11954 results about "Pre treatment" patented technology

The invention relates to the technical field of hydraulic engineering monitoring control, and discloses an automatic monitoring method and system for ecological hydraulic engineering. The method comprises the steps that water body parameters are collected through a sensor and preprocessed to obtain standard monitoring data; performing noise reduction alignment and correlation analysis on the monitoring data to obtain an optimized data set and a feature library; evaluating the water environment state according to the data set and the feature library to obtain an evaluation index and an early warning signal; analyzing the treatment scheme according to the index and the signal to obtain a regulation and control instruction; adjusting equipment operation parameters according to the instruction to obtain operation state data; and performing cycle analysis and strategy optimization based on the state data to obtain regulation and control parameters. Multi-dimensional correlation analysis of water quality parameters is realized, an accurate water environment regulation and control scheme is formulated on the basis, and the pertinence and efficiency of water quality improvement are improved.

The invention provides a sewage treatment data real-time monitoring method and system based on the Internet of Things, and relates to the technical field of data processing.The method comprises the steps that a sensor network is arranged at key nodes in the sewage treatment process, and multiple kinds of original data including water quality parameters, flow, temperature, pressure and equipment operation states are collected in real time; transmitting the various original data to a cloud data center for preprocessing to generate processed data; according to the processed data, the water quality change trend is monitored in real time, pollution events or equipment faults are recognized, and real-time monitoring and analysis results are generated. Real-time monitoring and intelligent analysis of sewage treatment data are achieved through the Internet of Things, pollution events or equipment faults are effectively warned, a treatment scheme is automatically generated, emergency response is started in time, and the sewage treatment efficiency and safety are improved.

The invention discloses a method for inverting key parameters in an underground water pollutant biodegradation numerical model based on a neural network algorithm. Carrying out preprocessing and refined dimension reduction on the data, and processing high-dimensional multi-scale data; building a multilayer neural network architecture under a physical information neural network framework, embedding a related reaction equation of a pollutant natural attenuation process, and coupling a biochemical reaction mechanism and a data driving model; a neural network model is trained, errors in the training process are calculated, and the accuracy of an inversion result is improved by using a sub-when optimization algorithm; integrating observation data and a model prediction result, determining final parameter setting of a numerical model, and carrying out underground water damage real object quantification; comparative verification of a neural network parameter inversion model is carried out, and feasibility and accuracy of the parameter inversion method are ensured. According to the method, the problem that the key parameters of the biodegradation numerical model are difficult to accurately invert in the real object quantification process of underground water pollution under complex geological conditions is solved.

The invention provides a sewage treatment data optimization method and system based on cloud computing, and relates to the technical field of data processing.The method comprises the steps that real-time key information, including water quality indexes, equipment operation parameters and energy consumption data, collected by monitoring equipment in real time is received, the key information is preprocessed, and a processed data set is obtained; constructing a cloud database according to the processed data set, and performing data analysis including trend prediction and anomaly detection by using the cloud database to obtain an analysis result; and according to the analysis result, constructing a sewage treatment data optimization model, and inputting the water quality standard, the equipment operation efficiency and the energy consumption cost parameters into the sewage treatment data optimization model to generate an optimization scheme. By collecting and analyzing sewage treatment key information in real time and constructing and optimizing the data model, intelligent adjustment and optimization of the sewage treatment process are realized, the water quality is improved, the energy consumption is reduced, and the equipment operation efficiency and the overall treatment effect are improved.

The invention relates to the technical field of sewage treatment, and particularly discloses a sewage and wastewater treatment control system and method based on an intelligent optimization algorithm. Water quality data are collected through a data acquisition module, and a water quality characteristic matrix is generated through preprocessing. And the prediction module analyzes the feature matrix by using the trained water quality dynamic prediction model to obtain a water quality prediction result. And processing the prediction result by using a multi-objective optimization algorithm to obtain an initial control parameter. And the parameter optimization module calculates a water load fluctuation ratio, a model confidence coefficient and an equipment state according to the sewage and wastewater treatment data, inputs the water load fluctuation ratio, the model confidence coefficient and the equipment state into the adaptive fuzzy network and generates a multi-target parameter optimization suggestion. And the dynamic optimization module adjusts the multi-objective optimization algorithm parameters according to the parameters, and processes the prediction result again to obtain optimization control parameters. And the control module regulates and controls sewage and wastewater treatment according to the optimized parameters. The system realizes closed-loop management from data acquisition, prediction and optimization to control, can dynamically adapt to water quality change, and operates stably and efficiently.

