1942 results about "Water consumption" patented technology

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 provides an intelligent maintenance method and system for the wall of a squat silo, and the method comprises the steps: applying controllable environment parameters to a concrete test block group with the same proportion through an environment simulation device, and obtaining test block deformation data, moisture content data and stress data; performing time sequence correlation mapping on the test block deformation data, the moisture content data and the stress data and environmental parameters to obtain a maintenance decision data set; acquiring a real-time environment vector through a sensor network deployed on the site of the squat silo; inputting the real-time environment vector into a maintenance decision model, and generating a spraying control instruction by matching the maintenance decision data set; and driving a spraying system to execute operation according to the spraying control instruction, and dynamically correcting spraying parameters based on moisture monitoring data. According to the method, through data-driven intelligent maintenance decision and dynamic closed-loop control, the crack risk of the wall of the squat silo is remarkably reduced, meanwhile, water resource consumption is reduced, and double breakthrough of maintenance quality and resource efficiency is achieved.

The invention discloses an irrigation area scale irrigation water consumption monitoring method based on physical constraint machine learning, and is applied to the technical field of irrigation water consumption monitoring. Comprising the following steps: constructing a space-time continuous sequence of irrigation response characteristics of an irrigation area by using a multi-source data integration and assimilation technology; the irrigation water amount balance equation and the earth surface energy conservation law are used as regularization terms of physical constraints to be embedded into the training process of the long and short term memory deep learning model, so that an irrigation water amount prediction model is established; and dynamically estimating the irrigation water consumption of the irrigation area based on the irrigation water consumption prediction model obtained by training. According to the method, the water balance equation and the energy conservation law are embedded into the machine learning model, so that the model prediction result follows the basic physical law, and water consumption estimation violating the physical common sense is avoided. The introduction of the physical constraint improves the robustness and generalization ability of the model under an unconventional climate condition or a limited training data condition, and enhances the credibility and interpretability of a result.

The invention discloses an agricultural and pastoral area water network regulation and storage project scale determination method and system. The method comprises the following steps: collecting historical data of an agricultural and pastoral area, dividing the agricultural and pastoral area into a plurality of secondary sub-areas, respectively calculating the water demand and available water consumption of each secondary sub-area, and identifying a water shortage time period and a water shortage area; constructing a regulation and storage project layout scheme set, and calculating the regulation and storage capacity and project parameters of each regulation and storage project; constructing a single-target optimization model, simulating a regulation and storage result of each regulation and storage project layout scheme, evaluating each regulation and storage project layout scheme, and screening out an optimal regulation and storage project layout scheme; and constructing an irrigation scheduling optimization model based on the optimal regulation and storage project layout scheme, solving the model to dynamically optimize and adjust the water storage time, the water discharge time and the water discharge amount of the regulation and storage project, and generating an agricultural and pastoral area water network regulation and storage project layout scheme. The water resource utilization efficiency of agricultural and pastoral areas is improved, the water resource supply and demand contradiction is relieved, and sustainable development of agriculture and animal husbandry and ecological environment improvement are promoted.

The invention discloses an intelligent water affair management method and system based on artificial intelligence, and relates to the technical field of intelligent water affair management, and the method comprises the steps: initializing a pipe network digital twinborn model, loading pre-stored pipe network GIS data, collecting pipeline pressure data, water quality parameters and user water consumption in real time, inputting a risk assessment model, calculating a real-time risk probability, and carrying out the real-time risk assessment. When the real-time risk probability exceeds an early warning threshold value, generating a high-risk early warning signal; based on the high-risk early warning signal, a differential order parameter and an integral gain parameter are calculated through a pipe state fractional order controller, a voltage stabilization control instruction is generated and executed, and meanwhile a control error signal in the voltage stabilization process is extracted; inputting the control error signal and the water source type label into the element reinforcement learning model, and dynamically optimizing the differential order parameter and the integral gain parameter to obtain a water source risk coefficient; according to the method, personalized modeling and dynamic updating of risk assessment are realized by outputting the dynamic response characteristic data.

