7265 results about "Water use" patented technology

The invention relates to the technical field of agricultural intelligent irrigation, and discloses a meteorological data fused water-saving irrigation control method, device and equipment and a medium. According to the method, a historical meteorological data set is constructed through multi-source meteorological data fusion, a dynamic water demand table is generated in combination with a crop water demand characteristic database, and a crop water demand model is established based on soil moisture content data. A grid irrigation unit division and growth period coupling soil moisture content response matrix construction technology is adopted, a cooperative constraint is established between a water demand threshold value and a water saving benefit through a multi-objective optimization learning algorithm, and a personalized irrigation scheme is generated. A soil moisture content dynamic evaluation matrix containing a dynamic time warping operator is designed, and dynamic matching of the soil layered soil moisture content and a standard template is achieved. The contradiction between meteorological response lag and low water resource utilization rate in traditional irrigation is effectively solved, accurate irrigation decision is realized through multi-dimensional data fusion and an intelligent optimization algorithm, and the water-saving benefit and the agricultural water resource utilization efficiency are improved.

The invention discloses a digital twinning-based wastewater and dirty salt treatment process optimization method and system, and relates to the technical field of intelligent wastewater and dirty salt treatment. The method comprises the steps that a digital twinborn model synchronously mapped with a processing system is established, and the digital twinborn model integrates the technological parameter incidence relation of an evaporation section, a crystallization section and a filtering section; collecting component data and flow data of the polluted salt wastewater corresponding to each process section in real time; correcting model characteristic parameters of each process section in the digital twin model based on the component data and the flow data collected in real time; according to the corrected digital twin model and the optimization target, generating a process optimization strategy corresponding to each process section; and monitoring the component data of the treated discharge water, reuse water, industrial salt and solid waste in real time, and if the data does not reach the standard, triggering model parameter correction, optimization strategy iteration or cross-section collaborative logic recalculation. According to the invention, intelligent cooperation of wastewater and dirty salt treatment is realized.

The invention provides a water resource scheduling optimization method and system based on artificial intelligence. The method comprises the following steps: acquiring water resource multi-source heterogeneous data of a target area, generating a dynamic balance strategy according to a precipitation gradient change trend of precipitation distribution characteristic data and a consumption rate association relationship in user side water demand fluctuation mode data, and determining the water demand fluctuation mode of the user side on the basis of node pressure distribution characteristics of dynamic load data of a water delivery pipe network. Outputting a multi-target water delivery path optimization scheme in combination with a priority weight in a dynamic balance strategy, and converting the multi-target water delivery path optimization scheme into a scheduling instruction set for controlling a pump station valve in the geographic space topology; according to the technical scheme provided by the invention, the defects of non-uniform resource allocation, poor real-time response capability and the like of a traditional static scheduling strategy are overcome, the utilization rate of water resources is also improved, and the flexibility and the response speed of a water supply system are enhanced.

The invention discloses a modeling analysis method for optimizing an urban water supply pipe network, and belongs to the technical field of intelligent water affairs, and the method comprises the steps: building a comprehensive pipe network database, collecting hydraulic parameters through Internet of Things equipment, and fusing the hydraulic parameters with GIS topological data; constructing a dynamic hydraulic simulation model, coupling real-time data of a pressure sensor with an EPANET model, and correcting a friction resistance coefficient of a pipe section; a hybrid optimization algorithm architecture is designed, a double-target genetic algorithm is adopted for old pipe network transformation, and a constrained particle swarm algorithm is adopted for new pipe network planning; executing multi-stage optimization calculation, generating a pipe diameter feasible solution space in combination with geographical constraints, and iteratively outputting a scheme meeting a pressure threshold value; and generating a pipe network optimal configuration scheme, and outputting an engineering implementation list through spatial overlay analysis and valve regulation logic. According to the method, the problem of modeling distortion caused by a multi-source data island is solved, the defect that a static model is difficult to adapt to dynamic water demands is overcome, and the economical efficiency and the engineering implementability of a pipe network optimization scheme are remarkably improved.

