7634 results about "Hydrology" patented technology

The invention discloses a slope rainfall infiltration stability dynamic evaluation and landslide prediction method. The method comprises the following steps: constructing a numerical model coupling rainfall infiltration and slope stress field response; integrating multi-source data, and updating model boundary conditions in real time; performing dynamic stability evaluation based on a local safety coefficient method; and training a landslide prediction model and realizing intelligent early warning identification. The seepage-stress-strength three-field coupling numerical model is provided, the physical response process of the unsaturated soil body is dynamically simulated, the precursor mechanism of rainfall-induced landslide can be truly restored, and prediction scientificity and accuracy are improved; remote sensing topographic data, soil property survey parameters and real-time meteorological monitoring information are integrated, model boundary conditions and initial states are dynamically updated through a system interface, real-time response to environmental changes is achieved, and simulation reliability is improved; a local safety coefficient evaluation mechanism is introduced, and a continuous weak region discrimination algorithm is combined, so that space identification and time sequence tracking of a potential slip region and a damage zone are realized, and the slope stability analysis refinement level is improved.

According to the technical scheme, the automatic feeding system comprises a feeding bin, a pneumatic conveying pipeline, an air source device and a discharging bin, and further comprises a distributed sensor set used for monitoring the flux, pressure, temperature and vibration frequency spectrum of a preset point of the pneumatic conveying pipeline; comprising a flow sensor, a pressure sensor, a temperature sensor and a vibration acceleration sensor which are arranged at a plurality of preset points of a conveying pipeline, and a digital twin model building module which is used for building three-dimensional modeling of the pneumatic conveying pipeline and mapping state parameters of all positions of a pipeline distribution sensor group in real time; the blockage analysis module is used for marking a blockage position to three-dimensional modeling in real time based on data acquired by the distributed sensor group, extracting a multi-parameter change curve in a time period T before blockage, constructing a blockage feature library, and generating a blockage risk level through a weight change rate difference value of a feeding bin and a discharging bin; and the graded dredging execution module is configured with a differentiated dredging strategy, and the differentiated dredging strategy is started according to the blockage risk grade.

The invention discloses an urban rainwater pipe network blockage risk early warning method and system based on edge calculation, and relates to the technical field of urban drainage monitoring. The problems that existing pipe network blockage detection lags behind, and the early warning precision is insufficient are solved. Dynamic hydraulic parameters and sediment migration state data of a pipe section are collected in real time through edge calculation equipment deployed at a pipe network node, the hydraulic state deviation rate is calculated, and the local blockage risk is rapidly recognized; when the deviation rate exceeds a threshold value, a dynamic sensing network is established by the trigger nodes, and a pipe network hydraulic topological relation is established by integrating the liquid level and flow velocity characteristics of the upstream and downstream nodes; executing distributed collaborative analysis based on the topological relation, identifying an abnormal attenuation area, calculating a sediment dynamic equilibrium index, and generating a blockage diagnosis parameter; and further combining real-time rainfall intensity to predict an overflowing capacity attenuation curve, generating a multi-stage early warning instruction according to an attenuation slope, and distributing the multi-stage early warning instruction to an operation and maintenance terminal, thereby realizing accurate early warning and active regulation and control of the blockage risk of the rainwater pipe network.

The invention provides a hydraulic engineering information management method and system based on big data. The method comprises the following steps: firstly, uploading flood flow velocity dynamic data, terrain elevation data and spectral feature data to a cloud server, then carrying out multi-dimensional superposition on the flood flow velocity dynamic data, associating with a historical inundation path, and generating a superposition association result; constructing a three-dimensional geographic model based on terrain elevation data and spectral feature data, generating a spatial superposition map according to boundary features of a water body and a terrain abrupt change area and a flood routing path, performing position association with a material reserve position, and performing dynamic priority ranking according to a submerging probability gradient, and finally, according to the resource scheduling instruction sequence, correcting the flight path of the unmanned aerial vehicle cluster and the data acquisition area to form a closed-loop feedback mechanism. According to the technical scheme provided by the invention, the accuracy and timeliness of real-time data updating are ensured.

