Big data monitoring and intelligent early warning platform and early warning method for environmental protection

Abstract

The invention relates to the technical field of environmental protection, in particular to a big data monitoring and intelligent early warning platform and method for environmental protection. Comprising a multi-source data acquisition module, a data integration and preprocessing module, an environmental risk factor identification module, a real-time monitoring and anomaly detection module, a prediction model and early warning module, a self-adaptive early warning and feedback module and a decision support and visualization module. The data integration and preprocessing module preprocesses multi-source data, the environmental risk factor identification module identifies key risk factors by using an XGBoost algorithm, and carries out accurate prediction and early warning on risks through a random forest and long and short term memory network combination model, and an adaptive feedback mechanism dynamically adjusts an early warning threshold value, so that the early warning efficiency is improved. According to the invention, the intelligent level and the scientific level of environmental protection are obviously improved.

Description

Technical Field

[0001] The present invention relates to the field of environmental protection technology, and in particular to a big data monitoring and intelligent early warning platform and an early warning method for environmental protection. Background Art

[0002] Effective environmental monitoring and timely warning have become the focus of environmental protection organizations. Through big data technology, environmental monitoring platforms can achieve real-time collection and analysis of large amounts of environmental data, thereby more comprehensively understanding environmental dynamics and assessing potential risks.

[0003] However, the current environmental monitoring platform data collection is often limited to a single data source and cannot fully cover the multi-dimensional information of the environment. At the same time, it is difficult to achieve high-precision anomaly detection and effective nonlinear correlation analysis during data processing, resulting in low accuracy in risk identification. In addition, the traditional early warning mechanism lacks an adaptive feedback mechanism and cannot dynamically adjust the early warning threshold, which is prone to false alarms or missed alarms and is difficult to meet the flexibility requirements under complex environmental conditions. Summary of the invention

[0004] Based on the above objectives, the present invention provides a big data monitoring and intelligent early warning platform and early warning method for environmental protection.

[0005] The big data monitoring and intelligent early warning method for environmental protection includes the following steps:

[0006] S1, Environmental data collection: Deploy a multi-sensor network and combine satellite remote sensing technology to collect multi-source data in the air, water and soil in real time. The multi-source data includes pollutant concentration, temperature and humidity, and pH value. The collected multi-source data is pre-processed by edge computing devices and transmitted to the preset central data unit;

[0007] S2, data integration and preprocessing: Kalman filtering and K-means clustering algorithms are used to preprocess multi-source data. Kalman filtering is used to reduce noise and improve data accuracy. K-means clustering is used to detect and mark abnormal data. The preprocessed multi-source data is integrated through data fusion technology.

[0008] S3, Identification of environmental risk factors: Using the feature extraction method based on the XGBoost algorithm, the integrated multi-source data is analyzed to identify the risk factors in the environment;

[0009] S4, Real-time monitoring and anomaly detection: A real-time monitoring model based on support vector machines and autoencoders is used to build a multi-level anomaly detection system;

[0010] S5, Early warning based on prediction model: A combined prediction model is constructed using random forest and long short-term memory network to predict potential environmental threats based on identified risk factors;

[0011] S6, Adaptive early warning and feedback mechanism: Establish an adaptive feedback mechanism and use the threshold adjustment method based on Bayesian optimization to dynamically correct the risk threshold of the early warning mechanism;

[0012] S7, Decision support and visualization: The prediction results are displayed through a visual interface, including risk level maps and trend analysis charts, to support relevant decision-making units to understand the environmental status in real time.

[0013] Optionally, the S1 includes:

[0014] S11, sensor network configuration and deployment: a multi-sensor network is arranged in the target environment, wherein the multi-sensor network includes air quality sensors, water quality sensors and soil moisture sensors. The sensor installation locations are optimally arranged according to geographical features and monitoring requirements. During the arrangement process, the coverage radius and data collection range of the sensors are considered;

[0015] S12, acquisition frequency setting and calibration: set appropriate acquisition frequencies for different types of sensors and calibrate the sensors to ensure data accuracy and reduce drift and deviation;

[0016] S13, preliminary processing of multi-source data: the collected multi-source data is pre-processed by edge computing devices, including format standardization and noise filtering;

[0017] S14, data formatting and encrypted transmission: formatting the preliminarily processed multi-source data into a unified structure to facilitate subsequent analysis and integration, and using an encryption protocol (such as AES encryption) to transmit the formatted multi-source data to a central data unit through a network;

[0018] S15, data upload and confirmation mechanism: After the multi-source data is uploaded to the central data unit, a confirmation signal is generated to verify the integrity of the data. If data transmission is lost or erroneous, the lost data packets will be automatically retransmitted.

