28results about How to "Easy to predict" patented technology

Embodiments of the present disclosure disclose a resource warning method and device based on a computing node cluster and an electronic device. A specific implementation of the method comprises: determining whether to call the computing node cluster for resource processing; for each interactive object, determining the corresponding model calculation amount in the information prediction process; selecting a first computing node from the computing node cluster to support the call of each interactive object; for each interactive object, generating first historical resource overdue summary information and first future resource overdue summary information; generating second historical resource overdue summary information and second future resource overdue summary information; generating target resource quality information; and sending corresponding encrypted information to a target monitoring terminal to perform target warning under the condition that the resource quality warning condition is met. The implementation accurately and efficiently generates target resource quality information on the basis of reducing waste of computing resources through reasonable allocation of hardware resources.

The present application relates to the technical field of tunnel construction, in particular to a small-angle roof coal uncovering tunnel vault small-section coal uncovering process, comprising S1, excavating a ladder-type drilling field; S2, drilling and pumping a drainage hole; S3, setting up an extraction system; S4, performing extraction operation. The ladder-type drilling field is excavated in a limited ladder form in accordance with the overall trend of the bedding, and the drainage hole is drilled in a manner perpendicular to the step drilling surface, which can realize uniform distribution of drilling holes in the target area through drilling hole channels with approximately the same length. Since the direction of the drilling hole channel is determined, the drilling angle and depth can be quickly determined, the distance to the bedding surface can be easily predicted, the drilling speed and pressure of the drilling machine can be dynamically adjusted in advance, the construction safety is improved, the construction engineering quantity is reduced, the construction efficiency is improved, the hole completion rate is improved, and at the same time, the drilling hole channel can form a small-angle intersection with the bedding surface, so that the drainage hole can effectively contact more gas-containing bedding fissures, improve the coverage of the bedding surface area, and be beneficial to improving the mining efficiency.

The application discloses a mechanical drilling speed prediction method based on micro-inclination characteristics, comprising the following steps: acquiring real drilling wellbore logging while drilling and logging data as a first data set; preprocessing the first data set and extracting the best feature and the micro-inclination feature as input features; the preprocessing comprises data cleaning and correlation analysis; training a mechanical drilling speed prediction model by using the input features; and predicting the mechanical drilling speed of a target well by using the trained mechanical drilling speed prediction model. The micro-inclination feature introduced in the application can significantly improve the ROP prediction accuracy of the model. By introducing the micro-inclination feature in the MLP neural network, the model weight is optimized, and the prediction accuracy of the ROP is improved. By introducing the micro-inclination feature in the SVR model and selecting the optimal hyperparameter, the prediction effect is significantly improved. The method provides a new idea for improving drilling parameter prediction and control by using machine learning, and can be applied to the optimization of drilling speed and efficiency.

The invention discloses a bridge multi-segment prefabricated part assembling deviation correction method. The method comprises the following steps that three-dimensional point cloud scanning, theoretical model comparison and virtual assembling are conducted on all segment prefabricated parts of a bridge; obtaining deviation correction positions of all the sections needing deviation correction; according to the pre-correction condition group, each correction position in the correction position group is primarily screened; classifying each deviation correction position in the screening position group according to the deviation correction type condition group; ring opening error adjustment is carried out on the thermal correction set, the mechanical correction set and the disassembly reconstruction set; a fourth adjusting process is adopted for adjusting the segment axis and the elevation error; and continuously repeating the steps until all the deviation rectification positions in the deviation rectification position group meet the pre-deviation rectification condition. The method can solve the problems that the rework rate and the equipment occupation time are relatively long due to the reasons of dependence on manpower, low measurement precision, lack of error pre-judgment, large trial splicing resource consumption and the like when the existing bridge segments are spliced, and realizes the pre-correction of the splicing error.

