170 results about "Pollution index" patented technology

The invention discloses a composite interference-oriented cultivated land system toughness evaluation and optimization method, and relates to the technical field of cultivated land toughness optimization, and the method comprises the steps: collecting and preprocessing multi-source cultivated land data, constructing a toughness evaluation index system, and calculating the weight of each index; and calculating a comprehensive toughness score based on weight distribution, drawing a time sequence change curve based on the score, and performing region division to generate a toughness distribution diagram. According to the method, the single-factor pollution index, the Nemo comprehensive index and the interaction effect index of heavy metal pollution are combined, the soil pollution condition is accurately quantified through a weighted summation method, a scientific and reliable basis is provided for toughness evaluation, the dynamic toughness distribution diagram is generated in combination with the LSTM model and the GIS technology, the spatio-temporal dynamic characteristics of a cultivated land system under composite interference are revealed, and the method has the advantages of being high in reliability and high in reliability. According to the method, the defects of an existing model in space-time analysis are overcome, differentiated optimization strategies are formulated based on region division and weak region labeling, and the resource allocation efficiency and the pertinence of an optimization scheme are remarkably improved.

The invention provides an intelligent runoff pollution monitoring and response method and system, and the method comprises the steps: arranging a plurality of monitoring points in a runoff region, collecting water quality, hydrological and meteorological data in real time through a sensor, transmitting the data to a data processing center through a wireless network, carrying out the preprocessing, storage and analysis, and calculating the comprehensive pollution index of each monitoring point. And comparing with a set index threshold value. When the comprehensive pollution index exceeds a threshold value, the system identifies and evaluates key water quality parameters so as to determine a reasonable parameter threshold value; once data of a monitoring point exceeds a preset pollution threshold value, an alarm mechanism is triggered, then a pipe network water flow model and a geographic information system are combined to reversely track a pollution path, accurately position a pollution source and provide a basis for pollution control, and the process comprises the steps of screening suspected pollution source nodes, calculating a propagation path probability and preferentially checking a high-probability path. The traced pollution source or path probability is lower than a preset value.

The invention discloses a high-pressure soft water medium treatment and supply system, which belongs to the technical field of seawater desalination treatment, and comprises a multi-dimensional ion detection module for detecting the concentration distribution characteristics of target inhibition ions in original seawater to generate a corresponding coating material selection instruction; the electric field dynamic coupling module captures a termination signal according to the coating material selection instruction; the shock wave generation module starts a main pump to generate fluid shock waves to execute sediment breaking action according to the termination signal; the monitoring module is used for monitoring the finally output softened water ion concentration and reversely calculating the pollution index of the coating surface; and the heat cycle feedback device is used for starting a self-cleaning mode according to the pollution index and transmitting waste heat in the cleaning process to the pump set. The electric field dynamic coupling adsorption and fluid shock wave cooperative treatment technology is adopted, high-efficiency ion capture, dynamic sediment removal and waste heat recycling can be achieved, energy consumption is remarkably reduced, and continuous and stable operation of the system is guaranteed.

The invention belongs to the technical field of highway tunnel ventilation control, and provides a highway tunnel ventilation control method and system. The method comprises the following steps of multi-source heterogeneous data acquisition, multi-source data fusion, state estimation, traffic flow prediction, dynamic pollution index calculation and multi-target weight generation, rolling optimization with constraints, control execution, model updating and feedback correction. The state sensing precision is improved through multi-source data fusion, real-time accurate estimation and error reduction of a pollutant concentration field and a traffic flow state are realized through a heterogeneous sensor and a Rayleigh fusion technology, the problem of sensing deviation caused by single data in a traditional method is solved, future traffic flow is predicted based on a graph neural network, and the traffic flow sensing accuracy is improved. The fan power is optimized through model prediction control, the ventilation intensity is adjusted in advance, pollutants are prevented from exceeding the standard, the response speed is increased compared with threshold value control, and energy consumption is reduced.