The invention discloses a flexible load multi-target collaborative scheduling system and method, and relates to the technical field of collaborative optimization of power systems. The method is used for solving the problem of lack of accurate prediction and multi-target coordination of agricultural electricity and water utilization regulation and control. Firstly, based on meteorological data, soil moisture content and crop growth characteristics, an irrigation demand prediction model is constructed, irrigation water demand is predicted, and a water pump load power baseline is generated; then, a dynamic baseline constraint condition is generated in combination with historical behavior data and water pump start-stop logic; establishing a power grid side objective function, a user side objective function and a water affair side objective function, and introducing an underground water and carbon emission punishment mechanism; thirdly, dividing the power distribution network into sub-regions, adopting an alternating direction multiplier method to solve a region regulation and control strategy in parallel, coordinating water resource distribution conflicts through virtual interactive variables, and outputting a global scheduling instruction; and finally, collecting real-time response data and correcting the prediction model on line to realize closed-loop adaptive optimization.

The invention relates to the technical field of water pump control, and provides a water supply and drainage pump set assembly and a digital control system, which realize self-adaptive control under a complex working condition by fusing multi-source data such as pressure, flow and temperature, break through the dependence of traditional control on a single parameter, and improve the response capability of the control system to water fluctuation and equipment state change. Based on a long-short-term memory network trained by historical multi-source data, a control system can predict a pressure set value in advance, the pressure set value is solved through a particle swarm algorithm, and weight fusion is performed according to the predicted pressure set value and the solved pressure set value to generate an adjustment instruction, so that the starting and stopping times of equipment and mechanical wear are reduced, and the service life of the equipment is prolonged. The maintenance cost is reduced, and water resource waste is avoided through accurate control; and meanwhile, through a redundancy design and a fault-tolerant mechanism, the control system can still operate stably under the scenes of sensor faults, equipment abnormity and the like.

The invention relates to the technical field of water resource scheduling, and provides an urban inland river system intelligent resource scheduling management system and method based on dynamic data collaboration, and the method comprises the following steps: S1, collecting river hydrological data, water quality indexes, urban water demands and water transfer facility state data in real time, and carrying out the time-space alignment and abnormal value filtering; s2, a hydrodynamic force-water quality coupling model of multiple water sources is established, a multi-objective optimization model is established in combination with constraint conditions of ecological base flow and an engineering upper limit, and the multiple water sources include but not limited to river water, underground water, reservoir water, reclaimed water, rainwater and desalinated seawater; and S3, carrying out global dynamic optimization on the water transfer proportion and the water transfer facility parameters by adopting a model algorithm, and minimizing the water transfer cost and the ecological influence. By quantifying the maximum flow upper limit and the ecological base flow threshold value of each water source and optimizing the water transfer proportion and facility parameters, the water resource utilization efficiency is improved, and it is guaranteed that the water transfer cost and the ecological influence are balanced in the urban river water dispatching process.

The invention discloses a multi-target water supply scheduling method and system based on big data driving, and the method comprises the steps: collecting the multi-source data of a plurality of water supply targets, and enabling the scheme to obtain more reliable data support in the scheduling planning of the water supply targets; based on multi-source data serving as input features, the scheme combines the multi-source data with a graph space-time prediction model, a season statistical model and a gradient lifting regression model to obtain water consumption predicted by the three models respectively, prediction results of the three models are integrated through error adaptive weighting, more reliable predicted water consumption requirements are obtained, and the prediction efficiency is improved. And after the linear distribution model of the water source and the water supply targets is solved, initial water supply distribution schemes in one-to-one correspondence with the multiple water supply targets are obtained, the water supply accuracy of the water supply targets can be improved, multiple costs are used as targets to be minimized, and after multi-constraint combined solving, the water supply accuracy of the water supply targets can be improved. Therefore, the scheduling cost optimization is considered under the condition that the multi-target water supply scheduling meets flexibility and reliability.

