A dynamic monitoring method and system for river micropollutants based on artificial intelligence

Abstract

The present invention discloses a dynamic monitoring method and system for river micropollutants based on artificial intelligence, which relates to the field of environmental monitoring technology, and includes: establishing a micropollutant concentration prediction model according to the characteristic data and the particle analysis module; adjusting the model parameters of the micropollutant concentration prediction model according to climate change and changes in the use of river basin land; obtaining the concentration results of micropollutants through the micropollutant concentration prediction model after adjusting the model parameters, and performing graded warning according to the concentration results and warning thresholds. The present invention can timely discover and respond to changes in micropollutant concentrations through continuous data collection and real-time analysis, provide immediate warning information, and can dynamically adjust the model parameters and warning thresholds according to climate change and land use changes, so that the system can adapt to dynamic changes in the environment.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and in particular to an artificial intelligence-based dynamic monitoring method and system for river micropollutants. Background Art

[0002] With the development of social economy and the acceleration of urbanization, the development and utilization of water resources have become increasingly intensified, especially the large-scale implementation of water diversion projects, which have had a significant impact on the hydrological characteristics of natural rivers. Water diversion projects have changed the natural flow pattern of rivers, significantly shortened the water exchange cycle of water bodies, and increased the complexity of water flow conditions, which is manifested in the alternation of various forms of water flow such as laminar flow, turbulent flow and uniform flow. This change not only has an impact on the river ecosystem, but also leads to a more complex distribution of micropollutants in the water body.

[0003] Micropollutants, i.e. organic or inorganic compounds present in nanogram or microgram concentrations in water environments, have attracted widespread attention due to their difficulty in degradation, easy bioaccumulation, and potential threats to ecosystems and human health. These pollutants come from a wide range of sources, including but not limited to medical institutions, family life, animal husbandry, agriculture, and industry. After entering the water environment through various pathways, they migrate, transform, and accumulate in the water body, forming a complex pollution network.

[0004] The Chinese invention patent with publication number CN118032440A discloses a method for sampling water conservancy projects, including determining the hydrogeological characteristics of the target sampling area through hydrological model analysis; setting up multiple sampling grids inside the target sampling area, and designing a sampling plan based on the hydrogeological characteristics; using high-resolution remote sensing images and underwater sonar imaging technology to obtain the topography and water quality distribution data of the target sampling area; determining the sampling points based on the sampling grids and hydrogeological characteristics, and optimizing the sampling points in combination with water flow dynamics and water body stratification characteristics. This invention realizes accurate monitoring of the water environment through hydrological model analysis and optimized design of sampling plans, combined with high-resolution remote sensing images and underwater sonar imaging technology, and can accurately identify the hydrogeological characteristics of the target sampling area, and accurately determine the sampling points based on factors such as water flow dynamics, topography and geomorphology, providing a reliable data basis for water environment assessment.

[0005] The above-mentioned and similar detection and sampling methods can often only capture the pollutant status at a certain moment. However, the complex hydrodynamic environment causes the distribution of pollutants in the water body to show a high degree of spatial heterogeneity and temporal volatility. At the same time, during the water diversion process, it will also cause the types and concentrations of pollutants in the water body to change continuously due to water and sand disturbance and frequent input of exogenous pollutants. Therefore, it is impossible to fully and continuously reflect the true situation of micropollutants in the water body. At the same time, it is also impossible to effectively distinguish the distribution of pollutants on different particles, and it is easy to miss important information. Summary of the invention

[0006] The purpose of the present invention is to provide a method and system for dynamic monitoring of river micropollutants based on artificial intelligence to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solution: a dynamic monitoring method of river micropollutants based on artificial intelligence, comprising:

[0008] Establishing a micro-pollutant concentration prediction model: obtaining characteristic data through water body physical / chemical parameters and flow rate data, and establishing a particle analysis module, and establishing a micro-pollutant concentration prediction model based on the characteristic data and the particle analysis module;

[0009] Update the micropollutant concentration prediction model: adjust the model parameters of the micropollutant concentration prediction model according to climate change and changes in land use in the watershed;

[0010] Micropollutant concentration warning: The micropollutant concentration prediction model after adjusting the model parameters is used to obtain the micropollutant concentration results, and graded warnings are performed based on the concentration results and warning thresholds;

[0011] The method of obtaining the characteristic data includes:

[0012] According to the water flow dynamics characteristics and water flow pollution conditions, a dynamic scheduling model and a regional prediction model are established, and at the same time, the time interval and location of data collection are obtained through the dynamic scheduling model, and the pollution hotspot area is obtained through the regional prediction model;

[0013] According to the pollution hotspot area, the data collection locations are sorted, and according to the sorted collection locations and the time interval of data collection, the data of the collection locations are acquired;

[0014] Characteristic data are obtained from the data at the collection location according to monitoring objectives and environmental conditions.