[0019] Optionally, the S2 specifically includes:

[0020] S21, multi-source data consistency check: before data fusion, the data from different sensors are checked for consistency, and the data that does not meet the consistency requirements are marked and stored;

[0021] S22, noise filtering: Apply Kalman filtering algorithm to the data after consistency check to remove noise and optimize the smoothness of the data;

[0022] S23, abnormal data detection and marking: use K-means clustering algorithm to detect outliers in the data and mark the outliers;

[0023] S24, multi-source data integration and fusion: weighted fusion of data after noise filtering and anomaly detection.

[0024] Optionally, the S3 includes:

[0025] S31, feature variable screening: The XGBoost algorithm is used to screen feature variables. First, the feature importance score is performed on the multi-source data after data fusion to identify the key variables that affect environmental risks;

[0026] S32, nonlinear association analysis between variables: using the nonlinear feature processing capability of the XGBoost algorithm, nonlinear association analysis is performed on the selected feature variables to identify the complex relationship between environmental variables;

[0027] S33, risk factor model construction: using the identified high-importance feature variables, an environmental risk factor identification model was established based on the XGBoost algorithm, and the decision tree ensemble method was used to segment and combine the feature variables to optimize the accuracy of identifying environmental risk factors;

[0028] S34, model parameter optimization: Optimize the parameters of the environmental risk factor identification model through cross-validation and grid search methods to improve the prediction accuracy and generalization ability of the model.

[0029] Optionally, the S4 includes:

[0030] S41, preliminary classification of abnormal data: use the support vector machine algorithm to perform preliminary classification of multi-source data collected in real time and identify abnormal data points;

[0031] S42, deep anomaly detection: further analyze the data initially classified as abnormal, and use the autoencoder model to perform deep detection of abnormal data;

[0032] S43, dynamic threshold adjustment: based on the distribution of real-time monitoring data, adaptively adjust the threshold of anomaly detection to improve sensitivity to environmental changes;

[0033] S44, abnormal event recording and alarm: record confirmed abnormal events in the database and trigger the alarm mechanism so that environmental management personnel can understand potential environmental risks in a timely manner.

[0034] Optionally, the S5 includes:

[0035] S51, prediction model selection and combination: random forest and long short-term memory network are used to build a combined prediction model;

[0036] S52, model training and validation: Use historical multi-source data to train random forest and long short-term memory network models to optimize static feature prediction and dynamic trend prediction, respectively;

[0037] S53, combined prediction result fusion: the prediction results of the random forest and long short-term memory network models are fused by weighted average method;

[0038] S54, risk threshold setting and alarm triggering: setting a risk threshold based on the combined prediction results, and triggering an early warning signal if the prediction results exceed the preset threshold.

[0039] Optionally, the S6 includes:

[0040] S61, Feedback data collection and dynamic analysis: Collect feedback data, including real-time multi-source data, records of historical risk events and previous warning results, to adjust the response strategy of the warning mechanism;

[0041] S62, Threshold adjustment based on Bayesian optimization: Using the Bayesian optimization algorithm, the risk threshold of the early warning mechanism is dynamically adjusted according to the changes in feedback data to improve the adaptability and accuracy of the early warning;

[0042] S63, feedback result verification and optimization: verify the updated thresholds and warning results, and analyze the accuracy and response speed of the feedback results.

[0043] Optionally, the S7 includes:

[0044] S71, risk level map generation: generating a dynamic risk level map based on real-time prediction results and multi-source data;

[0045] S72, trend analysis chart generation: using historical monitoring data and prediction results, generate a visual chart reflecting the trend of environmental factor changes;

[0046] S73, real-time display of warning information: the warning information is displayed in real time on the visual interface. The warning information is presented in a combination of graphics and text to improve the clarity and readability of the information;

[0047] S74, automatic generation of decision support reports: Generate decision support reports based on risk level maps and trend visualization charts. The reports include risk overview, trend analysis, early warning suggestions and response measures to help environmental management personnel quickly formulate effective response strategies. After the reports are generated, they can be communicated to relevant personnel through email or platform notifications;

[0048] S75, User interaction and feedback mechanism: Set up user interaction function on the visual interface to allow managers to provide feedback on the warning results, such as confirmation, adjustment or remarks.

[0049] The big data monitoring and intelligent early warning platform for environmental protection is used to implement the above-mentioned big data monitoring and intelligent early warning method for environmental protection, and includes the following modules:

[0050] Multi-source data acquisition module: deploys a multi-sensor network and combines satellite remote sensing technology to collect multi-source data of air, water and soil in real time, including pollutant concentration, temperature, humidity and pH value. The collected data is initially processed by edge computing devices and then transmitted to the central data unit.