The invention discloses a water body new pollutant risk assessment method and system. Belongs to the technical field of environmental science. Comprising the steps of collecting multi-source heterogeneous data to generate an initial data set; performing data fusion in the initial data set by using a weighted Bayesian fusion algorithm and a space-time interpolation method to generate a target data set; importing the target data set into a deep learning model for prediction and obtaining a prediction result; and obtaining a preset early warning condition and combining with the prediction result to judge whether risk early warning needs to be carried out. Through constructing a pollutant data acquisition system and data fusion, the monitoring precision of the new pollutants in the water body is improved. The fusion algorithm is used for data preprocessing, and pollutant detection is comprehensive. And the space-time diffusion process of the pollutants can be accurately modeled.

The invention relates to the technical field of control systems, in particular to an intelligent control method and system for sewage treatment, and the method comprises the steps: collecting sewage treatment data; preprocessing the sewage treatment data; introducing a control variable, and constructing an incidence matrix between the control variable and the sewage treatment data; the control variables and the sewage treatment data serve as input, and the water quality is predicted through the water quality prediction model; determining a real-time control variable in the sewage treatment process through a reinforcement learning algorithm according to the incidence matrix and a water quality prediction result; and according to the real-time control variable, controlling the sewage treatment equipment to carry out sewage treatment. According to the method, relevance of all links of sewage treatment is represented through the incidence matrix, system states and system actions are constructed through the incidence matrix, optimization is carried out through a reinforcement learning method, regulation and control parameters meeting the water quality requirement are given, all-directional regulation and control of sewage treatment from beginning to end are achieved, and the sewage treatment efficiency is improved. And the management of the sewage treatment system is more refined and intelligent.

The present invention relates to the technical field of the control of wastewater treatment devices. Disclosed is an intelligent control method for wastewater treatment devices at a dry bulk cargo terminal, which is used for solving the problem of poor control of wastewater treatment devices at a terminal. The method comprises the following steps: installing a plurality of types of sensors at key locations of a dry bulk cargo terminal, and using edge computing nodes to perform real-time data collection and preprocessing; on the basis of historical features and temporal features, using a machine learning model to perform wastewater type classification, thereby realizing efficient dynamic adjustment of operating parameters of wastewater treatment devices; then, by means of weighted voting and confidence evaluation, integrating a plurality of classification results to ensure an optimal treatment effect; and analyzing actual wastewater treatment conditions to continuously optimize device control, thereby preventing faults, extending the service life of devices, and improving the wastewater treatment effect.

The invention discloses an accurate dosing control system for water treatment, relates to the technical field of dosing control, and is used for solving the problem of poor dosing control accuracy in the water treatment process. Comprising a data acquisition module, a condition library construction module, a multi-target processing module and an optimization decision module. According to the method, a sensor is arranged in a water body to collect pollutant parameters, data are preprocessed, the types and the concentrations of pollutants are recognized through a support vector machine model, after recognition, an optimal processing parameter combination is called from a removal condition library, the condition library is dynamically updated by using an Apriori algorithm, and when multiple pollutants coexist, the optimal processing parameter combination is called from the removal condition library. Removal conditions are automatically adjusted according to interaction rules among pollutants, collaborative removal is realized, an optimal decision is solved by constructing a multi-objective optimization model with removal efficiency, medicament cost, processing time and environmental adaptability as targets and combining a genetic algorithm and a particle swarm optimization algorithm, intelligent dosing control is realized, and the system is suitable for large-scale popularization and application. And the accuracy and efficiency of water treatment are improved.