[0015] Furthermore, the time interval and location of data collection are obtained through the dynamic scheduling model, including:

[0016] Acquiring water body physical / chemical parameters, meteorological data and land use data, and preprocessing the water body physical / chemical parameters, meteorological data and land use data;

[0017] A dynamic scheduling model is established through the pre-processed water body physical / chemical parameters, meteorological data and land use data, and a combination set of time intervals and locations for data collection is obtained through the dynamic scheduling model. The dynamic scheduling model is specifically:

[0018] ;

[0019] in: is the observation vector at time t, is a constant vector, is the autoregressive coefficient matrix, is the observation vector at time ti, is a white noise vector, is the total number of lag orders, is the index with large lag order;

[0020] By using the particle swarm optimization algorithm, the final data collection location is determined from the combination of the time interval and location of the data collection. At the same time, the time interval corresponding to the location of the final data collection is the time interval of the final data collection. The location of the final data collection is specifically:

[0021] ;

[0022] in: is the velocity vector of the jth data at the k+1th iteration, is the position vector of the jth data at the k+1th iteration, is the velocity vector of the jth data at the kth iteration, , is the acceleration constant, , is a random number, is the best position of the jth data at the kth iteration, is the position vector of the jth data at the kth iteration, is the global optimal position of the kth iteration, is the total number of iterations, The index of the data.

[0023] Furthermore, the pollution hotspot areas are obtained through the regional prediction model, including:

[0024] Acquire water body physical / chemical parameters, meteorological data and land use data, and pre-process the water body physical / chemical parameters, meteorological data and land use data, and simultaneously acquire influencing features from the pre-processed water body physical / chemical parameters, meteorological data and land use data by a recursive feature elimination algorithm;

[0025] According to the influencing features, a basic model is established, and meta-feature data is obtained through the basic model. The basic model is specifically:

[0026] ;

[0027] in: , , is the meta-feature, is the number of decision trees, is the predicted value of the mth decision tree, , , is the bias term, is the kernel function, is the label of the training sample, is the Lagrange multiplier, To influence the characteristics, is the weight matrix of the first layer, is the activation function, is the weight matrix of the second layer, is the index of the training sample, is the number of training samples, is the index of the decision tree;

[0028] According to the meta-feature data, the regional prediction model is established to obtain the prediction value corresponding to each monitoring area, and a prediction threshold is set at the same time, and the prediction value greater than the prediction threshold is determined. The monitoring area corresponding to the prediction value greater than the prediction threshold is the pollution hotspot area. The regional prediction model is specifically:

[0029] ;

[0030] in: For pollution hotspots, is the activation function, is the weight vector, is the meta-feature data, is the bias term.

[0031] Furthermore, a particle analysis module is established to obtain the distribution results of particle matter, including:

[0032] Acquiring distribution data, time data, geographic data and meteorological data of particulate matter, and preprocessing the distribution data, time data, geographic data and meteorological data of the particulate matter;

[0033] According to the preprocessed distribution data, time data, geographic data and meteorological data of the particulate matter, the spatial and temporal distribution data of the particulate matter are obtained by using a convolutional neural network, and the distribution data of the particulate matter at different scales are obtained by using a multi-scale analysis method;

[0034] The particle analysis module is established by fusing the particle distribution data in space and time and the particle distribution data in different scales. The output result of the particle analysis module is the particle distribution result. The particle analysis module is specifically:

[0035] ;

[0036] in: is the particle distribution characteristic vector, is the fusion weight, is the spatial and temporal distribution data of particulate matter, It is the distribution data of particles at different scales.

[0037] Furthermore, a micropollutant concentration prediction model is established, including:

[0038] The characteristic data and the distribution results of the particles are fused to obtain a fused characteristic vector;

[0039] According to the fused feature vector, the micro-pollutant concentration prediction model is established, specifically:

[0040] ;

[0041] in: is the predicted result of micropollutant concentration, is the weight of the y-th weak learner, is the concentration prediction result of micropollutants corresponding to the yth weak learner, is the number of weak learners, is the index of the weak learner.

[0042] Furthermore, the model parameters of the micropollutant concentration prediction model are adjusted, including:

[0043] Through climate change, obtain the rate of change of temperature, precipitation, humidity and wind speed;

[0044] Through the change of land use in the watershed, the rate of change between agricultural land, industrial land and residential areas is obtained;

[0045] According to the magnitude of the change rate and the alteration rate, the model parameters of the micro-pollutant concentration prediction model are adjusted, specifically:

[0046] When the rate of change and / or the rate of modification is greater than zero, increasing the model parameter;

[0047] When the change rate and / or the modification rate is equal to zero, the model parameters remain unchanged;

[0048] When the rate of change and / or the rate of modification is less than zero, the model parameter is reduced.

[0049] Furthermore, the warning threshold is set, including:

[0050] Obtaining a change threshold of the warning threshold according to the change rate of temperature, precipitation, humidity and wind speed, and the change rate between agricultural land, industrial land and residential areas;

[0051] The final warning threshold is determined by the change threshold and the initial warning threshold.