The invention discloses a regional water supply emergency scheduling method and system. The method comprises the following steps: fusing GIS and IoT data to construct a dynamic three-dimensional topology model; historical water consumption and meteorological data are decomposed through wavelet multi-scale decomposition, and a prediction model is input to obtain a water consumption prediction value of a scheduling day period; on the basis of reservoir water storage, flow, water quality, rainfall and pollution source data collected in real time, a flow-water quality coupling model is used for predicting the reservoir inflow and water quality of a dispatching day; the method comprises the following steps: establishing a multi-objective function by combining water consumption prediction, storage prediction and current water storage, embedding a water quality constrained multi-objective optimization model, and solving by adopting an NSGA-II algorithm to obtain a Pareto optimal scheduling scheme set; and finally, a scheme is selected to be issued and executed. According to the method, data-physical collaborative intelligent scheduling decision is realized, the water supply demand is met under the condition that the water supply quality is stable, the condition of water shortage of the user terminal is avoided, and the water quality of the user terminal can continuously reach the standard.

The invention discloses a rural water supply pipe network pressure monitoring analysis method and device based on digital twinning. The method comprises the following steps: collecting historical basic data of a rural prediction area; flow and pressure sensors are arranged at nodes of a rural water supply network to obtain monitoring data; uploading the data to a server through a data stream processing framework, and preprocessing the data to obtain preprocessed data; constructing a relational database management system to perform batch processing on the preprocessed data to obtain historical basic data and monitoring data after batch processing; constructing a rural pipe network water pressure prediction model; constructing a pipe explosion detection and water pressure control model; according to the physical data, constructing a rural water supply pipe network geometric model based on ArcGIS; and constructing a rural water supply pipe network system data model and realizing visualization of the operation state of the water supply pipe network. Rural water supply characteristics and water consumption characteristics are fully considered, intelligent management and control of the rural water supply system are achieved, and reliability and safety of rural water supply are effectively guaranteed.

The invention provides an intelligent water affair scheduling method based on LSTM and multi-objective optimization, and belongs to the technical field of intelligent water affair management and urban water supply system optimization. Comprising the following steps: based on water consumption data, performing analysis through a long short term memory neural network to obtain water consumption demand prediction; based on the water consumption demand prediction, combining pipeline information and pipe network layout data, performing pipe network pressure partition optimization, and obtaining a pipe network layout optimization result; and based on the pipe network layout optimization result, combining the historical operation data of the equipment and the water demand prediction, carrying out efficiency analysis on the pump station equipment to obtain an operation combination scheme of the equipment.

An intelligent fertilization and water supply method and system. The method comprises the following steps: step a, comprehensive soil monitoring, involving: comprehensively monitoring various soil indexes by means of a plurality of sensors, so as to obtain real-time data of the soil indexes; b, consideration of crop-specific requirements, involving: for the varieties and growth stages of different crops, thoroughly studying the unique moisture and nutrient requirements of the crops, so as to ensure the rationality and accuracy of a fertilization amount; c, comprehensive consideration of environmental factors, involving: collecting data of surrounding environmental factors; d, utilization of an intelligent irrigation mode, involving: by using a cyclic irrigation mode, an intermittent irrigation mode, etc., in combination with the water requirement pattern of each crop and a change in soil moisture, realizing efficient water utilization; e, implementation of a dynamic fertilization plan, involving: on the basis of a change in a crop growth condition and in soil fertility, dynamically adjusting fertilization time, frequency and dosage, so as to ensure that the nutrient supply matches the requirements of each crop; and f, implementation of a precise fertilization technique, involving: precisely delivering fertilizer to an absorption part of each crop, so as to improve the efficiency of nutrient utilization.

The invention discloses an intelligent precise farmland irrigation direction control system based on the Internet of Things, and relates to the technical field related to intelligent irrigation. Comprising a data acquisition module, a data processing and analyzing module, a data labeling and classifying unit, a three-dimensional coordinate model building module, a nozzle angle adjusting device, a nozzle water quantity adjusting unit and a control instruction execution unit. According to the invention, the distance between the nearest nozzle and the water-requiring position is calculated, so that irrigation water can reach the water-requiring area more efficiently, the distance is accurately calculated to ensure that water can be quickly and accurately conveyed to the required area, water loss and energy waste caused by long-distance water conveying are avoided, the water spraying direction is adjusted in combination with the wind direction and the wind speed, and the water spraying efficiency is improved. The device can effectively cope with the influence of natural wind on irrigation, and by monitoring wind direction and wind speed information in real time and combining the wind direction and wind speed information with water spraying parameters of the spray head, the system accurately calculates horizontal and vertical angle adjustment values of the spray head, so that sprayed water flow can accurately reach a water needing position.

