140 results about "Pollution index" patented technology

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 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 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 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 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 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 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 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.

The invention provides a vehicle carpet cleaning method and device, electronic equipment and a vehicle, relates to the technical field of vehicle cleaning, and aims at comprehensively and accurately evaluating the cleanliness of a vehicle carpet and executing corresponding cleaning measures. Based on the state data of the vehicle carpet, determining pollution indexes of the vehicle carpet in a plurality of environmental feature dimensions; determining a cleanliness score of the vehicle carpet based on the pollution indexes of the vehicle carpet in the plurality of environmental feature dimensions; and determining a cleaning mode of the vehicle carpet based on the cleanliness score of the vehicle carpet, and cleaning the vehicle carpet based on the cleaning mode.

The invention relates to the technical field of direct drinking water treatment, and provides a direct drinking water purification system and a dynamic regulation and control method, a pollution load index is calculated by combining an inflow water quality parameter and a pre-membrane pollution index, a historical performance attenuation degree is extracted based on historical water production data, and accurate and comprehensive basic data support is provided for subsequent regulation and control; through time sequence analysis on historical water consumption data, water consumption feature vectors are identified, and difference scenes are divided, so that a regulation and control strategy can adapt to water consumption requirements of different scenes in a targeted manner; on the basis of a near-end strategy optimization algorithm, a state vector is constructed by a pollution load index, an attenuation risk index and a water use feature vector, the quality of produced water, the operation energy consumption and the service life of a nanofiltration membrane are taken into optimization targets, and meanwhile, a membrane pressure difference constraint is embedded; by regularly recording simulation verification data and dynamically optimizing a near-end strategy optimization algorithm and a long-short-term memory network, the regulation and control effect is prevented from being attenuated along with the operation time, and the stability and accuracy of long-term operation of the system are ensured.

The invention relates to the technical field of hydroelectric power generation equipment state monitoring, in particular to a hydropower station equipment operation and maintenance risk assessment method and system based on big data analysis, and the method comprises the steps: 1, carrying out the fusion collection of specific data of a hydropower station; step 2, hydraulic-mechanical coupling feature extraction; step 3, dynamically constructing a fault propagation network; and 4, generating a dynamic risk field and outputting a decision: injecting the coupling factor and the wet pollution index into equipment network nodes, generating risk density distribution through three-dimensional space interpolation, dynamically adjusting a risk threshold in combination with a real-time working condition, and outputting a priority maintenance node and a maximum maintenance time window under a constraint condition. Through deep analysis of a hydraulic-mechanical coupling failure mechanism, an equipment cluster risk conduction path is quantified, and dynamic risk assessment is realized in combination with a big data analysis technology, so that the accuracy, real-time performance and efficiency of hydropower station equipment operation and maintenance are remarkably improved.

The application discloses a water quality and soil detection and analysis method and system based on multi-standard automatic evaluation and belongs to the technical field of environmental monitoring data processing. The system comprises a data import module, an intelligent identification module, a batch sample processing module, a multi-standard evaluation module, a comprehensive analysis module, a water chemistry verification module and a result output module. The system automatically analyzes the Excel table structure through an intelligent identification algorithm, extracts index names, units, detection results and background values, supports batch sample format identification, can analyze multiple samples at a time, integrates three environmental quality standards of underground water GB / T 14848-2017, surface water GB 3838-2002 and soil GB 36600-2018, automatically performs quality classification evaluation, calculates single pollution index P i , exceeding multiple, Nemerow comprehensive pollution index P N , performs water chemistry anion and cation balance verification, Shukarev classification, TDS and hardness checking, and supports single sample and batch sample export and summary table generation. The application solves the problems of weak Excel data recognition ability, batch sample processing difficulty, lack of multi-standard automatic switching, single comprehensive evaluation method and insufficient data quality checking in the prior art, and greatly improves the efficiency and accuracy of detection data analysis.

The present invention discloses an intelligent detection and dynamic early warning system based on machine learning and multi-source data fusion, which relates to the field of membrane separation process monitoring technology. It consists of a data acquisition and preprocessing part, a physical model and data-driven model part, a dynamic weight fusion and adaptive mechanism part, and a human-computer interaction and alarm part. The system collects parameters such as membrane flux, transmembrane pressure difference, solution concentration, temperature, water flow rate and operating pressure, and combines them with an improved XGBoost machine learning model to construct a comprehensive pollution index to quantify the degree of membrane pollution. The model uses feature engineering to achieve dual analysis of pollution mechanisms and data patterns. The system supports full closed-loop management from minute-level early warnings to hour-level cleaning decisions, and dynamically adjusts thresholds through Bayesian optimization, significantly improving the system's generalization and anti-interference capabilities. It is suitable for the intelligent operation and maintenance of membrane separation processes such as reverse osmosis and ultrafiltration.

The invention provides an air pollutant intelligent monitoring system and monitoring method, and relates to the field of environment air quality detection.The system comprises a basic information module used for obtaining basic user information of a user and the area category of a target monitoring area; the determining and monitoring module is used for determining monitoring pollutants according to the basic user information and the region category; the real-time monitoring module is used for acquiring the pollutant content of the monitoring pollutants in each target monitoring area; the real-time information module is used for acquiring real-time user information of the user; the pollution index module is used for determining the air pollution index of each target monitoring area according to the real-time user information and the pollutant content; the change index module is used for determining an air pollution change index according to the air pollution index; and the monitoring report module is used for generating a monitoring report according to the air pollution index and the air pollution change index. According to the invention, the intelligence and accuracy of air pollutant monitoring can be improved.