The invention belongs to the technical field of oil and gas field development reservoir transformation, and discloses a tracer agent for multilayer fracturing flowback monitoring, a preparation method and application, according to the preparation method, the effectiveness of the tracer agent is evaluated through an experimental means, and it is guaranteed that the effective tracer agent can be used for monitoring a flowback curve of fracturing fluid flowback volume along with time; according to the application method, a tracer agent is used for marking fracturing fluid, and the initial fracturing fluid dosage and the initial tracer agent dosage are recorded; after flowback is started, accumulated flowback volume is recorded at a fixed time interval, the real-time concentration of the tracer agent is measured, and the flowback volume of each section is calculated; according to the return volume of each section and the accumulated return volume of each section, the return curve and the accumulated return rate of each section are calculated, the liquid production contribution rate and the production sequence of each layer section can be accurately obtained, and the liquid production capacity can be calculated in a semi-quantitative mode. Through flow-back liquid sample detection and data analysis, the liquid drainage sequence, the flow-back contribution rate and the like of each layer section can be determined, and data support is provided for subsequent construction.

The application relates to the technical field of tunnel construction, and discloses a tunnel construction drainage device, which solves the problem that the cleaning mechanism of the existing drainage device has a complex structure, the complex matching structure is prone to faults in operation, and the cleaning of the silt is ineffective; the water flow drives the rotary drum to rotate, thereby providing power for the rotation of the filter cover; the driving mode has a circulating operation path, has no redundant components or complex interaction, has a low failure rate, is convenient for long-term stable operation in a complex underwater environment, the rotating filter cover and the non-porous part of the surface cooperate with the inclined shelf to work, the silt adsorbed on the surface of the filter cover is continuously cleaned, the filter cover keeps the water filtering efficiency, the suction assembly enables the suction cover to generate suction force to suck the cleaned silt, the silt is prevented from being active near the filter cover, and the workload of the inclined shelf for cleaning the silt is increased; the movement path of the above structure is circular, the operation process is orderly, the regularity is high, and the drainage operation is suitable for stable operation in a complex underwater environment.

The application discloses a kind of equipment failure evaluation system based on multi-dimensional data of Internet of Things, and the multi-dimensional data such as mixed sound, axial vibration, radial vibration and bearing temperature in the working process of motor are collected and analyzed, to comprehensively evaluate motor failure.The application obtains the motor maintenance danger evaluation coefficient that multi-dimensional data shows by collecting the multi-dimensional data such as noise, vibration and temperature of motor and carrying out the analysis of multi-dimensional data, reflects the dangerous degree of continued use after motor failure, and realizes the quantitative display of dangerous degree, is convenient for comprehensive evaluation and prediction to motor failure, reaches the effect of early warning and maintenance.

This invention belongs to the field of medium- and long-term power generation in new energy, and discloses a medium- and long-term power forecasting method and related equipment based on ERA5 reanalysis data. This method uses multi-year ERA5 reanalysis data to perform climate model analysis on the area where the power plant is located, obtains the climatological average irradiance, and constructs an error correction model by combining it with historical measured data from the power plant. This allows for medium- and long-term power forecasting using historical data from the same period of the power plant's climatological output. By using multi-year ERA5 reanalysis data to perform climate model analysis on the area where the power plant is located, this method can accurately capture the climatological average irradiance of the area, effectively solving the forecasting problem caused by the lack of historical data for newly built or short-term operating photovoltaic power plants. Using this method is of crucial practical significance for power plants to reasonably declare medium- and long-term trading volumes in electricity transactions and reduce revenue losses caused by positive and negative volume deviations, significantly enhancing the competitiveness of power plants in the electricity market.

The invention discloses a calculation method for adding an irregular sub-domain without a flux boundary condition in a computational domain, which comprises the following steps of: determining an irregular sub-domain of a substrate / impurity phase added in the computational domain, and drawing the irregular sub-domain into a substrate / impurity phase shape image; processing the image into data with a required model size; establishing a calculation equation of the calculation method coupled with the irregular sub-domain added with the flux-free boundary condition in the calculation domain; performing discrete difference on the calculation equation by adopting a finite difference method to obtain a discrete difference format equation; writing a phase field preposition program by applying a discrete difference format equation; importing the data into a phase field preposition program; taking an operation result of the phase field preposition program as an initial condition of a substrate / impurity phase, and importing the operation result into a constructed phase field model for describing a dendritic crystal growth process to obtain a calculation model coupled with the substrate / impurity phase with an irregular domain; a numerical solution of the calculation model is obtained through computer simulation. According to the invention, the operation efficiency and accuracy are improved.

