Ecological environment inspection and detection information management system based on environment AI large model

Abstract

The invention discloses an ecological environment inspection and detection information management system based on an environmental AI large model, and relates to the technical field of data management. A data acquisition module is used for collecting extreme weather and pollutant data in real time; the feature extraction module converts the data into a new feature set, the convolutional neural network training module trains the features and historical data, the generalization ability of the model under extreme conditions is evaluated through the A / B test module, and then the model is divided into a high generalization ability level and a low generalization ability level; the model with high generalization ability is retrained regularly through real-time data feedback, and the model with low generalization ability generates an early warning signal on the basis of prediction accuracy analysis and takes corresponding emergency measures; the technical problem that historical data cannot comprehensively cover future environment changes is effectively solved, the response speed and accuracy of an environment management department to extreme environment changes and sudden pollution events are improved, and potential ecological and public health risks are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to an ecological environment inspection and detection information management system based on an environmental AI big model. Background Art

[0002] Ecological environment inspection and testing information management based on environmental AI big model refers to the use of artificial intelligence (AI) big model technology to intelligently manage and analyze ecological environment-related inspection and testing data. Specifically, this management system integrates data from different monitoring points, such as air, water quality, soil and other environmental quality indicators, and uses big data analysis and machine learning technology to efficiently process data, identify patterns, detect anomalies, etc. This not only improves data processing efficiency, but also helps environmental management departments to promptly identify ecological and environmental problems and make scientific decisions, thereby ensuring that environmental protection measures are implemented more accurately and efficiently.

[0003] Secondly, the intelligence of ecological environment inspection and testing information management relies on the deep learning capabilities of AI big models, which can make predictions and risk assessments while processing large amounts of complex environmental data. For example, when the system detects abnormal air or water quality in a certain area, the AI ​​big model can quickly identify potential environmental problems and predict their possible expansion trends, thereby providing data support for environmental monitoring and governance. In addition, this intelligent management can also achieve remote control and fault diagnosis of environmental monitoring equipment, improve the degree of automation of monitoring work, and provide data basis for the optimization of environmental policies.

[0004] The prior art has the following deficiencies:

[0005] The training of large environmental AI models often relies on historical data, and historical data itself may not fully represent future environmental changes, especially in the case of climate change or sudden pollution events. Historical patterns may not accurately predict new pollution sources, sudden meteorological changes, or unusual pollution diffusion phenomena. For example, extreme climate conditions may cause pollutants to spread rapidly, and the AI ​​model may fail to identify this new situation in the training data, resulting in prediction errors. In addition, if the AI ​​model cannot accurately identify and predict sudden pollution events (such as chemical leaks, industrial accidents, etc.) or the spread of pollution caused by extreme climate, it may cause environmental management departments to fail to detect and respond to these abnormal situations in a timely manner. For example, under extreme weather conditions, pollutants may spread rapidly in a short period of time. If the AI ​​model fails to identify similar patterns in historical data, it may miss the early warning window for the spread of pollutants, delay emergency response, and cause the scope of pollution to expand, causing serious impacts on ecosystems and public health. Summary of the invention

[0006] The purpose of the present invention is to provide an ecological environment inspection and testing information management system based on an environmental AI big model to solve the shortcomings of the background technology.

[0007] In order to achieve the above-mentioned purpose, the present invention provides the following technical solutions: an ecological environment inspection and detection information management system based on an environmental AI big model, comprising a data acquisition module, a feature extraction module, a convolutional neural network training module, an A / B testing module, a model retraining and data expansion module, and an early warning and emergency response module;

[0008] Data collection module: using modern monitoring technology to collect real-time data related to extreme weather and sudden pollution events;

[0009] Feature extraction module: converts the collected real-time data of extreme meteorological conditions and pollutants into a new feature set of the AI ​​large model, which includes the characteristics of the rapid temperature change rate in extreme meteorological conditions and the characteristics of the degradation rate of pollutants under different environmental conditions;

[0010] Convolutional neural network training module: Use the convolutional neural network model to train the temperature rapid change rate characteristics, degradation rate characteristics and historical pollution data. During the training process, the training data is divided into multiple subsets, including historical pollution data sets and new feature set data sets;

[0011] A / B testing module: Conduct A / B testing on the convolutional neural network model using the historical pollution data set and the new feature set data set, compare the performance differences between the two in predicting sudden pollution events and extreme weather conditions, evaluate the generalization ability of the environmental AI large model under extreme conditions, and divide the generalization ability of the environmental AI large model under extreme conditions into high generalization ability level and low generalization ability level according to the evaluation results;

[0012] Model retraining extension module: For large environmental AI models with high generalization capability, real-time collected environmental data is fed back into the model for regular retraining;

[0013] Early warning and emergency response module: For environmental AI big models with low generalization ability levels, the prediction accuracy of the environmental AI big models under extreme conditions within a fixed time period is analyzed and warned, and corresponding emergency response measures are taken according to the different levels of early warning signals.

[0014] Preferably, in the feature extraction module, the temperature rapid change rate anomaly index is generated after analyzing the extracted temperature rapid change rate characteristics in the extreme meteorological conditions. The method for obtaining the temperature rapid change rate anomaly index is:

[0015] Obtain historical temperature data and real-time temperature data for which the rapid change rate needs to be calculated. Select a sliding window size W. In each sliding window, calculate the rapid change rate of temperature, expressed as the change in temperature. The rapid change rate of temperature is defined as the difference between the temperature at the current time point and the temperature at the previous time point: ΔT(s) = |T(s)-T(s-1)|; where ΔT(s) is the rapid change rate of temperature at time s, and T(s) is the temperature value at time s. For each sliding window W, calculate the mean value μ of the rapid change rate of temperature W and standard deviation σ W , respectively represent the center and fluctuation degree of the normal variation range; compare the temperature rapid change rate at the current time s with the mean and standard deviation of the current window, and calculate the standardized abnormal index TSC(s) in each sliding window, the expression is: The temperature rapid change rate anomaly index is obtained by weighted averaging the anomaly index TSC(s) in all sliding windows.