[0051] Data integration and preprocessing module: preprocess multi-source data through consistency checking, noise filtering and anomaly detection;

[0052] Environmental risk factor identification module: Based on the XGBoost algorithm, it performs feature screening and nonlinear correlation analysis, builds an environmental risk factor identification model, accurately identifies risk factors in the environment, and provides basic data for early warning;

[0053] Real-time monitoring and anomaly detection module: Uses support vector machine (SVM) and autoencoder models to perform real-time monitoring and multi-level anomaly detection of environmental data to ensure timely identification of significant anomalies in environmental parameters;

[0054] Prediction model and early warning module: A prediction model based on a combination of random forest and long short-term memory network, integrating static and dynamic feature data, and providing early warning of environmental risks by setting risk thresholds;

[0055] Adaptive warning and feedback module: collects historical multi-source data and currently monitored multi-source data, dynamically adjusts the warning threshold through Bayesian optimization, and combines the feedback mechanism to improve the adaptability and accuracy of the warning;

[0056] Decision support and visualization module: Provides decision support for environmental managers through risk level maps, trend analysis charts and real-time warning information display, automatically generates decision reports and supports user interaction and feedback, and continuously optimizes the platform's responsiveness.

[0057] Beneficial effects of the present invention:

[0058] The present invention, through the combination of multi-source data acquisition module with multi-sensor network and satellite remote sensing technology, the platform can obtain multi-dimensional environmental data such as air quality, water quality, soil moisture, etc. in real time over a wide geographical range. The data integration and preprocessing module ensures the accuracy and consistency of the data through consistency checking, noise filtering and anomaly detection, and performs weighted fusion of data from different sources to form a high-quality unified data input source. This method realizes all-round monitoring of the environment and provides a reliable data foundation for subsequent risk factor identification and anomaly detection.

[0059] The present invention uses the XGBoost algorithm to construct an environmental risk factor identification model, and accurately identifies potential environmental risk factors through feature screening and nonlinear correlation analysis. At the same time, the combined prediction model constructed by combining random forests and long short-term memory networks improves the prediction accuracy of the platform with the dual support of static features and time series trends. The dynamic threshold adjustment and adaptive feedback mechanism can flexibly adjust the warning threshold according to historical and real-time data, improve the adaptability and accuracy of environmental risk prediction, and enable the platform to respond to sudden and trend environmental risks in a timely manner.

[0060] The present invention generates dynamic risk level maps, trend analysis charts and automatically generated decision support reports through decision support and visualization modules, helping environmental management personnel to intuitively understand the risk status and environmental change trends of different regions. In addition, the display of real-time warning information and user interactive feedback functions enable decision makers to quickly respond to environmental risks, significantly improving the scientific nature and response efficiency of environmental management. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0062] Figure 1 A schematic diagram of the flow of the intelligent early warning method according to an embodiment of the present invention;

[0063] Figure 2 The figure is a flow chart of the intelligent early warning platform according to an embodiment of the present invention. DETAILED DESCRIPTION

[0064] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0065] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0066] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0067] like Figure 1 As shown, the big data monitoring and intelligent early warning method for environmental protection includes the following steps:

[0068] S1, Environmental data collection: Deploy a multi-sensor network and combine satellite remote sensing technology to collect multi-source data in the air, water and soil in real time. The multi-source data includes pollutant concentration (such as PM2.5, sulfur dioxide), temperature and humidity, and pH value. The collected multi-source data is pre-processed by edge computing devices and transmitted to the preset central data unit;

[0069] S2, data integration and preprocessing: Use Kalman filtering and K-means clustering algorithms to preprocess multi-source data. Kalman filtering is used to reduce noise and improve data accuracy. K-means clustering is used to detect and mark abnormal data to ensure data consistency and reliability. The preprocessed multi-source data is integrated through data fusion technology.

[0070] S3, Identification of environmental risk factors: The feature extraction method based on the XGBoost algorithm is used to analyze the integrated multi-source data and identify the risk factors in the environment. The XGBoost algorithm extracts environmental variables that are highly correlated with pollution diffusion, abnormal climate fluctuations, etc. through feature importance scoring, and realizes the identification of complex nonlinear relationships.