The invention provides a sewage treatment system and method based on a large language model and multi-agent collaboration, and the system comprises a sensor network which is used for collecting the water quality parameters of sewage in real time; the monitoring agent is used for performing data verification on the water quality parameters; the at least one edge computing node is used for performing data preprocessing on the water quality parameters; the cloud decision intelligent agent is used for generating a sewage treatment strategy according to the preprocessed water quality parameters based on a large language model; the execution agent is used for executing the sewage treatment strategy and treating the sewage; the evaluation agent is used for analyzing and evaluating the sewage treatment effect and feeding back the sewage treatment effect to the cloud decision agent so as to optimize the sewage treatment strategy; by constructing an intelligent and dynamic treatment system, real-time monitoring, intelligent analysis and self-adaptive treatment of the mariculture sewage are realized, so that the sewage treatment stability and environmental protection benefits are improved, and the sustainable development of the mariculture industry is promoted.

The invention provides a sewage plant effluent prediction method, system and equipment based on an improved Bi-LSTM model, and relates to the technical field of sewage treatment. The method comprises the following steps: acquiring historical operation data of a sewage plant, introducing an attention mechanism, a bidirectional structure and residual connection based on a standard LSTM unit, constructing a Bi-LSTM prediction model, taking a key kinetic equation of a simplified activated sludge model ASM as a physical constraint condition, inputting the operation data subjected to data preprocessing into the Bi-LSTM prediction model, and calculating the operation data of the sewage plant according to the operation data. The prediction result is subjected to multi-objective optimization based on the genetic algorithm to obtain an optimal process parameter combination, and the optimal process parameter combination is converted into an actual process control instruction to realize dynamic parameter adjustment, so that the prediction precision is greatly improved, and the energy consumption is reduced, the stability is improved and the abnormal working condition adaptive capacity is improved through multi-objective optimization.

The invention discloses a hydration heat monitoring and dynamic regulation and control method and system in a mass concrete pouring process, and relates to the technical field of concrete pouring, and the method comprises the steps: pre-burying a plurality of dual-mode temperature measurement probes in concrete, collecting dual-mode temperature field data in the concrete in real time, and synchronously collecting environmental parameters; carrying out preprocessing, dynamic weight distribution and data fusion on the obtained dual-mode temperature field data; according to the unsteady state heat transfer equation and the hydration heat model, a finite element heat conduction model is constructed, and the total hydration heat and the temperature difference trend are inverted through actually measured temperature field data; predicting a concrete core and surface temperature difference and a surface and environment temperature difference in a future set time period by combining an LSTM neural network model; and according to the predicted temperature difference result, the cooling water flow rate and the cooling water temperature are subjected to closed-loop regulation. Through the combination of dual-mode sensor temperature measurement and heat inversion, the hydration heat temperature difference in the concrete can be predicted in advance, a cooling scheme is regulated and controlled in real time in a linkage mode, and temperature stress cracks are avoided.

The invention discloses an intelligent control method of a zinc ash-containing tail gas purification system, which relates to the technical field of tail gas purification systems, and comprises the following steps of: firstly, continuously monitoring a condensation process of high-temperature zinc-containing steam in a cooler through a monitoring device, and collecting zinc steam condensation conditions and particle generation data of tail gas under different temperature and pressure states in real time; according to the invention, intelligent monitoring, signal analysis, machine learning identification and dynamic regulation and control are combined to realize accurate control of the zinc-ash-containing tail gas purification system. Key features are extracted through real-time monitoring and data preprocessing, intelligent recognition of particle concentration is achieved based on feature vectors and machine learning, the rotating speed of the centrifugal separator is adjusted in a self-adaptive mode, and efficient separation and system stability are ensured by linkage with pulse back-blowing dust removal. Compared with a traditional fixed rotating speed mode, the tail gas purification efficiency is improved, the maintenance cost is reduced, the service life of equipment is prolonged, the automatic operation capacity is enhanced, and the system is kept in the optimal working state during load fluctuation.

