A method and system for real-time monitoring of ecological risk of contaminated sites

Abstract

The application discloses a kind of pollution site ecological risk real-time monitoring method and system, it is related to the technical field of pollution site environmental supervision.A kind of pollution site ecological risk real-time monitoring system, including data acquisition and processing module, dynamic weight calculation module, risk transmission path modeling module, intelligent early warning threshold optimization module and risk assessment and processing module.The application constructs original data matrix by setting rolling time window, quantifies the variation degree of each index data based on information entropy to calculate initial dynamic weight, and adopts exponential smoothing method to fuse historical weight for smoothing processing, not only can be objectively weighted according to the discrete degree of data itself to avoid artificial subjective bias, but also can prevent weight from fluctuating sharply by historical weight smoothing, ensure the stability and continuity of risk assessment result, so as to significantly improve the sensitivity and reliability of risk identification, improve the monitoring and early warning effect of the pollution site ecological risk real-time monitoring method and system.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring technology for contaminated sites, specifically to a method and system for real-time monitoring of ecological risks at contaminated sites. Background Technology

[0002] Contaminated sites refer to areas where soil, groundwater, or surface water has been contaminated by hazardous substances (such as heavy metals, organic pollutants, and petroleum hydrocarbons) due to human activities, with concentrations exceeding environmental standards or acceptable levels in risk assessments. These contaminated sites typically originate from industrial relocation, historical unorganized emissions, accidental leaks, or illegal dumping. On the one hand, pollutants exhibit migration and transformation characteristics in the environment, potentially spreading to surrounding ecosystems through leaching, volatilization, or bioaccumulation, causing irreversible damage. On the other hand, traditional end-of-pipe treatment methods are costly and have delayed effects, while real-time monitoring enables dynamic tracking of pollution plumes, early warning of risk thresholds, and quantitative assessment of remediation effectiveness. This provides data support for precise management and scientific decision-making, thereby effectively reducing environmental risks and ensuring regional ecological security.

[0003] Existing ecological risk monitoring technologies for contaminated sites generally suffer from three main shortcomings: First, the use of fixed-period sampling and static threshold judgment models results in monitoring data lagging significantly behind dynamic changes in pollution, making real-time risk capture impossible. Second, they rely solely on pollutant concentration indicators, neglecting the migration and transformation patterns of pollutants between environmental media and their bioaccumulation effects, making it difficult to identify risk transmission pathways across media and trophic levels. Third, the early warning mechanisms are rigid, lacking the ability to learn from historical false alarms and missed reports, and unable to adaptively optimize early warning sensitivity based on the actual environmental characteristics of the site. Furthermore, existing technologies often operate monitoring, assessment, and early warning processes in isolation, failing to form a data-driven closed-loop feedback system. This leads to high rates of missed reports of high-risk events and delayed emergency responses, making it difficult to meet the actual needs of refined and intelligent management of contaminated sites.

[0004] Based on the above, this invention proposes a real-time monitoring method and system for ecological risks of contaminated sites with good monitoring and early warning effects. Summary of the Invention

[0005] To overcome the three major shortcomings of existing ecological risk monitoring technologies for contaminated sites: First, the use of fixed-period sampling and static threshold judgment modes results in monitoring data lagging significantly behind the dynamic changes in pollution, making it impossible to capture risks in real time; second, the reliance on pollutant concentration indicators ignores the migration and transformation patterns of pollutants between environmental media and the bioaccumulation effect, making it difficult to identify risk transmission pathways across media and trophic levels; third, the rigid early warning mechanism lacks the ability to learn from historical false alarms and missed alarms, and cannot adaptively optimize the early warning sensitivity based on the actual environmental characteristics of the site. In addition, existing technologies often operate the monitoring, assessment, and early warning links in isolation, failing to form a data-driven closed-loop feedback system, resulting in a high rate of missed alarms for high-risk events and a delayed emergency response, which is insufficient to meet the actual needs of refined and intelligent management of contaminated sites. Therefore, this invention proposes a real-time monitoring method and system for ecological risks of contaminated sites with good monitoring and early warning effects.

[0006] A method for real-time monitoring of ecological risks at contaminated sites includes the following steps:

[0007] Physicochemical data, pollutant concentration data, and ecotoxicity data of each monitoring node in the contaminated site are acquired by sensors. The physicochemical data, pollutant concentration data, and ecotoxicity data of each monitoring node are preprocessed to obtain preprocessed physicochemical data, pollutant concentration data, and ecotoxicity data.