[0071] S4, Real-time monitoring and anomaly detection: Based on the real-time monitoring model of support vector machines and autoencoders, a multi-level anomaly detection system is constructed. The support vector machine is used to identify the preliminary classification of abnormal events, and the autoencoder is further used for deep detection to ensure that significant deviations of environmental parameters are accurately captured;

[0072] S5, Early warning based on prediction model: A combined prediction model is constructed using random forest and long short-term memory network to predict potential environmental threats based on identified risk factors;

[0073] S6, Adaptive early warning and feedback mechanism: Establish an adaptive feedback mechanism, use the threshold adjustment method based on Bayesian optimization to dynamically correct the risk threshold of the early warning mechanism, and the feedback mechanism is based on the comparison of historical event data with current monitoring data to optimize the accuracy and adaptability of the prediction results;

[0074] S7, Decision support and visualization: The prediction results are displayed through a visual interface, including risk level maps and trend analysis charts, to support relevant decision-making units to understand the environmental status in real time.

[0075] S1 includes:

[0076] S11, sensor network configuration and deployment: deploy a multi-sensor network in the target environment. The multi-sensor network includes air quality sensors, water quality sensors, and soil moisture sensors. The sensor installation locations are optimized according to geographical features and monitoring requirements to ensure the representativeness of environmental monitoring data and the comprehensiveness of the collection range. During the deployment process, the coverage radius and data collection range of the sensors are considered to ensure that each sensor can achieve seamless coverage and data redundancy in the specified area.

[0077] S12, Collection frequency setting and calibration: Set appropriate collection frequencies for different types of sensors. For example, air quality sensors collect data every 10 minutes, while water quality sensors and soil moisture sensors collect data every hour to ensure that the frequency of changes in environmental parameters is met. Sensors are calibrated to ensure data accuracy and reduce drift and deviation.

[0078] S13, preliminary processing of multi-source data: The collected multi-source data is pre-processed by edge computing devices, including format standardization and noise filtering. The preliminary data processing formula is expressed as:

[0079] D processed =D raw -N noise ;

[0080] Among them, D processed represents the data after preliminary processing, D raw Represents the raw data collected by the sensor, N noise is the detected noise interference data;

[0081] S14, data formatting and encrypted transmission: formatting the preliminarily processed multi-source data into a unified structure to facilitate subsequent analysis and integration, and using an encryption protocol (such as AES encryption) to transmit the formatted multi-source data to a central data unit through a network;

[0082] S15, data upload and confirmation mechanism: After the multi-source data is uploaded to the central data unit, a confirmation signal is generated to verify the integrity of the data. If data transmission is lost or erroneous, the lost data packet will be automatically retransmitted to ensure the integrity and availability of the final uploaded data;

[0083] Through scientific sensor network deployment, reasonable data collection frequency setting, real-time preprocessing of edge computing and encrypted transmission, the comprehensiveness, accuracy and security of multi-source environmental data are ensured, providing a reliable data foundation for subsequent data fusion and risk factor identification.

[0084] S2 specifically includes:

[0085] S21, multi-source data consistency check: Before data fusion, the data from different sensors are checked for consistency to ensure the consistency of the data unit, timestamp and geographic location. Data that does not meet the consistency requirements are marked and stored for subsequent analysis. The check formula is expressed as:

[0086] D consistent ={D|unit(D)=standard unit, timestamp(D)∈time range};

[0087] Among them, D consistent Indicates the data after consistency check. Unit(D) and timestamp(D) indicate the unit and timestamp of the data respectively.

[0088] S22, noise filtering: Apply the Kalman filter algorithm to the data after the consistency check to remove noise and optimize the smoothness of the data to improve the accuracy of the data. The update equation of the Kalman filter is as follows:

[0089]

[0090] in, represents the estimated value of the current state, z k is the current observation value, H is the observation matrix, K k is the Kalman gain, which is used to weight the influence of the observed value on the estimated value;

[0091] S23, abnormal data detection and marking: Use K-means clustering algorithm to detect outliers in the data and mark them for subsequent analysis. The definition of abnormal data is based on the threshold of cluster center distance. The formula is as follows:

[0092]

[0093] Among them, d i Represents data point x i The distance between the cluster center μ and the cluster center μ is i When the preset threshold is exceeded, x i Marked as abnormal data;

[0094] S24, multi-source data integration and fusion: The data after noise filtering and anomaly detection are weighted and fused. The data fusion adopts the weighted average method, and different weights are given to each data source according to the reliability of the data source. The fusion formula is expressed as:

[0095]

[0096] Among them, D fused represents the fused data, w i is the weight of data source i, D i is the corresponding data value, and

[0097] Through consistency checking, noise filtering, anomaly detection and labeling, and weighted fusion of multi-source data, the reliability, consistency and accuracy of multi-source environmental data are ensured, providing a high-quality data foundation for subsequent identification and prediction of environmental risk factors.