The invention provides a solid waste filling material ratio optimization method of a magnesium slag-steel slag composite cementing material, and relates to the technical field of solid waste filling. The method comprises the following steps: mixing magnesium slag, steel slag, desulfurized gypsum and mineral slag to prepare a cementing material, and carrying out pretreatment and physicochemical property analysis on raw materials by taking tailings and waste rocks as filling aggregates; then designing a cementing material proportioning scheme and carrying out strength test to obtain an optimal ratio of the cementing material; then considering different slurry concentrations and plastic-to-bone ratios, and carrying out an all-solid waste filling slurry rheological test and an all-solid waste cementitious body strength test; and finally, according to the two test results, establishing a multi-objective optimization model taking the on-way resistance and the filling material cost as optimization objectives and taking the strengths of cementitious bodies at different ages as constraint conditions, and determining optimal parameters of the full-solid waste filling material. According to the invention, resource utilization of the metallurgical solid waste and the mine solid waste is realized, the bottlenecks of high carbon emission and high cost of a cement-based filling material are broken through, and a solution is provided for co-processing of the mine solid waste.

The invention provides a sewage treatment plant terminal effluent carbon source quality detection and evaluation method, which realizes full-flow dynamic evaluation and regulation of carbon source quality through on-line monitoring and intelligent algorithm fusion. According to the method, an online water quality full-spectrum detector is used for collecting original spectrum data flow, and after preprocessing such as variational mode decomposition denoising and mutual information feature selection, organic matter content quantification, variation trend analysis and anomaly detection are completed in combination with algorithms such as a support vector machine and an autoregressive moving average model. An entropy weight method is introduced to dynamically adjust the weight of the evaluation model, model parameters are optimized based on a gradient descent algorithm, a process adjustment instruction is generated through reinforcement learning and fuzzy logic, and an automatic system is linked to execute regulation and control. According to the method, the problems of hysteresis and singleness of traditional offline analysis are solved, multi-dimensional real-time evaluation, abnormal quick response and process dynamic optimization of the quality of the carbon source are realized, the sewage treatment efficiency and the effluent quality stability are improved, and a technical support is provided for continuous standard reaching of the quality of the carbon source.

The invention provides a sewage treatment operation cloud platform based on Internet of Things and GIS, and the platform is characterized in that a data collection layer is used for obtaining user water consumption data and sewage pipe network data, and distributing all the data to each edge calculation node; the data storage layer is used for temporarily storing the data preprocessed by the edge computing node into a high-frequency storage chain, aggregating to obtain sewage pipe network comprehensive data, and permanently storing the sewage pipe network comprehensive data into a low-frequency storage chain; the data analysis layer is used for extracting sewage pipe network comprehensive data in the low-frequency storage chain, constructing a multi-dimensional dynamic GIS model by utilizing a GIS system, analyzing and predicting the multi-dimensional dynamic GIS model through a sewage treatment rectification analysis model, and generating a sewage treatment rectification plan of the sewage pipe network according to an analysis and prediction result; and the application service layer is used for acquiring all the sewage treatment and rectification plans and distributing all the sewage treatment and rectification plans to a management client held by a sewage pipe network manager. The sewage treatment device has the effect of efficiently and intelligently realizing sewage treatment.

The invention provides a sewage AI intelligent management and control system based on a neural network algorithm, which comprises multi-source data acquisition units, an algorithm processing unit and an execution feedback unit, and is characterized in that the multi-source data acquisition units are deployed at a water inlet and a water outlet of a sewage treatment assembly line and in a biological reaction tank; comprising a fast parameter detection array composed of a pH sensor, a conductivity sensor, a dissolved oxygen sensor, a turbidity sensor, a temperature sensor and an ORP sensor. The process state monitoring group consists of an aeration equipment rotating speed sensor, a reflux pump flowmeter and a sludge concentration meter; the fast parameter detection array and the process state detection group are connected with the edge computing node through an industrial bus, perform sliding window mean filtering preprocessing on original data, generate regulation and control instructions of aeration rate, reflux ratio and sludge discharge frequency through model calculation, and send the regulation and control instructions to the execution unit; the complete refined AI intelligent control sewage treatment system based on the neural network algorithm is realized.

The integrated intelligent self-adaptive waste gas purification system comprises a waste gas collection unit, a pretreatment unit, a washing spray tower, a dehumidification unit, an activated carbon adsorption unit and an intelligent control system which are sequentially arranged according to a waste gas treatment flow, wherein the waste gas collecting unit is used for collecting waste gas from a plurality of production units; the pretreatment unit is used for removing particulate matters and part of water in the waste gas; the washing spray tower is used for removing harmful substances in the waste gas; the dehumidification unit is used for removing water vapor in the waste gas; the activated carbon adsorption unit is used for adsorbing harmful substances and malodorous substances in the waste gas; and the intelligent control system is used for controlling the purification data parameters of all the processing units. In addition, the invention further discloses an integrated intelligent self-adaptive waste gas purification treatment method. By combining physical, chemical and adsorption technologies, a complete waste gas purification process is formed, the purification efficiency is improved, meanwhile, the operation cost of enterprises is reduced, and remarkable social and economic benefits are achieved.

