839 results about "Smart water" patented technology

The invention relates to the technical field of intelligent water affairs, in particular to an intelligent water affairs dynamic monitoring system and method based on digital twinning, and aims to detect data drift in real time, calculate a standard deviation change rate to generate abnormal judgment, analyze model stability and drift rate synchronism to output deviation indexes, screen data abnormal items to form quality evaluation, and realize dynamic monitoring of the intelligent water affairs. And comparing node synchronization difference to trigger compensation correction, and generating a monitoring adjustment result through integrity verification. According to the invention, through comparison of the multi-cycle standard deviation change rate and the environmental parameters, sensor drift classification and confidence evaluation are realized, the reliability of the model is enhanced by constructing a composite attenuation coefficient, abnormal sampling is positioned by combining the data change rate and an outlier map, and data synchronization is guaranteed by adopting a difference matrix and state feedback. And version verification and repair records are introduced to realize closed-loop tracing.

The invention discloses an intelligent discharge port dynamic regulation and control system and method based on multi-source time sequence fusion, and relates to the technical field of intelligent water affairs, and the system comprises a data collection module which is used for obtaining the multi-source data of a target area in real time; the space-time fusion module is used for mapping the meteorological radar data to the topological space of the drainage pipe network through a space-time alignment encoder, dynamically fusing the meteorological radar data, the surface runoff sensor data and the historical drainage data by adopting a multi-head attention mechanism, and generating a space-time fusion tensor; the prediction model module is used for constructing a TCN-LSTM hybrid model, taking the space-time fusion tensor as input and taking future drainage flow as output; and the dynamic regulation and control module is used for optimizing the gate opening degree and the pump station power control sequence in a rolling manner based on a model predictive control algorithm, and performing dual-target optimization by combining real-time electricity price data and overflow risks. According to the system, the prediction precision of the drainage flow can be improved, and double-target balance of the overflow risk and the energy consumption cost can be achieved.

The invention discloses an intelligent water affair monitoring management method and system based on the Internet of Things, particularly relates to the technical field of water affair management, and is used for solving the problems of control instruction mismatching, redundant execution and equipment overload caused by the fact that a static topology model cannot sense the dynamic change of a pipe network in real time in the prior art. Through real-time collection of pipe network operation data and analysis of time-space correlation characteristics of water flow propagation delay parameters and pressure mutation, a pipe network topology change event is dynamically perceived; the flow direction sudden change reasonability is verified in combination with fluid mechanics conservation constraint, and a corrected topological mapping table is generated through reverse calculation; candidate paths are screened based on water flow inertial parameters and pressure gradient threshold values, a safety control instruction set is generated through a multi-stage verification rule, dynamic matching of a pipe network regulation and control instruction and a real topological structure is achieved, the matching degree of the control instruction and the physical state of a pipe network is effectively improved, the leakage risk and energy waste are reduced, the manual maintenance requirement is reduced, and the safety of the pipe network is improved. The service life of equipment is prolonged.

The invention provides a digital twin water conservancy hyper-fusion method for coupling multi-source data and an all-in-one machine, and relates to the technical field of intelligent water conservancy. By constructing a unified water conservancy spatio-temporal data link protocol, efficient fusion of multi-source heterogeneous data of hydrology, meteorology, engineering operation and the like is realized, and the problem of water conservancy system data islands is solved; a coupling distributed hydrological model and a hydrodynamic model are established, and real-time accurate simulation of the watershed hydrological process and the river channel hydrodynamic process is achieved; a multi-target dynamic weight reinforcement learning algorithm is adopted to optimize a water conservancy scheduling scheme, model parameters are dynamically corrected in real time through a time sequence error regression analysis model, and closed-loop adaptive optimization is achieved; according to the method, closed-loop cooperation of water conservancy data fusion, real-time accurate simulation, intelligent scheduling optimization and dynamic parameter self-correction is realized, and the real-time performance and accuracy of water conservancy intelligent decision making are remarkably improved.