The invention relates to the technical field of mining area pollution monitoring, and discloses an atmospheric pollution monitoring system based on a mining area and a monitoring method thereof.The atmospheric pollution monitoring system based on the mining area comprises the steps that firstly, geographic environment data, outdoor environment data and well area environment data are obtained through a network deployment module and stored; the real-time transmission module is used for preprocessing the acquired data and transmitting the preprocessed data to the comprehensive analysis module, and the comprehensive analysis module is used for analyzing the atmospheric pollution index, judging whether to send out pollution early warning or not according to the calculation result of the atmospheric pollution index, and sending out a pollution alarm signal under the condition that the pollution early warning needs to be sent out; the mining area air pollution monitoring system can comprehensively monitor the mining area air pollution in a multi-dimensional manner, accurately identify the main pollution source and the dynamic change of the main pollution source, break through the limitation of traditional fixed site monitoring, flexibly capture the spatial and temporal change of the mining area pollution, accurately identify the pollution source, improve the monitoring accuracy and improve the monitoring efficiency. And an alarm signal is sent timely.

The invention relates to the technical field of industrial pollution monitoring, in particular to an industrial pollution source tracing method and system based on machine learning, which can accurately and efficiently determine a pollution source in an industrial park and meet high requirements of environmental protection laws and regulations and the public on environmental protection. The method is used in an industrial park, when an industrial pollution monitoring point monitors that pollution indexes exceed the standard and the emission of each factory in the park meets the emission standard for pollution discharge, pollution is subjected to traceability analysis, and a pollution source with the maximum participation degree in the pollution exceeding event is determined. The method comprises the following steps: acquiring pollution monitoring data information monitored by an industrial pollution monitoring point; carrying out standard-exceeding pollutant feature extraction on the pollution monitoring data information to obtain a park pollutant feature portrait; the park pollutant feature portrait comprises the components of the standard-exceeding pollutants in the park and the real-time concentration of each standard-exceeding pollutant; and matching the park pollutant feature portraits in a pre-established factory pollutant discharge database.

The invention relates to the technical field of water quality monitoring, and discloses a water quality multi-parameter real-time monitoring system. The system comprises a monitoring point position configuration module, a water quality parameter acquisition module, a pollution index calculation module, an anomaly detection module, a central control unit and the like. The monitoring point position configuration module sets monitoring point positions and generates acquisition rules; the water quality parameter acquisition module acquires a water quality parameter set according to rules; the pollution index calculation module calculates a pollution index; the anomaly detection module obtains an anomaly detection index; and the central control unit comprehensively analyzes and generates water quality evaluation parameters. In addition, the system is further provided with a pollution source tracking module, a storage unit and an alarm module, pollution sources can be tracked, data can be stored, and an alarm can be given according to evaluation parameters. The system realizes multi-parameter real-time monitoring, accurate analysis and evaluation and pollution source tracking, provides comprehensive support for water quality management, effectively improves the water environment monitoring level, and guarantees the safety of water resources.

The invention relates to the technical field of rural sewage treatment, and discloses a rural sewage intelligent monitoring and aeration regulation and control system based on Internet of Things. The system comprises a pollution parameter sensing layer, a pollution index analysis layer, an aeration path generation layer, a process parameter matching layer, a dynamic monitoring focusing layer, a cross-modal mapping layer, an equipment scheduling decision-making layer and an aeration parameter generation layer. Wherein the pollution parameter sensing layer collects operation parameters in real time by means of a multi-source sensing terminal; the pollution index analysis layer divides pollution parameter intervals and calculates a distribution coefficient; the aeration path generation layer generates an aeration intensity grading path in sequence; the process parameter matching layer outputs a process deviation parameter set; dynamically monitoring a focusing layer to adjust the density of monitoring points; the cross-modal mapping layer establishes space-time correlation mapping of water quality parameters and aeration intensity; the equipment scheduling decision-making layer sorts the calling sequence of the aeration equipment; and the aeration parameter generation layer converts the parameters into executable regulation and control parameters. The system realizes intelligent monitoring of rural sewage and accurate aeration regulation and control.