[0008] Based on the preprocessed physicochemical data, pollutant concentration data, and ecotoxicity data, the initial dynamic weights of each data indicator are obtained using the improved dynamic entropy weight method. The initial dynamic weights are then smoothed based on historical weight data to obtain the final dynamic weights of each data indicator.

[0009] Geological and biological distribution data of contaminated sites are acquired, and pollutant migration and transformation matrix and bioaccumulation response model are constructed accordingly. Based on the pollutant migration and transformation matrix and bioaccumulation response model, an ecological risk transmission network is established, and the risk intensity value of each transmission path is calculated in real time to obtain a real-time ecological risk transmission path map of the contaminated site.

[0010] Initial thresholds for various data indicators are set according to national environmental quality standards. Historical early warning data of contaminated sites are obtained, and support vector machines are used to dynamically optimize and adjust the initial thresholds of various data indicators to obtain dynamic thresholds for various data indicators.

[0011] The comprehensive risk index of each monitoring node in the contaminated site is calculated based on the final dynamic weight and dynamic threshold of each data indicator. The contaminated site is divided into regions according to the real-time ecological risk transmission path map, and the comprehensive risk index of each region is calculated. Based on the comprehensive risk index of each region and the preset classification rules, the ecological risk of each region of the contaminated site is classified. According to the classification results, the corresponding level of early warning and treatment plan is adopted for each region of the contaminated site.

[0012] As a preferred aspect of the invention, the physicochemical data includes pH data, temperature data, humidity data, redox potential data, and water level data for each monitoring node; the pollutant concentration data includes the content data of heavy metals and organic matter in different media for each monitoring node; and the ecotoxicological data includes microbial activity data, plant physiological damage index data, and animal behavioral response data for each monitoring node.

[0013] As a preferred aspect of the invention, the specific steps for preprocessing the physicochemical data, pollutant concentration data, and ecotoxicological data of each monitoring node to obtain preprocessed physicochemical data, pollutant concentration data, and ecotoxicological data are as follows:

[0014] Using dates as an index, the physicochemical data, pollutant concentration data, and ecotoxicity data of each monitoring node are converted into time series format. The starting reference time point of all time series is unified, and the time frequency of all time series is unified. The missing values ​​of time series with low time frequency at new time points are filled by interpolation methods such as linear interpolation or polynomial interpolation.

[0015] To handle null or missing values ​​in a time series, interpolation methods such as linear interpolation or polynomial interpolation can be used to fill in the missing values, or the mean or median of the time series can be used directly to fill in the missing values.

[0016] Outliers in a time series that do not conform to the expected pattern can be identified using the Z-Score or IQR method. Outliers can be removed and replaced with the mean or median of the time series, or interpolation methods can be used to repair outliers.

[0017] Min-max standardization is used to standardize data of different dimensions. The formula for calculating min-max standardization is as follows: ,in Represents the original data value. This represents the minimum value in a time series. Represents the maximum value in a time series. This represents the standardized data value.

[0018] As a preferred aspect of the invention, the specific steps for obtaining the initial dynamic weights of each data indicator based on preprocessed physicochemical data, pollutant concentration data, and ecotoxicological data using an improved dynamic entropy weighting method, and smoothing the initial dynamic weights according to historical weight data to obtain the final dynamic weights of each data indicator are as follows:

[0019] The length of the rolling time window and the sliding step size are set. The original data matrix is ​​constructed based on the preprocessed physicochemical data, pollutant concentration data and ecotoxicity data. The original data matrix is ​​normalized to eliminate the difference in dimensions and obtain a standardized matrix.

[0020] Each data point in the preprocessed physicochemical data, pollutant concentration data, and ecotoxicity data is regarded as a data indicator. The information entropy of each data indicator is calculated to quantify its data variation. The smaller the entropy value, the stronger the risk identification ability of the indicator. The difference coefficient is calculated based on the information entropy of each data indicator and converted into the initial dynamic weight.

[0021] Based on historical weight data, the initial dynamic weights of each data indicator are smoothed using the exponential smoothing method. After smoothing, normalization is performed to obtain the final dynamic weights of each data indicator.