The invention relates to the technical field of Internet of Things, in particular to a water meter data acquisition method based on an open source gap, which comprises the following steps of: based on an open source gap distributed architecture, acquiring water meter sensing and communication data, analyzing flow fluctuation, marking low-efficiency nodes, optimizing communication and energy consumption matching, screening abnormal links, and identifying over-limit nodes. And generating an exception and switching control table. According to the method, intelligent division of a data fluctuation interval is realized by analyzing a flow change trend in unit time, on the basis, a data acquisition task and an energy consumption state are matched in combination with an operation period and electric quantity distribution, meanwhile, signal strength and delay in a transmission path are comprehensively evaluated, and the flow output change is continuously tracked, so that the flow output change is accurately determined. And abnormal operation nodes are identified and switching control signals are triggered, so that the stability and accuracy of data acquisition in a complex environment are improved, the coordination and execution efficiency of the whole process are enhanced, the information loss risk caused by communication faults is reduced, and the intelligent response level is improved.

The invention discloses a gravity type submerging net cage parameter design optimization method, and relates to the field of mariculture equipment structure optimization, and the method comprises the steps: collecting the multi-period marine environment data of a target sea area, and constructing an environment load sequence with a time-varying characteristic; establishing a three-dimensional coupling finite element model, performing disturbance analysis on a plurality of structure control parameters, and identifying a key design parameter set based on a response sensitivity function; and constructing a multi-objective optimization model including floating and sinking time control, maximum stress minimization and attitude offset constraint, performing global optimization by adopting an adaptive genetic algorithm, obtaining an optimal parameter solution, substituting the optimal parameter solution into the simulation model for dynamic response verification, and if a response index is converged to a target interval, outputting the optimal parameter solution as final design configuration. According to the method, systematic optimization of structural parameters of the submerging and surfacing net cage can be achieved, the stability and safety of the submerging and surfacing net cage under complex sea conditions are improved, and the method has high engineering applicability.

A system and method for determining optimal sampling parameters is described. The method gathers soil data from a soil data source, which is associated with a soil organic carbon (SOC) project area. The crop prediction engine then determines that the soil data is less than optimal, but that the soil data is sufficient to generate an optimal sampling plan. The method completes a Monte Carlo simulation, which generates an empirical sampling distribution. The optimal sampling plan is determined by defining a margin of error, which provides a deviation from a predictive analysis of measured soil chemistry for a plurality of collected soil samples, and performing Monte Carlo simulations that include one Monte Carlo simulation having a lowest sampling density that satisfies the margin of error. The optimal sampling plan has the lowest sampling density and includes one or more sampling locations.

The invention relates to the technical field of soil erosion monitoring, in particular to a shallow mountain belt abrupt slope shrub land soil erosion monitoring method and system, and the method comprises the following steps: S1, collecting topographic point cloud data and vegetation reflectivity data; s2, spatial interpolation is carried out on the terrain point cloud data to generate three-dimensional terrain grid data, and meanwhile, boundary coordinates of a shrub coverage area are taken; s3, calculating a root system corrosion resistance strength value of each grid; s4, generating a real-time erosivity index of each grid; s5, extracting a real-time erosivity index of the grid where the corresponding node is located and a terrain parameter of the associated grid; and S6, calculating the soil erosion amount grid by grid, and generating a three-dimensional erosion space distribution diagram. According to the invention, through integration of multi-source data acquisition, real-time data processing and three-dimensional visual monitoring, accurate evaluation and dynamic early warning of the soil erosion process of the shrub forest land with the abrupt slope in the shallow mountain are realized, so that decision support is provided for water and soil conservation and ecological restoration.