The invention relates to the technical field of computer data processing, and particularly provides an intelligent washing parameter optimization decision-making method based on multi-modal perception fusion, which comprises the following steps: acquiring stain characteristic parameters of a fabric to be cleaned in real time through a multi-source acquisition module; constructing a collaborative regulation model, determining the type and proportion of the detergent and mechanical force parameters based on the stain characteristic parameters, training the collaborative regulation model by taking the maximization of the stain removal rate as a target and taking the energy consumption and the water consumption as constraint conditions, and outputting an optimal washing strategy; controlling the washing device to execute corresponding operation according to the washing strategy; stain residues and energy consumption abnormal data in the washing process are monitored in real time, the washing rotating speed and the washing duration are dynamically corrected, and the detergent supplementing and putting amount is synchronously adjusted according to chemical component distribution. The limitation of the existing single-mode recognition technology on complex stain analysis is broken through, resource waste and fabric damage caused by traditional extensive washing are avoided, and the technical contradiction between the decontamination effect and resource consumption in the prior art is effectively solved.

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 relates to the technical field of reservoir flood control dispatching, and discloses a dam hydraulic opening and closing control method, device and equipment and a storage medium. The method comprises the steps that multi-source hydrological data, weather forecast information and unit operation states of a current reservoir are collected; inputting the multi-source hydrological data and the weather forecast information into a preset hydrological prediction model, and outputting a hydrological prediction result; based on the hydrological prediction result and the real-time reservoir area water level and the unit operation state in the multi-source hydrological data, a gate opening control instruction is generated through a reservoir optimization scheduling model; and a hydraulic opening and closing system is adjusted according to the gate opening control instruction, and the current reservoir flood discharge gate opening is controlled. According to the method, data such as upstream incoming water, reservoir area water level and water consumption are utilized, weather forecast information is combined, a hydrological prediction model is constructed, a reservoir dispatching scheme is optimized according to a hydrological model prediction result, the reservoir area water level and unit operation conditions, and on the premise that flood prevention safety is guaranteed, reservoir dispatching is optimized, the reservoir area water level is automatically adjusted, and power generation benefits are improved.

The invention relates to the technical field of water meter flow, in particular to an ultrasonic water meter flow curve fitting system, which is characterized in that an edge computing device is used for primarily processing original data at a data acquisition end, a mean filtering algorithm is used for removing noise data, and the data after acquisition is subjected to filtering, denoising, abnormal value elimination and normalization processing; a dynamic evaluation model is constructed by adopting a time sequence prediction algorithm, the model calculates and predicts various data of future ultrasonic water meter flow by continuously inputting real-time data, and the model is optimized according to a prediction result, so that errors of the model are continuously reduced in the training process, and the prediction accuracy of the model is improved; modeling and fitting are conducted on the flow curves, calculated by the neural network model, of the ultrasonic water meter in different working states through the fitting model, the fitted curves can serve as a basic data source of a user water consumption behavior and system load characteristic advanced analysis model, and energy-saving analysis and fault positioning are facilitated.

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.

The present invention relates to the technical field of ultrasonic therapy, and particularly to a water circuit arrangement structure of an ultrasonic therapy device and an ultrasonic therapy device. The water circuit arrangement structure of the ultrasonic therapy device includes an ultrasonic therapy head, a water supply pipeline, and a water receiving tray. The ultrasonic therapy head has a cavity for accommodating a breast and performing treatment, and the inner wall of the cavity is an arc surface. A first connection channel communicating with the water supply pipeline is provided at the bottom of the ultrasonic therapy head. During ultrasonic therapy, a water medium enters the cavity from the bottom of the ultrasonic therapy head and overflows from the top of the cavity of the ultrasonic therapy head. The water receiving tray is arranged outside the ultrasonic therapy head and is used for receiving the water medium overflowing from the cavity of the ultrasonic therapy head. The beneficial effects of the present invention are as follows: The ultrasonic therapy head directly serves as a carrier for holding water, reducing water consumption, and reducing pipeline settings, avoiding winding interference. The structure is simple and the layout is compact, and the treatment operation is simple, convenient, and flexible.