[0016] Preferably, in the feature extraction module, the degradation rate characteristics of the extracted pollutants under different environmental conditions are analyzed to generate a pollutant concentration degradation rate fluctuation index, and the method for obtaining the pollutant concentration degradation rate fluctuation index is:

[0017] Collect the pollutant concentration data series {C(t)}, where C(t) is the pollutant concentration at time point t, and calculate the corresponding pollutant degradation rate Where Δt is the time step; for the pollutant concentration degradation rate series Perform discrete wavelet transform to obtain multi-scale decomposition. The discrete wavelet transform formula is: Among them, ψ j (t) is the wavelet basis function of scale j, W j is the decomposition coefficient; through wavelet transform, the degradation rate signal Decompose into coefficients of multiple scales, set the wavelet transform to decompose the signal into J scales W j (t), then the decomposition coefficient {W j (t)}, calculate the pollutant concentration degradation rate fluctuation index, the expression is: Where N is the number of sample points, that is, the length of the data sequence, J is the scale number of wavelet transform, and DRFI is the pollutant concentration degradation rate fluctuation index.

[0018] Preferably, a convolutional neural network model is used to train the temperature rapid change rate characteristics, degradation rate characteristics and historical pollution data, specifically:

[0019] Extract pollutant concentration data C(t) from historical monitoring data as basic input features, convert the temperature rapid change rate anomaly index and pollutant concentration degradation rate fluctuation index into comprehensive feature vectors and combine them with historical pollution data to form an input data set, input data set X: X = [C(t), EMC, DRFI], where EMC is the temperature rapid change rate anomaly index, and DRFI is the pollutant concentration degradation rate fluctuation index;

[0020] During the training process, the dataset is divided into multiple subsets, namely, the historical pollution dataset Xhistorical, which only contains historical pollution data C(t); the new feature set dataset Xnew contains the temperature rapid change rate feature and the degradation rate feature;

[0021] The input layer receives the features X = [C(t), EMC, DRFI] obtained from the data preprocessing stage, with a shape of (N, T, F), where N is the number of samples, T is the number of time steps, and F is the number of features in each time step; the convolution layer is used to extract local features from the input data. For time series data, a one-dimensional convolution operation is applied, and the convolution operation formula is: y(t) = (X*M)(t)+b; where X is the input data, M is the convolution kernel, b is the bias term, and y(t) is the output of the convolution layer;

[0022] The pooling layer follows the convolutional layer to reduce the amount of computation and improve the abstraction ability of features. The maximum pooling operation is used. The pooling process formula is: y(t) = max(X(t) k ); represents a window of the input signal, and max selects the maximum value in the window;

[0023] The features extracted by the convolutional layer and the pooling layer will be flattened and passed to the fully connected layer for the final classification or regression task. The calculation process of the fully connected layer is: y = M·X+b; where y is the output;

[0024] According to the goal of the task, the output layer will be adjusted accordingly. For the predicted pollutant concentration, the error MSE between the predicted value and the true value is calculated, and the expression is: Where: y pred (i) is the pollutant concentration predicted by the model, y true (i) is the true value.

[0025] Preferably, the generalization ability of the environmental AI large model under extreme conditions is evaluated, and according to the evaluation results, the generalization ability of the environmental AI large model under extreme conditions is divided into a high generalization ability level and a low generalization ability level, specifically:

[0026] Extract pollutant concentration data C(t) from historical monitoring data;

[0027] The temperature rapid change rate anomaly index and pollutant concentration degradation rate fluctuation index are converted into comprehensive feature vectors and combined with historical pollution data to form an input data set;

[0028] Divide the data into training and testing sets;

[0029] Group A training: A convolutional neural network model trained using a historical pollution dataset, with the input feature being the pollutant concentration C(t) to predict changes in pollutant concentration;

[0030] Group B training: The convolutional neural network model was trained using the historical pollution data set and the new feature set data set. The input features were the temperature rapid change rate anomaly index and the pollutant concentration degradation rate fluctuation index, which were converted into comprehensive feature vectors and historical pollution data to predict changes in pollutant concentrations.

[0031] According to the A / B test results, the generalization ability of the environmental AI large model under extreme conditions is evaluated. If the mean square error of the group B model is smaller than the mean square error of the group A model, it means that the environmental AI large model exhibits strong prediction accuracy and good generalization ability, and can effectively respond to extreme weather and sudden pollution events. At this time, no abnormal signal is generated, and the generalization ability of the environmental AI large model under extreme conditions is classified as a high generalization ability level; if the mean square error of the group B model is greater than or equal to the mean square error of the group A model, it means that the environmental AI large model performs poorly under extreme weather and sudden pollution events, cannot accurately identify, and has weak generalization ability. At this time, an abnormal signal is generated, and the generalization ability of the environmental AI large model under extreme conditions is classified as a low generalization ability level.

[0032] Preferably, in the early warning and emergency response module, for the environmental AI big model with low generalization ability level, the prediction accuracy of the environmental AI big model under extreme conditions within a fixed time period is analyzed and warned, and corresponding emergency treatment measures are taken according to different levels of early warning signals, specifically:

[0033] For environmental AI large models with low generalization ability levels, that is, the mean square error of the group B models generated within a fixed time period is greater than or equal to the mean square error of the group A models, the mean square errors of the group B models generated within a subsequent fixed time period that are greater than or equal to the mean square error of the group A models are collected, and the corresponding data set is established. The mean and standard deviation of the data set are calculated, and after analysis, the prediction accuracy of the environmental AI large model under extreme conditions is determined.

[0034] Preferably, if the mean square error of the group B model in the data set is greater than or equal to the reference threshold of the mean square error of the group B model, and the standard deviation of the mean square error of the group B model is less than the reference threshold of the standard deviation of the mean square error of the group B model, it means that the model is not accurate enough in overall performance, but the prediction error is stable. At this time, a first-level warning signal is generated, feature data under extreme conditions are added, and the group B model is optimized by introducing a new training data set;

[0035] If the mean square error of the model in group B is greater than or equal to the reference threshold of the mean square error of the model in group B, and the standard deviation of the mean square error of the model in group B is greater than or equal to the reference threshold of the standard deviation of the mean square error of the model in group B, it indicates that the prediction of the model under extreme conditions is neither accurate nor stable. At this time, a secondary warning signal is generated, and incremental learning technology is used to introduce real-time data to update the model;

[0036] If the mean square error of the model in group B is less than the reference threshold of the mean square error of the model in group B, and the standard deviation of the mean square error of the model in group B is greater than or equal to the reference threshold of the standard deviation of the mean square error of the model in group B, a third-level warning signal is generated to analyze the source of the error under specific environmental conditions and determine the root cause of the error fluctuation;

[0037] If the mean square error of the model in group B is less than the reference threshold of the mean square error of the model in group B, and the standard deviation of the mean square error of the model in group B is less than the reference threshold of the standard deviation of the mean square error of the model in group B, no emergency adjustment is required. At this time, no warning signal is generated, the model status quo is maintained, and the data is updated regularly to ensure its stability.