The invention relates to the technical field of tail gas exhaust control, and particularly discloses an automatic control system and method for tail gas exhaust treatment, the system is provided with a data transmission detection module, a tail gas detection module and a tail gas exhaust detection module, and a data transmission quality index is obtained by performing transmission evaluation on a tail gas detection data transmission process; the stability and reliability of data transmission can be ensured, the tail gas pollution index is obtained by analyzing tail gas property data, the pollution degree of tail gas emission can be reflected, the tail gas treatment efficiency and effect can be improved, it is ensured that tail gas emission meets the environmental protection requirement, and the exhaust performance data in the tail gas exhaust treatment process is analyzed; according to the method, the operation state and performance of tail gas exhaust treatment can be comprehensively and accurately known, the exhaust performance index is obtained, the method is an important basis for evaluating the performance of a tail gas exhaust treatment system, the fault occurrence probability and the influence degree can be reduced, and stable operation of tail gas exhaust treatment is guaranteed.

The invention relates to the technical field of industrial cameras, and discloses a visual positioning control system and method for an industrial camera, and the system carries out the calculation of an environment pollution index and an image definition change value through monitoring the internal environment of a working region and monitoring the definition of an image shot by the industrial camera. And calculating a lens pollution index by combining the environmental pollution index and the image definition change value, judging whether the current lens needs to be cleaned or not according to the lens pollution index, sending a cleaning signal to a cleaner to clean the lens under the condition that cleaning is needed, and cleaning the lens according to the change of the environmental factors and the image definition according to the change of the environmental factors and the image definition. According to the method, the probability of pre-judging lens pollution can be visually found, so that the lens pollution can be cleaned conveniently, the complexity of manual inspection is avoided, misjudgment and missed judgment are reduced, the working sensitivity of the industrial camera is improved, the problem of industrial camera lens pollution is effectively solved, and the working efficiency and the image quality of the industrial camera are improved.

The invention discloses an intelligent detection and dynamic early warning system based on machine learning and multi-source data fusion, which relates to the technical field of membrane separation process monitoring and consists of a data acquisition and preprocessing part, a physical model and data driving model part, a dynamic weight fusion and self-adaptive mechanism part and a man-machine interaction and alarm part. According to the system, parameters such as membrane flux, transmembrane pressure difference, solution concentration, temperature, water inlet flow and operation pressure are collected, an improved XGBoost machine learning model is combined, and a comprehensive pollution index is constructed to quantify the membrane pollution degree; the model realizes double analysis of a pollution mechanism and a data rule through feature engineering. The system supports full-closed-loop management from minute-level early warning to hour-level cleaning decision making, the threshold value is dynamically adjusted through Bayesian optimization, the generalization and anti-interference capacity of the system are remarkably improved, and the system is suitable for intelligent operation and maintenance of membrane separation processes such as reverse osmosis and ultrafiltration.

The invention discloses a water quality monitoring method and system based on big data analysis, and the method comprises the steps: obtaining water quality parameter data collected by multiple types of sensors, carrying out the format unification processing of the water quality parameter data through a standardization algorithm, and carrying out the elimination through a median filtering algorithm if abnormal data points are detected, thereby obtaining a standardized data set; aiming at the standardized data set, performing feature extraction on the chemical parameters, the spectral features and the biological indexes by adopting a principal component analysis algorithm to obtain feature vectors, and if the variance contribution rate of the feature vectors exceeds a preset threshold value, retaining the feature vectors and generating a dimension reduction feature data set; according to the dimension reduction feature data set, a random forest algorithm is adopted to construct a pollution evaluation model, a pollution index is calculated, if the pollution index exceeds a preset threshold value, a high pollution state mark is generated, and a pollution evaluation result is output. According to the invention, real-time monitoring, evaluation, early warning and traceability of water quality pollution are realized, and comprehensive technical support is provided for water environment protection.