The invention discloses a rice field irrigation control method, and belongs to the field of rice field irrigation, and the method comprises the steps: obtaining soil humidity, water level and meteorological data from a rice field through a sensor network, storing the data in a pre-established distributed database, and obtaining a multi-source data set; according to the data set in the unified format, key features of soil humidity, water level and meteorological data are extracted by adopting a principal component analysis method, and a feature vector set is obtained; if the water consumption of the predicted irrigation scheme exceeds a preset threshold value, the scheme is optimized and adjusted through a linear programming algorithm, and an optimized irrigation scheme is obtained; according to the optimized irrigation scheme, the water consumption in the irrigation process is monitored in real time through flow meter equipment, and accurate measurement data are obtained; and the accurate measurement data is distributed through a quota management module, a regional water resource allocation scheme is generated by adopting a dynamic quota algorithm, and a quota management result is obtained.

The invention belongs to the technical field of water supply network adjustment, and particularly relates to a water supply network pressure and flow adjusting method. The method comprises the following steps: firstly, collecting pressure, flow and water temperature data of a monitoring node at present and in a preset time period, wherein the preset time period is set according to a water consumption period; preprocessing flow data through a sliding window method and fitting a curve, and marking an instantaneous disturbance time period; the dynamic response coefficient of each disturbance time period is calculated, and water temperature correction is needed; combining the flow standard score, the dynamic response coefficient and the real-time deviation coefficient to obtain a flow dynamic regulation index; a pressure stability index is obtained through pressure and water temperature trend feature vector matching; the two are subjected to weighted fusion to form a pipe network comprehensive regulation factor, and the valve opening and the water pump power are regulated accordingly. The method can accurately identify instantaneous disturbance, improve the adjustment precision and response speed, and guarantee stable water supply.

The invention discloses an intelligent water meter operation monitoring method and system based on the Internet of Things, and relates to the technical field of intelligent water meters. According to the method, a disturbance consistency scoring index Ipd and a behavior map variation score Simv are constructed, and a multi-parameter coupling backtracking risk grade scoring function Rtrace is introduced; the disturbance sudden change intensity, the period stability, the flow direction consistency and the historical behavior deviation degree can be comprehensively considered, and the recognition precision of the unnatural disturbance water flow and the potential abnormal water consumption behavior is effectively improved. Particularly, under the condition that disturbance abnormity and behavior map abnormity are coupled, secondary comparison evaluation is carried out on a backtracking risk grade scoring function Rtrace and a risk interval threshold value, so that risk judgment and risk grade division with higher distinction can be realized, illegal water taking, backflow and backward flow and other abnormal water consumption behaviors can be accurately locked under the condition of a low misjudgment rate, and the accuracy and the reliability of the system are improved. And the intelligent sensing capability and the behavior modeling reliability of the urban water supply system are enhanced.

A combined dust suppression system for a metal ore unloading station, and a using method. The combined dust suppression system consists of an ore unloading station tunnel wall surface accumulated dust high-pressure water flushing device, an ore unloading station tunnel floor spraying, watering, and wetting device, and an ore unloading station ore train floor bidirectional high-pressure spray dust suppression device. The ore unloading station tunnel wall surface accumulated dust high-pressure water flushing device is used for performing manual flushing on a tunnel by means of high-pressure water in the travel direction of an ore unloading train, the ore unloading station tunnel floor spraying, watering, and wetting device is used for performing spraying, watering, and wetting on the tunnel, and the ore unloading station ore train floor bidirectional high-pressure spray dust suppression device isolates dust generated during ore unloading at the ore unloading station. The water consumption can be reduced, excessive water in an ore unloading pit that causes adhesion and blockage can be effectively avoided, the field applicability of high-pressure spray dust suppression measures is improved, and on-demand spray dust removal of a high-pressure spray device is achieved; high-pressure water is used for flushing accumulated dust on the tunnel wall surfaces of ore unloading stations, and the tunnel floors of ore unloading stations are sprayed, watered, and wetted, thereby achieving significant comprehensive dust suppression and dust control effects, and notable energy-saving effects.