[0038] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0039] 1. This invention effectively overcomes the shortcomings of traditional models that rely only on historical data and cannot adapt to extreme climate change and sudden pollution events by introducing new features and advanced convolutional neural network training methods. The generalization ability of the model is evaluated through A / B testing, which can accurately predict the diffusion trend of pollutants under extreme meteorological conditions, thereby providing timely warnings for environmental management departments and ensuring that emergency response measures are taken in the early stages of pollution diffusion. In addition, the system has regular retraining and real-time data feedback functions, so that the model always maintains good prediction accuracy in long-term operation.

[0040] 2. The present invention also ensures that low generalization models can promptly identify and respond to potential prediction errors through a detailed early warning and emergency response mechanism. In the event that the model's generalization ability is insufficient, dynamic adjustments are made through multi-level early warning signals (such as primary, secondary, and tertiary warnings) to optimize the model's performance. Combined with incremental learning, feature data introduction, and real-time data updates, the system can be continuously improved and adjusted to cope with extreme weather and sudden pollution events that may occur in the future. This technical solution significantly improves the reliability and accuracy of environmental AI models, provides strong support for environmental pollution monitoring and emergency response, and minimizes risks to ecosystems and public health. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0042] Figure 1 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] For examples, see Figure 1 As shown, the ecological environment inspection and detection information management system based on the environmental AI big model described in this embodiment includes a data acquisition module, a feature extraction module, a convolutional neural network training module, an A / B testing module, a model retraining and data expansion module, and an early warning and emergency response module;

[0045] Data collection module: using modern monitoring technology to collect real-time data related to extreme weather and sudden pollution events;

[0046] Feature extraction module: converts the collected real-time data of extreme meteorological conditions and pollutants into a new feature set of the AI ​​large model, which includes the characteristics of the rapid temperature change rate in extreme meteorological conditions and the characteristics of the degradation rate of pollutants under different environmental conditions;

[0047] Convolutional neural network training module: Use the convolutional neural network model to train the temperature rapid change rate characteristics, degradation rate characteristics and historical pollution data. During the training process, the training data is divided into multiple subsets, including historical pollution data sets and new feature set data sets;

[0048] A / B testing module: Conduct A / B testing on the convolutional neural network model using the historical pollution data set and the new feature set data set, compare the performance differences between the two in predicting sudden pollution events and extreme weather conditions, evaluate the generalization ability of the environmental AI large model under extreme conditions, and divide the generalization ability of the environmental AI large model under extreme conditions into high generalization ability level and low generalization ability level according to the evaluation results;

[0049] Model retraining extension module: For large environmental AI models with high generalization capability, real-time collected environmental data is fed back into the model for regular retraining;

[0050] Early warning and emergency response module: For environmental AI big models with low generalization ability levels, the prediction accuracy of the environmental AI big models under extreme conditions within a fixed time period is analyzed and warned, and corresponding emergency response measures are taken according to the different levels of early warning signals.

[0051] In the data acquisition module, extreme meteorological events (such as extreme temperature changes, strong winds, heavy rains, droughts, etc.) have a profound impact on environmental quality. Real-time monitoring of these meteorological parameters is crucial for predicting pollution spread, emergency response, etc.

[0052] Meteorological sensors specifically include: Temperature, humidity and air pressure sensors: used to monitor rapid changes in temperature, humidity, etc. Rapid fluctuations in temperature and humidity are often closely related to the spread of pollutants, especially in sudden pollution incidents (such as chemical leaks), changes in climate conditions may accelerate the spread of pollutants. Wind speed and direction sensors: Real-time monitoring of wind speed and direction can help determine the direction and speed of pollutant diffusion, which is especially important in the prediction of atmospheric pollution diffusion. Precipitation sensors: Extreme precipitation events such as heavy rain or heavy snow can have a significant impact on the sedimentation, diffusion or removal process of pollutants. By real-time monitoring of precipitation, the dissipation process of atmospheric pollution can be predicted.

[0053] Meteorological satellites and remote sensing technologies include: Satellite remote sensing technology: Satellites can monitor large-scale climate change and surface temperature, as well as extreme weather events such as heavy rains and storms. Remote sensing images can provide high-resolution real-time data to monitor the spread of pollutants over a large area, especially in hard-to-reach areas (such as oceans or remote areas). Meteorological satellites: Such as NOAA and European meteorological satellites (such as MetOp) can provide global weather data to help identify extreme weather events and associate them with changes in pollutants.

[0054] Ultra-low temperature and high temperature sensors include: Ultra-low temperature sensors: used to monitor low temperature extreme climate events, such as extremely cold weather or a sharp drop in temperature caused by a cold front. High temperature sensors: used to monitor high temperature extreme climate events (such as heat waves or forest fires). These extreme high temperatures often aggravate air pollution, affect air quality, and even directly cause environmental disasters such as fires.

[0055] Sudden pollution events (such as chemical leaks, industrial accidents, pollution source ruptures, etc.) can quickly cause significant harm to the ecological environment and public health. Real-time monitoring of these events is essential for timely response and mitigation of their impact.

[0056] Fixed air quality monitoring stations: By setting up fixed air quality monitoring stations around cities and industrial areas, pollutant concentration data such as PM2.5, CO2, NO2, SO2, etc. are continuously collected. Real-time data can reflect changes in air quality and provide a basis for predicting the spread of pollutants.

[0057] Mobile air quality monitoring equipment: Mobile sensors deployed using equipment such as drones, automated vehicles or balloons can monitor pollution near pollution sources in real time, especially in areas where fixed monitoring is difficult (such as disaster sites or remote areas).

[0058] Water quality sensors and monitoring platforms: used to detect pollutant concentrations in rivers, lakes and groundwater, such as heavy metals, toxic chemicals (such as benzene, cyanide), etc. Water quality sensors are usually equipped with sensors (such as pH, conductivity, dissolved oxygen, temperature, etc.) to detect changes in water bodies.

[0059] Unmanned underwater detectors: These devices can be quickly deployed after a pollution incident occurs, transmit real-time pollutant concentration data and monitor the flow and diffusion trends of water bodies, helping to analyze the impact range of the pollution source.