The invention provides a WIOTa wireless communication-based roof photovoltaic automatic cleaning and cooling method, which is applied to a WIOTa wireless communication-based roof photovoltaic automatic cleaning and cooling system, and the system comprises a distribution box strong current control unit, a gateway control cabinet, end equipment, an environment and working condition sensor and an upper control platform. Setting a cleaning interval period by combining power station region or industry characteristics; according to the sensing data and the interval period, triggering a cleaning or cooling mode according to a preset rule, generating a task queue after an inhibition condition is eliminated, receiving returned state data after execution according to the task queue, correcting parameters in real time, and recording a state. The system realizes wireless communication by means of WIoTa, reduces wiring cost, realizes on-demand operation and maintenance, improves automation level, optimizes water consumption and energy consumption through comprehensive pollution indexes and a dual-temperature threshold judgment mode in combination with pressure control and water consumption management, has mechanisms of retransmission, safe pump stop and the like, and is stable in operation.

The invention discloses a radiator filth blockage intelligent cleaning system based on multi-sensor data fusion. The radiator filth blockage intelligent cleaning system comprises a controller, a cleaning execution device and a differential pressure sensor. The cleaning execution device is arranged between a fan and a radiating grid of the radiator; the pressure difference sensor, the infrared scattering type particulate matter sensor and the infrared thermal imaging sensor are used for detecting the radiator to obtain pressure difference data, dust concentration data and temperature difference data; the controller calculates the pollution index of the radiator according to the pressure difference data, the dust concentration data and the temperature difference data, and when the pollution index reaches a set threshold value G, a cleaning instruction is generated and sent to the cleaning execution device; the cleaning execution device is used for cleaning the heat dissipation grid according to the cleaning instruction; the filth blockage condition of the radiator is detected in real time, and the radiator is dynamically cleaned according to the filth blockage condition of the radiator; and the problems that the cleaning execution device cannot clean the radiator in time and cannot clean the radiator in place are solved.

This invention relates to the field of drilling fluid testing equipment technology, specifically to an evaluation device and method for simulating the dynamic pollution of drilling fluids by CO2. The evaluation device includes a sealed pressure vessel, an injection assembly, a circulation system, a control module, a CO2 injection volume detection module, a first monitoring module, a second monitoring module, and an information processing and analysis system. The information processing and analysis system can establish a first evaluation model based on drilling fluid performance parameters before and after pollution, establish a second evaluation model related to CO2 consumption based on drilling fluid pollution index parameters, and establish an overall pollution degree evaluation model related to CO2 injection pressure, pollution time, and pollution temperature based on the first and second evaluation models. The evaluation device of this invention can achieve a comprehensive and dynamic assessment of the CO2 pollution process of drilling fluids and can simulate the complex effects of CO2 on drilling fluids under real-world pollution conditions.

The invention discloses a self-adaptive anti-interference water quality detection method, system, equipment and medium, belongs to the technical field of water quality detection, and aims to solve the technical problem of low accuracy of a water quality detection result in the prior art. The method comprises the steps of task parameter loading, global path cruising, pollution core area identification, pollution core area sampling, digital twin map generation, map transmission and signal denoising. When the pollution core area is identified, suspected pollution points are screened preliminarily, the comprehensive pollution index of the suspected pollution points is calculated, and the pollution core area with the highest pollution degree is locked; and when map transmission and signal denoising are carried out, the signal receiver carries out denoising and purification treatment on the transmitted signal through a Dig Flow denoising algorithm, and uploads the signal to a shore-based platform after the signal is verified to be correct, so that water quality detection is completed. A pollution assessment scheme of multi-parameter algorithm fusion, CPI comprehensive assessment and a DigFlow denoising algorithm is adopted, the pollution identification accuracy is improved, the traceability error is small, the signal error rate is reduced, and the signal-to-noise ratio is improved.

The invention relates to the technical field of data control, in particular to a goat feeding environment comprehensive control method and system. According to the method, a health state evaluation value is determined according to the overall difference degree of feed, drinking water daily consumption and daily average weight gain among regions; determining the breeding environment adaptation degree by combining the health state evaluation value, the daily consumption fluctuation characteristics of feed and water drinking and the daily average weight gain; marking the region with the breeding environment adaptation degree not greater than a preset threshold value as a target region; determining an excrement accumulation pollution index according to the historical ammonia concentration change trend of each region, the instantaneous change characteristics of the daily consumption of feed and drinking water at adjacent cleaning moments and the current excrement accumulation amount; determining an ammonia diffusion potential value according to the spacing distance between the target area and the peripheral adjacent area and the excrement accumulation pollution index; determining a time adjustment coefficient according to the ammonia diffusion potential value and the excrement accumulation pollution index; and correcting the initial cleaning frequency by using the time adjustment coefficient to obtain a final cleaning frequency, thereby improving the cleaning frequency.