The invention relates to an intelligent decision-making method, system, equipment and medium for sewage medicament addition, and the method comprises the following steps: detecting and preprocessing sewage water quality parameters, extracting feature vectors, and carrying out clustering analysis to obtain water quality categories. Historical dosing data is retrieved and subjected to statistical analysis, and an initial dosing scheme is generated; a water quality change curve is obtained by simulating the scheme, and then the optimal dosing scheme is obtained through optimization. And finally, generating and issuing an agent adding control instruction, and executing treatment, so that the technical problems that the traditional agent adding mode mostly depends on artificial experience or simple automatic control, and is difficult to cope with the complex working condition of dynamic change of water quality and water quantity, so that the agent adding is inaccurate, and the agent waste is possibly caused are solved.

The invention discloses a charging pile liquid cooling three-level leakage prevention and pump obstacle prevention intelligent control method and system, and relates to the technical field of liquid cooling control. The charging pile liquid cooling three-level leakage prevention and pump obstacle prevention intelligent control method comprises the steps that S1, liquid cooling monitoring data are periodically collected, and the liquid cooling monitoring data are preprocessed; s2, judging whether an abnormal period exists or not, and evaluating whether a cooling liquid leakage trend exists or not in combination with real-time liquid cooling monitoring data; s3, performing coolant leakage risk grade evaluation, judging the leakage risk grade in the current abnormal period, and executing a corresponding leakage prevention strategy; and S4, in the execution process of the anti-leakage strategy, the running state of the circulating pump is evaluated, and whether the pump obstacle prevention and control strategy is triggered or not is judged. The problem that thermal runaway is caused by the fact that cooling abnormity is not intervened in time due to the fact that leakage of trace cooling liquid in an existing liquid cooling system is difficult to recognize in time and pump obstacles are difficult to pre-judge accurately is solved.

The invention provides a sewage treatment whole-process control method and device, and relates to the technical field of sewage treatment, and the method comprises the steps: collecting multi-dimensional parameter data in a sewage treatment process through a real-time sensor; preprocessing the multi-dimensional parameter data to generate multi-scale feature data; adjusting the original language model based on the sewage treatment historical corpus data and general data in the fields of environmental science and chemistry to obtain a sewage treatment control parameter prediction model; inputting the multi-scale characteristic data into the sewage treatment control parameter prediction model to generate a sewage treatment control parameter prediction result; and controlling the sewage treatment equipment based on the sewage treatment control parameter prediction result. According to the invention, complex scene data characteristics can be fully combined and sewage treatment control parameters can be optimized in real time, so that the sewage treatment operation efficiency, the resource recovery capability and the carbon emission control effect are improved.

The invention relates to the technical field of membrane bioreactors, and discloses an integrated sewage treatment membrane bioreactor and a sewage treatment method. The reactor comprises a treatment pond, a pretreatment unit and a membrane treatment unit are arranged in the treatment pond, the membrane treatment unit comprises a support, a pump body is fixedly installed at the top of the treatment pond, a channel is formed in the support, and the extraction end of the pump body is communicated with the channel through an extraction pipe. According to the invention, the root of a membrane filament can be clamped by a pair of moving clamping plates after the membrane filament is broken, so that the membrane filament is closed after being broken, sewage is prevented from entering the membrane filament without being filtered by filter holes and polluting the whole membrane treatment unit, the clamping plates can be protected, impurities are prevented from being accumulated on the surfaces of the clamping plates, and the service life of the membrane treatment unit is prolonged. And protection on the clamping plates can be cancelled during use, so that the clean clamping plates are exposed outside, the subsequent clamping plates cannot be influenced by impurities when clamping and sealing membrane filaments, and the sealing effect on the membrane filaments is better.