[0098] S3 includes:

[0099] S31, feature variable screening: The XGBoost algorithm is used to screen feature variables. First, the feature importance score is performed on the multi-source data after data fusion to identify the key variables that affect environmental risks. The calculation formula for the feature score is as follows:

[0100]

[0101] Among them, I(f) represents the importance score of feature f, T represents the number of decision trees, and I t(f) is the importance score of feature f in decision tree t. Features with higher scores will be given priority for subsequent risk factor analysis;

[0102] S32, nonlinear correlation analysis between variables: Using the nonlinear feature processing capability of the XGBoost algorithm, nonlinear correlation analysis is performed on the selected feature variables to identify the complex relationship between environmental variables. The mutual influence of different environmental factors, such as the interaction effect between pollutant concentration and climate conditions, is analyzed through modeling to determine potential environmental risk factors.

[0103] S33, risk factor model construction: Using the identified high-importance feature variables, an environmental risk factor identification model is established based on the XGBoost algorithm. The decision tree ensemble method is used to segment and combine the feature variables to optimize the accuracy of identifying environmental risk factors. The prediction expression of the model is expressed as:

[0104]

[0105] Among them, F(x) represents the final risk factor prediction result, M is the number of decision trees, and α m is the weight f of decision tree m m (x) is the prediction result of the mth decision tree;

[0106] S34, model parameter optimization: Optimize the parameters of the environmental risk factor identification model through cross-validation and grid search methods to improve the prediction accuracy and generalization ability of the model. Specific parameter adjustments include learning rate, decision tree depth and number of decision trees. Through repeated adjustment and verification, the optimal parameter combination is selected;

[0107] Through feature screening, nonlinear correlation analysis, risk factor model construction and parameter optimization, accurate identification of environmental risk factors is achieved, which provides key risk variables for subsequent early warning and prediction, and improves the system's adaptability and predictive capabilities under complex environmental conditions.

[0108] S4 includes:

[0109] S41, preliminary classification of abnormal data: Use the support vector machine algorithm to perform preliminary classification on the multi-source data collected in real time and identify abnormal data points. The classification decision function of the support vector machine is as follows:

[0110] f(x)=sign(w·x+b);

[0111] Among them, f(x) represents the classification result, w is the weight vector, x is the data vector, and b is the bias term. When f(x)>0, the data point is judged as abnormal data; otherwise, it is judged as normal data. SVM is used to quickly detect potential abnormal events and provide a basis for subsequent deep anomaly detection.

[0112] S42, deep anomaly detection: further analyze the data initially classified as abnormal, and use the autoencoder model to perform deep detection on the abnormal data. The autoencoder identifies anomalies through the reconstruction error of the data. The calculation formula of the reconstruction error is:

[0113]

[0114] Where L represents the reconstruction error, x i is the original input data, For the reconstructed data, if the reconstruction error L exceeds the preset threshold, the data point is confirmed to be abnormal and further reported to the early warning unit;

[0115] S43, dynamic threshold adjustment: Based on the distribution of real-time monitoring data, the threshold of anomaly detection is adaptively adjusted to improve sensitivity to environmental changes. The dynamic threshold adjustment strategy uses historical monitoring data as a benchmark and is updated through changes in the mean and variance of real-time data. The calculation formula is as follows:

[0116] Threshold new =μ+kσ;

[0117] Among them, μ is the mean of the current data, σ is the standard deviation, and k is the adjustment coefficient. This strategy enables the system to flexibly adjust the sensitivity of anomaly detection according to the real-time environmental conditions;

[0118] S44, abnormal event recording and alarm: record confirmed abnormal events in the database and trigger the alarm mechanism so that environmental management personnel can understand potential environmental risks in a timely manner. The recorded data includes the type, time of occurrence, location and detection parameters of the abnormal event for subsequent analysis and evaluation;

[0119] Through preliminary classification, in-depth detection, dynamic threshold adjustment and abnormal event recording of abnormal data, the platform has effectively improved its detection accuracy and response speed to environmental abnormal conditions, providing reliable technical support for timely identification and response to environmental risks.

[0120] S5 includes:

[0121] S51, prediction model selection and combination: random forest and long short-term memory network are used to build a combined prediction model. Random forest is used to analyze the static characteristic variables of the environment, and long short-term memory network is used to process the time series information in the environmental data. Through this combination, the model's prediction ability for sudden and trend risks is improved;

[0122] S52, model training and verification: Use historical multi-source data to train random forest and long short-term memory network models to optimize static feature prediction and dynamic trend prediction respectively. Random forest uses decision tree ensemble to predict static environmental state, and its loss function is classification error. Long short-term memory network optimizes its prediction of time series through mean square error (MSE), which is expressed as:

[0123]

[0124] Among them, y i is the actual value, is the predicted value, n is the number of samples;