The invention relates to the technical field of water affair pipe network operation and maintenance, in particular to an intelligent analysis processing and decision support method for mass data of an intelligent water affair integrated platform based on a GIS (Geographic Information System) and the Internet of Things. According to the method, GIS topology and data of the internet of things are fused, the problems that the estimation deviation of the influence range of traditional pipe explosion is large, and the valve closing instruction lacks accuracy are solved, and the valve closing effectiveness is verified. The pipe explosion emergency response efficiency is improved, the water resource waste is reduced, the water supply stability is guaranteed, and the method is suitable for urban water refinement management.

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.

The invention discloses an intelligent water conservancy three-dimensional twinborn scene construction method and system, and relates to the technical field of intelligent water conservancy, and the method comprises the steps: collecting scene data in real time through a multi-source sensor disposed at a water conservancy facility site, including hydrological parameters, facility structure data, environment image data and ecological monitoring data; performing space-time alignment and deviation correction on the scene data by using a deep learning driven multi-modal feature fusion algorithm to generate a three-dimensional water conservancy grid model of a unified coordinate system; according to the real-time data updating frequency and the environment change trend, a local or global updating strategy of the three-dimensional water conservancy grid model is adaptively adjusted, the optimized three-dimensional water conservancy grid model and a virtual reality engine are integrated, and a twin scene supporting gesture interaction is constructed; an ecological simulation module is embedded in the twinborn scene to dynamically evaluate the influence of water conservancy activities on water quality and biological habitats.

The invention relates to the technical field of intelligent water affairs, in particular to an intelligent water affairs management and control system based on the Internet of Things, and aims to solve the problems that in the prior art, multi-source water quality data and user behavior characteristics can be fused to construct a risk assessment model based on fuzzy logic and a Bayesian network, dynamic assessment of a complex water quality state cannot be achieved, and the risk assessment accuracy is poor. Abnormity cannot be quickly recognized through a similarity matching mechanism, and the response speed and accuracy of early warning are reduced; multi-source water quality data and user behavior characteristics are fused through the water quality intelligent early warning module, a risk assessment model based on fuzzy logic and a Bayesian network is constructed, the method has high nonlinear modeling and uncertainty processing capacity, dynamic assessment of a complex water quality state is achieved, abnormity is rapidly recognized through a similarity matching mechanism, and the risk assessment efficiency is improved. The method improves the early warning response speed and accuracy, combines the countercurrent tracking and GIS technology, accurately locates the pollution source, and enhances the emergency disposal and decision support capability.

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 relates to the technical field of monitoring management, in particular to an intelligent water affair monitoring management system based on the Internet of Things, the system comprises a processor and a memory, and the processor executes a computer program stored in the memory to realize the following steps: acquiring data correlation coefficients and differential correlation coefficients corresponding to different monitoring moments, obtaining a target abnormal coefficient of the first clustering cluster and a target abnormal coefficient of the second clustering cluster according to a data volume ratio and a variable coefficient in the first clustering cluster and the second clustering cluster obtained by clustering the data correlation coefficient and the difference correlation coefficient corresponding to the monitoring moment; and obtaining a target abnormal index value corresponding to the current monitoring moment according to the data correlation coefficient corresponding to the current monitoring moment and the target abnormal coefficient of the cluster to which the differential correlation coefficient belongs, and monitoring the water affair system according to the target abnormal index value. And the timeliness and the reliability of abnormal monitoring and early warning of the water affair system can be improved.

The invention relates to the technical field of remote monitoring, in particular to a remote monitoring method and system for intelligent water purification equipment. The system comprises a sensing execution unit, a data routing unit, an intelligent central unit and an interactive service unit. The intelligent central unit is used for comparing underground water quality data collected in real time with corresponding month threshold values over the years, water quality fluctuation caused by natural changes such as seasonal replacement can be accurately distinguished, the water quality data interpretation accuracy is improved, when the water quality data deviates from a normal range and influences of natural laws are eliminated, the system introduces real-time weather data, and the water quality data interpretation accuracy is improved. And the analysis module considers the effect of weather on water quality by means of a dynamic structure equation model and resets a threshold value. The intelligent self-adaptive mechanism can effectively distinguish water quality abnormity caused by non-natural laws, for example, normal fluctuation of water quality after rainstorm is recognized, accuracy and intelligence of water quality monitoring are enhanced through multiple times of accurate judgment, and abnormity can be quickly recognized and early warned.