[0022] As a preferred aspect of the invention, the specific steps for obtaining geological environmental data and biological distribution data of the contaminated site, constructing a pollutant migration and transformation matrix and a bioaccumulation response model based on these data, establishing an ecological risk transmission network based on the pollutant migration and transformation matrix and the bioaccumulation response model, and calculating the risk intensity value of each transmission path in real time to obtain a real-time ecological risk transmission path map of the contaminated site are as follows:

[0023] Geological and meteorological data of the contaminated site were obtained, and the probability and speed of pollutant diffusion from each monitoring node to the surrounding area were calculated to obtain the pollutant migration and transformation matrix. Biological distribution data of the contaminated site were obtained, and a cumulative model of pollutant enrichment efficiency in different organisms was constructed based on the food chain relationship of the contaminated site to obtain the biological cumulative response model.

[0024] Based on the pollutant migration and transformation matrix and the bioaccumulation response model, the risk intensity value of each transmission path is calculated in real time and a visual transmission map is generated to obtain a real-time ecological risk transmission path map of the contaminated site. The arrows indicate the direction of risk flow, the thickness of the arrows indicates the transmission intensity, and the color of the arrows indicates the degree of risk.

[0025] As a preferred aspect of the invention, the specific steps for obtaining historical early warning data of contaminated sites and dynamically optimizing and adjusting the initial thresholds of various data indicators using a support vector machine to obtain dynamic thresholds for each data indicator are as follows:

[0026] Historical early warning data of contaminated sites are obtained and events are labeled as positive and negative samples based on whether ecological anomalies occur. The temporal features of monitoring data of each monitoring node are extracted from the historical early warning data and the support vector machine model with RBF kernel is trained accordingly. The decision boundary is optimized with the goal of maximizing the F1 score and the initial thresholds of each data indicator are dynamically adjusted according to the false positive rate or the underreporting situation to obtain the dynamic thresholds of each data indicator.

[0027] As a preferred aspect of the invention, the specific steps for calculating the comprehensive risk index of each monitoring node in the contaminated site based on the final dynamic weights and dynamic thresholds of various data indicators, and for dividing the contaminated site into regions according to the real-time ecological risk transmission path map and calculating the comprehensive risk index of each region are as follows:

[0028] Based on the pre-processed physicochemical data, pollutant concentration data, ecotoxicity data, and dynamic thresholds, the risk index of each data indicator is calculated. Based on the final dynamic weights, the risk indices of each data indicator are normalized and weighted to obtain the comprehensive risk index of each monitoring node.

[0029] Based on the real-time ecological risk transmission path map and the comprehensive risk index of each monitoring node, the contaminated site is divided into regions. The comprehensive risk index of each region is obtained by averaging the comprehensive risk index of multiple monitoring nodes in each region.

[0030] A real-time monitoring system for ecological risks of contaminated sites includes:

[0031] The data acquisition and processing module is used to acquire physicochemical data, pollutant concentration data and ecotoxicity data of each monitoring node in the contaminated site through sensors, and to preprocess the physicochemical data, pollutant concentration data and ecotoxicity data of each monitoring node to obtain preprocessed physicochemical data, pollutant concentration data and ecotoxicity data.

[0032] The dynamic weight calculation module is used to obtain the initial dynamic weights of each data indicator based on the preprocessed physicochemical data, pollutant concentration data, and ecotoxicity data and through the improved dynamic entropy weight method. The initial dynamic weights are then smoothed according to historical weight data to obtain the final dynamic weights of each data indicator.

[0033] The risk transmission path modeling module is used to acquire geological environmental data and biological distribution data of contaminated sites and construct pollutant migration and transformation matrix and bioaccumulation response model accordingly. Based on the pollutant migration and transformation matrix and bioaccumulation response model, an ecological risk transmission network is established and the risk intensity value of each transmission path is calculated in real time to obtain a real-time ecological risk transmission path map of the contaminated site.

[0034] The intelligent early warning threshold optimization module is used to set the initial thresholds of various data indicators according to national environmental quality standards, obtain historical early warning data of contaminated sites, and use support vector machines to dynamically optimize and adjust the initial thresholds of various data indicators to obtain the dynamic thresholds of various data indicators.

[0035] The risk assessment and treatment module is used to calculate the comprehensive risk index of each monitoring node in the contaminated site based on the final dynamic weight and dynamic threshold of various data indicators. It divides the contaminated site into regions according to the real-time ecological risk transmission path map and calculates the comprehensive risk index of each region. Based on the comprehensive risk index of each region and the preset classification rules, it classifies the ecological risk of each region of the contaminated site. Based on the classification results, it adopts the corresponding level of early warning and treatment plan for each region of the contaminated site.