The invention discloses an urban resident water consumption behavior classification method based on an intelligent water meter, and relates to the technical field of intelligent water affairs. The method is used for improving water consumption behavior recognition and anomaly detection precision. The method comprises the steps that firstly, a water flow change direction sequence is generated in real time through an intelligent water meter, and flow gradient and timestamp density characteristics are captured and extracted based on a stable persistence trigger event; then, eliminating water hammer noise by adopting an adaptive filtering mode, extracting a key turning point in combination with curvature analysis, comparing the key turning point with a behavior pattern library topology, and generating a dual-channel behavior feature vector; further, metering grids are divided according to valve distribution in the building water supply system, distribution clustering is executed in combination with the pressure gradient, and a cross-grid abnormal feature conduction path is constructed; and finally, tracing an abnormal diffusion path by means of a multilayer graph convolutional network, positioning a water pressure abrupt change inflection point, mapping the water pressure abrupt change inflection point to a building component, and outputting a leakage point identifier and a behavior mode label, thereby realizing high-precision classification and abnormal identification of residential water consumption behaviors.

The invention discloses an agricultural water utilization right distribution and transaction management method suitable for a high-water area, and particularly relates to the technical field of agricultural water utilization right management. A self-adaptive water right scheduling model is constructed, the water surplus and shortage conditions in the future 2-24 hours are calculated, the agricultural water quota is dynamically adjusted through a regional water right redistribution mechanism, and meanwhile, a water right transaction is divided into a long-term contract transaction and a short-term instant transaction based on an intelligent contract system of a block chain, so that the water right transaction efficiency is improved. The peasant households are allowed to match and dispense water resources in real time through the intelligent water right transaction terminal, the water right transaction matching rule is further adjusted in combination with historical transaction data, a water transfer execution effect and peasant household water consumption behavior analysis, and a regional water resource scheduling strategy is adaptively adjusted; accurate irrigation, efficient water right circulation and intelligent scheduling are achieved, the utilization efficiency of agricultural water resources is effectively improved, stable production of farmlands is guaranteed, meanwhile, a water right market circulation mechanism is adjusted, and the flexibility and the intelligent level of water resource regulation and control are improved.

The invention discloses a stepped treatment process for multi-path recycling of oil and gas field drilling mud, which comprises the following steps: oxidizing and breaking gel of the drilling mud to scatter the mud structure, and then granulating and coagulating; then, carrying out the following gradient treatment: step 1, filtering the outlet water of granulation and coagulation, and taking the filtrate as reinjection water; step 2, carrying out nucleation granulation reaction on the filtrate obtained in the step 1, using effluent for preparing drilling fluid, and carrying out solid waste treatment on solid-phase crystal seeds and solid-phase mud cakes subjected to granulation and coagulation; and step 3, carrying out ozone flotation reaction on the effluent obtained in the step 2, wherein the effluent is used for preparing the guanidine gum fracturing fluid. According to the method, resource recycling of the waste drilling mud is achieved, the usage amount of a gel breaker in a traditional gel breaking and filter pressing technology is reduced, a filter pressing plate is omitted, the treatment efficiency in the mud recycling treatment process is improved, the treatment cost of the waste water-based drilling mud is reduced, different recycling ways can be met, and the method is suitable for popularization and application. And specific operation is flexible and changeable.

The invention provides an intelligent management method and system for reducing pollution and carbon of rural domestic sewage. According to the method, sewage flow velocity, COD concentration, ammonia nitrogen content data, user water use time and detergent use amount are collected, and a database is constructed. And weighting the flow velocity and COD to generate a pollution load evaluation value, and calculating an organic matter pollution influence value in combination with ammonia nitrogen and detergent data. A porous ceramic medium is arranged in the biological treatment device, and the dissolved oxygen concentration is adjusted according to the pollution load. A correlation curve of a COD concentration peak value and water consumption time is established, a prediction model is constructed in combination with dissolved oxygen parameters, the gas injection and reflux ratio is adjusted through graded pressure instructions, and the critical value of dissolved oxygen is maintained. The day and night temperature difference is fused to correct the degradation rate, and the optical detection data is combined to optimize the critical range and the instruction priority, so that the cooperative control of sewage treatment and carbon emission reduction is realized. According to the invention, self-adaptive adjustment of dissolved oxygen in sewage treatment is realized, and the sewage reduction and carbon reduction management efficiency of domestic sewage is improved.