The invention provides an intelligent irrigation district management method and system based on the Internet of Things, and the method comprises the steps: obtaining the real-time irrigation water amount and water pump energy consumption data of each water conveyance channel in a target irrigation district; according to the real-time irrigation water amount and the water pump energy consumption data, generating a dynamic water resource supply and demand relation graph; determining a target water delivery channel corresponding to the water resource transaction request of the water consumption terminal of each irrigation area from a target irrigation area, and if any target water delivery channel has a condition that a water pump is overloaded or a deposition diffusion coefficient value is greater than a preset diffusion coefficient threshold value, determining the water resource transaction request of the water consumption terminal of each irrigation area based on a dynamic water price adjustment rule and a dynamic water resource supply and demand relationship graph, generating a water resource transaction price of the target water conveyance channel; and according to the water resource transaction prices of all the target water conveyance channels and the node states in the dynamic water resource supply and demand relationship graph, generating target water conveyance optimization parameters of each target water conveyance channel. The water resource allocation efficiency of the irrigation area and the operation stability of the water conveying system are improved.

The invention provides an irrigation district water resource scheduling optimization method based on cloud computing, and the method comprises the following steps: collecting irrigation district multi-source data in real time through a space-air-ground integrated sensing network, and uploading the data to a cloud platform after preprocessing; the method comprises the following steps: constructing digital twin bodies of an irrigation area in a cloud platform, dividing the irrigation area into a plurality of sub-areas according to water consumption attributes, and integrating hydraulics and hydrological agriculture models; real-time data and future weather information are fused, and a water demand prediction value is generated through a water demand prediction model by taking the sub-region as a unit; a scheduling scheme is generated by adopting a multi-objective optimization algorithm based on the predicted value, and the minimum economic benefit, water resource utilization rate and energy consumption are taken as optimization objectives; and performing simulation evaluation on the scheme by using the digital twinborn body, outputting a performance index aided decision, and finally determining an optimal scheduling scheme. According to the method, accurate prediction, dynamic optimization and intelligent scheduling of the water resources in the irrigation area are realized, and the utilization efficiency of the water resources and the irrigation management level are improved.

The invention discloses a hydrological data model fusion analysis method based on water resource scheduling decision support, and relates to the field of water resource scheduling, and the method comprises the steps: loading an HMM-BP noise detection model, obtaining a multi-source data fusion model, and outputting a space-time grid data cube; constructing a water consumption behavior prediction unit through a TPB decision tree model, adding a water right transaction engine, and outputting a water consumption prediction value, a behavior of an intelligent contract protocol and a transaction cooperation model; an incremental method is adopted to generate N combined scene matrixes of rainfall and temperature rise correlation adjustment, an ecological stability evaluator is combined, a climate and ecological coupling model is constructed, and a scene and risk mapping table and a resource re-allocation weight are output; and constructing an intelligent rule base platform to obtain an executable decision logic tree. The system has the advantage that full-chain intelligentization of water resource scheduling is realized through modular integration. Therefore, social and market collaborative prediction, climate and ecological risk quantification and adaptive decision support are achieved.

The invention discloses a control method of a gas water heater, the gas water heater and a storage medium. The method comprises the steps that the gas water heater is controlled to be shut down; a fan in the gas water heater is controlled to execute post-cleaning operation, and whether a water using signal of the gas water heater exists or not is detected; when the water consumption signal is detected, the fan is controlled to stop executing post-cleaning operation, and the gas water heater is controlled to execute ignition operation; and when the water consumption signal is not detected, the fan is controlled to maintain execution of post-cleaning operation until the operation duration of the post-cleaning operation is longer than the target duration. The invention aims to effectively shorten the restarting time of the gas water heater after the gas water heater is shut down and ensure the use safety of equipment at the same time.