[0036] The present invention has the following advantages:

[0037] 1. This invention constructs an original data matrix by setting a rolling time window, quantifies the degree of variation of each indicator data based on information entropy to calculate the initial dynamic weights, and uses exponential smoothing to integrate historical weights for smoothing. This not only objectively assigns weights based on the dispersion of the data itself to avoid subjective human bias, but also prevents drastic fluctuations in weights through historical weight smoothing, ensuring the stability and continuity of risk assessment results. This significantly improves the sensitivity and reliability of risk identification, and enhances the monitoring and early warning effect of this real-time monitoring method and system for ecological risks of contaminated sites.

[0038] 2. This invention constructs a pollutant migration and transformation matrix and a bioaccumulation response model, establishes an ecological risk transmission network, and calculates the risk intensity value of each transmission path in real time. It generates a visualized transmission map where the thickness of the arrows represents the transmission intensity and the color depth represents the risk level. This can overcome the limitations of traditional single-point monitoring, realize the quantitative tracking of the migration patterns of pollutants between environmental media and the enrichment effects between biological trophic levels, thereby predicting high-risk areas in advance, gaining valuable time for precise control and emergency response, and improving the monitoring and early warning effect of this real-time ecological risk monitoring method and system for contaminated sites.

[0039] 3. This invention trains an RBF kernel support vector machine model using historical early warning data, optimizes the decision boundary with the goal of maximizing the F1 score, and dynamically adjusts the initial threshold according to the false positive rate or missed reporting. This not only transforms the early warning threshold from a static fixed mode to a data-driven adaptive optimization mode, but also enables self-correction through continuous learning of historical false and missed reporting events. This effectively improves the accuracy of early warning and significantly reduces the missed and false reporting rates of high-risk events, thereby enhancing the monitoring and early warning effect of this real-time monitoring method and system for ecological risks of contaminated sites. Attached Figure Description

[0040] Figure 1 This is a flowchart illustrating a real-time monitoring method for ecological risks of contaminated sites, as used in an embodiment of the present invention.

[0041] Figure 2 This is a schematic diagram of a real-time monitoring system for ecological risks of contaminated sites used in an embodiment of the present invention. Detailed Implementation

[0042] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this invention.

[0043] Example 1: A method for real-time monitoring of ecological risks at contaminated sites, such as... Figure 1 As shown, it includes the following steps:

[0044] Physicochemical data, pollutant concentration data, and ecotoxicity data of each monitoring node in the contaminated site are acquired by sensors. The physicochemical data, pollutant concentration data, and ecotoxicity data of each monitoring node are preprocessed to obtain preprocessed physicochemical data, pollutant concentration data, and ecotoxicity data.

[0045] Based on the preprocessed physicochemical data, pollutant concentration data, and ecotoxicity data, the initial dynamic weights of each data indicator are obtained using the improved dynamic entropy weight method. The initial dynamic weights are then smoothed based on historical weight data to obtain the final dynamic weights of each data indicator.

[0046] Geological and biological distribution data of contaminated sites are acquired, and pollutant migration and transformation matrix and bioaccumulation response model are constructed accordingly. Based on the pollutant migration and transformation matrix and bioaccumulation response model, an ecological risk transmission network is established, and the risk intensity value of each transmission path is calculated in real time to obtain a real-time ecological risk transmission path map of the contaminated site.

[0047] Initial thresholds for various data indicators are set according to national environmental quality standards. Historical early warning data of contaminated sites are obtained, and support vector machines are used to dynamically optimize and adjust the initial thresholds of various data indicators to obtain dynamic thresholds for various data indicators.

[0048] The comprehensive risk index of each monitoring node in the contaminated site is calculated based on the final dynamic weight and dynamic threshold of each data indicator. The contaminated site is divided into regions according to the real-time ecological risk transmission path map, and the comprehensive risk index of each region is calculated. Based on the comprehensive risk index of each region and the preset classification rules, the ecological risk of each region of the contaminated site is classified. According to the classification results, the corresponding level of early warning and treatment plan is adopted for each region of the contaminated site.

[0049] The physicochemical data includes pH, temperature, humidity, redox potential, and water level data for each monitoring node; the pollutant concentration data includes the content of heavy metals and organic matter in different media for each monitoring node; and the ecotoxicological data includes microbial activity data, plant physiological damage index data, and animal behavioral response data for each monitoring node.

[0050] The specific steps for preprocessing the physicochemical data, pollutant concentration data, and ecotoxicological data of each monitoring node to obtain preprocessed physicochemical data, pollutant concentration data, and ecotoxicological data are as follows:

[0051] Using dates as an index, the physicochemical data, pollutant concentration data, and ecotoxicity data of each monitoring node are converted into time series format. The starting reference time point of all time series is unified, and the time frequency of all time series is unified. The missing values ​​of time series with low time frequency at new time points are filled by interpolation methods such as linear interpolation or polynomial interpolation.

