14results about How to "Solve forecasting problems" patented technology

The invention belongs to the field of heating and ventilation equipment fault prediction, and provides a heating and ventilation equipment fault probability prediction method suitable for multiple working conditions. Comprising the steps of defining equipment types and equipment parameters, collecting recorded data, constructing a data-fault mapping relation, constructing an equipment fault probability formula, reconstructing data, cleaning data, acquiring equipment operation and maintenance parameters, adjusting the equipment operation and maintenance parameters, predicting the equipment operation and maintenance parameters and classifying predicted operation and maintenance parameters. Through real-time operation and maintenance data prediction and fault judgment, the defect that an existing method depends on a single monitoring point, upper and lower limit alarm and manual tracking is overcome, and prediction and early warning of equipment faults are achieved; reliable prediction of different working conditions is realized by constructing an environment parameter matrix and a working condition set; by adjusting the operation and maintenance parameters of the equipment, the influence of manual operation on the operation and maintenance parameters of the equipment is reduced, and the reliability and accuracy of a final prediction result are improved.

The invention discloses an anti-seismic fault crossing system and method based on self-adaption. The system comprises a distributed optical fiber module, a micro electro mechanical system module, a central processing module, a material control module and an execution module. The distributed optical fiber module and the micro-electro-mechanical module monitor temperature, strain and dynamic response data of a building in real time through the optical fiber node and the micro-electro-mechanical node respectively, and transmit the data to the central processing module; the central processing module is responsible for analyzing and predicting data in real time and generating a control instruction; after the material control module receives a control instruction, the control instruction is converted into an electric signal or a magnetic field control signal through the signal processor, the drive circuit and the interface unit, a material assembly in the execution module is driven to deform or change physical characteristics, and the structural rigidity and damping of a building are dynamically adjusted. The rigidity and damping of the building structure are dynamically adjusted, the shock resistance of the building in the fault area is improved, and the safety and stability of the building are improved.

This invention relates to the field of oil and gas field exploration and development technology, and particularly to a method for constructing classification curves for low-permeability gas reservoirs in the sea using integrated static and dynamic data. The method includes: Step S1, preprocessing logging data and core data from the entire well section; Step S2, based on Step S1, delineating target sections with low-permeability gas reservoirs, selecting the main influencing parameters related to reservoir static layers and production capacity, and extracting curves for each main influencing parameter of the target section; Step S3, based on Step S2, normalizing the extracted curves for each main influencing parameter, and constructing reservoir classification curves according to the correlation between each main influencing parameter curve and production capacity; Step S4, based on Step S3, verifying the accuracy of the reservoir classification curves using test production capacity data from different dynamic layers of the reservoir within the target section. Using this reservoir classification curve for the classification of low-permeability gas reservoirs in the sea has high accuracy and operability, and can be used to accurately predict production capacity.

The application relates to the technical field of data analysis and intelligent marketing, and particularly discloses a market trend prediction and precision marketing system based on deep learning. The system comprises a data interface module, an interlocking collaborative analysis engine and a strategy generation module. The interlocking collaborative analysis engine sequentially performs market trend prediction and user behavior analysis, and through a two-way coupling feedback step, converts the market prediction result into an adjustment signal for a user interest model, and feeds the emerging pattern identified in the user behavior to the market prediction model as a correction input; and then updates the two models based on the coupled new data, to realize collaborative evolution. The strategy generation module generates marketing instructions under resource constraints according to the collaborative analysis result. The application realizes the deep integration and co-evolution of market trends and user preferences by forcibly establishing and continuously optimizing a two-way interaction channel between macro prediction and micro analysis, so that the foresight, precision and self-adaptive ability of the marketing strategy are improved.

The present invention relates to the field of intelligent workbench search optimization technology, and in particular to an intelligent workbench search optimization method and system based on multi-scenario business. The method comprises the following steps: collecting user behavior data of platform users to obtain user behavior data; performing behavior time series analysis on user behavior data to obtain user behavior sequence; constructing a user tag library according to the user behavior sequence to obtain a user tag library; extracting content information from the workbench and constructing a content feature vector library to obtain a content feature vector library; constructing a scene context library according to the user tag library and the content feature vector library to obtain a scene context library. The present invention solves the problem of lack of intelligent recommendation and prediction in traditional methods by taking users as the center, actively perceiving user needs, and providing accurate and personalized information push and search assistance, thereby improving the intelligence level, user experience and work efficiency of the intelligent workbench.