[0060] Gas sensor: used to monitor the concentration of toxic gases (such as ammonia, chlorine, methane, etc.) in the air in real time. In the event of sudden pollution incidents such as chemical leaks, gas sensors can detect the leakage of dangerous gases in a timely manner.

[0061] Infrared thermal imaging and gas leak detection: Using infrared imaging technology can help detect invisible gas leaks (such as methane, etc.). This technology captures infrared radiation signals and identifies the location of the leak source.

[0062] By establishing a huge IoT sensor network, environmental data (weather, air quality, water quality, etc.) from different locations are collected in real time, and the data is aggregated, stored and analyzed through the cloud platform. Sensors include temperature and humidity, air pressure, pollutant concentration sensors, etc. In this way, a large area can be monitored in real time. The cloud computing platform is used to process and analyze the large amount of data collected from various sensors, identify the trend of pollutant diffusion and potential extreme weather changes, and feed the results back to the decision-making system.

[0063] GIS technology is used to combine real-time data from monitoring equipment (such as air quality, meteorological data, pollution source information, etc.) with geographic spatial data to generate pollution distribution maps, pollutant diffusion trend maps, etc. in real time, helping decision makers and management departments to quickly understand the impact range and propagation path of sudden pollution events. Based on GIS data, combined with meteorological and pollutant data, environmental simulation tools (such as air flow simulation, pollutant diffusion models, etc.) can be used to predict pollutant diffusion trends and formulate emergency response plans based on geographical characteristics.

[0064] In the feature extraction module, the collected real-time data of extreme meteorological conditions and pollutants are converted into a new feature set of the AI ​​large model, which includes the characteristics of the rapid temperature change rate in extreme meteorological conditions and the degradation rate characteristics of pollutants under different environmental conditions;

[0065] It is necessary to collect data related to extreme weather and pollutants in real time from a variety of sensors and monitoring equipment. These data include meteorological conditions such as temperature, humidity, wind speed, precipitation, air pressure, and pollution data such as air quality, water quality, and pollutant concentration.

[0066] Extreme weather conditions data: collected in real time through meteorological sensors, remote sensing satellites, weather stations and other equipment, including temperature, humidity, wind speed, air pressure, precipitation, temperature fluctuation rate, etc. Pollutant data: pollutant data in air and water quality collected through air quality monitoring stations, sewage monitoring equipment, pollution source sensing equipment, etc., such as PM2.5, CO, NOx, SO2, nitrogen and phosphorus compounds, heavy metals and other concentrations.

[0067] The collected raw data usually needs to be preprocessed so that it can be converted into a format that can be input into the AI ​​model. Remove outliers and missing data to ensure the integrity and accuracy of the data. Standardize or normalize data of different dimensions (such as temperature, wind speed, etc.) so that different features are in the same range to avoid excessive impact of certain data on model training. Since meteorological data and pollutant data are usually time series data, it may be necessary to organize the data by time window and process time delays, timing characteristics, etc.

[0068] By extracting key features from raw data, AI models can more effectively learn and predict the behavior of extreme meteorological conditions and pollutants. The temperature variability refers to the rapid fluctuation of temperature in a short period of time, which is usually closely related to extreme meteorological events (such as cold fronts, heat waves, cold waves, etc.).

[0069] The temperature change rate is calculated by calculating the temperature difference in the time series. The expression is: Among them, T i and T i+1 are the temperatures at time points i and i+1 respectively, and Δs is the time interval.

[0070] A change threshold is set. For example, when the temperature change rate is greater than a certain predetermined value, it is considered that a sharp change has occurred. The threshold can be adjusted according to actual conditions.

[0071] Extract statistical features of the rapid rate of temperature change, such as the maximum, minimum, mean, and standard deviation of the rate of change. These features can reflect the dramatic fluctuations in temperature in a short period of time and help AI models determine the impact of extreme weather on pollution diffusion.

[0072] The pollutant degradation rate describes the speed at which pollutants naturally degrade or dissipate in the environment, which is affected by meteorological conditions (such as temperature, humidity, wind speed, etc.) and pollutant types (such as organic matter, inorganic matter, etc.). The steps to extract the pollutant degradation rate characteristics are as follows:

[0073] Use empirical formulas or reaction rate models to describe the degradation process of pollutants under different environmental conditions. For example, for pollutants in the air, the commonly used degradation model is: C(t) = C 0 e -kt ; Where C(t) is the pollutant concentration at time t, C 0 is the initial concentration, and k is the degradation rate constant. Based on the literature or field experimental data, determine the effect of meteorological conditions such as temperature and humidity on the degradation rate. For example, higher temperatures generally accelerate chemical reactions and pollutant degradation.

[0074] Based on the real-time pollutant concentration data and meteorological data, the above model is used to calculate the degradation rate of pollutants. The calculated degradation rate is input into the AI ​​big model as a feature. Statistical features are extracted from the degradation rate data, such as the maximum degradation rate, the minimum degradation rate, the average degradation rate, the standard deviation of the degradation rate change, etc.

[0075] After analyzing the characteristics of the temperature rapid change rate in the extracted extreme meteorological conditions, the temperature rapid change rate anomaly index is generated. The method for obtaining the temperature rapid change rate anomaly index is as follows:

[0076] Get historical temperature data and real-time temperature data for which you need to calculate the rate of rapid change. Set a time window, usually selected based on the time scale of temperature change (such as hours, days, weeks, etc.). Select a sliding window size W (usually a few hours or days), and the data in the window is used to calculate the mean and standard deviation of the temperature change rate. Sliding window size W: that is, the length of the time period for each sliding calculation (such as temperature data for the past W hours).

[0077] In each sliding window, the temperature rapid change rate is calculated, which is expressed as the change in temperature. The temperature rapid change rate can be defined as the difference between the temperature at the current time point and the temperature at the previous time point: ΔT(s) = |T(s)-T(s-1)|; where ΔT(s) is the temperature rapid change rate at time s, and T(s) is the temperature value at time s. For each sliding window W, the mean value μ of the temperature rapid change rate is calculated. W and standard deviation σ W , respectively representing the center and fluctuation degree of the normal variation range.

[0078] Compare the temperature rapid change rate at the current time s with the mean and standard deviation of the current window, and calculate the standardized anomaly index TSC(s) in each sliding window. The expression is: The temperature rapid change rate anomaly index is obtained by weighted averaging the anomaly index TSC(s) in all sliding windows.