[0052] To handle null or missing values ​​in a time series, interpolation methods such as linear interpolation or polynomial interpolation can be used to fill in the missing values, or the mean or median of the time series can be used directly to fill in the missing values.

[0053] Outliers in a time series that do not conform to the expected pattern can be identified using the Z-Score or IQR method. Outliers can be removed and replaced with the mean or median of the time series, or interpolation methods can be used to repair outliers.

[0054] Min-max standardization is used to standardize data of different dimensions. The formula for calculating min-max standardization is as follows: ,in Represents the original data value. This represents the minimum value in a time series. Represents the maximum value in a time series. This represents the standardized data value.

[0055] The specific steps for obtaining the initial dynamic weights of each data indicator based on preprocessed physicochemical data, pollutant concentration data, and ecotoxicological data using an improved dynamic entropy weighting method, and then smoothing the initial dynamic weights according to historical weight data to obtain the final dynamic weights of each data indicator are as follows:

[0056] The length of the rolling time window and the sliding step size are set. The original data matrix is ​​constructed based on the preprocessed physicochemical data, pollutant concentration data and ecotoxicity data. The original data matrix is ​​normalized to eliminate the difference in dimensions and obtain a standardized matrix.

[0057] Each data point in the preprocessed physicochemical data, pollutant concentration data, and ecotoxicity data is regarded as a data indicator. The information entropy of each data indicator is calculated to quantify its data variation. The smaller the entropy value, the stronger the risk identification ability of the indicator. The difference coefficient is calculated based on the information entropy of each data indicator and converted into the initial dynamic weight.

[0058] Based on historical weight data, the initial dynamic weights of each data indicator are smoothed using the exponential smoothing method. After smoothing, normalization is performed to obtain the final dynamic weights of each data indicator.

[0059] The above steps construct the original data matrix by setting a rolling time window, quantify the degree of variation of each indicator data based on information entropy to calculate the initial dynamic weights, and use exponential smoothing to integrate historical weights for smoothing. This not only objectively assigns weights based on the dispersion of the data itself to avoid human subjective bias, but also prevents drastic fluctuations in weights through historical weight smoothing, ensuring the stability and continuity of risk assessment results. This significantly improves the sensitivity and reliability of risk identification, and enhances the monitoring and early warning effect of this real-time monitoring method and system for ecological risks of contaminated sites.

[0060] The specific steps for obtaining geological environmental data and biological distribution data of the contaminated site, constructing a pollutant migration and transformation matrix and a bioaccumulation response model based on these data, establishing an ecological risk transmission network based on the pollutant migration and transformation matrix and the bioaccumulation response model, and calculating the risk intensity value of each transmission path in real time to obtain a real-time ecological risk transmission path map of the contaminated site are as follows:

[0061] Geological and meteorological data of the contaminated site were obtained, and the probability and speed of pollutant diffusion from each monitoring node to the surrounding area were calculated to obtain the pollutant migration and transformation matrix. Biological distribution data of the contaminated site were obtained, and a cumulative model of pollutant enrichment efficiency in different organisms was constructed based on the food chain relationship of the contaminated site to obtain the biological cumulative response model.

[0062] Based on the pollutant migration and transformation matrix and the bioaccumulation response model, the risk intensity value of each transmission path is calculated in real time and a visual transmission map is generated to obtain a real-time ecological risk transmission path map of the contaminated site. The arrows indicate the direction of risk flow, the thickness of the arrows indicates the transmission intensity, and the color of the arrows indicates the degree of risk.

[0063] The above steps construct a pollutant migration and transformation matrix and a bioaccumulation response model, establish an ecological risk transmission network, and calculate the risk intensity value of each transmission path in real time. This generates a visualized transmission map where the thickness of the arrows represents the transmission intensity and the color depth represents the risk level. This approach breaks through the limitations of traditional single-point monitoring, enabling the quantitative tracking of pollutant migration patterns between environmental media and the enrichment effects between biological trophic levels. As a result, high-risk areas can be predicted in advance, providing valuable time for precise control and emergency response. This improves the monitoring and early warning effect of the real-time ecological risk monitoring method and system for this contaminated site.