[0052] A dynamic monitoring system for river micropollutants based on artificial intelligence, the dynamic monitoring system for river micropollutants comprising:

[0053] The feature module obtains feature data from the collected data of pollution hotspot areas;

[0054] Analysis module, to obtain the distribution results of particles;

[0055] A concentration module determines the micro-pollutant concentration result according to the characteristic data and the distribution result of the particulate matter;

[0056] An adjustment module is used to adjust the determined micropollutant concentration result;

[0057] Threshold module, which sets different levels of early warning thresholds based on climate change and changes in watershed land use;

[0058] The early warning module performs graded early warning according to the micropollutant concentration results and early warning thresholds of different levels.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] First, the present invention can timely detect and respond to changes in micropollutant concentrations through continuous data collection and real-time analysis, provide immediate warning information, and dynamically adjust model parameters and warning thresholds according to climate change and land use changes, so that the system can adapt to dynamic changes in the environment;

[0061] Second: The present invention improves the richness of model input information by integrating multiple data sources such as water body physical / chemical parameters, meteorological data and land use data, thereby improving the accuracy and reliability of prediction;

[0062] Third: The present invention can capture the characteristics of different scales through a multi-scale analysis method, providing more comprehensive information on the distribution of micropollutants. At the same time, it also combines factors such as meteorology and land use, making the model's prediction results more comprehensive and consistent with actual results. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a schematic diagram of the process of the method for dynamic monitoring of river micro-pollutants of the present invention;

[0064] Figure 2 It is a schematic diagram of the process of obtaining the data collection time interval and location of the present invention;

[0065] Figure 3 A schematic diagram of the process of obtaining the pollution hotspot area of ​​the present invention;

[0066] Figure 4 It is a schematic diagram of the process of obtaining the particle distribution result of the present invention;

[0067] Figure 5 The system block diagram of the river micro-pollutant dynamic monitoring system of the present invention. DETAILED DESCRIPTION

[0068] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0069] Due to the frequent input of water and sand disturbance and exogenous pollutants during the water diversion process, the types and concentrations of pollutants in the water body change with time and space and become extremely complex. However, the normal water monitoring method can only capture the pollutant status at a certain moment, so it is impossible to fully and continuously reflect the true situation of micropollutants in the water body. At the same time, it is difficult to effectively distinguish the distribution of pollutants on different particles, which further limits the accurate assessment and effective management of the water pollution status. The technical solution provided by this application obtains the time interval and location of data collection through a dynamic scheduling model, obtains the pollution hotspot area through a regional prediction model, and sorts the pollution hotspot area at the same time. After the data of the collection location is obtained according to the arrangement order, the corresponding characteristic data is obtained according to the monitoring target and environmental conditions. At the same time, a micropollutant concentration prediction model is established through the characteristic data and particle analysis module. And according to climate change and changes in the use of land in the basin, the model parameters of the micropollutant concentration prediction model are adjusted. Finally, the river water body can be dynamically monitored by the adjusted micropollutant concentration prediction model, thereby improving the monitoring accuracy and management level of water pollution status, which helps to improve the protection of the water environment.

[0070] Embodiment 1:

[0071] refer to Figure 1-Figure 4This embodiment provides a method for dynamic monitoring of river micropollutants based on artificial intelligence, and the method for dynamic monitoring of river micropollutants includes the following steps:

[0072] Step S1: Establish a micro-pollutant concentration prediction model. That is, through the water body physical / chemical parameters and flow rate data, obtain the corresponding characteristic data, and establish a particle analysis module. At the same time, according to the characteristic data and the particle analysis module, establish a micro-pollutant concentration prediction model. It includes the following steps:

[0073] Step S1.1: By establishing a dynamic scheduling model and a regional prediction model, characteristic data is obtained from water body physical / chemical parameters and flow rate data, and the distribution results of particles are obtained through the particle analysis module. At the same time, the characteristic data and the distribution results of particles are fused to obtain a fused characteristic vector.

[0074] In this embodiment, the process of acquiring feature data includes the following steps:

[0075] Step M1: According to the water flow dynamics characteristics and water flow pollution situation, establish the corresponding dynamic scheduling model and regional prediction model. At the same time, through the established dynamic scheduling model, obtain the time interval and location of data collection, and through the established regional prediction model, obtain the pollution hotspot area.

[0076] Specifically, the time interval and location of data collection are obtained through the established dynamic scheduling model, including the following steps:

[0077] Step MA1: Obtain water body physical / chemical parameters (such as pH value, dissolved oxygen, conductivity, temperature and turbidity), meteorological data (such as temperature, precipitation, humidity, wind speed and other data) and land use data (such as land use type (such as agricultural land, industrial land, residential area, etc.) change data), and pre-process water body physical / chemical parameters, meteorological data and land use data. It is worth noting that the pre-processing in this embodiment includes data cleaning, standardization and feature extraction.

[0078] Step MA2: A dynamic scheduling model is established through the water body physical / chemical parameters, meteorological data and land use data preprocessed in step MA1, so as to obtain a combination set of time intervals and locations for data collection through the output of the dynamic scheduling model.

[0079] In this embodiment, the dynamic scheduling model is specifically:

[0080] ;

[0081] in: is the observation vector at time t, is a constant vector, is the autoregressive coefficient matrix, is the observation vector at time ti, is a white noise vector, is the total number of lag orders, is the index with large lag order.

[0082] It is worth noting that the observation vector at time t is It includes water body physical / chemical parameters, meteorological data and land use data. In other words, the observation vector at time t obtained by the dynamic scheduling model output , which is the combination of the time interval and location of data collection.

[0083] Step MA3: Using the particle swarm optimization algorithm, the final data collection location is determined from the combination of the time intervals and locations of data collection obtained in step MA2. It is worth noting that the time interval corresponding to the location of the final data collection is the time interval of the final data collection.

