under each thermal stratification stage, a three-dimensional effective concentration field of each target heavy metal under each stratification stage is constructed; wherein, the set data point in the three-dimensional effective concentration field of the target heavy metal under the set thermal stratification stage represents the effective concentration value of the set target heavy metal at the set reservoir location coordinate point under the set thermal stratification stage. Based on the environmental factor data of the target reservoir under different thermal stratification stages, a dynamic environmental weight coefficient field is constructed. The set data points in the dynamic environmental weight coefficient field represent the environmental weights at a set reservoir water level depth under a set thermal stratification stage. Based on the three-dimensional effective concentration field of each target heavy metal in each thermal stratification stage, the preset risk benchmark and dynamic environmental weight coefficient field, the three-dimensional weighted risk market of each target heavy metal in each thermal stratification stage is calculated. The three-dimensional weighted risk market for each target heavy metal at each thermal stratification stage is calculated by summing the heavy metal dimension and the thermal stratification dimension to obtain the full-cycle comprehensive ecological three-dimensional risk field, which serves as the heavy metal ecological risk assessment result for the target reservoir.

2. The method according to claim 1, characterized in that, Obtain environmental factor data for the target reservoir at different thermal stratification stages, and obtain the effective concentration values ​​of each target heavy metal at sampling points in each reservoir location at each thermal stratification stage, including: The current thermal stratification stage of the target reservoir is identified by detecting water sample data from the target reservoir. When the target reservoir is identified as being in the target thermal stratification stage, a gradient diffusion film device is deployed at sampling points at each reservoir location that matches the target thermal stratification stage to collect heavy metals from each target reservoir within a preset exposure time. During the exposure time, environmental factor data of the target reservoir under the target thermal stratification stage are collected simultaneously by a multi-parameter water quality analyzer. After the preset exposure time is reached, the gradient diffusion film recovery device performs data analysis to obtain the effective concentration values ​​of each target heavy metal at each sampling point in the target reservoir during the target thermal stratification stage.

3. The method according to claim 1, characterized in that, Based on the effective concentration values ​​of each target heavy metal at sampling points in each reservoir during each thermal stratification stage, a three-dimensional effective concentration field of each target heavy metal at each stratification stage is constructed, including: The effective concentration values ​​of each target heavy metal at each reservoir sampling point are obtained under each thermal stratification stage, wherein each reservoir sampling point corresponds to a set three-dimensional coordinate value in three-dimensional space. The underwater topographic digital elevation model of the target reservoir is used as a three-dimensional boundary constraint for spatial interpolation. Based on the effective concentration values ​​of each target heavy metal at each reservoir sampling point under each thermal stratification stage, and the three-dimensional boundary constraints, a preset three-dimensional kriging interpolation algorithm is used to calculate and generate the three-dimensional effective concentration field of each target heavy metal under each stratification stage.

4. The method according to claim 1, characterized in that, Before calculating the three-dimensional weighted risk market for each target heavy metal at each thermal stratification stage based on the three-dimensional effective concentration field of each target heavy metal at each thermal stratification stage, the preset risk benchmark field, and the dynamic environmental weighting coefficient field, the following steps are also included: Chronic toxicity data of various target heavy metals on multiple aquatic organisms were screened from authoritative toxicology databases. Using the selected chronic toxicity dataset, a statistical distribution function was used to fit the species sensitivity distribution curve; The concentration values ​​of species that pose a preset percentage of harm are calculated from the species sensitivity distribution curve, and risk benchmarks corresponding to each target heavy metal are derived based on these concentration values.

5. The method according to claim 4, characterized in that, Based on the three-dimensional effective concentration field of each target heavy metal at each thermal stratification stage, the preset risk benchmark and dynamic environmental weighting coefficient field are used to calculate the three-dimensional weighted risk market for each target heavy metal at each thermal stratification stage, including: Obtain the current three-dimensional effective concentration field of the target heavy metal in the current thermal stratification stage, and obtain the current risk benchmark that matches the target heavy metal; The current initial risk quotient is obtained by dividing the current effective concentration value at each current reservoir location coordinate point in the current three-dimensional effective concentration field by the current risk benchmark. The initial risk quotient value at each current reservoir location coordinate point is multiplied by the current environmental weight selected from the dynamic environmental weight coefficient field that matches the current reservoir location coordinate point and the current thermal stratification stage to obtain the three-dimensional weighted risk market of the current target heavy metal at the current thermal stratification stage. In the three-dimensional weighted risk market, the set data points represent the weighted risk quotient value under the set reservoir location coordinates.

6. The method according to claim 5, characterized in that, The three-dimensional weighted risk quotients of each target heavy metal at each thermal stratification stage are summed and calculated using the heavy metal dimension and the thermal stratification dimension to obtain the comprehensive ecological three-dimensional risk field for the entire cycle, including: The weighted risk quotients of all target heavy metals at each reservoir location coordinate point under each thermal stratification stage are summed to obtain the three-dimensional mixed ecological risk index field corresponding to each thermal stratification stage. Among them, the set data points in the three-dimensional hybrid ecological risk index field represent the hybrid ecological risk index values ​​under the set reservoir location coordinates; Based on the duration weight of each thermal stratification stage, the three-dimensional hybrid ecological risk index fields corresponding to each thermal stratification stage are weighted and summed to generate a full-cycle comprehensive three-dimensional ecological risk field. Among them, the set data points of the full-cycle integrated ecological three-dimensional risk field represent the full-cycle integrated ecological risk value under the set reservoir location coordinates.

7. The method according to claim 6, characterized in that, Based on the duration weights of each thermal stratification stage, the three-dimensional hybrid ecological risk index fields corresponding to each thermal stratification stage are weighted and summed to generate a full-cycle comprehensive three-dimensional ecological risk field, including: Calculate the proportion of time each thermal stratification stage represents in the entire cycle based on the duration of each thermal stratification stage. Based on the time proportions, determine the duration weights corresponding to each thermal stratification stage; Based on the duration weights corresponding to each thermal stratification stage, the full-cycle comprehensive ecological risk values ​​at each reservoir location coordinate point in the three-dimensional hybrid ecological risk index field corresponding to each thermal stratification stage are weighted and summed to generate a three-dimensional risk field of full-cycle comprehensive ecological risk.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the heavy metal ecological risk assessment method for the reservoir according to any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are used to cause a processor to execute the method for assessing the heavy metal ecological risk of a reservoir according to any one of claims 1-7.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method for assessing the heavy metal ecological risk of a reservoir according to any one of claims 1-7.

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