The invention discloses a digital twin water affair dynamic monitoring and evaluation method based on big data, and relates to the technical field of electronic digital data processing. Firstly, pipeline information of each water affair dynamic monitoring sub-area is analyzed, the operation condition of the pipeline can be recognized in time, then water quality parameters of each pipeline normal water affair dynamic monitoring sub-area are analyzed, water quality abnormity can be found in time, and corresponding measures are taken to guarantee water supply safety. And finally, analyzing to obtain a water affair dynamic comprehensive evaluation value of the normal water affair dynamic monitoring sub-region of each pipeline, which can reflect the water supply capacity and the water demand of each monitoring sub-region, thereby realizing reasonable utilization of water resources. Meanwhile, the water resources are scheduled and adjusted according to the water affair dynamic comprehensive evaluation values of the normal water affair dynamic monitoring sub-areas of all the pipelines, the water supply strategy can be rapidly adjusted, and the stability and reliability of water supply are ensured.

The invention discloses a unit area irrigation water consumption estimation method and system based on multi-source data fusion, and relates to the field of irrigation water consumption estimation. Multi-source heterogeneous data such as remote sensing images, meteorological data, soil humidity and crop growth information are fused, and observation and model errors are reduced through data assimilation; estimating crop evapotranspiration under an energy balance principle; estimating daily-scale irrigation water consumption by adopting a water balance method; a crop actual irrigation area identification model based on deep learning is introduced, actual irrigation areas of different crop types are effectively distinguished, and the influence of temporal and spatial changes on irrigation efficiency is dynamically reflected. According to the method, high-precision and rapid estimation of the comprehensive unit area irrigation water consumption of a single crop and multiple crops on different regional scales can be realized, the timeliness and accuracy of irrigation water quota evaluation are remarkably improved, and the method has strong adaptability to climate change, soil difference and crop diversity and is suitable for popularization and application. Powerful technical support is provided for precise irrigation and intelligent water conservancy management.

The invention relates to a secondary water supply optimal scheduling method and device for a building, and aims to realize efficient, stable and intelligent operation of a secondary water supply system through water supply demand prediction, multi-objective optimal scheduling and real-time monitoring and feedback. The method comprises the following steps: predicting future water demand by using a long short-term memory neural network, constructing a multi-objective optimization model containing energy consumption, water supply pressure and pump set life, and solving an optimal scheduling scheme by adopting a genetic algorithm. By monitoring water supply pressure and flow in real time and dynamically adjusting start and stop and frequency conversion speed regulation of the water pump, energy-saving operation of the system is ensured, and the service life of equipment is prolonged. The system is suitable for secondary water supply systems of high-rise buildings and large building groups.

The invention discloses a heat-pump water heater temperature control method based on artificial intelligence, relates to the technical field of heat-pump water heater temperature control, and aims to solve the problem that the COP (coefficient of performance) is reduced due to a low-temperature environment, the exhaust temperature of a compressor and the opening degree of an expansion valve are optimized in real time through a GA (genetic algorithm), and operating parameters of a heat-pump water heater are dynamically adjusted by taking COP maximization as a target; the operation frequency of a compressor and the flow of a water pump are dynamically adjusted through fuzzy control logic in combination with a GA optimization result, the current water temperature and the temperature set by a user, the stability of the water supply temperature is guaranteed, an optimal control strategy is calculated through defuzzification, and the adaptability under different environment conditions is improved; an ANN is adopted to learn and predict a user behavior pattern, an LSTM network is utilized to analyze time sequences of historical water consumption data, target temperature and environment temperature, future water consumption peak and temperature requirements are predicted, a preheating function is started in advance, and insufficient hot water supply in the water consumption peak is avoided.