The invention discloses multi-pump collaborative variable-frequency energy-saving water supply equipment and a control method, belongs to the technical field of intelligent control of water supply equipment, and aims to solve the problems of high energy consumption and poor adaptability of traditional water supply equipment. The method comprises the following steps: collecting water consumption data, extracting features through a sliding window, screening parameters through principal component analysis to construct a feature parameter set, dividing feature clustering groups in combination with time attribute clustering, and associating water consumption time periods and demand intensity grades to construct a typical water consumption mode; according to the water pump performance curve and the attenuation index, constructing an updated dynamic capability matrix; matching a target water consumption mode, screening water pumps to construct an execution pump set, decomposing a flow demand and distributing a load to generate a regulation and control instruction; and executing the instruction and counting the deviation accumulated duration, and judging whether to update the instruction. Dynamic adaptation of the water pump set and the water demand is achieved, and the water supply stability and the energy saving performance are improved.

The invention provides a cascade reservoir operation risk and benefit evaluation method considering prediction uncertainty, and the evaluation method comprises the steps: building a day-ahead wind-light-water optimal complementary model, and employing a two-stage robust method to process the power generation amount deviation generated by the wind-light uncertainty; the method comprises the following steps: identifying the worst condition of photovoltaic power output which causes maximum water consumption and power imbalance to obtain a day-ahead total power generation plan of the wind-solar hybrid system capable of realizing maximum resource utilization rate; according to the evaluation method, a benefit and risk evaluation model based on power generation complementary benefit, power generation stability, power supply reliability and water level fluctuation is further provided, the risk of complementary operation of the reservoir system is quantified through indexes, the risk and benefit of cascade reservoir operation can be reasonably evaluated and balanced on the basis of considering the uncertainty of wind and light power generation, and the method is suitable for popularization and application. And the overall efficiency and reliability of the power system are improved.

The invention belongs to the technical field of intelligent household appliances, and particularly relates to a water inlet control method, device and equipment of clothes treatment equipment and a storage medium. After the clothes weight and the fabric material of the clothes in the inner drum are obtained, the water absorption rate of the clothes is determined according to the clothes material, then the clothes weight and the water absorption rate of the clothes, a washing program selected by a user and an expected water level height value are comprehensively considered, the washing water consumption needed in the treatment process of the clothes is determined, and finally the water consumption is reduced. And controlling the clothes processing equipment to execute water inlet operation according to the washing water consumption. According to the method, the problem that the water level cannot reach the preset water level height after primary water inflow due to inaccurate calculation of the primary water inflow is solved, the problem that the water inflow time is prolonged due to the fact that a water inflow program needs to be started for the second time to complement the water level is solved, and the operation efficiency and the user experience of the clothes treatment equipment are improved.

The invention relates to the field of agricultural irrigation, in particular to an intelligent water distribution system and method for root space-time competition of an intercropping system, and is applied to agricultural precise irrigation, efficient utilization of water resources and management of the intercropping system. According to the system, the ground penetrating radar and the multispectral laser technology are fused, and crop root system distribution, activity and nutrient data are accurately obtained. Based on transfer learning and multi-agent reinforcement learning algorithms, a root system competition model is constructed, and an optimal water distribution strategy is intelligently decided. The deformable drip irrigation execution module achieves precise irrigation, the water consumption is greatly reduced by 20%-35%, and waste is effectively avoided. Meanwhile, the system balances the growth requirements of intercropping crops, relieves root competition, improves the overall yield and stability, and the amplification reaches 15%-25%. According to the system, the utilization efficiency of water resources is remarkably improved, and powerful support is provided for sustainable development of modern agriculture.

The invention discloses a hydropower line economic dispatching and load collaborative optimization system and method, and relates to the technical field of hydropower dispatching, and the system comprises a data collection module which is used for collecting hydropower station unit water consumption characteristic curve data, power grid real-time load demand data, reservoir water level dynamic data and unit vibration area constraint data; the model construction module is used for preprocessing the data acquired by the data acquisition module and constructing a multi-target optimization model with the minimum total water consumption and the minimum line transmission loss as targets in combination with constraint conditions; the improved optimization module is used for receiving the target function and the constraint condition output by the multi-target optimization model module and dynamically calculating the optimal load distribution scheme of each unit; and the result output module is used for receiving the optimal load distribution scheme output by the improvement and optimization module and realizing visual output and decision assistance of the scheme. The problem that a traditional scheduling scheme is low in traditional scheduling decision-making efficiency is solved, and the scheduling decision-making efficiency and engineering practicability are improved.