The invention relates to the technical field of sewage treatment, in particular to a sewage treatment control process based on dynamic regulation and control of aeration and carbon source addition, which comprises the following steps: acquiring microbial metabolism heat change data in real time; biochemical treatment: monitoring the dielectric constant of a water body and the Reynolds number of sewage flow in real time, and constructing a multi-parameter coupling regulation and control model and dynamically regulating and controlling the aeration rate in combination with microbial metabolism heat change data obtained in the pretreatment step; accurately adding the carbon source according to a calculation result of the carbon source demand prediction model; sewage subjected to biochemical treatment is subjected to ultrafiltration through a nanofiber membrane and then subjected to combined disinfection treatment through ultraviolet rays and ozone, treatment parameters are regulated and controlled in real time according to the membrane flux attenuation trend in the treatment process, and dynamic and accurate control over sewage treatment is achieved. According to the method, the problem of energy consumption waste caused by excessive aeration or low degradation efficiency caused by insufficient aeration in a traditional process is solved, and accurate matching of dissolved oxygen supply and microbial requirements is realized.

The invention discloses ultra-high performance concrete and a preparation method thereof. The ultra-high performance concrete comprises the following components in percentage by mass: 15%-25% of cement, 3%-6% of nano SiO2, 30%-35% of fly ash or steel slag powder, 35%-40% of recycled aggregate, 1%-4% of fiber, 0.5% of a microbial remediation agent and 1%-3% of nano TiO2. The preparation method comprises the following steps: S1, pretreating the recycled aggregate; and S2, packaging the microbial remediation agent: mixing the bacillus pasteurii spores with calcium lactate, and wrapping the mixture in calcium phosphate microspheres. S3, step-by-step stirring; S3.1, dry mixing: uniformly stirring the cement, the fly ash, the nano SiO2, the nano TiO2 and the recycled aggregate; s3.2, wet mixing is carried out, water solution fibers containing the polycarboxylic acid water reducing agent are added, uniform stirring is carried out, and the water-binder ratio of the water solution containing the polycarboxylic acid water reducing agent is 0.18; s3.3, final mixing: adding the packaged microbial remediation agent, and uniformly stirring; and S4, directional curing: performing steam curing at 80 DEG C for 48 hours, performing ultraviolet irradiation, and performing constant-temperature and constant-humidity curing for 7 days. The ultra-high performance concrete has the effects of low cost, low carbon emission, multi-function integration and repeatable crack self-repairing.

The invention discloses a sewage denitrification dosing method and system based on machine learning and a storage medium, and belongs to the technical field of sewage treatment.The method includes the steps that data are collected and preprocessed, and variable data influencing biochemical pool carbon source dosing behaviors are obtained; and lagging influence of carbon source input on the denitrification amount index is analyzed, and the duration time range of the drug effect is determined. And adopting the trained prediction model, and based on the denitrification amount index and the prediction variable of the future t + X period, obtaining the dosage of the (t + 1) th period. Through a correlation analysis method, the correlation rule of nitrogen conversion in the future X period after the carbon source is added is analyzed, the duration time of the drug effect is determined, the lag effect is accurately quantified, and the problem of mismatching of regulation and control opportunities is avoided. The hysteresis effect is captured and subjected to multi-factor coupling analysis based on the prediction model, the carbon source adding amount and time are optimized, system load fluctuation caused by excessive carbon sources or incomplete nitrogen removal caused by insufficient carbon sources are avoided, and the stability of an original sewage ecological system is gradually improved.

The invention provides a multiphase collaborative purification intelligent treatment system for heavy metal wastewater, which relates to the technical field of wastewater treatment and comprises a pretreatment module, a main treatment module, an advanced treatment module, a post-treatment and recycling module, a sludge treatment module and an intelligent control module. The intelligent control module comprises a data acquisition layer, a control layer and a management layer, and intelligent control on each treatment module is realized by monitoring parameters such as pH value, ORP (oxidation-reduction potential), conductivity and heavy metal ion concentration of the wastewater on line. The data acquisition layer acquires processing parameters in real time through various sensors; the control layer executes control logic based on the PLC / DCS and monitors the operation state through SCADA (Supervisory Control And Data Acquisition); the management layer adopts AI algorithms such as a water quality-energy consumption correlation regression model and a time sequence water quality fluctuation learning model to optimize processing parameters. According to the system, the treatment process can be intelligently adjusted according to the characteristics of the wastewater, the maximization of the heavy metal wastewater treatment efficiency, the minimization of chemical consumption and the minimization of energy consumption are realized, and the treatment effect and the system stability are improved.

