9results about How to "Good data base" patented technology

The invention discloses a computer data management system and method, and relates to the technical field of data management, and the method comprises the steps: reading a heterogeneous data set, dividing the heterogeneous data set into text, time sequence and binary data groups according to data types, extracting a core field of each data group, and generating a deduplication feature and an index feature, setting a sliding window based on the data type and generating a window index feature abstract; calculating a comprehensive similarity score between the data blocks according to the sliding window, screening a similar data block set, determining a reference data block and a redundant data block, clustering the reference data block to generate an initial cluster, and if the global deduplication rate and the clustering preprocessing validity do not meet preset conditions, re-initializing the sliding window; according to the method, the cluster structure is optimized and the cluster feature abstract is generated based on the retrieval popularity predicted value of the initial clustering cluster, the three-layer index structure comprising the global index, the intra-cluster index and the feature index is constructed, the retrieval efficiency is tested, the index hierarchy is optimized, and the storage efficiency and the retrieval performance of heterogeneous data management are remarkably improved.

This application relates to the field of industrial data processing and intelligent control technology, and discloses a method for dynamic modeling and prediction of industrial big data combined with digital twins. The method includes: acquiring operating status data, process execution data, control command records and prediction results, and constructing a sequence of control action events; segmenting the operating status data according to the sequence of control action events to form natural operating data segments and intervention operating data segments, extracting distribution characteristics, fluctuation characteristics and response characteristics, and generating a segmented behavioral feature set; calculating the control reflexive influence quantity and generating a reflexive drift marker, dividing the natural evolution dataset and the control feedback dataset, and predicting them separately; generating control risk constraint quantity, screening, delaying or limiting the control commands to be executed, and iteratively correcting the dataset according to the execution results; this method can distinguish between control feedback changes and natural evolution changes, reduce drift misjudgments, and suppress multi-round oscillations and parameter fluctuations.

The application discloses a chloride-based multi-parameter humidity and conductivity comprehensive calculation method, comprising the following steps: S1, collecting basic parameter information of chloride materials, and performing relevant data arrangement, and comparing after entering a system; S2, then setting an environmental humidity change range, and obtaining moisture absorption and surface water layer thickness under different relative humidity conditions; S3, establishing a relevant coupling model, and considering ion mobility, charge transmission channel number and interface polarization effect of the chloride materials under different humidity; S4, introducing a multi-parameter regulation factor, correcting environmental temperature, pressure and material surface state, and obtaining correct data; the method has high application prospect and popularization value.

The application relates to a chemical short-range order measurement method and system based on an atomic pair distribution function, and the method comprises the following steps: performing a total scattering experiment on a polycrystalline powder sample to obtain total scattering data, a scattering function and an atomic pair distribution function; based on the total scattering data, an initial three-dimensional atomic structure model is constructed; further, the initial three-dimensional atomic structure model is fitted and reconstructed by taking minimization of a fitting goodness factor as a target, so that a three-dimensional atomic structure model after fitting and reconstruction is obtained; according to the three-dimensional atomic structure model after fitting and reconstruction, a radial distribution function and a bulk average distribution concentration are calculated, and a chemical short-range order parameter calculation cutoff is set according to a near-neighbor atomic cutoff distance; further, the probability of an atom finding other types of atoms under the calculation cutoff is calculated; and the chemical short-range order parameter is calculated based on the probability and the bulk average distribution concentration. Compared with the prior art, the application has the advantage of wide application scenarios.

This invention discloses a method for constructing a road cavity detection model based on 3D ground-penetrating radar (GPR). The method includes constructing a hybrid spatiotemporal detection model, which comprises a feature preprocessing module, a soft clipping module, a spatiotemporal feature extraction module, and an output module. The feature preprocessing module performs feature pre-extraction on preprocessed and labeled data, outputting a pre-extracted feature map. The soft clipping module performs spatiotemporal perception on the pre-extracted feature map, outputting a clipped feature map. The spatiotemporal feature extraction module analyzes the clipped feature map, outputting feature information with global spatiotemporal information. The output module integrates the feature information and predicts the existence of cavities and the range of their occurrence channels. By combining the correlations between data in the spatiotemporal dimension, it accurately detects underground cavities, solving the problems of high computational load and difficulty in adjusting model parameters in existing 3D GPR high-resolution data interpretation. This provides an efficient and reliable technical solution for intelligent road cavity detection and infrastructure safety maintenance.