The invention belongs to the technical field of cement production, and provides a cement production water footprint metering and on-line control method, which comprises the following steps: collecting whole-flow water consumption data in a cement production process in real time, constructing a cement production real-time water consumption database, and establishing a theoretical water consumption calculation model according to the real-time water consumption data; water consumption deviation analysis is conducted on the real-time water consumption and the theoretical water consumption, abnormal events are extracted, and whether the water consumption is abnormal or not is judged according to the two indexes of the abnormal events in the water consumption deviation and the interval event frequency of the abnormal events. According to the method, the abnormal events are screened based on the deviation rate limit value, the abnormal judgment index is calculated through fusion of the number of the abnormal events and the interval mean value, the abnormal link is rapidly identified, stable change and non-stable change are distinguished by using two-dimensional inspection of Spearman rank correlation coefficient and sliding window variance analysis for abnormal data, misjudgment of a single index is avoided, and the accuracy of the abnormal data is improved. And a key basis is provided for accurate control.

The invention provides an irrigation district water diversion quantity multistage distribution method under total quantity index constraint, comprising the following steps: dividing an irrigation district into a plurality of calculation units based on an administrative division boundary and a canal system network; respectively calculating the initial water demand of each calculation unit under the living water demand, the industrial water demand, the agricultural water demand and the ecological water demand; based on the initial water demand and according to multiple water resource rigid constraint indexes, performing at least one round of checking and pressure reduction on the initial water demand of each calculation unit to obtain an initial distribution water amount of each calculation unit; and carrying out secondary distribution on surplus water generated after checking and pressure reduction, and compensating the calculation units of which the water demand is not met to obtain the final distribution water of each calculation unit. According to the method, fair, reasonable and efficient distribution of water resources in a complex system with cross coupling of administrative division and canal system networks is realized, and the problems of conflict between a scheme and actual management requirements, water consumption contradiction and the like caused by a traditional single-dimension distribution method are avoided.

The multi-field coupling collaborative modeling and parameter autonomous intelligent decision-making method for the roller type quenching process comprises the steps that four key process parameters including the nozzle flow, the water ratio, the roller gap distance and the plate passing speed are collected and subjected to normalization preprocessing, and a training set and a test set are divided; the four key process parameters of the training set serve as input, the water-cooling convective heat transfer coefficient and the phase change plasticity coefficient serve as output, a BP neural network is trained, and the workpiece surface water-cooling convective heat transfer coefficient and the material internal phase change plasticity coefficient are predicted through the trained BP neural network; a multi-field coupling model composed of a temperature field, a structure phase change field and a stress-strain field is solved, and quantitative calculation of quenching core quality indexes is achieved; by taking water consumption minimization as a target, constructing a nonlinear optimization model under the condition that multiple core quality index constraints of the plate temperature, the outlet hardness and the plate shape are met; and performing global optimization on the optimization model by adopting a differential evolution algorithm, and autonomously deciding an optimal process parameter set value.

The invention provides an analysis method and device for flow, efficiency and water consumption rate of a hydraulic power plant generator set. A distributed flow measurement unit based on a domestic PLC controller and a domestic operating system is constructed, and each unit is independently configured with the flow measurement unit to realize decentralization of data acquisition and processing; active power, guide vane opening, water head difference, instantaneous flow and volute inlet pressure data are collected in real time through a distributed flow measurement unit, and a unit efficiency and water consumption rate calculation model is dynamically fitted based on the data; sending an output result of the calculation model to a monitoring system through an anti-interference communication network, wherein the communication network adopts layered wiring and a double-shielded twisted pair to reduce the signal bit error rate; and the IMC integrated management and control platform is used for remote monitoring and diagnosis of the flow measurement unit, and seamless connection with a smart power plant data medium station is realized. The system reliability and the maintenance flexibility are improved, and the autonomous controllable requirements of domestic substitution and intelligent power plants are comprehensively met.