The application relates to the technical field of hardware equipment quality tracing, and discloses a hardware full-life-cycle quality tracing method and system based on IoT, which comprises the following steps: acquiring multi-dimensional real-time data flow and arranging the same into a real-time running data set; extracting distribution characteristics based on a historical data archive to obtain a benchmark reference space; projecting the real-time data to calculate a deviation quantitative value, correcting environmental deviation when the deviation quantitative value exceeds a threshold value to obtain a deviation result; extracting degradation characteristics to match historical trajectories to locate a degradation process; performing time sequence analysis to predict residual life to obtain a failure time window; and generating and arranging maintenance intervention instructions into a scheduling instruction set. The method realizes hardware full-life-cycle quality tracing and early abnormality early warning, improves maintenance accuracy, and reduces fault loss.

The application provides a human pose estimation method based on attention and adversarial network, a medium and equipment; wherein the method is: inputting an image to be predicted into a data enhancement module based on an adversarial network for data enhancement processing to obtain a data enhanced image; inputting the data enhanced image into a human pose estimation network based on multi-scale spatial attention to obtain an output heat map; converting the output heat map into spatial coordinates of each key point to generate a human pose, and then obtaining a human pose estimation result. The method can obtain spatial attention from different scale features, simultaneously perform multi-scale fusion on the attention, combine global and local attention features, finally generate more fine spatial attention, and can solve the prediction problem of occluded images and complex background images.

The invention belongs to the technical field of hydrological forecasting, and provides a natural and actually-measured annual runoff forecasting method, electronic equipment and a storage medium, and the method comprises the steps: obtaining a historical data set of a to-be-measured section, the historical data set comprising natural annual runoff, actually-measured annual runoff and sunspot actually-measured data of a corresponding time period; training the long-short-term memory network model by adopting a historical data set to obtain a natural annual runoff prediction model, an actually measured annual runoff prediction model and a sunspot prediction model; obtaining a sunspot prediction value according to the sunspot prediction model; and according to the sunspot forecast value, the natural annual runoff forecast model and the actually measured annual runoff forecast model, obtaining a natural annual runoff forecast value and an actually measured annual runoff forecast value of the section to be measured. According to the method, the runoff prediction value is solved based on the prediction value of the sunspots by constructing the long-short-term memory network model of the runoff series and the sunspot series, so that the prediction precision is improved.

The invention provides a station flow long-term prediction method and device and computer equipment, and the method comprises the steps: firstly calculating the year-on-year trend ratio of the product of the current passenger flow of a reference day and the average passenger flow before a prediction day relative to the average passenger flow before the reference day after obtaining necessary data, and then carrying out the prediction according to historical weather data and historical passenger flow data, a regression analysis method is adopted, flow influence coefficients under different weathers are obtained through fitting, then a first station flow prediction model is constructed by adopting a multi-condition summation method according to a year-on-year trend ratio and the flow influence coefficients, and then a passenger flow increment ratio generated by activities of a prediction day is calculated by taking the current day passenger flow of a reference day as a benchmark according to activity characteristics of the prediction day. And finally, according to the first station flow prediction model and the standardized passenger flow increment ratio, integrating to form a second station flow prediction model, and calculating the current-day passenger flow of the prediction day by adopting the second station flow prediction model. Compared with the prior art, the method is more suitable for long-term prediction of predictable events.

This invention discloses a method, modeling method, system, and application for predicting dynamic contact angle distribution, belonging to the technical field of predicting dynamic contact angle distribution of micro-protrusions. The method for predicting dynamic contact angle distribution provided by this invention constructs a joint probability density function of horizontal distance and height difference based on the normal distribution law of the horizontal distance and height difference between adjacent micro-protrusions on a rough surface; derives the probability density function of the static micro-protrusion contact angle through integral transformation and Leibniz's differentiation rule; derives the first and second relations through cyclic load experiments; and constructs a probability density function model of the dynamic contact angle based on the first and second relations and the probability density function of the static contact angle. By integrating the static model and the dynamic relations, it achieves the prediction of contact angle distribution under different cycles, adapting to the evolution of contact characteristics under varying loads, and effectively solving the problem that traditional static models cannot reflect the dynamic evolution of the contact angle under cyclic loads.