The application discloses a multi-relation contrast learning drug interaction prediction method, system, medium and equipment, belongs to the technical field of graph neural networks and drug interaction prediction, and aims to solve the technical problem of how to capture implicit information about drug interaction and improve the accuracy of drug interaction prediction.The technical solution is as follows: obtaining SMILES sequences and molecular graph features of two drugs to be predicted, integrating multi-dimensional feature information of the two drugs to be predicted, and capturing comprehensive relationship features inside and between the two drugs to be predicted; converting the SMILES sequences of the two drugs to be predicted into drug molecular graphs by using an RDKit tool; extracting drug features from the drug molecular graphs by using a TrimNet method; obtaining a DDI network graph of the two drugs to be predicted and drugs with known interaction relationships; taking the feature vectors of the drug molecules as the feature vectors of the DDI network graph, fusing the drug molecular feature vectors and external DDI features by using a multi-layer GAT network layer, and obtaining updated drug representation vectors.

The application discloses a stability and surface position error comprehensive prediction method and equipment for milling finishing, and belongs to the technical field of milling finishing in mechanical processing. The method comprises the following steps: acquiring milling cutter information; calculating the cutting edge angle position, cutting radius, feed amount and total cutting force in X and Y directions at any time; constructing milling dynamics time-delay differential equation groups and transforming into space state equations; performing integral calculation on the space state equations to determine the spindle speed range, corresponding discrete numbers, axial cutting depth range, corresponding discrete numbers and single tooth cutting period discrete numbers; calculating state items, time-delay items and static force items in the space state equations, constructing the state transmission relationship of the milling finishing system in a single time period, and determining the stability of the milling finishing system; extracting corresponding steady-state coefficient vectors Y direction coefficients to predict dynamic machining errors; and obtaining stability petal diagrams and surface position error prediction diagrams. The application has good accuracy and timeliness.

The application provides a human posture estimation behavior analysis method, comprising: processing human image information into picture data, obtaining global posture features of each group of pictures, and obtaining an image frame sequence with human two-dimensional key point information based on the global posture features; establishing a motion model according to the image frame sequence, estimating the task motion state in the image frame sequence, constructing a Transformer model, and generating posture hypotheses based on a multi-hypothesis generator of the Transformer model to regress each group of posture hypotheses to generate posture hypothesis information; constructing a behavior prediction network, learning based on pre-collected posture data to obtain optimal parameters of the network; applying the optimal parameters to the behavior prediction network, predicting the posture hypothesis information based on the behavior prediction network, and finally outputting predicted posture estimation data. The application does not require manual parameter setting, improves the efficiency and accuracy of neural network parameter searching, is simple to operate, and is convenient for staff to use.

This invention provides a water hammer prediction system and device. The invention relates to the field of pipeline fluid transport safety technology. The water hammer prediction system is configured to: acquire static topology and physical attribute information and multi-dimensional real-time operating condition data of the fluid transport system, and pre-calibrate a dynamic simulation model based on the information and data; when a water hammer triggering event is detected, invoke the pre-calibrated dynamic simulation model to perform an initial prediction to generate an initial prediction result, and issue a pre-control command to the first-level protection device based on the initial prediction result; acquire the measured impact index at the first-level protection device and compare it with the initial prediction result to determine the prediction error; and, based on the prediction error, correct the dynamic simulation model in real time, and drive the corrected dynamic simulation model to perform cascade control of the next-level protection device.

The application provides a quantitative analysis method for ore pore and fissure structure, and relates to the technical fields of mineral processing, metallurgical engineering and geological analysis. The method realizes accurate prediction of effective porosity of heap leaching ore by coupling on-site particle size investigation, laboratory "quasi on-site" heap construction, ore block fissure microscopic measurement and mass-volume conservation calculation. The method effectively overcomes the technical problems that the traditional method depends on single scale measurement, and is difficult to comprehensively reflect the complex structure from nanoscale pore to centimeter scale fissure, especially for the mixed ore of coarse and fine particle grades. The existing technology often processes different particle grades respectively, lacks systematic integration and mutual correction, and causes deviation of the calculation results of porosity and fissure rate.