The invention relates to the technical field of nuclear power internal disaster analysis, and discloses a nuclear power plant internal flooding experiment verification method, device, equipment and medium, and the method comprises the steps: carrying out the modeling analysis of a target plant prototype, and determining a plurality of model parameter values of an experiment scaling model; constructing an experiment scaling model according to the plurality of model parameter values, and determining experiment parameters of an internal water logging experiment; performing an internal water logging experiment on the experimental scale model by using the experimental parameters to obtain an internal water logging experiment result of the experimental scale model; and obtaining an internal water logging simulation result of the numerical model, comparing the internal water logging simulation result with an internal water logging experiment result, and verifying the accuracy of the internal water logging simulation result according to a comparison result. According to the method, the experiment scale model of the nuclear power plant prototype is constructed, the flooding spreading conditions under different working conditions are simulated and compared with the flooding simulation result, the accuracy of flooding simulation calculation is verified, and more accurate guidance is provided for follow-up nuclear power project internal flooding analysis.

The invention relates to the technical field of reservoir dam safety monitoring, and discloses a reservoir dam siltation dynamic monitoring and early warning system. Multi-dimensional sensing node arrays of the system are arranged at key positions of a dam body structure and a reservoir area, and sediment thickness distribution data, water flow velocity field data and sediment concentration gradient data are synchronously collected. And the edge computing node receives the original monitoring data, executes data cleaning and space-time alignment processing, and generates a standardized siltation feature data set. And the cloud analysis platform receives the data set, calculates a deposition evolution trend matrix through a space-time coupling prediction model, and outputs a reservoir area deposition risk level distribution map. And the dynamic visualization engine analyzes the risk level distribution map, generates a three-dimensional dynamic deposition situation model, and marks the space coordinates of the abnormal deposition area. And the early warning decision center generates a graded early warning instruction set according to the space coordinates of the abnormal region, and triggers a corresponding emergency response strategy.

The invention discloses a flood disaster monitoring and early warning system and a flood disaster monitoring and early warning method. A cloud, rain, water and I integrated sensing network is constructed through a full-chain monitoring capability; a hybrid prediction model coupled with HEC-HMS and SWMM physical models and an LSTM-Transformer deep learning architecture is established, parameter deviation is dynamically corrected through NSGA-II and a symbolic regression multi-objective optimization algorithm, the flood prediction period is prolonged to 10 days (the precision of the southern watershed is larger than or equal to 90%, and the precision of the northern watershed is larger than or equal to 70%), the flood peak time error is compressed to be within 30 minutes, and compared with a scheme based on a static flood risk model, the method has the advantage that the flood prediction efficiency is greatly improved. The false alarm rate is reduced from 20% to 5% through the dynamic threshold calibration technology; hierarchical response and survivability communication are adopted, Beidou satellite and NB-IoT dual-channel redundant transmission is deployed, and a Mesh ad hoc network and frequency modulation subcarrier technology are combined, so that the direct rate of early warning information in extreme weather is ensured to be greater than or equal to 99%; and three-dimensional GIS platform dynamic rendering is supported, and collaborative visualization of a submerging thermodynamic diagram, a material scheduling path and ecological flow monitoring is realized.

The invention discloses a multi-source data-combined pit-pond water depth calculation and storage capacity calculation method, which comprises the following steps of: acquiring actually measured water depth data, performing data preprocessing, and dividing the data into a training sample point set and a test sample point set; acquiring a multispectral remote sensing image of the reckoning area, and executing image preprocessing; unifying to the same coordinate system; fractal dimensions of each wave band and the combined wave band are calculated, and a water body region is segmented; establishing a mapping relation between the water depth values of the training sample points and the standardized multispectral apparent reflectance; establishing a water depth inversion model, and verifying the water depth inversion model by using the test sample point set; calculating discrete water depth points of the pit pond by using the optimal water depth inversion model; and establishing a minimum energy curved surface to obtain the water storage amount of a single pit pond, and accumulating to generate the total water storage amount. According to the method, actual measurement and remote sensing data are fused, the energy minimization water depth inversion model is constructed, accurate calculation of the water storage capacity of the pit pond is achieved, and the method has the advantages of being high in precision and high in applicability.