[0125] S53, combined prediction result fusion: The prediction results of the random forest and long short-term memory network models are fused by weighted average method. Different weights are given to the prediction results of each model to balance the short-term prediction accuracy and the stability of long-term trend prediction. The fusion formula is as follows:

[0126] P fused =w RF ·P RF +w LSTM ·P LSTM

[0127] Among them, P fused is the combined prediction result, P RF and P LSTM are the prediction results of random forest and long short-term memory network, w RF and w LSTM is the corresponding weight, and w RF +w LSTM =1;

[0128] S54, risk threshold setting and alarm triggering: risk threshold is set based on the combined prediction results. If the prediction results exceed the preset threshold, the warning signal is triggered. The risk threshold is set with reference to the frequency of historical risk events and the characteristics of environmental changes to ensure the sensitivity and accuracy of the prediction and warning.

[0129] Through the design, training and verification of the combined prediction model, the fusion of results and the setting of risk thresholds, the system's early warning capabilities for potential environmental risks have been enhanced, effective predictions of sudden and trend environmental risks have been achieved, and more comprehensive and accurate risk warning information has been provided to environmental management personnel.

[0130] S6 includes:

[0131] S61, Feedback data collection and dynamic analysis: Collect feedback data, including real-time multi-source data, records of historical risk events and previous warning results, to adjust the response strategy of the warning mechanism;

[0132] S62, Threshold adjustment based on Bayesian optimization: Using the Bayesian optimization algorithm, the risk threshold of the early warning mechanism is dynamically adjusted according to the changes in feedback data to improve the adaptability and accuracy of the early warning. Bayesian optimization constructs a priori probability distribution and performs a posteriori update based on the current monitoring data to find the threshold that minimizes the early warning error. The objective function of Bayesian optimization is:

[0133]

[0134] Among them, f(θ) is the threshold error function, y is the actual value, is the predicted value, θ is the threshold parameter;

[0135] S63, feedback result verification and optimization: verify the updated thresholds and warning results, analyze the accuracy and response speed of the feedback results, and further adjust the feedback parameters if there is a deviation in the warning effect or a large error;

[0136] Through the collection of feedback data, threshold adjustment through Bayesian optimization, and feedback verification, a flexible early warning mechanism has been formed, which enables the system to dynamically adjust the early warning threshold according to changes in environmental conditions, improves the accuracy and adaptability of the early warning, and provides timely and accurate risk early warning support for environmental management departments.

[0137] S7 includes:

[0138] S71, Risk level map generation: Based on real-time prediction results and multi-source data, a dynamic risk level map is generated. Different risk areas are divided in the map, and the risk level is represented by color coding. It is automatically updated according to the changes in real-time monitoring data. The risk level map helps decision makers quickly understand the risk status of different areas and facilitates the rational allocation of resources;

[0139] S72, trend analysis chart generation: using historical monitoring data and forecast results, generate visual charts reflecting the changing trends of environmental factors, such as time series charts, line charts and bar charts. Trend analysis charts show the changes of major environmental parameters (such as pollutant concentrations, temperature and humidity) in different time periods, providing decision makers with an intuitive understanding of environmental dynamic trends;

[0140] S73, real-time display of warning information: the warning information is displayed in real time on the visual interface. The warning information is presented in a combination of graphics and text to improve the clarity and readability of the information;

[0141] S74, automatic generation of decision support reports: Generate decision support reports based on risk level maps and trend visualization charts. The reports include risk overview, trend analysis, early warning suggestions and response measures to help environmental management personnel quickly formulate effective response strategies. After the reports are generated, they can be communicated to relevant personnel through email or platform notifications;

[0142] S75, user interaction and feedback mechanism: set up user interaction functions on the visual interface to allow managers to provide feedback on the warning results, such as confirmation, adjustment or remarks;

[0143] Through the generation of risk level maps and trend analysis charts, real-time warning information display, decision support report automation and user feedback mechanism integration, it provides intuitive and efficient decision support functions, enabling managers to accurately grasp the environmental risk status and take timely actions, effectively improving the response efficiency and scientific nature of environmental risk management.

[0144] like Figure 2 As shown, the big data monitoring and intelligent early warning platform for environmental protection is used to implement the above-mentioned big data monitoring and intelligent early warning method for environmental protection, and includes the following modules:

[0145] Multi-source data acquisition module: deploys a multi-sensor network and combines satellite remote sensing technology to collect multi-source data of air, water and soil in real time, including pollutant concentration, temperature, humidity and pH value. The collected data is initially processed by edge computing devices and then transmitted to the central data unit.