The invention belongs to the technical field of air pollution detection, and particularly relates to an air environment pollution degree detection system and method based on big data. According to the method, detection and early warning of the air environment pollution degree are achieved by mining and analyzing the multi-source environment data, and a dynamic weight distribution strategy is adopted when the pollution comprehensive index in the monitoring area is determined, so that the calculation precision of the pollution comprehensive index is effectively improved, the accuracy and timeliness of air quality monitoring are improved, and the method is suitable for popularization and application. Besides, by constructing the space-time incidence matrix and calculating the correlation strength coefficient, the correlation between abnormal types can be automatically identified, a scientific basis is provided for emergency response, meanwhile, a bimodal early warning mechanism provided by the invention can distinguish instantaneous anomalies and normalized anomalies, and the accuracy of emergency response is improved. The problems of resource waste and untimely emergency response caused by false alarm or missing alarm are avoided, and the continuity and stability of air quality monitoring are ensured.

The invention relates to the field of underground water pollution monitoring, in particular to a dynamic intelligent monitoring method and system for underground water pollution. The method comprises the following steps: constructing and updating an underground water solute transport model; generating a comprehensive pollution feature vector by using the obtained multi-source data which is related to the groundwater pollution and comprises a plurality of pollution indexes; respectively constructing a pollution source positioning model and a pollution diffusion model; triggering a pollution alarm according to the comprehensive pollution feature vector; according to the multi-source data and the current hydrogeological data, utilizing the pollution source positioning model to obtain a pollution source position; respectively utilizing the pollution diffusion model and the groundwater solute transport model to carry out prediction simulation to obtain first pollution distribution changing along with time and second pollution distribution changing along with time; and carrying out weighted fusion on the first pollution distribution and the second pollution distribution to generate predicted pollution distribution changing along with time. The problem that underground water pollution monitoring is not timely and accurate enough in the prior art is solved.

The invention discloses an environmental risk step-by-step assessment method in a multi-medium and multi-scale complex scene, and belongs to the technical field of environmental risk assessment. The method comprises the following steps: obtaining the heavy metal concentration of the waste residue through a leaching experiment, and determining the toxicity grade by using a single pollution index method and a potential ecological risk index method; dividing volume grades based on quantiles, dividing local diffusion risk grades by using an inverse distance weighted interpolation method and the like, dividing remote receptor sensitivity grades according to a surrounding land and water source construction model, and generating priority control grades by integrating the grades; and finally, fusing multi-source data to construct a spatio-temporal conjoint analysis model verification result. Therefore, by integrating pollution source toxicity, volume, diffusion risk and receptor sensitivity to carry out risk step-by-step assessment, a multi-source data fusion and spatio-temporal conjoint analysis method is proposed for the first time, and the problems of single assessment dimension, insufficient data fusion, lack of dynamic verification and the like of an existing assessment method in a multi-medium and multi-scale scene are effectively solved.

The invention relates to the technical field of mine disaster monitoring and prevention and control, in particular to a deep mine disaster big data analysis and prevention and control decision-making system. Comprising a data pollution quantification unit used for collecting a multi-source heterogeneous data stream and reference prediction data and carrying out noise pollution quantitative analysis to obtain a data pollution index; the toughness entropy evaluation unit is used for collecting physical risk factors, resolving the basic safety entropy, correcting the basic safety entropy according to the data pollution index to obtain the toughness entropy of the mine system, and judging the toughness entropy of the mine system to obtain a risk level; the anti-fragility decision-making unit is used for dynamically adjusting a risk aversion coefficient according to the risk level, and performing utility function solution on a preset decision to be selected to obtain an optimal decision; and the closed-loop scheduling control unit is used for matching a preset scheduling instruction set according to the optimal decision and the risk level, and generating and executing a hierarchical control instruction. The problem that a traditional risk assessment model fails due to serious pollution of deep mine monitoring data is solved.

The invention discloses a graded discharge management system and method for urban sewage, multiple water quality parameters are collected in real time from different monitoring nodes of a pipe network through a multi-source data fusion collection module, and time and position information are integrated to form a multi-dimensional data set, so that the actual condition of the sewage is comprehensively reflected; the complex water quality evaluation and grading module applies a multi-factor dynamic weight comprehensive pollution index calculation formula, integrates multiple parameters and dynamically adjusts the weight, so that the water quality evaluation is more scientific and accurate, the pollution degrees of different time and regions can be truly presented, the limitation of the traditional fixed weight is overcome, and the comprehensive pollution index and the multi-level grading threshold value of the sewage are calculated based on the accurate sewage comprehensive pollution index and the multi-level grading threshold value. The system realizes fine discharge grade grading, different grades correspond to different treatment requirements and standards, sewage is guided to corresponding treatment units and channels, accurate graded discharge is realized, the sewage with low pollution degree is subjected to cost reduction by adopting a simple process, and the sewage with high pollution degree is subjected to enhanced treatment to meet the standard.