The present application relates to artificial intelligence, and provides a drug candidate library construction method, device, equipment and storage medium. The method performs site mutation on the initial protein to obtain a new protein, performs graph characterization on the new protein and the initial protein to obtain a new characterization vector and an initial characterization vector, calculates a spatial structure loss value of the new protein and the initial protein, inputs the new characterization vector and the spatial structure loss value into a protein property prediction model to obtain a predicted property, and screens a target protein from a plurality of new proteins. Amino acid sequences are generated based on antigen-antibody complexes and disease antigens, and the target protein and the amino acid sequences are combined to improve the rationality of generating the drug candidate library. In addition, the present application also relates to blockchain technology, and the drug candidate library can be stored in the blockchain.

The invention provides a method for measuring and simulating six-degree-of-freedom non-linear rigidity and damping of a shock absorber, and relates to the technical field of photoelectric pod shock absorbers. According to the measuring method, loads and displacement curves of the shock absorber in three translation directions and torques and angular displacement curves of the shock absorber in three rotation directions are measured through a test bed; the method comprises the following steps: calibrating non-linear translational stiffness and non-linear torsional stiffness, and measuring frequency response curves of the shock absorber in three translational directions and frequency response curves of the shock absorber in three rotating directions by adopting a force hammer excitation mode to obtain damping ratio parameters in the translational directions and the torsional directions; according to the simulation method, the six-degree-of-freedom nonlinear rigidity and damping data of the shock absorber are applied to finite element calculation, and the influence of the shock absorber on the shock absorption effect of a product is analyzed; the problems that according to an existing simulation method, a nonlinear material is difficult to accurately simulate, the nonlinear rigidity and damping characteristics of the shock absorber in six directions cannot be completely simulated, and the shock absorber effect is difficult to predict in the state that a prototype does not exist are solved.

The invention relates to a paleo-tidal range recovery method, device, equipment and medium based on the thickness of a medium tidal plateau, in particular to the field of paleo-tidal range recovery in a sedimentary environment, and the paleo-tidal range recovery method comprises the following steps: obtaining the deposition of the medium tidal plateau in different deposition periods, and counting the thickness of the corresponding deposition of the medium tidal plateau; calculating a preliminary paleo-tidal range by using the thickness of the middle tidal plateau deposition; acquiring an ancient landform by using a source-sink system; obtaining the paleo-water depth based on the Milankovitch cycle; calculating and counting an offshore distance by using the obtained ancient landform; and calibrating the preliminary paleo-tidal range by using the paleo-landform, the paleo-water depth and the offshore distance to obtain a paleo-tidal range value, and then drawing to obtain a paleo-tidal range plane graph. According to the method provided by the invention, preliminary restoration of the paleo-tidal range is carried out by applying the relationship between the deposition thickness of the tidal plateau and the tidal range, numerical fitting is carried out on the paleo-tidal range, the paleo-landform, the paleo-water depth and the offshore distance of the research area, the paleo-tidal range is corrected, and finally plane restoration of the paleo-tidal range of the research area is realized.

The invention discloses a dynamic replenishment method and system based on consumption trend prediction, and relates to the technical field of inventory management. The method comprises the following steps: step 100, constructing an inventory full-link digital twin, synchronizing physical inventory dynamic data in real time and identifying potential bottlenecks; step 200, collecting multi-dimensional core data, and constructing a consumption behavior inventory state external environment causal link graph based on a knowledge graph technology; step 300, inputting the causal link map and an inventory constraint factor into a causal reasoning model, and outputting a consumption trend prediction result and an inventory feasibility verification report in combination with quantum lightweight calculation; and step 400, generating an initial replenishment scheme based on a federated learning driven inventory health degree scoring system, and outputting an optimal replenishment scheme after the digital twinborn scene rehearsal optimization. According to the invention, accurate matching of dynamic replenishment prediction and inventory actual bearing capacity is realized, real-time response and cross-scene self-adaption characteristics are realized, and the feasibility of replenishment scheme landing is greatly improved.