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 provides a water network monitoring method and system based on intelligent water supply and drainage, and the method comprises the steps: obtaining multi-source heterogeneous monitoring data of a water network in real time, carrying out the unified format conversion and time-space synchronization processing of the multi-source heterogeneous monitoring data based on a protocol adaptive fusion mechanism, and obtaining a standardized multi-source time series data set; performing exception identification processing on the standardized multi-source time sequence data set based on the time-varying statistical characteristics and the dynamic control limit to obtain a candidate exception event set; performing multi-sensor cross validation processing on the candidate abnormal event set based on a pipe network topological space association knowledge graph to obtain a high-confidence abnormal event set; performing reverse scene matching analysis on the high-confidence abnormal event set based on a hydraulic numerical simulation model to obtain a preliminary positioning area; performing positioning optimization processing on the preliminary positioning area based on a graph theory pressure wave propagation algorithm to obtain an abnormal position; and performing multi-dimensional fusion display processing on the abnormal position to generate a monitoring report. According to the method, accurate positioning of the abnormal position is realized.

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.

The invention discloses a municipal pipe network overflow detection and intelligent repair method, and relates to the technical field of municipal pipe network management, and the method comprises the following specific steps: data fusion and basic analysis: employing a plurality of sensors to collect various data in real time, and carrying out the fusion and preprocessing in a data processing center, comparing and analyzing historical and real-time data to set a threshold value, and finding out an external environment change factor influencing the operation of the pipe network to provide basic data; through deep integration of meteorological, hydrological and municipal pipe network related data and by means of an intelligent hydrodynamic simulation technology, high-precision simulation of water flow in a pipe network can be realized, the occurrence position, time and influence range of overflow can be accurately predicted in advance, and the risk of overflow can be accurately evaluated based on accurate overflow risk evaluation. The system can automatically formulate and adjust a pipe network operation parameter regulation and control strategy in real time, parameters of the drainage pumping station and the opening degree of a pipe network valve are intelligently adjusted according to different overflow risk grades, and therefore the pipe network operation parameters can be effectively optimized.

The invention relates to an intelligent water affair remote monitoring and control system based on the Internet of Things, in particular to the field of intelligent water affair remote monitoring and control, and the system comprises the steps: firstly, pre-training a long and short-term memory network model through a cloud, and generating a lightweight edge prediction model to reduce calculation and transmission loads; the data transmission module effectively reduces the occupation of transmission bandwidth based on a dynamic quantization threshold value of a residual error and a differential coding technology, meanwhile, the high precision of the data is kept, the verification feedback module monitors the accuracy of the transmission data in real time through confidence coefficient detection, and triggers model re-calibration when the confidence coefficient is reduced, so that the long-term stability of the system is ensured, and the reliability of the system is improved. The sampling optimization module dynamically adjusts the sampling frequency according to the confidence coefficient and the residual fluctuation characteristics, balance is achieved between bandwidth occupation and reconstruction precision by using the Lyapunov optimization theory, the real-time performance and accuracy of water affair monitoring are improved, and meanwhile bandwidth consumption and energy consumption are effectively reduced.

The invention discloses a meteorological water conservancy disaster intelligent grading early warning linkage call routing method and a meteorological water conservancy disaster intelligent grading early warning linkage call routing system. A meteorological water conservancy two-factor early warning matrix is constructed, a cross-department data fusion channel is established, an early warning coupling model is developed, and multi-dimensional early warning fusion is achieved. And formulating a water conservancy special response strategy library, wherein the water conservancy special response strategy library comprises river water level grading routing and mountain torrent risk area customization strategies. And developing an intelligent water conservancy enhancement module, such as a flood control project topology analysis engine and a water and rain work condition semantic analyzer. The method is characterized in that a three-element risk rating model is constructed by an initiative dynamic coupling algorithm, and a water conservancy emergency response topology network based on double dimensions and a water conservancy project BIM data and meteorological early warning space superposition analysis module are adopted. The accuracy and comprehensiveness of disaster early warning can be improved, the timeliness and effectiveness of emergency response are ensured, and the intelligent level and disaster response capacity of the system are improved.

