3660 results about "Water consumption" patented technology

A distributed water resource allocation method and which includes: acquiring a sub-basin in a setting region based on data of a digital elevation model, and dividing the sub-basin into a plurality of allocation units by applying a multi-attribute overlaying method; determining a spatial topological relationship between water users and water sources in the various allocation units according to a water supply priority of the water source and a water use priority of the water user; acquiring water demand data in an administrative region, and calculating a water supply amount of each water source as well as a daily amount of water allocation, water consumption, water discharge and pollutant discharge of each water user in the administrative region; constructing objective functions and constraint conditions; and resolving the objective functions based on the constraint conditions and by adopting a genetic algorithm, to obtain water resource allocation data.

The invention relates to the technical field of soil monitoring, in particular to a farmland irrigation management and control system based on the Internet of Things, and the system comprises a soil monitoring module, a crop water demand analysis module, an irrigation strategy making module, an irrigation execution control module, a water source management module, a fertilizer distribution module, an irrigation effect evaluation module and an adaptability adjustment module. According to the method, the crop water demand is analyzed by combining the decision tree, the water consumption is accurately calculated, the irrigation strategy optimizes the time and frequency based on the linear programming and simulated annealing method, the scheme adaptation degree is improved, the irrigation link adopts an automatic control and dynamic adjustment mechanism, the environment and crop demand changes are adapted in real time, and the irrigation efficiency is improved. Water source allocation ensures efficient distribution of multiple water sources on the basis of dynamic optimization, the type and quantity of fertilizer feeding are accurately controlled according to crop requirements, the irrigation effect is subjected to self-adaptive optimization of an irrigation plan after crop data evaluation, and accurate, flexible and efficient farmland resource management is integrally achieved.

The invention relates to the technical field of agricultural irrigation management, in particular to a method and system for estimating the agricultural irrigation water consumption of an irrigation area. The method comprises the following steps: collecting soil, weather and crop data through a sensor and a detector, processing the collected data through an edge computing node, and then selecting an optimal transmission path to transmit the data through an Internet of Things gateway based on an https protocol; an irrigation area agricultural irrigation water consumption prediction model is constructed based on an intelligent algorithm of deep learning, the model stores, preprocesses and analyzes received data, then modeling and prediction are carried out on soil humidity, meteorological conditions and crop growth conditions, and finally an irrigation water consumption estimation result is obtained; and updating the irrigation area agricultural irrigation water consumption prediction model to ensure the accuracy and timeliness of model prediction. According to the method, through an advanced sensor technology, the Internet of Things, big data analysis and a machine learning algorithm, accurate estimation and optimal management of the agricultural irrigation water consumption of the irrigation area can be realized.

The invention discloses an intelligent irrigation control method and system based on meteorological information, and relates to the technical field of irrigation control, an area to be irrigated is divided into a plurality of sub-areas, and future rainfall (first rainfall) is predicted through a preset meteorological early warning model; acquiring water absorption data of each sub-region, calculating a water absorption coefficient, and determining absorbable rainfall (second rainfall) in combination with the first rainfall; and judging the water consumption of each sub-region according to the crop growth data and the historical data, and comparing the water consumption with the second rainfall. If the water consumption is greater than the second rainfall, the sub-region needs to be irrigated; according to the method, the soil water content of each sub-region can be finely judged, and whether rainfall meets the water demand or not is evaluated, so that whether irrigation is needed or not is judged, excessive irrigation and water resource waste are avoided, meanwhile, it is ensured that crops obtain enough water, water resource management is optimized, and the crop growth effect and agricultural production efficiency are improved.

The invention relates to the technical field of intelligent water affair management, and discloses a method and system for dynamically adjusting the reporting moment of an NB water meter, and the system comprises NB water meter terminal equipment which is responsible for collecting water meter data in real time and uploading the data to a data management platform through an NB-IoT network; the data management platform is responsible for receiving, processing and storing data from the plurality of NB water meter terminal devices; the reporting moment adjusting module is used for dynamically adjusting the reporting moment of the NB water meter terminal equipment according to the strategy generated by the data management platform; the communication management module is responsible for communication between the NB water meter terminal equipment and the data management platform; the intelligent alarm and event processing module is responsible for monitoring the running state of the system in real time; and an equipment remote control module. By dynamically adjusting the reporting time, the system can reduce unnecessary frequent reporting, especially in a time period when the water consumption is relatively small or relatively stable, so that the power consumption of the equipment is remarkably reduced. Therefore, the battery life of the NB water meter terminal equipment can be prolonged, and the maintenance cost is reduced.