The invention is suitable for the technical field of glasses rubber plate manufacturing, and particularly relates to a digitized pattern pressing method and equipment for a cellulose acetate glasses rubber plate, and the method comprises the following steps: obtaining the temperature of a pressing mold in real time, and sending a first standby signal when the temperature of the pressing mold reaches a first temperature; when it is detected that the plate is fixed to the pressing area, a second standby signal is sent, and when it is detected that the plate is not fixed to the pressing area, the second standby signal is canceled; when the first standby signal and the second standby signal are prepared at the same time, the pattern digital pressing equipment carries out a pressing process of a fixed pressure-time relation curve, when the pressing process is finished, a second temperature is recorded, and the first standby signal is cancelled; the second temperature is compared with the third temperature, and the first thickness in the grinding and polishing process is determined according to the difference between the third temperature and the second temperature. According to the method, the depth consistency of the finally obtained patterns can be improved, and the problems that an existing printing and pressing method is poor in printing consistency and low in production speed are solved.

The present application belongs to the technical field of frozen ground foundation engineering, and in particular to a frozen ground foundation bearing behavior prediction method with multi-pile length cooperation. A three-dimensional geological-temperature database of the frozen ground foundation is constructed, stratum distribution, water content, fine particle content and geothermal gradient data are integrated, frozen layers and unfrozen layers are divided, and high, medium and low frost heaving sensitive zones are identified. Based on the sensitive zone distribution, a three-level differentiated pile type of "short pile-medium pile-long pile" is designed, a coupling relationship between pile length and frost heaving sensitivity is established, a group pile soil resistance correction model considering pile length difference is constructed by introducing a multi-pile length cooperation effect coefficient, and a frozen layer rigid shell effect correction model and an unfrozen layer bearing decay model are integrated. Through the differentiated pile type design and the cooperation effect model, the problem that the traditional method cannot handle the spatial variability of frozen soil, resulting in inaccurate bearing behavior prediction, is solved, and the present application has the advantages of accurate prediction, optimized design, improved safety and economy.

The application discloses a short-term and nowcasting precipitation prediction method based on a multimodal RNN, relates to the technical field of short-term and nowcasting precipitation prediction, and aims to provide a short-term and nowcasting precipitation prediction method with high accuracy, which comprises the following steps: acquiring a radar graph and a meteorological element graph; training a multimodal model by using the radar graph and the meteorological element graph; and performing precipitation prediction by using the trained multimodal prediction model; wherein the multimodal prediction model comprises a radar module and a meteorological element module, and the multimodal prediction model obtains a short-term and nowcasting precipitation prediction result according to a radar prediction graph; the method takes radar information as the main part and meteorological element information as the auxiliary part to perform precipitation prediction, fuses the two modes, and improves the accuracy of short-term and nowcasting precipitation prediction.

This invention provides an auxiliary puncture positioning device, relating to the field of medical navigation technology. It includes a positioning section and a puncture section. The positioning section has an arc-shaped flat plate structure, and its upper surface is provided with positioning patches configured for identification by a medical navigation system. At least three positioning patches are provided. The puncture section has a puncture hole, the axis of which is perpendicular to the plane of the upper surface of the positioning section. The lower end of the puncture section extends below the lower surface of the positioning section. The arc-shaped flat plate structure of the positioning section minimizes obstruction of the operator's field of vision, facilitating judgment based on the operator's conventional surgical experience (not supported by medical navigation systems). The overall size of the auxiliary puncture positioning guide plate can be kept within a small range, facilitating operator operation while achieving the effect of medical navigation. The axis of the puncture hole is perpendicular to the plane of the upper surface of the positioning section, allowing the operator to visually judge and estimate the angle or direction of the puncture hole by observing the positioning surface.