The invention relates to the field of milk powder proportion control, in particular to an intelligent milk powder and water accurate proportion control method and system of a milk powder brewing machine. The method comprises the following steps: collecting milk powder blanking acoustic signals of a user, and performing deep audio analysis and dynamic density estimation to obtain actual density state information of the current batch of milk powder; pressure parameters and humidity parameters in the discharging process are obtained, environmental deviation sensing is carried out on the actual density state information, milk powder volume dynamic compensation correction is carried out, and therefore accurate milk powder density condition information is obtained; a preset optimal powder-water ratio is obtained, optimal ratio demand calculation and intelligent water injection dissolution regulation and control are carried out on the accurate milk powder density condition information, and an intelligent water injection dissolution strategy is constructed; and performing real-time water injection control according to the intelligent water injection dissolution strategy, and collecting a milk powder dissolution monitoring image. According to the method, accurate correction in the last step is realized through fine-grained water supplementing / powder supplementing adjustment, and accurate control over the dissolution ratio of the milk powder to the water is achieved.

The embodiment of the invention discloses a remote state monitoring method and system based on an intelligent water meter, and the method comprises the steps: obtaining pipeline BIM model data of an intelligent water meter pipeline network of a target building, and synchronously collecting a real-time monitoring data set of a plurality of sensing nodes in the intelligent water meter pipeline network; performing abnormal state recognition processing on the real-time monitoring data set, and generating an abnormal state distribution map matched with the spatial position of the pipeline BIM model data; based on the abnormal state distribution map and a preset pipeline topology constraint condition, performing dynamic visual reconstruction processing on the pipeline BIM model data, and generating a visual output interface with an abnormal state mark; and performing association mapping on the visual output interface and the real-time monitoring data set to generate a state monitoring feedback strategy, and triggering maintenance response operation of the intelligent water meter pipeline network through a remote service port.

The invention provides an aquaculture intelligent feeding and growth monitoring system based on the Internet of Things, and relates to the technical field of intelligent aquaculture, and the system comprises the following modules: a multi-mode sensing module, a data processing module, a central processing module, an intelligent feeding module, and a monitoring module. The aquatic product feeding monitoring module is used for accurately calculating the appropriate feeding amount by combining data of current water quality, the number of aquatic products and the growth stage, and analyzing aquatic product images collected by the camera device. An image recognition technology and a deep learning algorithm are used for recognizing aquatic product types, individual sizes and health conditions to carry out three-dimensional form modeling; according to the method, the scientificity and accuracy of breeding decision making are remarkably improved, and detailed and visual growth information is provided for the breeding decision making; and through cooperative work of the intelligent feeding module and the central processing module, accurate adjustment of the feeding amount is achieved, and the feed utilization rate is increased.

The invention provides a smart city water supply pipe network emergency supervision Internet of Things large model system and method, and relates to the field of city water pipe monitoring, and the system comprises a government supervision management platform, a government supervision sensing network platform, a government supervision object platform, a water affair company sensing network platform, and a smart water affair equipment object platform. The government supervision and management platform is configured to determine time sequence flow corresponding to the multiple water pipe node groups based on water flow characteristics of the multiple water pipe nodes in the water supply network; based on the time sequence flow, determining water and soil loss coefficients corresponding to the one or more target areas; based on the water and soil loss coefficient, temporary control parameters are generated through a parameter generation model; and on the basis of the temporary control parameters, the valve opening degrees of the corresponding valves in the one or more target areas are controlled. The system can determine the control parameters of the adjusting device according to the water and soil loss coefficient of the target area, and realizes informatization and intelligence of urban water pipe emergency monitoring.

The invention provides an intelligent gate control access method and system based on driver identification and data mapping, and is applied to the field of data processing. The gateway edge computing node is arranged in the water conservancy terminal, automatic identification and protocol adaptation of the gate control equipment are achieved, the tedious process of traditional manual configuration of a communication channel and protocol driving is avoided, manual intervention is effectively reduced, after the equipment is accessed, the variable format and the equipment function of the equipment can be analyzed, and the control strategy can be automatically matched and executed; if the strategy cannot be directly applied, dynamic verification and behavior analysis can be carried out based on simulation data, the linkage relation between the equipment and the upstream and downstream areas is further constructed, the strategy adaptability and the equipment universality are enhanced through the mechanism, the intelligent level, the operation reliability and the automation capability of the gate control system can be improved, and the application prospect is wide. The method is especially suitable for intelligent water conservancy scenes of large-scale equipment heterogeneous access and unified control.