[0084] In this embodiment, the formula for obtaining the location of final data collection is specifically:

[0085] ;

[0086] in: is the velocity vector of the jth data at the k+1th iteration, is the position vector of the jth data at the k+1th iteration, is the velocity vector of the jth data at the kth iteration, , is the acceleration constant, , is a random number, is the best position of the jth data at the kth iteration, is the position vector of the jth data at the kth iteration, is the global optimal position of the kth iteration, is the total number of iterations, The index of the data.

[0087] Specifically, in the process of obtaining the final data collection location through the particle swarm optimization algorithm, each data represents a combination of a data collection time interval and location. At the same time, in the iterative process, it can determine the data update speed according to the historical best position and the global best position of the data, and update the data location according to the data update speed.

[0088] During the implementation process, the following data can be obtained: water physical / chemical parameters (pH, dissolved oxygen, conductivity, temperature and turbidity), meteorological data (temperature, precipitation, humidity and wind speed), and land use data (agricultural land, industrial land and residential areas).

[0089] After preprocessing the above water body physical / chemical parameters, meteorological data and land use data, a dynamic scheduling model can be established based on the preprocessed water body physical / chemical parameters, meteorological data and land use data. Set to 3. Similarly, in the process of data optimization through the particle swarm optimization algorithm, the total number of iterations Set to 100. That is to say, after obtaining the combination of the time interval and location of data collection through the dynamic scheduling model, the final result can be obtained by optimizing the data through the particle swarm optimization algorithm. That is, the time interval: collect once every 2 hours. Collection location: monitoring station A, monitoring station B and monitoring station C.

[0090] In this embodiment, the pollution hotspot area is obtained by establishing a regional prediction model, including the following steps:

[0091] Step MB1: Obtain water body physical / chemical parameters (such as pH value, dissolved oxygen, conductivity, temperature and turbidity), meteorological data (such as temperature, precipitation, humidity, wind speed and other data) and land use data (such as land use type (such as agricultural land, industrial land, residential area, etc.) change data), and pre-process water body physical / chemical parameters, meteorological data and land use data. It is worth noting that the pre-processing in this embodiment includes data cleaning, standardization and feature extraction. At the same time, the recursive feature elimination algorithm is used to obtain the influencing features from the pre-processed water body physical / chemical parameters, meteorological data and land use data.

[0092] Step MB2: Establish multiple basic models based on the influencing features obtained in step MB1. At the same time, the prediction results of the multiple basic models are used as meta-features to form a meta-feature vector.

[0093] In this embodiment, the basic model is set to three, specifically:

[0094] ;

[0095] in: , , is the meta-feature, is the number of decision trees, is the predicted value of the mth decision tree, , , is the bias term, is the kernel function, is the label of the training sample, is the Lagrange multiplier, To influence the characteristics, is the weight matrix of the first layer, is the activation function, is the weight matrix of the second layer, is the index of the training sample, is the number of training samples, is the index of the decision tree.

[0096] Step MB3: According to the meta-feature vector obtained in step MB2, a regional prediction model is established, and the prediction value corresponding to each monitoring location is obtained. At the same time, a prediction threshold is set, and the prediction value corresponding to each monitoring location is compared with the prediction threshold, and a prediction value greater than the prediction threshold is determined, and the monitoring location corresponding to the prediction value is the pollution hotspot area. It is worth noting that the setting of the prediction threshold is not fixed, and it can be specifically set according to actual data requirements, so the size setting will not be specifically explained in this embodiment.

[0097] In this embodiment, the regional prediction model is specifically:

[0098] ;

[0099] in: For pollution hotspots, is the activation function, is the weight vector, is the meta-feature data, is the bias term.

[0100] During the implementation process, the data are obtained and preprocessed, specifically: water physical / chemical parameters (pH value, dissolved oxygen, conductivity, temperature and turbidity), meteorological data (temperature, precipitation, humidity and wind speed), land use data (agricultural land, industrial land and residential areas), and influencing characteristics (pH value, dissolved oxygen, temperature, precipitation and industrial land).

[0101] According to the above determined influencing features, three different basic models are established, and the prediction results of the three different basic models are obtained. At the same time, the prediction results are formed into a meta-feature vector, and a regional prediction model is established according to the formed meta-feature vector.

[0102] In this embodiment, the weight vector Set to [0.5, 0.3, 0.2], bias term Set to 0.1, activation function Set to Sigmoid function. Further, the meta-feature data corresponding to monitoring station A is [0.8, 0.7, 0.9], the meta-feature data corresponding to monitoring station B is [0.6, 0.5, 0.7], the meta-feature data corresponding to monitoring station C is [0.9,0.8,0.95].

[0103] That is to say, the predicted value corresponding to monitoring station A is 0.71, the predicted value corresponding to monitoring station B is 0.66, and the predicted value corresponding to monitoring station C is 0.73. It is worth noting that in this embodiment, the monitoring threshold is set to 0.7. Therefore, the area where monitoring stations A and C are located is a pollution hotspot area.

[0104] Step M2: Sort the data collection locations according to the pollution hotspot areas determined in step MB3, and acquire the data of the collection locations in sequence and intervals according to the sorted collection locations and the time intervals of data collection.