The present invention relates to urban rainfall runoff pollution prediction in urban water systems, and provides an urban rainfall runoff pollution prediction method based on integrated rolling decomposition method and deep learning algorithm. A rolling decomposition method is firstly used to decompose rainfall runoff sequence data into different sub-sequences; then decomposition is sequentially performed on added data, and future data is excluded, to prevent information leakage; a recurrent neural network is used to model and predict the sub-sequences; and finally, predicted results of the sub-sequences are summed to obtain the predicted result of rainfall runoff pollution.

The invention discloses a method and a system for analyzing a pollution source of a rainwater pipe network. The method comprises the following steps of: 1, obtaining a first key fluorescence characteristic parameter combination according to basic physicochemical indexes by adopting a fluorescence characteristic parameter prediction model; 2, three-dimensional fluorescence characteristic parameters of the pollution source are obtained, and a pollution source three-dimensional fluorescence characteristic data set is obtained; screening by adopting a multivariate statistical method to obtain a second key fluorescence characteristic parameter combination; 3, determining a fluorescence characteristic parameter combination according to the first key fluorescence characteristic parameter combination and the second key fluorescence characteristic parameter combination; 4, inputting the fluorescence characteristic parameter combination into the Bayesian mixture model to obtain the contribution rate of the pollution source to the receptor water quality, and completing the analysis of the pollution source of the rainwater pipe network; according to the method, the DOM fluorescent fingerprint information is predicted by adopting basic data; and based on the three-dimensional fluorescence characteristic parameters and in combination with a Bayesian mixture model, precise quantitative analysis of the pipe network pollution source is realized.

The invention discloses a wireless layered water injection communication method and device, electronic equipment and a storage medium. The method comprises the steps that water absorption characteristic curve information of stratums corresponding to all water distributors in a target well is obtained, a target stratum is determined according to the water absorption characteristic curve information, an instruction for obtaining characteristic parameters of a reference stratum is received, and a sound wave signal generator corresponding to the reference stratum is controlled to emit a sound wave signal; the sound wave signal is transmitted to a target water distributor corresponding to the target stratum, so that the target water distributor sends a pressure pulse signal to a wellhead controller of the target well, characteristic parameters of the reference stratum are obtained, and the pressure pulse signal is a signal used for indicating the characteristic parameters of the reference stratum. After the target stratum is determined, the sound wave signal is transmitted to the target water distributor corresponding to the target stratum through the sound wave signal generator, then the target water distributor sends the underground data to the wellhead controller through the pressure pulse signal, and the problems that the whole well communication efficiency is low and the communication reliability of partial layers is poor are solved.

The invention provides an in-tunnel inrush water pressure monitoring method and system based on dual-acquisition multi-modal data, and belongs to the technical field of inrush water pressure monitoring. Comprising the steps of collecting video data by adopting a high-definition camera; sequentially performing water flow area intelligent segmentation, optical flow calculation and ConvLSTM time sequence prediction on the video data to predict the water flow velocity; an infrared camera is adopted to collect temperature data; self-adaptive temperature gradient calculation and temperature anomaly detection are sequentially carried out on the temperature data to predict the water flow temperature; aligning and fusing the predicted data of the water flow velocity and the water flow temperature, and constructing a water pressure calculation model to obtain a water pressure predicted value; and determining an early warning level according to the water pressure predicted value so as to adjust the monitoring mode of the inrush water pressure in the tunnel. According to the method, the infrared technology and the vision field technology are combined, accurate prediction of the inrush water pressure in the tunnel can be achieved, and a corresponding early warning monitoring mode is provided according to the prediction result.

The invention discloses a coastal zone carbon sink data acquisition management system and method, and particularly relates to the technical field of carbon sink evaluation, and the method comprises the following steps: dividing carbon flux parameter partitions based on vegetation types, sediment characteristics and historical carbon storage levels, and establishing initial reference values; plant biomass, soil organic carbon content and non-carbon dioxide greenhouse gas flux are regularly collected according to fixed sampling points, and a carbon sink observation data sequence is formed; when data exception triggers an evaluation process, generating a carbon sink credibility index and an ecological interference response index; inputting the two indexes into a prediction model to output an adjustment vector, generating an adjustment strategy in combination with a weight, and dynamically updating an initial carbon flux reference value of each partition; according to the method, regionalized management is realized by constructing carbon flux parameter partitions, a carbon sink observation data sequence is established, an evaluation index is dynamically generated, intelligent updating of a carbon flux reference value is completed in combination with a prediction model, and the precision, responsiveness and adaptive adjustment capability of carbon sink monitoring are improved.