The invention discloses a regulation and control method for stably improving the water quality of water produced by a reverse osmosis system of a power plant, and relates to the field of water quality regulation and control, which comprises the following steps: configuring a water inlet flow regulation device, performing initial setting, configuring the position of an online sensor, performing data acquisition, setting judgment rules of turbidity, differential pressure and transmembrane pressure difference, and determining the water quality. And configuring a flocculant adding device and adding control logic, configuring a backwashing and adding linkage control state machine, and carrying out data smoothing, trend analysis and self-adaptive threshold adjustment. When the water quality of the raw water fluctuates for a short time or the pollution load is increased, dynamically adjusting the water inlet flow, the backwashing intensity and the flocculant adding concentration based on parameters such as turbidity, pollution index, conductivity and pressure difference which are acquired by an online sensor in real time; and meanwhile, the concentrated water reflux proportion and the high-pressure pump frequency are automatically corrected by continuously calculating the desalting rates of the first-stage and second-stage reverse osmosis devices, so that the running state of the membrane component is kept in an optimal interval.

The invention relates to the technical field of Internet and environment monitoring, in particular to an Internet-based river water quality remote monitoring system, which comprises a water quality data acquisition module, a pollution detection module, a water quality grading module and a mobile terminal early warning module, the water quality data acquisition module is used for acquiring water quality parameters and water quality pollution parameters of each river section and storing water quality parameter data and water quality pollution parameter data; the pollution detection module is used for calculating a water quality pollution index of each river section; the water quality pollution level of each river section is determined; the early warning module is used for generating river interval water quality early warning signals; the water quality grading module is used for calculating a water quality health index of each river section; the mobile terminal early warning module is used for displaying the segmented linear river graph by using the water quality pollution grade data and the water quality health grade data corresponding to each river interval in different colors; and a corresponding river interval alarm is given.

The invention discloses a method for evaluating, depicting and dynamically monitoring the ecological self-purification capacity of a site, which comprises the following steps of: primarily screening indexes for evaluating, depicting and dynamically monitoring the ecological self-purification capacity of the site, determining a core ecological self-purification index and determining a weight; defining a pollution core area, a transition area and a background area; the terrain deviation is corrected, the threshold value is dynamically adjusted in combination with seasonal monitoring data, and high-precision demarcation of the ecological boundary is achieved; site investigation is carried out, and historical data of each pollution index is collected; determining a relationship between the evaluation index and the ecological self-cleaning capability, constructing a comprehensive evaluation model of the ecological self-cleaning capability of the site, and calculating a main component and an ecological self-cleaning capability score; according to the principal component score and the ecological self-cleaning ability score, drawing a single-index thermodynamic diagram and a comprehensive self-cleaning ability thermodynamic diagram, and realizing GIS space visualization ecological analysis; and performing dynamic detection and model optimization verification. According to the method, spatial description and optimization of the ecological self-purification capacity are achieved, and precise treatment and enhanced treatment of a polluted site are facilitated.

The invention relates to the field of data processing, in particular to a full-spectrum water quality detection method based on big data, and the method comprises the following steps: collecting a full-spectrum waveform curve of a preprocessed to-be-detected water body and full-spectrum waveform curves of various historical water body detection samples; dividing the full spectrum into an ultraviolet region, a visible light region and an infrared region, dividing a full spectrum waveform curve into a plurality of wavelength bands in each region, and calculating the independence degree of each wavelength band; selecting the wavelength section corresponding to the maximum independence degree in each region as an independent wavelength section of the region, and calculating a weighted concentration matrix of the to-be-detected water body based on the independence degrees of all the most independent wavelength sections and the concentration matrix of the to-be-detected water body in each region; and calculating a comprehensive pollution index of the to-be-detected water body, and when the comprehensive pollution index is greater than a preset threshold value, judging that the to-be-detected water body is polluted. According to the invention, efficient, accurate and scientific detection and evaluation of water body pollution are realized.