The application relates to the technical field of equipment predictive maintenance, and discloses a bearing residual service life prediction method based on feature migration, which comprises the following steps: obtaining equipment failure data and equipment full-cycle degradation data, and performing data preprocessing; a source network is constructed, the source network comprises a failure feature extraction module and a failure classifier; a failure classification loss function is designed, and the source network is trained by using the equipment failure data; a target network is constructed, the target network comprises a degradation feature extractor and a residual service life predictor; a residual service life prediction loss function is designed, and the target network is trained by using the equipment full-cycle degradation data; and a failure feature migration loss function is designed to perform failure feature migration. The application obtains failure features and feature migration by performing failure diagnosis on equipment, so that the requirement of an existing method for equipment full-cycle degradation data is reduced, and better results are obtained.

The invention discloses an idiopathic atmospheric hydrocephalus biomarker, a diagnostic kit and application, belongs to the technical field of biological medicine, and particularly relates to the idiopathic atmospheric hydrocephalus biomarker which is a protein biomarker and comprises NBL1, TREM2, COL3A1, DPP7, PLA1A, SELENBP1, FGB, CCK, WFIKKN2, KNG1, HBA2, GFRA3 and CSF1. The invention relates to an application of an idiopathic atmospheric hydrocephalus biomarker in preparation of an idiopathic atmospheric hydrocephalus diagnostic kit. Differential proteins are screened through a DIA technology, protein expression is verified through a PRM technology, the idiopathic atmospheric hydrocephalus biomarker is obtained through screening and used for early screening, risk assessment and prognosis judgment of iNPH, early, accurate and objective diagnosis of iNPH is achieved, the clinical diagnosis period is shortened, and the diagnosis and treatment efficiency is improved.

This application relates to a method, apparatus, equipment, storage medium, and computer program for quantitative prediction of tight sandstone. The method includes: acquiring basic seismic geological data; interpreting seismic horizons of post-stack seismic data based on the basic seismic geological data to obtain a target seismic stratigraphy; determining the source direction of the target seismic stratigraphy based on the target seismic stratigraphy; calculating Kriging interpolation parameters based on the source direction; performing low-frequency modeling using the Kriging interpolation parameters to obtain a low-frequency model; performing post-stack seismic impedance inversion based on the low-frequency model and post-stack seismic data to obtain an inverted impedance data volume; and calculating the predicted value of tight sandstone corresponding to the basic seismic geological data based on the inverted impedance data volume. This application improves the accuracy of quantitative prediction of tight sandstone.

The application relates to an effluent BOD concentration prediction method based on a WSFA-AFE ILSTM neural network and relates to the field of artificial intelligence.The application proposes a WSFA-AFE method aiming at the problem that the input characteristic variable and input history step length are difficult to determine when a neural network is used to predict a multivariate time sequence of effluent BOD.The method can adaptively extract dynamic characteristic variables in the multivariate time sequence, so that the neural network can better predict the effluent BOD concentration.The application proposes an ILSTM neural network aiming at the problems that the standard LSTM neural network has a large number of structure parameters and the training process is time-consuming.The application simplifies the recursive term weight in the structure equation, reduces the number of required training parameters in the network, and accelerates the convergence speed through a parameter updating algorithm.The application realizes efficient, accurate and low-cost prediction of effluent BOD concentration at future time according to the data collected in the sewage treatment process.

The invention discloses a model for estimating carbon reserves of soil microbial death residues, which considers that the carbon of the microbial death residues has a great contribution to the formation of total organic carbon in soil, and establishes a set of estimation algorithm representing the microbial death residues and develops four modules for realizing the algorithm, so that the carbon reserves of the soil microbial death residues are estimated. The reserve of microbial residual carbon in soil types can be estimated. In order to solve the problem that the carbon reserves of dead soil microorganism residues are difficult to accurately estimate on a large spatial scale, a neural network machine learning method is combined in an algorithm module, and a regional and even global soil microorganism residue carbon reserve map can be drawn through measurement data of some observation points. The core technology of the method is based on a theoretical algorithm for estimating microbial residues, computer software for realizing the algorithm is developed, the computer software comprises four modules, measurement data of field observation points are input, and spatial distribution of soil microbial residue carbon reserves and contribution to soil total organic carbon can be automatically, rapidly and accurately estimated.