[0105] In the specific implementation process, it can be seen from the above data that the predicted value corresponding to monitoring station A is 0.71, the predicted value corresponding to monitoring station B is 0.66, and the predicted value corresponding to monitoring station C is 0.73. At the same time, the area where monitoring stations A and C are located is a pollution hotspot area. In other words, in the process of collecting data in the area, data collection is carried out in the following order, specifically: monitoring station C and monitoring station A.

[0106] At the same time, according to the time interval of data collection obtained in step MA3, it can be known that data is collected once every 2 hours. That is to say, in the process of regional data collection, the collection process is divided into two parts: the order of the collection areas and the interval of data collection. The order of the collection areas is: monitoring station C and monitoring station A. The interval of data collection is: once every 2 hours.

[0107] Step M3: Acquire characteristic data from the data collected in the pollution hotspot area according to the monitoring target and environmental conditions in the pollution hotspot area.

[0108] During the specific implementation process, monitoring stations C and A collected the following data respectively:

[0109] Monitoring Station C:

[0110] ;

[0111] Monitoring Station A:

[0112] ;

[0113] That is to say, based on the above data, the characteristic data that can be obtained include pH value, dissolved oxygen, temperature, precipitation and land use type. The characteristic data of monitoring station C are: pH value [7.2, 7.3, 7.1], dissolved oxygen [8.5, 8.6, 8.4], temperature: [25, 26, 25], precipitation [10, 12, 9] and land use type industrial land. Similarly, the characteristic data of monitoring station A are: pH value [7.0, 7.1, 7.2], dissolved oxygen [8.0, 8.1, 8.2], temperature [24, 25, 24], precipitation [8, 7, 9] and land use type agricultural land.

[0114] In this embodiment, a particle analysis module is established to obtain the distribution results of the particle matter, including the following steps:

[0115] Step N1: Obtain the distribution data of particulate matter (such as PM2.5, PM10 and other particulate matter concentration data), time data (data collection timestamp), geographic data (geographic location of data collection) and meteorological data (such as temperature, precipitation, humidity, wind speed and other meteorological data), and pre-process the distribution data of particulate matter, time data, geographic data and meteorological data. The pre-processing includes data cleaning, standardization and feature extraction.

[0116] Step N2: Based on the distribution data, time data, geographic data and meteorological data of the particulate matter preprocessed in step N1, the distribution data of the particulate matter in space and time is obtained through a convolutional neural network, and the distribution data of the particulate matter at different scales is obtained through a multi-scale analysis method. That is to say, the distribution data, time data, geographic data and meteorological data of the particulate matter are used as inputs of the convolutional neural network, and the features extracted from the above data are output for classification and regression. At the same time, features of different scales are extracted through convolution kernels of different sizes, and features of different scales are fused to form a multi-scale feature vector.

[0117] Step N3: Fuse the distribution data of the particles in space and time and the distribution data of the particles at different scales, and establish a particle analysis module based on the fused particle data. The output result of the particle analysis module is the distribution result of the particles. In this embodiment, the particle analysis module is specifically:

[0118] ;

[0119] in: is the particle distribution characteristic vector, is the fusion weight, is the spatial and temporal distribution data of particulate matter, It is the distribution data of particles at different scales.

[0120] In the process of specific implementation, the distribution data of particulate matter (PM2.5: [10,12,15,14,13,11,10,12], PM10: [20,22,25,24,23,21,20,22]), time data (timestamp [1,2,3,4,5,6,7,8]), meteorological data (temperature [25,26,27,26,25,24,23,24], precipitation [0,0,1,0,0,0,0,0], Humidity [50,52,55,54,53,52,51,50], Wind speed [2,3,4,3,2,1,2,3]), Geographic data (Longitude: [116.39,116.40,116.41,116.42,116.43,116.44,116.45,116.46], Latitude: [39.90,39.91,39.92,39.93,39.94,39.95,39.96,39.97]).

[0121] Furthermore, based on the above data, the spatial and temporal distribution data of particulate matter can be obtained. , distribution data of particles at different scales , which are: [0.8, 0.7, 0.9, 0.85, 0.75, 0.78, 0.82, 0.8], [0.6, 0.55, 0.65, 0.62, 0.58, 0.6, 0.63, 0.61]. At the same time, in this embodiment, the fusion weights Set it to 0.7, so that the distribution results of the particles corresponding to the above data can be obtained, specifically: [0.74, 0.655, 0.825, 0.781, 0.7, 0.726, 0.763, 0.743].

[0122] Step S1.2: Based on the fused feature vector obtained in step S1.1, a micropollutant concentration prediction model is established, specifically:

[0123] ;

[0124] in: is the predicted result of micropollutant concentration, is the weight of the y-th weak learner, is the concentration prediction result of micropollutants corresponding to the yth weak learner, is the number of weak learners, is the index of the weak learner.

[0125] Step S2: Update the micropollutant concentration prediction model. That is, adjust the model parameters of the micropollutant concentration prediction model established in step S1.2 according to climate change and changes in the use of watershed land. The details are as follows:

[0126] Step S2.1: Obtain the change rates of temperature, precipitation, humidity and wind speed through climate change, that is, obtain the change rate of temperature over time, the change rate of precipitation over time, the change rate of humidity over time and the change rate of wind speed over time.