The invention discloses a pouring environment monitoring system and method, and relates to the field of pouring monitoring, and the pouring environment monitoring system comprises a management module which serves as a central interaction platform and provides interaction authority for each functional module after identity verification; the environment scanning module is used for deploying a sensor in a pouring area to periodically scan a pouring environment in real time and collecting area state data; the evaluation unit is used for analyzing and processing the temperature information in the area according to the area state data provided by the environment scanning module, and extracting a temperature index, a pollution index and a concrete solidification index; according to the method, the dynamic environment condition can be rapidly adjusted, prediction of the future pouring abnormal coefficient is more accurate, the safety of the construction process is improved, a scientific basis is provided for construction quality control, the accuracy and effectiveness of decision making are improved, and the safety and emergency processing efficiency of the construction process are improved.

The invention discloses a central air conditioner pollution degree autonomous evaluation robot based on multi-modal perception, and relates to the technical field of intelligent detection, the robot comprises a voxel space division module, a data acquisition module, a pollution characteristic mapping modeling module, a physical pollution detection module, a chemical pollution detection module and a biological pollution detection module; through three-dimensional point cloud data scanning and real-time data acquisition, the air pipe wall surface roughness, the temperature delay time, the gas fluctuation, the heat retention degree and the microbial metabolism gas concentration are monitored in real time, and a three-dimensional pollution characteristic mapping model is generated according to the pollution characteristic data set; by calculating a physical pollution index PPI, a chemical pollution index CPI and a biological pollution index BPI and comparing and analyzing with corresponding threshold values, whether dust retention, gas concentration and microorganism quantity in an air pipe exceed standards or not is judged in real time, cleaning, filtering and sterilizing strategies are automatically triggered, and the air quality and operation safety of a central air conditioning system are effectively guaranteed.

The invention relates to the technical field of civil air defense building ventilation equipment, in particular to efficient-purification and self-cleaning civil air defense building ventilation opening protective door equipment which comprises a door frame, a door body, a driving unit, a sensing system, a purification device, a self-cleaning execution unit and a control unit. The sensing system comprehensively collects environment and equipment data, and the control unit adopts a weighted fusion algorithm to calculate a comprehensive pollution index and determine a pollution level; the protection and ventilation efficiency coefficients under different door body opening and closing angles are calculated based on fluid analogue simulation; dynamically solving the optimal opening and closing angle of the door body and the optimal operation parameters of the purification device through a multi-objective optimization algorithm; and the self-cleaning effect is intelligently triggered and verified according to the filter screen state and the pollution level. Self-adaptive intelligent regulation and control of the protective door in a complex pollution environment are achieved, the safety protection and ventilation efficiency is effectively balanced, and the purification capacity and the automatic maintenance level are improved.

The invention relates to the technical field of energy and environmental protection, and discloses a boiler environmental protection island system based on an energy collaborative optimization technology, which comprises a data acquisition module, a data analysis module, an optimization decision module, a command execution module and a feedback adjustment module, a data analysis module in the system uses a formula to calculate a data set collected by the data analysis module, a boiler emission pollution index Qrl and a post-denitration air pollution index Mej are obtained through calculation, a comprehensive environmental protection coefficient Uqm is obtained through calculation by combining the two data results, and an optimization decision module optimizes the system according to the comprehensive environmental protection coefficient Uqm. The command execution module converts a data result and executes an operation command, the feedback adjustment module is connected with the data analysis module again after receiving information to form closed-loop control, and the system solves the problem that air pollutants generated by a traditional boiler pollute the environment by continuously circulating the system process.