[0079] The larger the temperature rapid change rate anomaly index is, the greater the current temperature change is, which means that the amplitude of the current temperature change is far beyond the normal fluctuation range in historical data, which usually indicates that sudden or extreme climate change has occurred in the environment. If the AI ​​large model can effectively identify and accurately predict these abnormal fluctuations, it means that the model has a strong generalization ability, can adapt to extreme conditions that are different from the training data, and promptly respond to the impact of climate change on environmental quality. The high anomaly index reflects that the environmental AI model can quickly identify and make corresponding adjustments when faced with new and unprecedented extreme climate events, showing good adaptability and early warning capabilities.

[0080] On the contrary, if the temperature rapid change rate anomaly index is small, it means that the current temperature change has not significantly deviated from the historical normal range, which may indicate that the model has failed to identify potential extreme climate events or pollution diffusion phenomena. This may be because the generalization ability of large AI models is weak and it is difficult to cope with new environmental changes outside the training data. For example, in extreme weather or sudden pollution events, if the model cannot accurately predict the environmental risks brought about by rapid temperature changes, it may lead to deviations in prediction results and inability to respond to emergencies in a timely manner, thereby affecting its decision-making ability and early warning effect under extreme conditions.

[0081] After analyzing the degradation rate characteristics of the extracted pollutants under different environmental conditions, the pollutant concentration degradation rate fluctuation index is generated. The method for obtaining the pollutant concentration degradation rate fluctuation index is as follows:

[0082] Collect pollutant concentration data series {C(t)}, where C(t) is the pollutant concentration at time point t. Calculate the corresponding pollutant degradation rate based on environmental conditions (such as temperature, humidity, etc.) or historical data It can be obtained by difference method or numerical integration method: Here, Δt is the time step.

[0083] Select wavelet basis function, commonly used ones are Haar wavelet, Daubechies wavelet, Morlet wavelet, etc. Wavelet function has localized characteristics and is suitable for processing sudden or non-stationary signals.

[0084] The Daubechies 4 (db4) wavelet basis function is selected for transformation. Perform discrete wavelet transform to obtain multi-scale decomposition. The basic steps of discrete wavelet transform are to decompose the signal through high-pass filter and low-pass filter, and then analyze each frequency band (scale). The discrete wavelet transform formula is: Among them, ψ j (t) is the wavelet basis function of scale j, W j is the decomposition coefficient. The degradation rate signal can be decomposed into low-frequency (approximation) and high-frequency (details) parts through wavelet transform.

[0085] Through wavelet transform, the degradation rate signal Decompose into coefficients of multiple scales. Each scale contains the information of the signal at that scale. Set the wavelet transform to decompose the signal into J scales W j (t), then the decomposition coefficient {W j (t)}, these coefficients reflect the changes in pollutant concentrations at different time scales. The pollutant concentration degradation rate fluctuation index is calculated as follows: Where N is the number of sample points, that is, the length of the data sequence, J is the scale number of wavelet transform, and DRFI is the pollutant concentration degradation rate fluctuation index.

[0086] The larger the pollutant concentration degradation rate fluctuation index, the more significant the fluctuation of the pollutant degradation rate under different environmental conditions, which may reflect the complex degradation dynamics and strong interference of extreme environmental conditions. In this case, if the environmental AI large model can accurately capture the drastic changes in the degradation rate and successfully predict the behavior of pollutants under extreme conditions, it means that the model has strong generalization ability and can cope with complex environmental changes. A larger fluctuation index challenges the adaptability of the model, but also highlights its robustness and accuracy under dynamic conditions.

[0087] On the contrary, the pollutant concentration degradation rate fluctuation index is small, indicating that the pollutant degradation rate changes steadily under different environmental conditions and the environmental interference effect is low. In this case, if the model still cannot accurately predict the degradation rate or behavior pattern, it means that its generalization ability is weak and may be limited to simple training data, making it difficult to show sufficient adaptability in practical applications. The situation with a small index is the minimum requirement for the generalization ability of the model. If the model performs poorly in this environment, its applicability needs to be further improved.

[0088] Convolutional neural network training module: The convolutional neural network model is used to train the characteristics of the rapid change rate of temperature, the degradation rate characteristics and the historical pollution data. During the training process, the training data is divided into multiple subsets, including historical pollution data sets and new feature set data sets.

[0089] The pollutant concentration data C(t) is extracted from the historical monitoring data as the basic input feature. The temperature rapid change rate anomaly index and the pollutant concentration degradation rate fluctuation index are converted into comprehensive feature vectors and combined with the historical pollution data to form an input data set: X = [C(t), EMC, DRFI], where EMC is the temperature rapid change rate anomaly index and DRFI is the pollutant concentration degradation rate fluctuation index.

[0090] During the training process, in order to better train the model, the data set can be divided into multiple subsets, namely:

[0091] Historical pollution dataset Xhistorical: contains only historical pollution data C(t); New feature set dataset Xnew: contains the temperature rapid change rate feature and degradation rate feature;

[0092] To ensure that the model can better handle different input features, you can partition the dataset by time windows or feature distributions:

[0093] Training dataset: A combination of the historical pollution dataset Xhistorical and the new feature set dataset Xnew.

[0094] Validation data set: used to verify the generalization ability of the model and ensure that there is no overfitting.

[0095] Test dataset: used for final evaluation of model performance.

[0096] CNN is usually used to process input with spatial structure or sequence data. When processing time series data, CNN extracts local features by sliding windows. The convolutional neural network architecture we designed consists of the following main parts:

[0097] The input layer receives the features X = [C(t), EMC, DRFI] obtained from the data preprocessing stage, with a shape of (N, T, F), where N is the number of samples, T is the number of time steps, and F is the number of features in each time step (for example, 3 features: pollutant concentration, temperature rapid change rate, and degradation rate).

[0098] The convolution layer is used to extract local features from the input data. For time series data, a one-dimensional convolution operation (1D CNN) can be applied. The convolution operation formula is: y(t) = (X*M)(t) + b; where X is the input data, M is the convolution kernel (filter), b is the bias term, and y(t) is the output of the convolution layer. The convolution kernel extracts local features by sliding on the time series data, capturing important patterns related to the diffusion of pollutants, such as degradation rate and temperature changes.

[0099] The pooling layer is usually followed by the convolution layer to reduce the amount of calculation and improve the abstraction ability of features. Using the maximum pooling operation, the pooling process formula is: y(t) = max(X(t) k ); represents a window of the input signal, and max selects the maximum value within the window. Pooling operation helps capture the most important features.