The utility model discloses an electrolytic bath bipolar plate with micropore flow channels and an electrolytic bath, a micropore flow channel network is arranged on the side surface of an anode of a plate body, and the micropore flow channel network comprises a plurality of groups of micropore flow channel units which are arranged at intervals along the width direction of the plate body; each micropore flow channel unit comprises a main flow channel arranged in the length direction of the plate body and a plurality of first-stage branch flow channels arranged in the length direction of the main flow channel at intervals, a plurality of short branch flow channels are arranged on the two sides of each first-stage branch flow channel at intervals, and a jet hole is formed in the top of each short branch flow channel. The jet flow holes are formed in the direction perpendicular to the side surface of the anode, the length of the short branch runner is smaller than that of the first-stage branch runner, and the short branch runner is perpendicular to the first-stage branch runner. And the microporous flow channel network adopts a structure of combining tree fractal and T-shaped nodes, so that the mass transfer uniformity of water flow in the microporous flow channels can be improved, and the water-membrane contact efficiency is improved, thereby ensuring the hydrogen production efficiency of the bipolar plate.

The invention provides a digital twinning-based water flow dynamic analysis method and a big data analysis system, and the method comprises the steps: obtaining a historical water flow data set of a target water area, collecting a real-time water flow monitoring data set, and constructing a digital twinning water flow model corresponding to the target water area; performing multi-modal feature fusion processing on the historical water flow data set and the real-time water flow monitoring data set to generate a dynamic water flow feature set, loading the dynamic water flow feature set into a digital twin water flow model based on a spatial distribution topological structure of the digital twin water flow model to generate a water flow dynamic change rule set, and performing dynamic water flow change on the dynamic water flow change rule set. And generating a water flow optimization strategy set according to the water flow dynamic change rule set, and feeding back the water flow optimization strategy set to the water flow regulation and control system to execute dynamic regulation and control operation. By adopting the technical scheme of the invention, the space adaptability and the dynamic response capability of the water flow optimization strategy decision can be obviously improved.

The invention discloses a near-shore wave-flow field-sediment coupling simulation method considering a wave breaking nonlinear effect, and belongs to the field of near-shore hydrodynamics, and the method comprises the steps: inputting WRF high-resolution wind field, tide boundary conditions and water depth information, and initializing a wave module, a flow field module and a sediment module; calculating local wave breaking dissipated energy by adopting an improved wave breaking energy dissipation formula to obtain a wave energy density spectrum and a breaking probability; a near-shore flow field is calculated under radiation stress nonlinear correction, and hydrodynamic boundary conditions are updated; calculating sediment suspension and deposition by using a coupling diffusion coefficient, and updating sediment concentration distribution; waves, flow fields and sediment are mutually coupled through loop iteration, and coastal wave-flow field-sediment simulation is achieved under extreme conditions such as typhoons, storm surge and strong storm waves. The method can improve the prediction precision of the wave height, the flow velocity and the sediment concentration under the condition of offshore strong storm waves, and is used for typhoon storm surge prediction, harbor basin deposition evaluation and offshore ecological environment management.