The invention relates to the technical field of power plant condensate polishing systems, and provides a condensate polishing resin regeneration scrubbing operation control optimization device and method.The method comprises the following steps that 1, water quality parameters of a high-speed mixed bed inlet mother pipe are monitored through a condensate water quality monitoring instrument; 2, the water quality parameters are fed back to an automatic adjustment control system, and a water quality pollution index is calculated; 3, based on the water quality pollution index, the final scrubbing frequency is calculated through an automatic adjusting control system, and an air scrubbing step sequence system is dynamically adjusted. According to the scheme, the optimal air scrubbing scheme can be automatically selected, the resin regeneration efficiency is improved while the resin scrubbing effect is guaranteed, and the operation cost is reduced.

The invention discloses an intelligent home environment monitoring and linkage system and method based on multi-parameter fusion, and relates to the technical field of home environment monitoring, and the method comprises the following steps: S1, collecting environment data in different directions through a monitor; s2, judging the type of a pollution scene according to the difference value of the pollution indexes in all directions; s3, in combination with pre-stored indoor environment layout information, corresponding pollutant traceability analysis is executed; s4, generating and executing an intelligent linkage instruction according to the pollution scene type and the pollutant traceability judgment result; and S5, verifying a linkage effect and adaptively adjusting a subsequent monitoring strategy or a linkage instruction. Through fusion calculation of directional sampling and a multi-parameter sensor, directional traceability of the pollution source is realized, so that a user can intuitively and quickly position the pollution source, the limitation that in the prior art, only alarming can be performed, and positioning cannot be performed is fundamentally changed, and a key basis is provided for taking efficient and accurate treatment measures.

The invention discloses a regional water environment quality evaluation system and method based on artificial intelligence, and relates to the technical field of water environment evaluation.The regional water environment quality evaluation method comprises the steps that pollution detection is conducted on a river channel in a to-be-detected region through a sensor, and obtained pollution detection feature information is bound with the corresponding river channel position in the to-be-detected region; calculating a single-factor pollution index of each pollutant based on the pollution detection feature information; based on the corrected Nemerow index method, analyzing the comprehensive pollution index of each position in the river channel; constructing a pollution index change model of each position in the river channel through the comprehensive pollution index, and analyzing an association score between the river channel sections by using the pollution index change model in combination with the position information of the river channel; predicting the comprehensive pollution index; and analyzing the pollution index change value of the river channel section based on the prediction result. The accuracy of system analysis is improved; and automation of water environment quality assessment and early warning is realized.

The embodiment of the invention provides a sponge city high terrain rainwater management method and system, and the method comprises the steps: obtaining the historical rainfall distribution and pollution index of a target region, building a stepped storage facility according to the historical rainfall distribution, and setting a filtering material of the stepped storage facility based on the pollution index; acquiring the real-time rainfall capacity of the target area, and generating a corresponding rainfall terrain map; the initial water level of the stepped storage and stagnation facility is obtained, a terrain water level model is established in combination with a rainfall terrain map, the water pumping rate and the water discharging rate of the stepped storage and stagnation facility are adjusted based on the terrain water level model, and the water level of the stepped storage and stagnation facility is kept within a preset range; the drainage requirements corresponding to different terrains are obtained, the water quality requirements of the corresponding terrains are determined based on the drainage requirements, the water level and the water quality of the stepped storage facility are monitored in real time, and whether the water level is kept within a preset range or not and whether the water quality meets the water quality requirements of the corresponding terrains or not are judged.

The invention discloses an urban environment quality monitoring method based on deep learning. The urban environment quality monitoring method is used for predicting an air pollution index API in the future 6 hours. The method comprises the following steps: collecting concentration data of six pollutants, namely PM2.5, PM10, SOO, NOO, Oand CO, and constructing a deep learning model comprising a double-layer time sequence feature extraction network and a pollution index prediction network; in the double-layer time sequence feature extraction network, a time scale layer adopts an LSTM structure to capture short-term change features, and a periodic scale layer adopts an attention-enhanced GRU structure to capture periodic features; after the time scale feature and the periodic scale feature are spliced, calculating a weighted feature through a self-attention mechanism, and inputting the weighted feature into a full-connection layer to generate a predicted value; according to the method, the prediction precision of the atmospheric pollution index is remarkably improved, the model robustness is enhanced, and a scientific basis is provided for environment decision making.