The invention relates to the technical field of agriculture, in particular to a greenhouse energy-saving water consumption monitoring system which is used for solving the problems that in the prior art, historical data and real-time data cannot be combined to accurately predict irrigation water consumption, scientificity and rationality of an irrigation strategy cannot be guaranteed, and the irrigation cost is low. And different environmental conditions and crop growth stages cannot be dynamically adapted. Historical data and real-time data are combined through the irrigation water consumption adjusting module, the irrigation water consumption is accurately predicted through a machine learning model, automatic adjustment is achieved to improve efficiency, multiple factors are comprehensively considered, scientificity and reasonability of an irrigation strategy are guaranteed, different environment conditions and crop growth stages are dynamically adapted, water resource utilization is optimized, and the irrigation efficiency is improved. Cost is reduced, personal errors are reduced through automatic operation, and various crops and complex growth environments are supported.

The invention relates to an inter-basin water saving amount transaction and scheduling optimization method, which comprises the following steps of: performing dynamic contrast analysis on water saving behaviors and water resource response on the basis of historical water consumption behaviors, resource regulation and control records and meteorological basic data, judging the autonomy and continuity of the water saving behaviors, and obtaining the right-confirmable water saving amount; according to the watershed water resource tension degree, the water ecology sensitivity and the water transfer cost, a cross-watershed conversion rule is adopted, a conversion scale factor is set, and resource value transverse butt joint of water saving behaviors of different areas is achieved; calculating the flow attenuation, the regional bearing threshold and the water quality compatibility in the allocation path based on the water-saving amount obtained by the water-saving transaction, and determining the available transmission range and the sustainable allocation amount of the water-saving amount; and according to the distribution of the water-saving quotas and the corresponding value grades, carrying out combined screening according to the use priority, the basin compensation relationship and the space-time adaptation requirements, outputting a resource allocation scheme, and forming a cross-basin water-saving-quantity-driven water resource optimal allocation structure.

The invention discloses a water outlet control method, system and device for an intelligent water valve, and relates to the technical field of water outlet control of water valves. The water outlet control method for the intelligent water valve comprises the following steps of water outlet prediction, intelligent water valve adjustment evaluation and water outlet control effect evaluation. The real-time water outlet data corresponding to each intelligent water valve control mode is collected, the water outlet prediction index is obtained in combination with the corresponding historical water outlet data so as to adjust the opening degree of the valve, then the intelligent water valve adjustment evaluation index is obtained according to the real-time water consumption data and the real-time water return data, and whether the intelligent water valve control mode is switched or not is judged. Finally, water outlet control adjustment measures are taken according to the water outlet control effect evaluation index, the effect of improving the water outlet control effectiveness of the intelligent water valve is achieved, and the problem that in the prior art, water outlet control of the intelligent water valve is not sufficiently associated with user requirements is solved.

The invention relates to the technical field of remote water meter abnormity early warning, and discloses a remote water meter abnormity early warning method and system based on historical water volume, and the method comprises the steps: dividing a user water meter into three classes A, B and C according to the installation time of the user water meter, and employing a differentiated abnormity detection strategy. For the A-type users, establishing a water consumption prediction model based on historical consumption data of the A-type users; for the B-type users, finding similar reference water meters from the A-type users by using a K-means clustering algorithm; and for the C-type users, monitoring whether the accumulated water consumption for continuous N days is completely equal or not after the C-type users are installed. After real-time usage data is obtained, usage deviations of various users are calculated respectively, if the deviations exceed a set threshold value, abnormal early warning is triggered, and the early warning level is dynamically adjusted according to the number of abnormal duration days. According to the method, technologies such as machine learning and clustering analysis are comprehensively utilized, a set of comprehensive, flexible and efficient water consumption abnormity early warning system is constructed, and the refinement and intelligence level of urban water supply management can be remarkably improved.