The invention relates to the technical field of smart city operation, and discloses a pressure regulation and loss reduction method based on steam simulation and condensate water analysis, comprising the following steps: step S1, collecting operation parameters of a steam pipe network system; s2, converting the preprocessed operation parameters into scale space parameters; s3, establishing a condensate water source term equation, a heat source term equation and a condensate water generation model to obtain pressure, temperature and condensate water content; s4, solving the optimal pressure adjustment amount and the condensate water adjustment amount by combining the pressure and condensate water coupling matrix, and optimizing the circulation degree; and S5, correcting model parameters in the condensate water source term equation, the heat source term equation and the condensate water generation model based on a comparison result. By coupling and simulating the condensate water source item, the heat source item and the generation model, dynamic distribution of condensate water can be accurately analyzed, and the problems that in the prior art, calculation efficiency and model precision are difficult to balance, and dynamic coupling is lacked are solved.

The invention relates to the technical field of information, and discloses an agricultural sewage treatment control method and system based on the Internet of Things, and the method comprises the steps: obtaining sewage water quality data in different time periods, and carrying out the preprocessing of the data, and obtaining standard water quality data; extracting time sequence data of the water quality indexes from the water quality indexes, and obtaining smoothed time sequence data by using a time sequence analysis method; according to the smoothed time sequence data, through a trained long-short-term memory neural network model, obtaining prediction data of a future time sequence of the water quality index; according to the prediction data, constructing an objective function of an optimization problem so as to establish a multi-objective optimization model; solving the multi-objective optimization model by adopting a particle swarm optimization algorithm to obtain an optimal combination of process parameters; and converting the optimal combination into a control instruction, issuing the control instruction to execution equipment of the sewage treatment system, and controlling the operation state of the execution equipment. According to the method, the process parameters can be adjusted in time, and the efficiency and the effect of agricultural pollution treatment are improved.

The invention discloses an irrigation district water resource allocation method based on double layers and multiple targets, which comprises the following steps of: 1, collecting related data of an irrigation district, and preprocessing all the data to obtain a data set; 2, according to the data set, various parameters in the model are determined, and an upper-layer model and a lower-layer model are constructed; 3, processing a multi-target problem by adopting a fuzzy mathematical programming method, and converting interval parameters into deterministic parameters by applying an optimal-worst method; step 4, adopting a genetic algorithm, setting a proper population scale and genetic operation probability parameters, and on the basis of satisfying a target function and constraint conditions, realizing optimal configuration of water resources in the whole irrigation area through data interaction and iterative optimization of the upper-layer model and the lower-layer model; and 5, analyzing the final scheme, and evaluating economic benefits and social effects of water resource allocation in the irrigated area. The invention relates to the technical field of water resource optimal configuration, the water resource configuration is considered thoroughly, and the adjusting efficiency is high.

The invention discloses a BIM-based water conservancy project construction quality intelligent management method and system, and relates to the technical field of intelligent management. During operation of the system, various parameters related to the water conservancy project construction quality are acquired based on construction of a BIM model, data from a data acquisition module are preprocessed and integrated, and a BIM model is established; the method comprises the following steps: establishing a mathematical model for water conservancy project quality monitoring, establishing a dynamically updated and optimized model through regression analysis of historical data and a machine learning algorithm, predicting potential risks and quality problems in a construction process, and calculating a dynamic monitoring index Dtjk according to parameters output by a BIM model establishment module, the real-time monitoring and early-warning module is used for monitoring a BIM model, comparing the real-time monitoring and early-warning module with a preset qualified threshold value in the BIM model, giving out early warning according to the preset threshold value, reminding management personnel to take intervention measures, and providing improvement suggestions and optimizing a construction management scheme according to feedback information of the real-time monitoring and early-warning module and changes of a dynamic monitoring index Dtjk.