[0127] During the implementation, the climate change data can be obtained: temperature, precipitation, humidity and wind speed, which are [25, 26, 27, 26, 25, 24, 23, 24], [0, 0, 1, 0, 0, 0, 0], [50, 52, 55, 54, 53, 52, 51, 50], [2, 3, 4, 3, 2, 1, 2, 3] respectively.

[0128] Furthermore, based on the above climate change data, we can obtain the following change rates: temperature change rate, precipitation change rate, humidity change rate and wind speed change rate, which are [1,1,−1,−1,−1,−1,1], [0,1,−1,0,0,0,0], [2,3,−1,−1,−1,−1,−1], [1,1,−1,−1,−1,1,0] respectively.

[0129] Step S2.2: Obtain the change rate between agricultural land, industrial land and residential area through the change of land use in the watershed, that is, obtain the change rate of agricultural land area over time, the change rate of industrial land area over time and the change rate of residential area over time.

[0130] In the process of specific implementation, the data on land use changes in the basin can be obtained: industrial land area, agricultural land area, residential area, which are [50,55,60,65,70,75,80,85], [100,105,110,108,105,102,100,98], [30,32,35,38,40,42,45,48] respectively.

[0131] Furthermore, based on the above-mentioned watershed land use change data, we can obtain the change rates: agricultural land change rate, industrial land change rate, and residential area change rate, which are [5,5,−2,−3,−3,−2,−2], [5,5,5,5,5,5,5], [2,3,3,2,2,3,3], respectively.

[0132] Step S2.3: According to the change rate obtained in step S2.1 and the change rate obtained in step S2.2, the model parameters of the micro-pollutant concentration prediction model are adjusted, specifically:

[0133] When the rate of change and / or the rate of change is greater than zero, the model parameters are increased. When the rate of change and / or the rate of change is equal to zero, the model parameters remain unchanged. When the rate of change and / or the rate of change is less than zero, the model parameters are decreased.

[0134] It is worth noting that in the process of increasing or decreasing the model parameters, the preset size is increased or decreased according to the size of the data. Therefore, the specific increase or decrease value of the model parameters can be set according to the needs of the user, and there is no specific and clear setting.

[0135] In the specific implementation process, in this embodiment, the initial model parameters are set to 1.0, and the increase or decrease value is set to 0.1. At the same time, the initial model parameters are adjusted in sequence according to the above data, specifically:

[0136] According to the temperature change rate [1,1,−1,−1,−1,−1,1], the model parameter for the first adjustment is obtained: 1.0+0.1+0.1−0.1−0.1−0.1−0.1+0.1=1.0. In other words, the model parameter for the first adjustment is 1.0. Further, according to the precipitation change rate [0,1,−1,0,0,0,0], the model parameter for the second adjustment is obtained: 1.0+0.1−0.1=1.0. In other words, the model parameter for the second adjustment is 1.0. Further, according to the humidity change rate [2,3,−1,−1,−1,−1,−1], the model parameter for the third adjustment is obtained: 1.0+0.1+0.1−0.1−0.1−0.1−0.1−0.1=0.7. In other words, the model parameter for the third adjustment is 0.7. Further, according to the wind speed change rate [1,1,−1,−1,−1,1,0], the model parameters for the fourth adjustment are obtained: 0.7+0.1+0.1−0.1−0.1−0.1+0.1=0.7. In other words, the model parameters for the fourth adjustment are 0.7. Further, according to the agricultural land change rate [5,5,−2,−3,−3,−2,−2], the model parameters for the fifth adjustment are obtained: 0.7+0.1+0.1−0.1−0.1−0.1−0.1=0.5. In other words, the model parameters for the fifth adjustment are 0.5. Further, according to the industrial land change rate [5,5,5,5,5,5,5], the model parameters for the sixth adjustment are obtained: 0.5+0.1+0.1+0.1+0.1+0.1+0.1+0.1+0.1=1.3. That is to say, the model parameter adjusted for the sixth time is 1.3. Further, according to the residential area change rate [2,3,3,2,2,3,3], the model parameter adjusted for the seventh time is obtained: 1.3+0.1+0.1+0.1+0.1+0.1+0.1+0.1=2.1. That is to say, the model parameter adjusted for the seventh time is 2.1.

[0137] That is to say, after dynamically adjusting the model parameters according to the rate of change and the positive or negative sign of the change rate, the model parameters in this embodiment are changed from 1.0 to 2.1.

[0138] Step S3: Micropollutant concentration warning. That is, the concentration results of micropollutants are obtained through the micropollutant concentration prediction model after adjusting the model parameters in step S2.3. At the same time, according to the actual use requirements, the corresponding warning threshold is set, and the obtained micropollutant concentration results are compared with the warning threshold, so as to carry out graded warning.

[0139] In this embodiment, the process of setting the warning threshold is as follows:

[0140] Step W1: Obtain the change threshold of the warning threshold according to the change rate of temperature, precipitation, humidity and wind speed, and the change rate between agricultural land, industrial land and residential areas obtained in step S2.1.

[0141] During the implementation process, the data can be obtained: temperature change threshold 0.5, precipitation change threshold 0.5, humidity change threshold 0.5, wind speed change threshold 0.5, agricultural land change threshold 2, industrial land change threshold 2, residential area change threshold 2.