The invention discloses a three-dimensional modeling method and system for port whole-process management and control. The three-dimensional modeling method comprises the following steps: acquiring initial laser point cloud data, initial millimeter wave point cloud data, dust concentration and a visibility estimation value; performing dust point cloud elimination on the initial laser point cloud data and the initial millimeter wave point cloud data to obtain laser point cloud data and millimeter wave point cloud data; determining a laser point cloud weight and a millimeter wave point cloud weight; based on the laser point cloud weight and the millimeter wave point cloud weight, performing weighted fusion on the laser point cloud data and the millimeter wave point cloud data to obtain final point cloud data; and performing three-dimensional modeling based on the final point cloud data. According to the method, the point cloud belonging to the dust in the initial point cloud data is removed, only the required point cloud data is reserved, the influence caused by the dust is avoided, and a good data basis is provided for modeling from unloading of a car dumper and transportation of a belt conveyor to stockpiling of a storage yard in the whole-process operation.

The invention discloses a steel-concrete nondestructive testing method and device based on time-varying analysis, and relates to the field of steel-concrete structure detection.The steel-concrete nondestructive testing device comprises an acquisition module, a data processing module, a data processing module and a data processing module, collecting a stress wave signal, a vibration response signal, a sound wave propagation signal and an elastic wave signal of the structure at a preset interval continuous moment and a temporary moment of stress change; the processing module is used for receiving the signals acquired by the acquisition module, performing noise reduction and time sequence alignment processing on the signals, and synchronously establishing association mapping between the signals and corresponding acquisition moments; when the system is applied to steel-concrete structure detection, multi-dimensional signals of key stress positions can be accurately collected, signal time-varying laws are mined after processing, health state parameters are matched to generate visual health assessment, data can be dynamically updated, structural abnormity can be early warned in time, core influence factors can be marked, potential damage can be effectively prevented and controlled, and the system is high in practicability. Long-term safety of the structure is guaranteed, and service life is prolonged.

The invention relates to the technical field of medical instruments, in particular to a multifunctional operation arm suitable for hysteromyoma resection under a laparoscope, which comprises an intelligent operation system and a support, a plurality of mechanical arms are mounted on the support, and end effectors with different functions are mounted at the tail ends of the mechanical arms. The mechanical arm operation system comprises a patient analysis module, an operation planning module, a real-time feedback module and a mechanical arm control module. The operation planning module is used for constructing an operation planning engine through a CNN (Convolutional Neural Network); the operation planning engine is used for inputting an operation three-dimensional model and generating an operation suggestion scheme. The operation planning engine is used for generating corresponding operation suggestion schemes for different operation three-dimensional models, a doctor is effectively assisted in constructing and adjusting the operation schemes, then through the design of the mechanical arm control module, the doctor is assisted in performing operation, and the operation effect is effectively improved.

The invention discloses a shale oil highly-deviated well stress and rock mechanics calculation method and system. According to the method, a conventional logging data set along a shaft is obtained, the conventional logging data set generally comprises data columns such as interval transit time data, resistivity, density data, natural gamma data and neutron porosity data, and the data columns are selected according to corresponding characteristics of regional logging; selecting the same reservoir, and establishing a relationship between natural gamma data and sound wave data; selecting the same reservoir, and establishing a relationship between neutron porosity data and sound wave data; and through the GRAC and the CNLAC obtained in the step 3d, mechanical parameters of a certain depth are obtained through an indoor rock mechanical test, and a weight coefficient is obtained. Through the field test fracturing test, the actually measured minimum horizontal principal stress at the perforation can be obtained, and the weight coefficient can be obtained. Therefore, an alternative formula verified by measured data is obtained, the stress and mechanical parameter profile of the deep fractured highly-deviated well can be obtained more reliably, and the pertinence of fracturing design is improved.