The invention discloses a dynamic load adjusting method and system for dish-washing machine energy efficiency and water efficiency test, and the method comprises the steps: setting a test set of different dirt degrees and types of tableware proportions, carrying out the transient performance analysis of each washing mode, predicting the fluid flow and temperature distribution in a cavity of a dish-washing machine, and recognizing an abnormal region; a first objective function based on the washing effect is constructed to optimize water pump parameters to improve a water flow path, a second objective function is constructed to adjust heating element parameters, energy efficiency and water efficiency testing is conducted to determine the optimal washing period, the water pump parameters and the heating element parameters as optimal operation parameters, and a decision tree algorithm is introduced; and learning a mapping relation between the test condition and the optimal operation parameters, and adjusting the operation parameters of the dish-washing machine in real time by means of a control algorithm according to the tableware dirt degree and proportion. According to the method, the energy efficiency and water efficiency balance of the dish washing machine under different load conditions can be effectively improved, the washing effect is enhanced, energy consumption and water consumption are reduced, and meanwhile good interpretability is achieved.

The invention relates to the field of electric digital data processing, in particular to an intelligent management and control method and system for building water. The method comprises the steps of collecting real building water consumption data, training a data expansion model based on a conditional generative adversarial network algorithm adopting a dynamic recoding strategy, training a feature extraction model, training a classifier of a deep neural decision tree algorithm based on local structure entropy, and collecting new real building water consumption data. And performing feature extraction on the new real building water consumption data, inputting the new real building water consumption data after feature extraction into the trained classifier, identifying the energy consumption level of the new real building water consumption data, and performing corresponding behaviors. An existing building water intelligent management and control system and method have the problem of poor data analysis accuracy. The building water intelligent management and control method and system provided by the invention are high in data analysis accuracy.

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 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 urban water supply pipe network leakage monitoring method, relates to the technical field of urban water supply pipe networks, and aims to reduce dependence on additional equipment and data and improve estimation precision. The method comprises the steps that a dynamic water supply pipe network model is constructed, and leakage flow and the water consumption for metering DMA in a partition mode under the leakage working condition are obtained through simulation based on the constructed dynamic water supply pipe network model; constructing a neural network water consumption prediction model, and predicting normal water consumption when no leakage occurs through training learning; a neural network leakage monitoring model is constructed, and the neural network leakage monitoring model takes normal water consumption and water consumption of DMA subarea metering under the leakage working condition as input parameters to judge whether leakage occurs in each DMA subarea or not; and for the DMA partition with the leakage, establishing a leakage positioning model by taking the minimum square error of the monitoring value and the simulation value of the pressure measuring point when the leakage occurs as an objective function, and solving the leakage positioning model to position the leakage position of the DMA partition.

The invention provides a campus direct drinking water energy consumption optimization method and system based on energy-saving control. The method comprises the following steps: collecting key data of campus direct drinking water to construct a water demand prediction model based on a multilayer long short-term memory network; according to a prediction result of the water demand prediction model, generating a dynamic equipment scheduling strategy and executing the dynamic equipment scheduling strategy on the campus direct drinking water equipment; monitoring the data after the dynamic equipment scheduling strategy is executed in real time, and calculating the power deviation between the dynamic equipment scheduling strategy and the actual power of the real-time monitoring equipment; adaptively optimizing the corrected dynamic equipment scheduling strategy according to the actual feedback data for executing the corrected dynamic equipment scheduling strategy; and performing dynamic closed-loop feedback adjustment according to an equipment scheduling strategy in combination with power deviation and water consumption deviation in actual operation. The intelligent energy-saving system effectively overcomes the defects that in the prior art, equipment operation is not matched with the water demand, and water leakage is not detected in time, and intelligent energy-saving optimization of the campus direct drinking water system is achieved.

The invention relates to the technical field of automatic control, in particular to an intelligent Internet of Things water meter control method and system.The intelligent Internet of Things water meter control method comprises the steps that the water consumption of a water meter at the current time node is obtained, the water consumption of the current time node is input into an improved machine learning model, the water consumption state of the current time node is output, and if the water consumption state is abnormal, the water meter is started; if yes, controlling the water meter to send out an early warning signal and stopping water supply; wherein the improved machine learning model comprises the steps of carrying out iterative training on the machine learning model by utilizing a loss function and calculating a loss function value of a current time node, and the historical loss function value is positively correlated with the abnormal degree of the water consumption of the historical time node and is inversely correlated with the surge coefficient of the water consumption of the historical time node. According to the method, the problem that the overall performance and reliability of the intelligent water meter are not high possibly due to potential insufficiency of an existing algorithm in the aspects of detection efficiency and accuracy is solved.