[0142] That is, the above-mentioned change threshold and modification threshold are both compared with zero, specifically: when the change threshold and / or modification threshold is greater than zero, the change threshold is increased. When the change threshold and / or modification threshold is equal to zero, the change threshold remains unchanged. When the change threshold and / or modification threshold is less than zero, the change threshold is decreased.

[0143] It is worth noting that in the process of increasing or decreasing the change threshold, the preset size is increased or decreased according to the size of the data. Therefore, the specific increase or decrease value of the change threshold can be specifically set according to the needs of the user, and there is no specific and clear setting.

[0144] Specifically, in this embodiment, the preset increase or decrease value is 5, so according to the above data, it can be known that the change threshold in this embodiment is 35.

[0145] Step W2: Determine the final warning threshold value through the change threshold value obtained in step W1 and the set initial warning threshold value.

[0146] In the specific implementation process, the initial warning threshold of PM2.5 is set to 50, and the initial warning threshold of PM10 is set to 100. According to the change threshold and the alteration threshold, the final warning threshold of PM2.5 is 85, and the final warning threshold of PM10 is 135.

[0147] Furthermore, in this embodiment, a graded warning is performed according to the final warning threshold value set. Specifically:

[0148] Level 1 warning: The concentration of micropollutants exceeds 5% of the final warning threshold. Level 2 warning: The concentration of micropollutants exceeds 20% of the final warning threshold. Level 3 warning: The concentration of micropollutants exceeds 35% of the final warning threshold.

[0149] Furthermore, the predicted results for PM2.5 are: [12, 14, 17, 16, 15, 13, 12, 14], and the predicted results for PM10 are: [24, 26, 29, 28, 27, 25, 24, 26]. In other words, the predicted results for PM2.5 and PM10 do not exceed the first-level warning threshold, so no warning will be issued.

[0150] Embodiment 2:

[0151] refer to Figure 5 This embodiment provides a dynamic monitoring system for river micropollutants based on artificial intelligence, which includes a feature module, an analysis module, a concentration module, an adjustment module, a threshold module and an early warning module. The feature module, the analysis module, the concentration module, the adjustment module, the threshold module and the early warning module interact with each other. That is, through the collaborative work of the six modules, real-time monitoring and early warning of river micropollutants can be achieved, which helps to improve the efficiency of environmental pollution control.

[0152] In this embodiment, the feature module is used to obtain feature data from the collected data of the pollution hotspot area. That is, it can deploy water quality monitoring stations through various sensors (such as temperature, pH value, conductivity, etc.) and sampling equipment. At the same time, drones or satellite remote sensing technology are used to regularly take images of rivers and surrounding areas for analyzing the spread of pollutants. Furthermore, in the feature module, it can pre-process the collected data to identify characteristic variables related to micropollutants.

[0153] Furthermore, the analysis module is used to obtain the distribution results of particulate matter. The concentration module is used to determine the concentration of micropollutants based on the characteristic data obtained by the characteristic module and the distribution results of particulate matter obtained by the analysis module, that is, it can convert the characteristic data into corresponding concentration values. The adjustment module is used to adjust the micropollutant concentration results determined by the concentration module, that is, it can make specific adjustments based on actual climate changes and changes in the use of watershed land, so that the determined micropollutant concentration results can better meet actual needs.

[0154] Furthermore, the threshold module is used to set the warning threshold according to climate change and changes in the use of watershed land, and to classify the size of the warning threshold accordingly. The warning module is used to perform graded warnings based on the micropollutant concentration results adjusted by the adjustment module and the different levels of warning thresholds set by the threshold module.

[0155] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is limited by the attached embodiments and their equivalents.