The invention provides a high-temporal-spatial-resolution full-profile real-sea accurate corrosion test method and system, and the method comprises the steps: dividing the sea level into three layers of monitoring regions, namely an atmospheric near-sea-level layer, a sea-gas boundary layer and a seawater mixing layer, deploying corresponding sensor arrays in each monitoring region, and synchronously collecting the environmental parameters of each layer in real time; a standard corrosion coupon and a corrosion monitoring sensor for monitoring the local corrosion state of the sample are arranged in each monitoring area; performing space-time alignment on the acquired data to form an environment-corrosion strong association data set; an atmosphere-ocean coupling forecasting system is constructed, simulation and prediction of future environmental parameters are achieved, simulation results serve as input variables of a corrosion prediction model, and a material corrosion life prediction model is established in combination with historical test data. According to the invention, synchronous monitoring of multi-dimensional environmental parameters and material corrosion data within a range of 30 meters above and below the sea level can be realized, and an accurate basis is provided for material selection and corrosion prediction of marine equipment.

A system and method of monitoring a downhole tool implemented in a wellbore that include identifying, from surface parameters, a surface downlink sent from a surface of the wellbore to the downhole tool. The surface downlink indicates a downlink command for the downhole tool to implement. The system and method further include confirming that the downhole tool received the surface downlink based on comparing downhole telemetry data received from the downhole tool to the surface downlink. The system and method additionally include, determining a status of the downhole tool indicated by the surface downlink based on confirming that the downhole tool received the surface downlink.

The invention provides an oil-water separation real-time monitoring system in a petroleum extraction process, and belongs to the field of image processing, and the method comprises the following steps: obtaining an oil-water separation image of each frame and an emulsion layer area in the oil-water separation image of each frame; obtaining the separation degree of the oil-water separation image of each frame according to the gray level distribution of the pixel points in the emulsion layer area of each frame and the distribution difference of the gradient values of the pixel points in the emulsion layer areas of each frame and the first frame; and setting the length of the time interval, and comparing the separation degrees of the oil-water separation images of different frames in the same time interval to obtain the oil-water separation effect in each time interval. The invention aims to solve the problem that the oil-water separation effect in each time interval cannot be accurately judged directly through an oil-water separation image due to low image discrimination in the oil-water separation process in the oil extraction process.

The invention relates to the technical field of carbon sink calculation, in particular to a forestry carbon sink calculation model, a forestry carbon credit calculation method and a storage medium. The model comprises an overground biological carbon sink amount estimation module used for calculating overground biological carbon sink data of a target area, an underground soil carbon sink amount estimation module used for calculating soil carbon sink data of the target area, and a litter carbon reserve estimation module used for calculating litter carbon sink data of the target area. The combined loss calculation module is used for calculating carbon loss data; and the final carbon sink total quantity fusion module is used for calculating the carbon sink quantity according to the aboveground biological carbon sink data, the soil carbon sink data, the litter carbon sink data and the carbon loss data. A forestry carbon sink calculation model based on multiple dimensions is established through multiple types of carbon sink estimation modules, and the problem that the requirement for rapid authentication of the global carbon market cannot be met due to high manual dependency is solved; the problems of low calculation precision, high cost and the like caused by insufficient model dynamics, single model calculation dimension and the like are solved.

The invention relates to an earthwork balance calculation method, system and device and a storage medium, and relates to the technical field of earthwork balance design. The method comprises the steps of performing fusion processing on oblique photography data and laser radar point cloud data to generate a live-action three-dimensional model; performing grid division on the live-action three-dimensional model to obtain a grid model; based on the gridding model, defining a decision variable set including the design elevation of each grid point, the overall slope of the site, the slope height of each slope unit and the earthwork allocation amount, and constructing a comprehensive objective function including an earthwork balance objective, an economical objective and a safety objective; and under the engineering constraint condition, performing multi-objective optimization solution on the comprehensive objective function to obtain a Pareto optimal solution set, and selecting a final implementation scheme from the Pareto optimal solution set. According to the method, the problem of topographic data missing of the dense vegetation region is effectively solved, the limitation of traditional single-target optimization is broken through, and the maximization of comprehensive benefits is realized.