The invention discloses an intelligent water operation management and control system based on the Internet of Things, and relates to the technical field of industrial Internet of Things crossing, and the system comprises the steps: collecting multi-dimensional operation data in a water supply network, carrying out the preliminary time mark alignment and preprocessing, and generating a multi-source synchronous perception data set; importing the comprehensive risk list into a digital twinborn body, dynamically injecting corresponding fault and abnormal scene parameters in a digital twinborn environment, and performing large-scale parallel deduction through a Monte Carlo simulation algorithm to generate a rehearsal consequence data set; and carrying out multi-objective decision analysis on the rehearsal consequence data set, carrying out prediction comprehensive plan utility scoring based on a preset safety weight and an economic weight, and calculating an optimized management and control plan through iterative optimization. According to the invention, the multi-source heterogeneous data is deeply fused through the graph neural network of the risk identification module, the dynamic impedance characteristics of the pipe network are accurately extracted, the early abnormality is identified, and advanced diagnosis of the multi-modal coupling risk is realized.

The invention discloses an intelligent water affair water resource tracing and transaction management system fused with a block chain technology, and the system comprises the following steps: a data collection and preprocessing module which is used for collecting real-time water affair data, carrying out the preprocessing of the real-time water affair data, and forming a structured data file; the parameter optimization and intelligent early warning module is used for constructing an anomaly detection model and performing global adjustment; the consensus verification module is used for performing consensus verification based on a practical Byzantine fault-tolerant consensus algorithm; the secure uplink and transaction management module is used for performing secure uplink on the data after the consensus verification to form a non-tampering data storage certificate; and the automatic response and decision support module is used for automatically triggering an intelligent contract and executing water resource scheduling, transaction management and emergency response operation. According to the invention, the block chain, the Internet of Things and an intelligent optimization algorithm are fused, real-time collection, accurate detection, safe chaining and automatic management of water resource data are realized, and the efficiency and transparency of the intelligent water affair system are significantly improved.

According to the efficient water treatment system based on the reverse osmosis membrane and the control method provided by the invention, gradient anti-pollution membrane arrangement and reconfigurable intelligent waterway design are creatively combined, so that a foundation is laid for high-flux and low-pollution operation from a physical structure; meanwhile, a full-closed-loop intelligent control system is constructed, and by fusing fuzzy logic, an optimization algorithm and a neural network prediction model, the system can integrate multi-dimensional information such as inlet water quality, membrane pressure difference and water yield, judge pollution risks in real time and adaptively and seamlessly switch among a high recovery mode, a segmented flushing mode and a prediction cleaning mode, so that the system is high in recovery efficiency and high in reliability. Meanwhile, parameters such as pump frequency and valves are dynamically optimized to find an optimal balance point of recovery rate and energy consumption; according to the invention, the recovery rate can be stably improved, the membrane pollution rate is greatly reduced, the chemical cleaning period and the service life of the membrane element are prolonged, and finally, the comprehensive operation and maintenance cost of the system is remarkably reduced while the water treatment efficiency and the water quality safety are improved.

The invention discloses an intelligent water meter recognition system based on a three-layer edge computing architecture, which has the following beneficial effects: (1) based on the three-layer edge computing architecture, the intelligent water meter recognition system gives full play to the advantages of edge computing, the load of a central server is reduced, and the service life of the central server is prolonged; and the service life of the water meter image data acquisition equipment is prolonged. (2) YOLO-METER is improved on the basis of YOLO11 and is used for water meter reading recognition, a FastC3k2 module and an MRFBlock are introduced into the YOLO-METER, and the FastC3k2 module can improve the image feature extraction performance and enhance the low-contrast feature extraction capability of a water meter image by reducing the number of parameters of a model and FLOPs; and the MRFBlock obviously expands the receptive field of the model, enhances the capability of autonomously selecting features, improves multi-scale fusion, and can optimize the feature selection of a digital roller region and improve the positioning capability of a water meter reading region. According to the invention, the calculation cost is reduced while the identification precision is improved.