The invention discloses a modeling method for a relationship between man-machine collaborative decision evaluation indexes. The method comprises the following steps: establishing a man-machine collaborative decision model; establishing a man-machine collaborative decision evaluation index system; carrying out a man-machine collaborative decision-making human factor experiment, and obtaining experiment data of each index; calculating the score of each man-machine collaborative decision evaluation index, and carrying out index correlation analysis to obtain a correlation coefficient between every two indexes; selecting strongly correlated man-machine collaborative decision evaluation indexes, and establishing a relation function among the evaluation indexes by using a multiple linear regression analysis method; and evaluating the accuracy of the relation function between the evaluation indexes by calculating an average absolute percentage error between a predicted value and an actual value of the relation function. According to the method, the quantitative relation between the man-machine collaborative decision evaluation indexes is determined, a complete man-machine collaborative decision evaluation index system is established and simplified, the quantitative evaluation problem of man-machine decision is effectively solved, and a quantitative standard is provided for improving the accuracy of man-machine decision.

The present application belongs to the technical field of intelligent video processing, and particularly relates to a future frame anomaly detection method based on meta learning and space-time relationship. The present application method proposes a meta learning module, and a model-based meta learning method enables the model to learn general features from multiple tasks, so that the system can learn more discriminative and generalizable feature representations from data. In different monitoring scenes and abnormal behaviors, the model can also accurately perform anomaly detection without overfitting or underfitting. The present application method introduces the meta learning module into the autoencoder, uses the learning characteristics of the meta learning module to extract, save and update the features extracted by the autoencoder, automatically acquires the feature importance of the input video frame through learning, and assigns important weights to the features that are more worthy of attention, which helps to improve the utilization rate of key features in the input stage of the future frame prediction network.

This invention discloses a method and system for monitoring collapses in unmanned transport and unloading areas of open-pit mines. The method includes: acquiring position data from several wireless position sensors scattered in the unloading area; determining the warning level based on the acquired position data and preset collapse detection rules; and issuing control commands to the unmanned transport truck according to the warning level. This invention can continuously monitor the status of the work area, promptly detect hazards in the unloading area, and solve the problem of untimely and accurate hazard prediction caused by the limited detection range of the unmanned transport truck's own sensing system and the influence of adverse external working conditions on reliability.

The embodiment of the invention provides a hydrate exploitation numerical simulation method and related equipment, and belongs to the technical field of fluid dynamics simulation. The method comprises the following steps: setting a current decomposition front in a hydrate exploitation process under a two-dimensional simulation condition; setting a basic mode of a current geophysical model based on the current decomposition front edge, and establishing the current geophysical model based on a rock physics formula; acquiring a current seismic wave signal of the current geophysical model in a preset time window, feeding back the current seismic wave signal to a hydrate exploitation site, and acquiring site exploitation data based on the hydrate exploitation site; reconstructing a dynamic mining model according to the current decomposition front edge through the current geophysical model, and performing mining simulation to obtain simulated mining data; and comparing the difference between the field mining data and the simulated mining data, and updating the current geophysical model according to the difference to obtain a second geophysical model. According to the embodiment of the invention, the calculation efficiency and real-time performance of physical detection of the hydrate exploitation area can be improved.

The invention discloses a photovoltaic output prediction method and device and electronic equipment. The method comprises the steps that a photovoltaic output prediction request for a power system is acquired, the photovoltaic output prediction request carries prediction parameters, and the prediction parameters comprise at least one of meteorological parameters and prediction time scales; a target probability distribution function corresponding to the prediction parameter is called, the target probability distribution function is determined according to an evaluation index and multiple probability distribution functions, the evaluation index comprises a histogram fitting index, and the multiple probability distribution functions comprise a parameterized probability density function and a non-parameterized kernel density fitting function; and determining predicted photovoltaic output data of the power system according to the target probability distribution function. According to the method and the device, the technical problems that a lot of data is needed, time and labor are wasted and prediction is possibly inaccurate when photovoltaic output prediction is carried out in the prior art are solved.