[0146] Data integration and preprocessing module: preprocess multi-source data through consistency check, noise filtering and anomaly detection to ensure data reliability and consistency, and integrate data using weighted fusion method to form a unified monitoring data input source;

[0147] Environmental risk factor identification module: Based on the XGBoost algorithm, it performs feature screening and nonlinear correlation analysis, builds an environmental risk factor identification model, accurately identifies risk factors in the environment, and provides basic data for early warning;

[0148] Real-time monitoring and anomaly detection module: Use support vector machines and autoencoder models to perform real-time monitoring and multi-level anomaly detection of environmental data to ensure timely identification of significant anomalies in environmental parameters;

[0149] Prediction model and early warning module: A prediction model based on a combination of random forest and long short-term memory network, integrating static and dynamic feature data, and providing early warning of environmental risks by setting risk thresholds;

[0150] Adaptive warning and feedback module: collects historical multi-source data and currently monitored multi-source data, dynamically adjusts the warning threshold through Bayesian optimization, and combines the feedback mechanism to improve the adaptability and accuracy of the warning;

[0151] Decision support and visualization module: Provides decision support for environmental managers through risk level maps, trend analysis charts and real-time warning information display, automatically generates decision reports and supports user interaction and feedback, and continuously optimizes the platform's responsiveness.

[0152] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0153] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. Big data monitoring and intelligent methods for environmental protection, characterized by: The following steps are involved: S1, Environmental data collection: Deploy a multi-sensor network and combine satellite remote sensing technology to collect multi-source data in the air, water and soil in real time. The multi-source data includes pollutant concentration, temperature and humidity, and pH value. The collected multi-source data is pre-processed by edge computing devices and transmitted to the preset central data unit; S2, data integration and preprocessing: Kalman filtering and K-means clustering algorithms are used to preprocess multi-source data. Kalman filtering is used to reduce noise and improve data accuracy. K-means clustering is used to detect and mark abnormal data. The preprocessed multi-source data is integrated through data fusion technology. S3, Identification of environmental risk factors: Using the feature extraction method based on the XGBoost algorithm, the integrated multi-source data is analyzed to identify the risk factors in the environment; S4, Real-time monitoring and anomaly detection: A real-time monitoring model based on support vector machines and autoencoders is used to build a multi-level anomaly detection system; S5, Early warning based on prediction model: A combined prediction model is constructed using random forest and long short-term memory network to predict potential environmental threats based on identified risk factors; S6, Adaptive early warning and feedback mechanism: Establish an adaptive feedback mechanism and use the threshold adjustment method based on Bayesian optimization to dynamically correct the risk threshold of the early warning mechanism; S7, Decision support and visualization: The prediction results are displayed through a visual interface, including risk level maps and trend analysis charts, to support relevant decision-making units to understand the environmental status in real time.

2. The big data monitoring and intelligent early warning method for environmental protection according to claim 1 is characterized in that: The S1 includes: S11, sensor network configuration and deployment: a multi-sensor network is arranged in the target environment, wherein the multi-sensor network includes air quality sensors, water quality sensors and soil moisture sensors. The sensor installation locations are optimally arranged according to geographical features and monitoring requirements. During the arrangement process, the coverage radius and data collection range of the sensors are considered; S12, acquisition frequency setting and calibration: setting acquisition frequencies for different types of sensors and calibrating the sensors; S13, preliminary processing of multi-source data: the collected multi-source data is pre-processed by edge computing devices, including format standardization and noise filtering; S14, data formatting and encrypted transmission: formatting the preliminarily processed multi-source data into a unified structure, and transmitting the formatted multi-source data to the central data unit through the network using an encryption protocol; S15, data upload and confirmation mechanism: After the multi-source data is uploaded to the central data unit, a confirmation signal is generated to verify the integrity of the data. If data transmission is lost or erroneous, the lost data packets will be automatically retransmitted.

3. The big data monitoring and intelligent early warning method for environmental protection according to claim 2 is characterized in that: The S2 specifically includes: S21, multi-source data consistency check: perform consistency check on data from different sensors, and mark and store data that does not meet the consistency requirements; S22, noise filtering: Apply Kalman filtering algorithm to the data after consistency check to remove noise and optimize the smoothness of the data; S23, abnormal data detection and marking: use K-means clustering algorithm to detect outliers in the data and mark the outliers; S24, multi-source data integration and fusion: weighted fusion of data after noise filtering and anomaly detection.