[0064] The specific steps for obtaining historical early warning data of contaminated sites and using support vector machines to dynamically optimize and adjust the initial thresholds of various data indicators to obtain dynamic thresholds for each data indicator are as follows:

[0065] Historical early warning data of contaminated sites are obtained and events are labeled as positive and negative samples based on whether ecological anomalies occur. The temporal features of monitoring data of each monitoring node are extracted from the historical early warning data and the support vector machine model with RBF kernel is trained accordingly. The decision boundary is optimized with the goal of maximizing the F1 score and the initial thresholds of each data indicator are dynamically adjusted according to the false positive rate or the underreporting situation to obtain the dynamic thresholds of each data indicator.

[0066] The above steps utilize historical early warning data to train an RBF kernel support vector machine model, optimize the decision boundary with the goal of maximizing the F1 score, and dynamically adjust the initial threshold based on the false positive rate or missed reporting. This not only transforms the early warning threshold from a static fixed mode to a data-driven adaptive optimization mode, but also enables self-correction through continuous learning of historical false and missed reporting events. As a result, the accuracy of early warning is effectively improved, and the missed and false reporting rates of high-risk events are significantly reduced, thereby enhancing the monitoring and early warning effect of this real-time monitoring method and system for ecological risks of contaminated sites.

[0067] The specific steps for calculating the comprehensive risk index of each monitoring node in the contaminated site based on the final dynamic weights and dynamic thresholds of various data indicators, and for dividing the contaminated site into regions according to the real-time ecological risk transmission path map and calculating the comprehensive risk index of each region are as follows:

[0068] Based on the pre-processed physicochemical data, pollutant concentration data, ecotoxicity data, and dynamic thresholds, the risk index of each data indicator is calculated. Based on the final dynamic weights, the risk indices of each data indicator are normalized and weighted to obtain the comprehensive risk index of each monitoring node.

[0069] Based on the real-time ecological risk transmission path map and the comprehensive risk index of each monitoring node, the contaminated site is divided into regions. The comprehensive risk index of each region is obtained by averaging the comprehensive risk index of multiple monitoring nodes in each region.

[0070] Example 2: A real-time monitoring system for ecological risks of contaminated sites, such as... Figure 2 As shown, it includes:

[0071] The data acquisition and processing module is used to acquire physicochemical data, pollutant concentration data and ecotoxicity data of each monitoring node in the contaminated site through sensors, and to preprocess the physicochemical data, pollutant concentration data and ecotoxicity data of each monitoring node to obtain preprocessed physicochemical data, pollutant concentration data and ecotoxicity data.

[0072] The dynamic weight calculation module is used to obtain the initial dynamic weights of each data indicator based on the preprocessed physicochemical data, pollutant concentration data, and ecotoxicity data and through the improved dynamic entropy weight method. The initial dynamic weights are then smoothed according to historical weight data to obtain the final dynamic weights of each data indicator.

[0073] The risk transmission path modeling module is used to acquire geological environmental data and biological distribution data of contaminated sites and construct pollutant migration and transformation matrix and bioaccumulation response model accordingly. Based on the pollutant migration and transformation matrix and bioaccumulation response model, an ecological risk transmission network is established and the risk intensity value of each transmission path is calculated in real time to obtain a real-time ecological risk transmission path map of the contaminated site.

[0074] The intelligent early warning threshold optimization module is used to set the initial thresholds of various data indicators according to national environmental quality standards, obtain historical early warning data of contaminated sites, and use support vector machines to dynamically optimize and adjust the initial thresholds of various data indicators to obtain the dynamic thresholds of various data indicators.

[0075] The risk assessment and treatment module is used to calculate the comprehensive risk index of each monitoring node in the contaminated site based on the final dynamic weight and dynamic threshold of various data indicators. It divides the contaminated site into regions according to the real-time ecological risk transmission path map and calculates the comprehensive risk index of each region. Based on the comprehensive risk index of each region and the preset classification rules, it classifies the ecological risk of each region of the contaminated site. Based on the classification results, it adopts the corresponding level of early warning and treatment plan for each region of the contaminated site.

[0076] It should be understood that those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims. Parts not described in detail in this specification are prior art known to those skilled in the art.