The invention discloses an expansive soil landslide monitoring and early warning system, and relates to the technical field of geological monitoring, the expansive soil landslide is monitored in real time by using multiple types of sensors so as to obtain multiple environmental data and displacement data of the expansive soil landslide in real time, and the environmental data with high correlation with the environmental data are extracted at the same time; fusion of multi-source data is realized, and an accurate early warning grade of the expansive soil landslide is established; meanwhile, the displacement data is decomposed into trend term displacement and periodic term displacement, polynomial fitting is used for predicting the trend term displacement to obtain a trend term displacement predicted value, PSO-LSTM is used for predicting the periodic term displacement to obtain a periodic term displacement predicted value, and displacement data representing displacement long-term deformation rules and short-term fluctuation are captured at the same time; and adding the trend term displacement predicted value and the periodic term displacement predicted value to obtain an accurate displacement predicted value, and realizing high-precision landslide monitoring and early warning according to the deformation rate obtained by the displacement predicted value and the early warning level to which the tangent angle belongs.

The invention discloses an ultrasonic water meter detection device and method, and relates to the technical field of ultrasonic flow metering, the device comprises an ultrasonic detection unit, a block chain evidence storage unit, a zero-knowledge proof module, a master control MCU and a low-power-consumption communication unit, the ultrasonic detection unit collects downstream and countercurrent ultrasonic propagation time difference delta t through symmetrically installed transducers, a time difference measurement module (precision + / -1ns) is combined with water temperature data to calibrate a delta t value, the block chain evidence storage unit constructs a Merkel tree for a delta t sequence according to a time window, and root hash is written into a private chain by improving a PBFT consensus mechanism (threshold 4 / 5 node confirmation). The method covers the whole process of data collection, evidence storage, verification and auditing, solves the problems that traditional water meter data is easy to tamper, privacy is leaked and installation is limited through a hash chain and zero-knowledge proof technology, and is suitable for an intelligent water affair scene.

The invention provides a prediction method for a sudden change type signal of gas dissolved in oil. The prediction method comprises the following steps: firstly, acquiring a time sequence signal of transformer gas monitoring; processing the time sequence signal according to a variational mode decomposition algorithm to obtain a plurality of mode components; according to the variational mode decomposition algorithm, the sum of confusion entropies of all mode components is used as an objective function of parameter optimization; and finally, according to a pre-established prediction model, performing prediction and superposition reconstruction on each modal component to obtain a prediction result of the abrupt change type signal of the gas dissolved in oil. According to the method, the VMD decomposition architecture optimized by the frost ice algorithm is introduced, the non-stationarity of the signal is quantified and obviously reduced through the chaos entropy index, higher prediction precision and robustness of the gas signal in the mutation type oil are realized, and the method has obvious innovativeness and superiority in the field of transformer fault trend prediction.

A system is for cooling at least one computing device using water. The system has a body of water defining a first waterline and a structure at least partially submerged in the body of water. An internal volume of water defines a second waterline. At least one outlet drains water from the internal volume of water to the body of water. A container is at least partially submerged in the internal volume of water and has at least one computing device that generates heat in use. An inlet pipe has a pump to move water from the body of water to the internal volume of water. A flow path of water is provided across the container towards the at least one outlet. The second waterline is at a height above the first waterline to drive the flow path to provide cooling of the container and computing device therein.

The invention discloses a sampling mechanism for water conservancy sand content detection, and belongs to the technical field of sampling mechanisms, the sampling mechanism comprises a supporting platform and a servo motor mounted at the upper end of the supporting platform, the output end of the servo motor is connected with a sampling barrel, an interception hole is formed in the side edge of the upper end of the sampling barrel, and a water leakage cover is fixed in the sampling barrel; a protective bottom cover is arranged at the bottom of the sampling barrel, a drainage pipe is installed on the protective bottom cover, a first air cylinder is installed at the upper end of the supporting platform, the telescopic end of the first air cylinder is connected with a sealing ring through a guide connecting rod, the sealing ring is used for controlling opening and closing of an interception hole, and sand blocking filter cloth is arranged in a water leakage cover. According to the sampling mechanism for water conservancy sand content detection, when a water body sample is sampled, a surrounding water area to be sampled is conveniently stirred, so that the sand content of the water area is equal, and the accuracy of a subsequent detection result is improved.