[0100] The features extracted by the convolutional layer and the pooling layer will be flattened and passed to the fully connected layer for the final classification or regression task. The calculation process of the fully connected layer is: y = M·X+b; where y is the output.

[0101] Depending on the goal of the task (predicting pollutant concentration, degradation rate, etc.), the output layer will be adjusted accordingly. If it is a regression problem, the output layer will contain one neuron for outputting the predicted value; if it is a classification problem, it may contain multiple neurons for classification.

[0102] For regression tasks (such as predicting pollutant concentrations), the error between the predicted value and the true value is calculated as: Where: y pred (i) is the pollutant concentration predicted by the model, y true(i) is the true value. The optimization algorithm usually uses gradient descent (such as Adam optimizer) to minimize the loss function and update the model parameters. The gradient is calculated by the back propagation algorithm and the network parameters are updated. The training is repeated multiple times until the loss function converges.

[0103] A / B testing module: Conduct A / B testing on convolutional neural network models using historical pollution datasets and new feature set datasets, compare their performance differences in predicting sudden pollution events and extreme weather conditions, evaluate the generalization ability of the environmental AI large model under extreme conditions, and divide the generalization ability of the environmental AI large model under extreme conditions into high generalization ability level and low generalization ability level based on the evaluation results.

[0104] Extract pollutant concentration data C(t) from historical monitoring data;

[0105] The temperature rapid change rate anomaly index and pollutant concentration degradation rate fluctuation index are converted into comprehensive feature vectors and combined with historical pollution data to form an input data set;

[0106] The test data set includes extreme weather data and sudden pollution event data over a period of time in the past. The data should include changes in pollutant concentrations under extreme climate conditions (such as rapid temperature changes, sudden pollution sources, etc.).

[0107] The data is divided into a training set (for training the model) and a test set (for evaluating the model's predictive performance).

[0108] Group A training: Convolutional neural network (CNN) model trained using historical pollution datasets. The input feature is pollutant concentration C(t), and the change of pollutant concentration is predicted.

[0109] Group B training: Convolutional neural network (CNN) model trained using historical pollution datasets and new feature set datasets. The input features are the temperature rapid change rate anomaly index and the pollutant concentration degradation rate fluctuation index converted into a comprehensive feature vector and historical pollution data to predict changes in pollutant concentrations.

[0110] According to the A / B test results, the generalization ability of the environmental AI large model under extreme conditions is evaluated. If the mean square error of the group B model is smaller than the mean square error of the group A model, it means that the environmental AI large model exhibits strong prediction accuracy and good generalization ability, and can effectively respond to extreme weather and sudden pollution events. At this time, no abnormal signal is generated, and the generalization ability of the environmental AI large model under extreme conditions is classified as a high generalization ability level; if the mean square error of the group B model is greater than or equal to the mean square error of the group A model, it means that the performance of the environmental AI large model under extreme weather and sudden pollution events is not ideal, and it cannot accurately identify or predict these situations, and its generalization ability is weak. At this time, an abnormal signal is generated, and the generalization ability of the environmental AI large model under extreme conditions is classified as a low generalization ability level.

[0111] Model retraining extension module: For large environmental AI models with high generalization capability, real-time collected environmental data is fed back into the model for regular retraining;

[0112] Through sensors, satellite remote sensing technology and monitoring equipment, real-time environmental data is collected, such as air quality index (AQI), water quality parameters, temperature changes, wind speed, humidity, etc., as well as data on sudden pollution events (such as chemical leaks, traffic accidents, etc.). The real-time collected environmental data is synchronized to the database to ensure the timeliness, completeness and accuracy of the data.

[0113] Process missing values, noise, and abnormal data in real-time data to ensure data quality. Interpolation, mean filling, and other methods can be used to fill missing values. Extract features from real-time data, such as rapid changes in temperature, pollutant degradation rate, wind speed, etc., and compare and analyze with historical data to form a new feature set.

[0114] Merge the newly collected environmental data with the original training data (historical data sets, extreme climate data, etc.) to build a new training set containing real-time data. Feed the new features and data back into the AI ​​big model to ensure that the model includes the latest environmental change information during the training process.

[0115] Retrain with updated datasets containing new features and real-time data at regular intervals (such as monthly, quarterly, etc.). This helps the model gradually adapt to environmental changes over time and improves prediction accuracy. If the real-time data changes significantly, incremental learning technology can be used to directly update the existing model without having to train from scratch.

[0116] Retrain a convolutional neural network (CNN) or other deep learning model using a new dataset (including historical contaminated data, new features, and real-time data). The training process may include adjusting model hyperparameters, updating convolutional layer weights, etc. Training expressions: For example: Among them, L(θ) is the loss function, is the model prediction value, y i is the actual value, N is the data sample size, and θ is the model parameter. During retraining, hyperparameters such as learning rate, batch size, and regularization parameter are optimized to ensure the convergence and prediction accuracy of the model.

[0117] Use an independent validation set to evaluate the trained model and calculate the error (such as mean square error MSE, accuracy, R 2 Scoring, etc.). If the model performs poorly, further tuning can be performed. After the model is retrained, it should be tested on real-time data to verify whether it can accurately respond to emerging environmental changes and sudden pollution incidents. Based on the performance of the model, the cycle of data feedback can be flexibly adjusted. For example, when sudden pollution incidents or extreme weather occur, the frequency of real-time data feedback can be increased to capture environmental changes in a timely manner. Use automated pipelines or workflow systems (such as MLOps platforms) to automate the training, updating, and deployment of models. Whenever real-time data is updated, the model can automatically receive data and optimize.

[0118] Early warning and emergency response module: For environmental AI big models with low generalization ability levels, the prediction accuracy of the environmental AI big models under extreme conditions within a fixed time period is analyzed and warned, and corresponding emergency response measures are taken according to the different levels of early warning signals.

[0119] For environmental AI large models with low generalization ability levels, that is, the mean square error of the group B models generated within a fixed time period is greater than or equal to the mean square error of the group A models, the mean square errors of the group B models generated within a subsequent fixed time period that are greater than or equal to the mean square error of the group A models are collected, and the corresponding data set is established. The mean and standard deviation of the data set are calculated, and after analysis, the prediction accuracy of the environmental AI large model under extreme conditions is determined.

[0120] If the mean square error of the group B model in the data set is greater than or equal to the reference threshold of the mean square error of the group B model, and the standard deviation of the mean square error of the group B model is less than the reference threshold of the standard deviation of the mean square error of the group B model, it means that the model is not accurate enough in overall performance, but the prediction error is stable. At this time, a first-level warning signal is generated to increase feature data under extreme conditions (such as rapid changes in temperature and diffusion rate of pollutants). The group B model is optimized by introducing a new training data set. The weight of extreme condition features is increased to reduce dependence on historical data.