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 relates to the technical field of agricultural production, in particular to a field corn and wheat drought tolerance data analysis and monitoring method which comprises the following steps: S1, data acquisition: acquiring environment and physiological data of field corn and wheat based on a multi-source sensor; s2, data preprocessing: preprocessing the collected environment and physiological data, and integrating the environment and physiological data into comprehensive data; s3, a leaf angle regulation and control transpiration mechanism: analyzing the influence of the leaf angle change of the corn and wheat on illumination absorption and transpiration rate, optimizing the leaf angle in real time to adapt to the change of illumination intensity, and predicting the water consumption conditions of the corn and wheat under different illumination conditions; s4, drought tolerance analysis: evaluating the drought tolerance of corn and wheat. According to the system, the utilization efficiency of water resources is improved, healthy growth of crops in a drought environment is ensured, and the practicability of farmland management and the scientificity of decision making are improved.

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 data statistics, in particular to an intelligent water meter missing data complementation method and system.The intelligent water meter missing data complementation method includes the steps that initial segmentation points of a water consumption data curve are determined by analyzing the slope change condition of the water consumption data curve, and the variation index of the initial segmentation points is further analyzed to determine segmentation points of the water consumption data curve; performing clustering analysis on all segmentation points in the water consumption data curve of the user in all days, and constructing a standard water consumption data curve of the user based on a clustering result; and respectively using a Bezier curve fitting method and a regression analysis method, and combining the change trend of the standard water consumption data curve to carry out data completion on the missing data segment. Through the method and the system provided by the invention, complementation data which conform to the overall trend and are in smooth transition can be obtained, the integrity and the accuracy of the data are remarkably improved, and a more reliable data basis is provided for subsequent water resource management and decision support.

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 the technical field of error verification, and particularly discloses an error verification method and system for an intelligent water meter, which adopts a neural network model based on deep learning to carry out fine-grained time sequence feature extraction on time sequence data of temperature and pressure of a water body flowing through a multifunctional intelligent water meter in a target time period; according to the method, the time sequence change mode of the water body temperature and the pressure in each local time domain is captured, time sequence integral coding is carried out on the time sequence change characteristics of the water body temperature and the pressure in each local time domain to understand the time sequence accumulation effect of the water body temperature and the pressure on the water consumption in the global time domain, and intelligent prediction of the water consumption is achieved. And error verification is carried out on the intelligent water meter according to the difference between the prediction result and the measured value of the water meter. In this way, the measuring error of the intelligent water meter can be reflected more accurately, and then the error checking precision and the intelligent level of water affair management are improved.

The invention provides a method for predicting node pressure of an urban water supply pipe network, which comprises the following steps of: constructing a corresponding water supply pipe network topological structure by taking water use nodes in the urban water supply pipe network as nodes and taking connected pipelines as edges; acquiring node pressure data of each sensor in the urban water supply network to construct a data set; constructing a prediction model based on a transform model framework and a water supply pipe network topological structure; training the prediction model by using the data set to obtain a node pressure prediction model for predicting the pressure of the sensorless water use node; and inputting the position information of the target water consumption node and the prediction time point into the node pressure prediction model to obtain node pressure data of the target water consumption node. According to the method provided by the invention, a water supply pressure data prediction model which does not depend on a hydraulic model can be constructed, the water demand pressure of a water demand node in the future is predicted, and good prediction precision is shown in a flat water supply area.

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 belongs to the field of demand prediction, and particularly relates to a water supply demand prediction and resource allocation method. Comprising the steps of obtaining data from an external data source and performing dynamic multi-stage standardization processing to form an input data set; constructing an adaptive weighted multi-scale time sequence convolutional network model to predict a water supply demand; calculating and distributing the optimal water supply quantity of each region based on the predicted demand quantity and the water resource reserve quantity; and acquiring actual water consumption data regularly, calculating deviation, and feeding back and adjusting parameters of the prediction model. According to the method, the data problem is solved through multi-source data fusion and multi-stage preprocessing, the prediction precision is improved by combining a multi-scale time sequence convolutional network with an adaptive weighting mechanism, the resource allocation is optimized by a dynamic allocation objective function, the long-term stability of a model is ensured by a feedback optimization mechanism, the efficiency of a water supply management system is effectively improved, and the method is suitable for the field of water supply management.

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 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 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 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.