4. The big data monitoring and intelligent early warning method for environmental protection according to claim 3 is characterized in that: The S3 includes: S31, feature variable screening: The XGBoost algorithm is used to screen feature variables. First, the feature importance score is performed on the multi-source data after data fusion to identify the key variables that affect environmental risks; S32, nonlinear association analysis between variables: using the nonlinear feature processing capability of the XGBoost algorithm, nonlinear association analysis is performed on the selected feature variables to identify the complex relationship between environmental variables; S33, risk factor model construction: using the identified characteristic variables, an environmental risk factor identification model was established based on the XGBoost algorithm, and the decision tree ensemble method was used to segment and combine the characteristic variables to optimize the accuracy of identifying environmental risk factors; S34, model parameter optimization: Optimize the parameters of the environmental risk factor identification model through cross-validation and grid search methods to improve the prediction accuracy and generalization ability of the model.

5. The big data monitoring and intelligent early warning method for environmental protection according to claim 4 is characterized in that: The S4 includes: S41, preliminary classification of abnormal data: use the support vector machine algorithm to perform preliminary classification of multi-source data collected in real time and identify abnormal data points; S42, deep anomaly detection: further analyze the data initially classified as abnormal, and use the autoencoder model to perform deep detection of abnormal data; S43, dynamic threshold adjustment: based on the distribution of real-time monitoring data, adaptively adjust the threshold of anomaly detection to improve sensitivity to environmental changes; S44, abnormal event recording and alarm: record confirmed abnormal events in the database and trigger the alarm mechanism so that environmental management personnel can understand potential environmental risks in a timely manner.

6. The big data monitoring and intelligent early warning method for environmental protection according to claim 5 is characterized in that: The S5 includes: S51, prediction model selection and combination: random forest and long short-term memory network are used to build a combined prediction model; S52, model training and validation: Use historical multi-source data to train random forest and long short-term memory network models to optimize static feature prediction and dynamic trend prediction, respectively; S53, combined prediction result fusion: the prediction results of the random forest and long short-term memory network models are fused by weighted average method; S54, risk threshold setting and alarm triggering: setting a risk threshold based on the combined prediction results, and triggering an early warning signal if the prediction results exceed the preset threshold.

7. The big data monitoring and intelligent early warning method for environmental protection according to claim 6 is characterized in that: The S6 includes: S61, Feedback data collection and dynamic analysis: Collect feedback data, including real-time multi-source data, records of historical risk events and previous warning results, to adjust the response strategy of the warning mechanism; S62, Threshold adjustment based on Bayesian optimization: Using the Bayesian optimization algorithm, the risk threshold of the early warning mechanism is dynamically adjusted according to the changes in feedback data to improve the adaptability and accuracy of the early warning; S63, feedback result verification and optimization: verify the updated thresholds and warning results, and analyze the accuracy and response speed of the feedback results.

8. The big data monitoring and intelligent early warning method for environmental protection according to claim 7 is characterized in that: The S7 includes: S71, risk level map generation: generating a dynamic risk level map based on real-time prediction results and multi-source data; S72, trend analysis chart generation: using historical monitoring data and prediction results, generate a visual chart reflecting the trend of environmental factor changes; S73, real-time display of warning information: the warning information is displayed in real time on the visual interface, and the warning information is presented in the form of a combination of graphics and text; S74, automatic generation of decision support report: Generate a decision support report based on the risk level map and trend visualization chart. The report includes risk overview, trend analysis, early warning suggestions and response measures; S75, User interaction and feedback mechanism: Set up user interaction function on the visual interface to allow managers to provide feedback on the warning results.

9. A big data monitoring and intelligent early warning platform for environmental protection, used to implement the big data monitoring and intelligent early warning method for environmental protection as described in any one of claims 1 to 8, characterized in that: Includes the following modules: Multi-source data acquisition module: deploys a multi-sensor network and combines satellite remote sensing technology to collect multi-source data of air, water and soil in real time, including pollutant concentration, temperature, humidity and pH value. The collected data is initially processed by edge computing devices and then transmitted to the central data unit. Data integration and preprocessing module: preprocess multi-source data through consistency checking, noise filtering and anomaly detection; Environmental risk factor identification module: Based on the XGBoost algorithm, feature screening and nonlinear correlation analysis are performed to build an environmental risk factor identification model to accurately identify risk factors in the environment; Real-time monitoring and anomaly detection module: Use support vector machines and autoencoder models to perform real-time monitoring and multi-level anomaly detection of environmental data to ensure timely identification of significant anomalies in environmental parameters; Prediction model and early warning module: A prediction model based on a combination of random forest and long short-term memory network, integrating static and dynamic feature data, and providing early warning of environmental risks by setting risk thresholds; Adaptive warning and feedback module: collects historical multi-source data and currently monitored multi-source data, dynamically adjusts the warning threshold through Bayesian optimization, and combines the feedback mechanism to improve the adaptability and accuracy of the warning; Decision support and visualization module: Provides decision support for environmental managers through risk level maps, trend analysis charts and real-time warning information display, automatically generates decision reports and supports user interaction and feedback.

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