According to the sewage discharge data management method based on the intelligent water affair, the sewage discharge data, the equipment state data and the environment data of an enterprise are subjected to edge calculation, the initial discharge data and the equipment evaluation data are generated, localized real-time processing of the data is achieved, and the transmission amount of invalid data is reduced. And performing data implicit anomaly identification and multi-modal data cleaning on the initial emission data and the equipment evaluation data based on the sewage component chemical association rule base, accurately capturing abnormal points which do not conform to chemical association rules in the data, and generating an abnormal mark list and intermediate emission data. Data association analysis is carried out based on the abnormal mark list, the intermediate emission data and enterprise operation data corresponding to the enterprise, a data logic verification map is constructed, errors in the intermediate emission data are automatically corrected according to a logic relation in the verification map, the processing efficiency is improved, and through cross verification of multiple data sources, the data processing efficiency is improved. And target emission data and a compliance report with high accuracy are generated.

The invention provides an error checking method and system of an intelligent water meter, and relates to the technical field of error checking of the intelligent water meter, and the method comprises the following steps: collecting multi-modal monitoring data of the intelligent water meter; performing signal preprocessing on the water flow acoustic signal and the mechanical vibration signal to generate an acoustic fusion signal, extracting time-frequency domain features of the acoustic fusion signal related to errors, and constructing an optimal error feature set; performing pattern recognition based on the optimal error feature set and a preset standard verification feature library to generate an error preliminary result; constructing an error prediction and verification model, verifying the error preliminary result in combination with the optimal error feature set and the flow data, and outputting an error verification result; when the error grade reaches a preset error grade threshold value, an early warning mechanism is triggered, and early warning information is pushed, so that the metering performance of the water meter can be evaluated in real time, the error change trend can be dynamically predicted, the timeliness and effectiveness of error verification are effectively guaranteed, and the dynamic error management and control requirements of the intelligent water meter are comprehensively met.

The invention relates to an expert question and answer auxiliary interaction method and system based on a large water affair model, and relates to the technical field of intelligent water affair. The method comprises the following steps: firstly, constructing a problem semantic vector fusing a current intention and a historical background by deeply analyzing user interaction data and a historical session; then, SCADA time sequence data, a static knowledge graph and a historical processing record are retrieved from an external multi-source data stream by taking the vector as an index, and are converted into a situation feature vector reflecting the running state constraint at the current moment through semantic mapping and time sequence feature engineering. By jointly inputting a problem semantic vector and a situation feature vector into a pre-trained water affair field large model, the system can perform verification reasoning under strict working condition constraints, and encapsulates a reasoning result into a structured expert reply based on a visual template. The technical problems that in the prior art, a general model is prone to generating illusion, the retrieval result accuracy is low, and end-to-end intelligent diagnosis cannot be provided are effectively solved.

The invention belongs to the technical field of soil sampling, particularly relates to a river dam soil sampling device for intelligent water conservancy, and aims to solve the problems that traditional equipment can only obtain samples with single depth through single-time drilling, different soil layers can only be collected through repeated drilling, the efficiency is low, soil layer structures are damaged, and an effective isolation mechanism is lacked. In order to solve the problems in the prior art that soil at different depths is easy to mix in the collection or extraction process, and the detection accuracy is influenced, the invention provides the following scheme: the device comprises a disc ring, and three finger grooves are formed in the outer side of the disc ring; and the three supporting leg positioning structures are uniformly mounted at the bottom of the disc ring. Through cooperation design of the rotary sampling cylinder and the sampling hole, the precise 60-degree indexing rotary locking handle and the bottom sealing structure of the integrated drill bit, soil of different depths can be collected, the collected soil can be prevented from being mixed, the stability of the sampling process is ensured through three-point synchronous propulsion, and the sampling efficiency is improved. And the deployment of the river dam is simplified through the magnetic foot-stepping positioning insertion rods.