Claims

1. A method for real-time monitoring of ecological risks at contaminated sites, characterized in that, Includes the following steps: Physicochemical data, pollutant concentration data, and ecotoxicity data of each monitoring node in the contaminated site are acquired by sensors. The physicochemical data, pollutant concentration data, and ecotoxicity data of each monitoring node are preprocessed to obtain preprocessed physicochemical data, pollutant concentration data, and ecotoxicity data. Based on the preprocessed physicochemical data, pollutant concentration data, and ecotoxicity data, the initial dynamic weights of each data indicator are obtained using the improved dynamic entropy weight method. The initial dynamic weights are then smoothed based on historical weight data to obtain the final dynamic weights of each data indicator. Geological and biological distribution data of contaminated sites are acquired, and pollutant migration and transformation matrix and bioaccumulation response model are constructed accordingly. Based on the pollutant migration and transformation matrix and bioaccumulation response model, an ecological risk transmission network is established, and the risk intensity value of each transmission path is calculated in real time to obtain a real-time ecological risk transmission path map of the contaminated site. Initial thresholds for various data indicators are set according to national environmental quality standards. Historical early warning data of contaminated sites are obtained, and support vector machines are used to dynamically optimize and adjust the initial thresholds of various data indicators to obtain dynamic thresholds for various data indicators. The comprehensive risk index of each monitoring node in the contaminated site is calculated based on the final dynamic weight and dynamic threshold of each data indicator. The contaminated site is divided into regions according to the real-time ecological risk transmission path map, and the comprehensive risk index of each region is calculated. Based on the comprehensive risk index of each region and the preset classification rules, the ecological risk of each region of the contaminated site is classified. According to the classification results, the corresponding level of early warning and treatment plan is adopted for each region of the contaminated site.

2. The method for real-time monitoring of ecological risks of contaminated sites according to claim 1, characterized in that, The physicochemical data includes pH, temperature, humidity, redox potential, and water level data for each monitoring node; the pollutant concentration data includes the content of heavy metals and organic matter in different media for each monitoring node; and the ecotoxicological data includes microbial activity data, plant physiological damage index data, and animal behavioral response data for each monitoring node.

3. The method for real-time monitoring of ecological risks of contaminated sites according to claim 2, characterized in that, The specific steps for preprocessing the physicochemical data, pollutant concentration data, and ecotoxicological data of each monitoring node to obtain preprocessed physicochemical data, pollutant concentration data, and ecotoxicological data are as follows: Using dates as an index, the physicochemical data, pollutant concentration data, and ecotoxicity data of each monitoring node are converted into time series format. The starting reference time point of all time series is unified, and the time frequency of all time series is unified. The missing values ​​of time series with low time frequency at new time points are filled by interpolation methods such as linear interpolation or polynomial interpolation. To handle null or missing values ​​in a time series, interpolation methods such as linear interpolation or polynomial interpolation can be used to fill in the missing values, or the mean or median of the time series can be used directly to fill in the missing values. Outliers in a time series that do not conform to the expected pattern can be identified using the Z-Score or IQR method. Outliers can be removed and replaced with the mean or median of the time series, or interpolation methods can be used to repair outliers. Min-max standardization is used to standardize data of different dimensions. The formula for calculating min-max standardization is as follows: ,in Represents the original data value. This represents the minimum value in a time series. Represents the maximum value in a time series. This represents the standardized data value.

4. The method for real-time monitoring of ecological risks of contaminated sites according to claim 3, characterized in that, The specific steps for obtaining the initial dynamic weights of each data indicator based on preprocessed physicochemical data, pollutant concentration data, and ecotoxicological data using an improved dynamic entropy weighting method, and then smoothing the initial dynamic weights according to historical weight data to obtain the final dynamic weights of each data indicator are as follows: The length of the rolling time window and the sliding step size are set. The original data matrix is ​​constructed based on the preprocessed physicochemical data, pollutant concentration data and ecotoxicity data. The original data matrix is ​​normalized to eliminate the difference in dimensions and obtain a standardized matrix. Each data point in the preprocessed physicochemical data, pollutant concentration data, and ecotoxicity data is regarded as a data indicator. The information entropy of each data indicator is calculated to quantify its data variation. The smaller the entropy value, the stronger the risk identification ability of the indicator. The difference coefficient is calculated based on the information entropy of each data indicator and converted into the initial dynamic weight. Based on historical weight data, the initial dynamic weights of each data indicator are smoothed using the exponential smoothing method. After smoothing, normalization is performed to obtain the final dynamic weights of each data indicator.