[0121] If the mean square error of the model in group B is greater than or equal to the reference threshold of the mean square error of the model in group B, and the standard deviation of the mean square error of the model in group B is greater than or equal to the reference threshold of the standard deviation of the mean square error of the model in group B, it indicates that the prediction of the model under extreme conditions is neither accurate nor stable. At this time, a secondary warning signal is generated, and incremental learning or online learning technology is used to introduce real-time data to update the model. When the low generalization ability model cannot provide reliable predictions, the backup rule model or physical model is started for prediction. Adjust the structure of the model in group B (such as adding convolutional layers and introducing hybrid models).

[0122] If the mean square error of the model in group B is less than the reference threshold of the mean square error of the model in group B, and the standard deviation of the mean square error of the model in group B is greater than or equal to the reference threshold of the standard deviation of the mean square error of the model in group B, it means that the overall accuracy of the model is good, but unstable predictions may occur under individual extreme conditions. At this time, a third-level warning signal is generated to analyze the source of errors under specific environmental conditions and determine the root cause of error fluctuations. Re-evaluate feature selection under extreme conditions and eliminate interfering features. Generate more data under extreme conditions through simulation technology for model optimization.

[0123] If the mean square error of the model in group B is less than the reference threshold of the mean square error of the model in group B, and the standard deviation of the mean square error of the model in group B is less than the reference threshold of the standard deviation of the mean square error of the model in group B, it means that the model performs well under extreme conditions and does not need emergency adjustment. At this time, no warning signal is generated, the model status is maintained, and data is updated regularly to ensure its stability. Pay attention to new data features to continuously improve model performance.

[0124] It should be noted here that the importance of the first-level warning signal is greater than that of the second-level warning signal, and the importance of the second-level warning signal is greater than that of the third-level warning signal. Relevant personnel can take corresponding adjustment measures according to different warning signal levels.

[0125] In this embodiment, multiple modules work together to improve the predictive ability of the environmental AI large model under extreme weather and sudden pollution events. First, the data acquisition module uses modern monitoring technology to collect extreme weather and pollutant data in real time. Next, the feature extraction module converts these real-time data into a new feature set, including the characteristics of the rapid change rate of temperature and the degradation rate of pollutants. Then, the convolutional neural network training module trains these features with historical pollution data, and evaluates the generalization ability of the model under extreme conditions through the A / B testing module, dividing it into high generalization ability and low generalization ability levels. For high generalization ability models, real-time data is fed back and retrained regularly; for low generalization ability models, the early warning and emergency response module generates early warning signals based on the prediction accuracy analysis results, and takes corresponding emergency treatment measures to ensure that the model can respond promptly and accurately when facing extreme environments.

[0126] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0127] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0128] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. An ecological environment inspection and detection information management system based on an environmental AI big model, characterized by: It includes data acquisition module, feature extraction module, convolutional neural network training module, A / B testing module, model retraining and data expansion module, and early warning and emergency response module; Data collection module: using modern monitoring technology to collect real-time data related to extreme weather and sudden pollution events; Feature extraction module: converts the collected real-time data of extreme meteorological conditions and pollutants into a new feature set of the AI ​​large model, which includes the characteristics of the rapid temperature change rate in extreme meteorological conditions and the characteristics of the degradation rate of pollutants under different environmental conditions; Convolutional neural network training module: Use the convolutional neural network model to train the temperature rapid change rate characteristics, degradation rate characteristics and historical pollution data. During the training process, the training data is divided into multiple subsets, including historical pollution data sets and new feature set data sets; A / B testing module: Conduct A / B testing on the convolutional neural network model using the historical pollution data set and the new feature set data set, compare the performance differences between the two in predicting sudden pollution events and extreme weather conditions, evaluate the generalization ability of the environmental AI large model under extreme conditions, and divide the generalization ability of the environmental AI large model under extreme conditions into high generalization ability level and low generalization ability level according to the evaluation results; Model retraining extension module: For large environmental AI models with high generalization capability, real-time collected environmental data is fed back into the model for regular retraining; Early warning and emergency response module: For environmental AI big models with low generalization ability levels, the prediction accuracy of the environmental AI big models under extreme conditions within a fixed time period is analyzed and warned, and corresponding emergency response measures are taken according to the different levels of early warning signals.

2. According to claim 1, an ecological environment inspection and detection information management system based on an environmental AI big model is characterized by: In the feature extraction module, the temperature rapid change rate anomaly index is generated after analyzing the extracted temperature rapid change rate characteristics in extreme meteorological conditions. The method for obtaining the temperature rapid change rate anomaly index is as follows: Obtain historical temperature data and real-time temperature data for which the rapid change rate needs to be calculated. Select a sliding window size W. In each sliding window, calculate the rapid change rate of temperature, expressed as the change in temperature. The rapid change rate of temperature is defined as the difference between the temperature at the current time point and the temperature at the previous time point: ΔT(s) = |T(s)-T(s-1)|; where ΔT(s) is the rapid change rate of temperature at time s, and T(s) is the temperature value at time s. For each sliding window W, calculate the mean value μ of the rapid change rate of temperature W and standard deviation σ W , respectively represent the center and fluctuation degree of the normal variation range; compare the temperature rapid change rate at the current time s with the mean and standard deviation of the current window, and calculate the standardized abnormal index TSC(s) in each sliding window, the expression is: The temperature rapid change rate anomaly index is obtained by weighted averaging the anomaly index TSC(s) in all sliding windows.

3. According to claim 2, an ecological environment inspection and detection information management system based on an environmental AI big model is characterized by: In the feature extraction module, the degradation rate characteristics of the extracted pollutants under different environmental conditions are analyzed to generate the pollutant concentration degradation rate fluctuation index. The method for obtaining the pollutant concentration degradation rate fluctuation index is as follows: Collect the pollutant concentration data series {C(t)}, where C(t) is the pollutant concentration at time point t, and calculate the corresponding pollutant degradation rate Where Δt is the time step; for the pollutant concentration degradation rate series Perform discrete wavelet transform to obtain multi-scale decomposition. The discrete wavelet transform formula is: Among them, ψ j (t) is the wavelet basis function of scale j, W j is the decomposition coefficient; through wavelet transform, the degradation rate signal Decompose into coefficients of multiple scales, set the wavelet transform to decompose the signal into J scales W j (t), then the decomposition coefficient {W j (t)}, calculate the pollutant concentration degradation rate fluctuation index, the expression is: Where N is the number of sample points, that is, the length of the data sequence, J is the scale number of wavelet transform, and DRFI is the pollutant concentration degradation rate fluctuation index.