Claims

1. A dynamic monitoring method for river micropollutants based on artificial intelligence, characterized in that: Included are: Establishing a micro-pollutant concentration prediction model: obtaining characteristic data through water body physical / chemical parameters and flow rate data, and establishing a particle analysis module, and establishing a micro-pollutant concentration prediction model based on the characteristic data and the particle analysis module; Update the micropollutant concentration prediction model: adjust the model parameters of the micropollutant concentration prediction model according to climate change and changes in land use in the watershed; Micropollutant concentration warning: The micropollutant concentration prediction model after adjusting the model parameters is used to obtain the micropollutant concentration results, and graded warnings are performed based on the concentration results and warning thresholds; The method of obtaining the characteristic data includes: According to the water flow dynamics characteristics and water flow pollution conditions, a dynamic scheduling model and a regional prediction model are established, and at the same time, the time interval and location of data collection are obtained through the dynamic scheduling model, and the pollution hotspot area is obtained through the regional prediction model; According to the pollution hotspot areas, the data collection locations are sorted, and according to the sorted collection locations and the time intervals for data collection, the data of the collection locations are acquired; Acquiring characteristic data from the data at the collection location according to monitoring objectives and environmental conditions; The dynamic scheduling model is used to obtain the time interval and location of data collection, including: Acquiring water body physical / chemical parameters, meteorological data and land use data, and preprocessing the water body physical / chemical parameters, meteorological data and land use data; A dynamic scheduling model is established through the pre-processed water body physical / chemical parameters, meteorological data and land use data, and a combination set of time intervals and locations for data collection is obtained through the dynamic scheduling model. The dynamic scheduling model is specifically: ； in: is the observation vector at time t, is a constant vector, is the autoregressive coefficient matrix, is the observation vector at time ti, is a white noise vector, is the total number of lag orders, is the index with large lag order; By using the particle swarm optimization algorithm, the final data collection location is determined from the combination of the time interval and location of the data collection. At the same time, the time interval corresponding to the location of the final data collection is the time interval of the final data collection. The location of the final data collection is specifically: ； in: is the velocity vector of the jth data at the k+1th iteration, is the position vector of the jth data at the k+1th iteration, is the velocity vector of the jth data at the kth iteration, , is the acceleration constant, , is a random number, is the best position of the jth data at the kth iteration, is the position vector of the jth data at the kth iteration, is the global optimal position of the kth iteration, is the total number of iterations, is the index of the data; Through the regional prediction model, the pollution hotspot areas are obtained, including: Acquire water body physical / chemical parameters, meteorological data and land use data, and pre-process the water body physical / chemical parameters, meteorological data and land use data, and simultaneously acquire influencing features from the pre-processed water body physical / chemical parameters, meteorological data and land use data by a recursive feature elimination algorithm; According to the influencing features, a basic model is established, and meta-feature data is obtained through the basic model. The basic model is specifically: ； in: , , is the meta-feature, is the number of decision trees, is the predicted value of the mth decision tree, , , is the bias term, is the kernel function, is the label of the training sample, is the Lagrange multiplier, To influence the characteristics, is the weight matrix of the first layer, is the activation function, is the weight matrix of the second layer, is the index of the training sample, is the number of training samples, is the index of the decision tree; According to the meta-feature data, the regional prediction model is established to obtain the prediction value corresponding to each monitoring area, and a prediction threshold is set at the same time, and the prediction value greater than the prediction threshold is determined. The monitoring area corresponding to the prediction value greater than the prediction threshold is the pollution hotspot area. The regional prediction model is specifically: ； in: For pollution hotspots, is the activation function, is the weight vector, is the meta-feature data, is the bias term; Establish a particle analysis module to obtain the particle distribution results, including: Acquiring distribution data, time data, geographic data and meteorological data of particulate matter, and preprocessing the distribution data, time data, geographic data and meteorological data of the particulate matter; According to the preprocessed distribution data, time data, geographic data and meteorological data of the particulate matter, the spatial and temporal distribution data of the particulate matter are obtained by using a convolutional neural network, and the distribution data of the particulate matter at different scales are obtained by using a multi-scale analysis method; The particle analysis module is established by fusing the particle distribution data in space and time and the particle distribution data in different scales. The output result of the particle analysis module is the particle distribution result. The particle analysis module is specifically: ； in: is the particle distribution characteristic vector, is the fusion weight, is the spatial and temporal distribution data of particulate matter, It is the distribution data of particles at different scales.

2. According to claim 1, a dynamic monitoring method of river micropollutants based on artificial intelligence is characterized in that: Establish a micropollutant concentration prediction model, including: The characteristic data and the distribution results of the particles are fused to obtain a fused characteristic vector; According to the fused feature vector, the micro-pollutant concentration prediction model is established, specifically: ； in: is the predicted result of micropollutant concentration, is the weight of the y-th weak learner, is the concentration prediction result of micropollutants corresponding to the yth weak learner, is the number of weak learners, is the index of the weak learner.

3. The method for dynamic monitoring of river micropollutants based on artificial intelligence according to claim 2 is characterized in that: The model parameters of the micropollutant concentration prediction model are adjusted, including: Through climate change, obtain the rate of change of temperature, precipitation, humidity and wind speed; Through the change of land use in the watershed, the rate of change between agricultural land, industrial land and residential areas is obtained; According to the magnitude of the change rate and the alteration rate, the model parameters of the micro-pollutant concentration prediction model are adjusted, specifically: When the rate of change and / or the rate of modification is greater than zero, increasing the model parameter; When the change rate and / or the modification rate is equal to zero, the model parameters remain unchanged; When the rate of change and / or the rate of modification is less than zero, the model parameter is reduced.

4. The method for dynamic monitoring of river micropollutants based on artificial intelligence according to claim 1 is characterized in that: It also includes setting the warning threshold, including: Obtaining a change threshold of the warning threshold according to the change rate of temperature, precipitation, humidity and wind speed, and the change rate between agricultural land, industrial land and residential areas; The final warning threshold is determined by the change threshold and the initial warning threshold.

5. A dynamic monitoring system for river micropollutants based on artificial intelligence, characterized in that: The method for dynamic monitoring of river micropollutants based on artificial intelligence described in any one of claims 1 to 4 is executed during operation, and the dynamic monitoring system for river micropollutants includes: The feature module obtains feature data from the collected data of pollution hotspot areas; Analysis module, to obtain the distribution results of particles; A concentration module determines the micro-pollutant concentration result according to the characteristic data and the distribution result of the particulate matter; An adjustment module is used to adjust the determined micropollutant concentration result; Threshold module, which sets different levels of early warning thresholds based on climate change and changes in watershed land use; The early warning module performs graded early warning according to the micropollutant concentration results and early warning thresholds of different levels.

Images