5. The method for real-time monitoring of ecological risks of contaminated sites according to claim 4, characterized in that, The specific steps for obtaining geological environmental data and biological distribution data of the contaminated site, constructing a pollutant migration and transformation matrix and a bioaccumulation response model based on these data, establishing an ecological risk transmission network based on the pollutant migration and transformation matrix and the bioaccumulation response model, and calculating the risk intensity value of each transmission path in real time to obtain a real-time ecological risk transmission path map of the contaminated site are as follows: Geological and meteorological data of the contaminated site were obtained, and the probability and speed of pollutant diffusion from each monitoring node to the surrounding area were calculated to obtain the pollutant migration and transformation matrix. Biological distribution data of the contaminated site were obtained, and a cumulative model of pollutant enrichment efficiency in different organisms was constructed based on the food chain relationship of the contaminated site to obtain the biological cumulative response model. Based on the pollutant migration and transformation matrix and the bioaccumulation response model, the risk intensity value of each transmission path is calculated in real time and a visual transmission map is generated to obtain a real-time ecological risk transmission path map of the contaminated site. The arrows indicate the direction of risk flow, the thickness of the arrows indicates the transmission intensity, and the color of the arrows indicates the degree of risk.

6. The method for real-time monitoring of ecological risks of contaminated sites according to claim 5, characterized in that, The specific steps for obtaining historical early warning data of contaminated sites and using support vector machines to dynamically optimize and adjust the initial thresholds of various data indicators to obtain dynamic thresholds for each data indicator are as follows: Historical early warning data of contaminated sites are obtained and events are labeled as positive and negative samples based on whether ecological anomalies occur. The temporal features of monitoring data of each monitoring node are extracted from the historical early warning data and the support vector machine model with RBF kernel is trained accordingly. The decision boundary is optimized with the goal of maximizing the F1 score and the initial thresholds of each data indicator are dynamically adjusted according to the false positive rate or the underreporting situation to obtain the dynamic thresholds of each data indicator.

7. The method for real-time monitoring of ecological risks of contaminated sites according to claim 6, characterized in that, The specific steps for calculating the comprehensive risk index of each monitoring node in the contaminated site based on the final dynamic weights and dynamic thresholds of various data indicators, and for dividing the contaminated site into regions according to the real-time ecological risk transmission path map and calculating the comprehensive risk index of each region are as follows: Based on the pre-processed physicochemical data, pollutant concentration data, ecotoxicity data, and dynamic thresholds, the risk index of each data indicator is calculated. Based on the final dynamic weights, the risk indices of each data indicator are normalized and weighted to obtain the comprehensive risk index of each monitoring node. Based on the real-time ecological risk transmission path map and the comprehensive risk index of each monitoring node, the contaminated site is divided into regions. The comprehensive risk index of each region is obtained by averaging the comprehensive risk index of multiple monitoring nodes in each region.

8. A real-time monitoring system for ecological risks of contaminated sites, applied to the real-time monitoring method for ecological risks of contaminated sites as described in any one of claims 1-7, characterized in that, Including: The data acquisition and processing module is used to acquire physicochemical data, pollutant concentration data and ecotoxicity data of each monitoring node in the contaminated site through sensors, and to preprocess the physicochemical data, pollutant concentration data and ecotoxicity data of each monitoring node to obtain preprocessed physicochemical data, pollutant concentration data and ecotoxicity data. The dynamic weight calculation module is used to obtain the initial dynamic weights of each data indicator based on the preprocessed physicochemical data, pollutant concentration data, and ecotoxicity data and through the improved dynamic entropy weight method. The initial dynamic weights are then smoothed according to historical weight data to obtain the final dynamic weights of each data indicator. The risk transmission path modeling module is used to acquire geological environmental data and biological distribution data of contaminated sites and construct pollutant migration and transformation matrix and bioaccumulation response model accordingly. Based on the pollutant migration and transformation matrix and bioaccumulation response model, an ecological risk transmission network is established and the risk intensity value of each transmission path is calculated in real time to obtain a real-time ecological risk transmission path map of the contaminated site. The intelligent early warning threshold optimization module is used to set the initial thresholds of various data indicators according to national environmental quality standards, obtain historical early warning data of contaminated sites, and use support vector machines to dynamically optimize and adjust the initial thresholds of various data indicators to obtain the dynamic thresholds of various data indicators. The risk assessment and treatment module is used to calculate the comprehensive risk index of each monitoring node in the contaminated site based on the final dynamic weight and dynamic threshold of various data indicators. It divides the contaminated site into regions according to the real-time ecological risk transmission path map and calculates the comprehensive risk index of each region. Based on the comprehensive risk index of each region and the preset classification rules, it classifies the ecological risk of each region of the contaminated site. Based on the classification results, it adopts the corresponding level of early warning and treatment plan for each region of the contaminated site.

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