The invention discloses a water supply energy-saving safe scheduling and purification intelligent control system, and relates to the field of water supply energy-saving purification, and the system comprises a sensing module, a calculation module, an acquisition terminal, a scheduling module and a purification module.A final water demand prediction value is obtained by constructing a time-sequence-dependent water use feature set and performing prediction and error correction on an LSTM model; performing water supply energy-saving safety scheduling analysis, generating a water supply pipeline equipment control parameter combination, performing pipeline state abnormity and pipeline fluid pressure analysis, calculating a pipeline state abnormity value, performing pressure loss state change analysis when the pipeline fluid flows to obtain a pipeline fluid pressure loss value, and performing image construction to obtain a water pipeline pressure difference value; the electronic valve works according to the adjusted valve opening value through the scheduling module, and the calculation module receives the water quality data obtained by the sensing module and performs corresponding valve control and purification treatment, so that responsibility complaints of users are greatly reduced, and resource waste is reduced.

The invention discloses a water quality monitoring method and device, a storage medium and a program product, and relates to the technical field of water quality treatment.The method comprises the steps that water quality data collected by multiple sensors are preprocessed to obtain standardized water quality data, and the multiple sensors are arranged at key nodes of a clean workshop; inputting the standardized water quality data into a water quality prediction model obtained by training based on a convolutional neural network model to obtain water quality change trend data; and when the water quality change trend data reaches an early warning threshold value, triggering an early warning mechanism, and inputting the water quality data into the Bayesian fault model to obtain a water quality optimization strategy. After the water quality data is collected, the water quality prediction model can be used for accurately predicting the water quality change trend, early warning is performed when the water quality is possibly abnormal, and adjustment is performed through the water quality optimization strategy. The situation of hysteresis of an existing water quality monitoring method is avoided, so that the water quality problem can be solved in advance, and the quality and efficiency of the production water of the clean workshop are guaranteed.

The invention discloses a water supply control method and system based on fluid flow self-adaptive adjustment. The efficiency and reliability of a water supply system are improved. The method comprises the steps that pipe network layout information of a target water supply area is acquired, a water supply network topological structure is constructed, and the layout position of a water supply monitoring sensor is determined; constructing a water supply monitoring simulation model based on a topological structure and sensor layout, monitoring a water supply condition in real time, and identifying water demand characteristics; constructing an adaptive flow regulation model according to the water demand characteristics, analyzing demands, determining water supply control parameters, and generating a water supply control strategy; the water supply control is realized through the strategy, the satisfaction degree of the water demand is evaluated in real time, and the water supply control strategy is optimized. The water supply system can intelligently adapt to water demand changes, and the operation efficiency and service quality of the water supply system are improved.

The invention discloses a time-phased intelligent pressure regulation management method and system for secondary water supply, and belongs to the technical field of water supply management, and the method specifically comprises the steps: dividing a day into a water consumption peak period and a water consumption low period based on historical water consumption flow data, and determining the starting and ending time nodes of each time period; through hydraulic calculation and actual water supply pressure monitoring, a pressure control point at the tail end of the water supply system is locked, and water supply pressure data of the point are collected in real time; in the water consumption peak period, a peak pressurization control strategy is executed; in the water consumption low-peak period, analysis is conducted according to historical water supply pressure data of a tail end pressure control point, and a low-peak period pressure safety threshold value is set; a pressure instruction is sent to a control system according to the threshold value, and water supply equipment is controlled to output pressure according to the threshold value; the water supply pressure of a tail end pressure control point is monitored in real time, and when the pressure is lower than a low-peak-period pressure safety threshold value, pre-alarm is triggered, and pressure changes are continuously recorded; and if the pre-alarm state is not finished continuously, switching to a peak-period peak pressurization control strategy.

The invention discloses a fire hydrant pressure adaptive regulation and control system based on Internet of Things data, and relates to the technical field of urban water supply pipe network fire hydrant pressure control, and the system comprises a multi-source data collection module which is used for collecting fire hydrant pressure time sequence data in real time from a plurality of Internet of Things nodes deployed in an urban water supply pipe network, and historical pressure data, pipeline topological structure data and user water consumption mode data of the water supply network are obtained from a historical database. Real-time and historical data are integrated through the multi-source data acquisition module, a comprehensive basis is provided for pressure regulation and control in combination with a pipe network topology and a user water consumption mode, and the integrity of data support is improved; the pipe network state prediction module is used for extracting multi-dimensional features and predicting pressure changes by means of an LSTM model, the pressure abnormal risk can be recognized in advance, and hysteresis of traditional regulation and control is avoided.