4. According to claim 3, an ecological environment inspection and detection information management system based on an environmental AI big model is characterized by: The convolutional neural network model is used to train the characteristics of rapid temperature change rate, degradation rate characteristics and historical pollution data, specifically: Extract pollutant concentration data C(t) from historical monitoring data as basic input features, convert the temperature rapid change rate anomaly index and pollutant concentration degradation rate fluctuation index into comprehensive feature vectors and combine them with historical pollution data to form an input data set, input data set X: X = [C(t), EMC, DRFI], where EMC is the temperature rapid change rate anomaly index, and DRFI is the pollutant concentration degradation rate fluctuation index; During the training process, the dataset is divided into multiple subsets, namely, the historical pollution dataset Xhistorical, which only contains historical pollution data C(t); the new feature set dataset Xnew contains the temperature rapid change rate feature and the degradation rate feature; The input layer receives the features X = [C(t), EMC, DRFI] obtained from the data preprocessing stage, with a shape of (N, T, F), where N is the number of samples, T is the number of time steps, and F is the number of features in each time step; the convolution layer is used to extract local features from the input data. For time series data, a one-dimensional convolution operation is applied, and the convolution operation formula is: y(t) = (X*M)(t)+b; where X is the input data, M is the convolution kernel, b is the bias term, and y(t) is the output of the convolution layer; The pooling layer follows the convolutional layer to reduce the amount of calculation and improve the abstraction ability of features. The maximum pooling operation is used. The pooling process formula is: y(t) = max(X(t)k); represents a window of the input signal, and max selects the maximum value in the window; The features extracted by the convolutional layer and the pooling layer will be flattened and passed to the fully connected layer for the final classification or regression task. The calculation process of the fully connected layer is: y = M·X+b; where y is the output; According to the goal of the task, the output layer will be adjusted accordingly. For the predicted pollutant concentration, the error MSE between the predicted value and the true value is calculated, and the expression is: Where: ypred(i) is the pollutant concentration predicted by the model, y true( i ) is the true value.

5. According to claim 4, an ecological environment inspection and detection information management system based on an environmental AI big model is characterized by: Evaluate the generalization ability of the environmental AI large model under extreme conditions, and divide the generalization ability of the environmental AI large model under extreme conditions into high generalization ability level and low generalization ability level according to the evaluation results, specifically: Extract pollutant concentration data C(t) from historical monitoring data; The temperature rapid change rate anomaly index and pollutant concentration degradation rate fluctuation index are converted into comprehensive feature vectors and combined with historical pollution data to form an input data set; Divide the data into training and testing sets; Group A training: A convolutional neural network model trained using a historical pollution dataset, with the input feature being the pollutant concentration C(t) to predict changes in pollutant concentration; Group B training: The convolutional neural network model was trained using the historical pollution data set and the new feature set data set. The input features were the temperature rapid change rate anomaly index and the pollutant concentration degradation rate fluctuation index, which were converted into comprehensive feature vectors and historical pollution data to predict changes in pollutant concentrations. According to the A / B test results, the generalization ability of the environmental AI large model under extreme conditions is evaluated. If the mean square error of the group B model is smaller than the mean square error of the group A model, it means that the environmental AI large model exhibits strong prediction accuracy and good generalization ability, and can effectively respond to extreme weather and sudden pollution events. At this time, no abnormal signal is generated, and the generalization ability of the environmental AI large model under extreme conditions is classified as a high generalization ability level; if the mean square error of the group B model is greater than or equal to the mean square error of the group A model, it means that the environmental AI large model performs poorly under extreme weather and sudden pollution events, cannot accurately identify, and has weak generalization ability. At this time, an abnormal signal is generated, and the generalization ability of the environmental AI large model under extreme conditions is classified as a low generalization ability level.

6. According to claim 1, an ecological environment inspection and detection information management system based on an environmental AI big model is characterized by: In the early warning and emergency response module, for environmental AI big models with low generalization ability levels, the prediction accuracy of the environmental AI big models under extreme conditions within a fixed time period is analyzed and warned, and corresponding emergency treatment measures are taken according to the different levels of early warning signals, specifically: For environmental AI large models with low generalization ability levels, that is, the mean square error of the group B models generated within a fixed time period is greater than or equal to the mean square error of the group A models, the mean square errors of the group B models generated within a subsequent fixed time period that are greater than or equal to the mean square error of the group A models are collected, and the corresponding data set is established. The mean and standard deviation of the data set are calculated, and after analysis, the prediction accuracy of the environmental AI large model under extreme conditions is determined.

7. According to claim 6, an ecological environment inspection and detection information management system based on an environmental AI big model is characterized by: If the mean square error of the group B model in the data set is greater than or equal to the reference threshold of the mean square error of the group B model, and the standard deviation of the mean square error of the group B model is less than the reference threshold of the standard deviation of the mean square error of the group B model, it means that the model is not accurate enough in overall performance, but the prediction error is stable. At this time, a first-level warning signal is generated, feature data under extreme conditions are added, and the group B model is optimized by introducing a new training data set; If the mean square error of the model in group B is greater than or equal to the reference threshold of the mean square error of the model in group B, and the standard deviation of the mean square error of the model in group B is greater than or equal to the reference threshold of the standard deviation of the mean square error of the model in group B, it indicates that the prediction of the model under extreme conditions is neither accurate nor stable. At this time, a secondary warning signal is generated, and incremental learning technology is used to introduce real-time data to update the model; If the mean square error of the model in group B is less than the reference threshold of the mean square error of the model in group B, and the standard deviation of the mean square error of the model in group B is greater than or equal to the reference threshold of the standard deviation of the mean square error of the model in group B, a third-level warning signal is generated to analyze the source of the error under specific environmental conditions and determine the root cause of the error fluctuation; If the mean square error of the model in group B is less than the reference threshold of the mean square error of the model in group B, and the standard deviation of the mean square error of the model in group B is less than the reference threshold of the standard deviation of the mean square error of the model in group B, no emergency adjustment is required. At this time, no warning signal is generated, the model status quo is maintained, and the data is updated regularly to ensure its stability.

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