152results about How to "Reliable data base" patented technology

The invention discloses a micro-seismic outrange waveform reconstruction method based on multi-station joint calibration, and the method comprises the steps: firstly obtaining outrange sections and outrange stations corresponding to different micro-seismic events of each station, and then for the same micro-seismic event, selecting a station which does not generate outrange saturation as a reference station, and carrying out the calibration of the outrange waveform. Carrying out time alignment on a seismic phase time window of the reference station and a corresponding time period of the outrange station, and then calculating an equivalent attenuation coefficient corresponding to the micro-seismic event by adopting a multi-station joint inversion mode, so as to avoid the use of missing data; and finally, mapping the complete waveform amplitude of the reference station in the outrange section to the outrange station by using the equivalent attenuation coefficient to realize the real waveform amplitude of the outrange station in the outrange section, so that each station can obtain continuous microseismic waveforms in different microseismic events. Through the method, the physical consistency and availability of the outrange micro-seismic waveform are improved, and a reliable data basis is provided for micro-seismic energy evaluation, risk analysis and a subsequent intelligent early warning model.

This invention discloses a comprehensive management method and system for base station power consumption, belonging to the field of base station power consumption. The method includes: topological mapping of the access relationships of AC / DC equipment within the base station to form a hierarchical power consumption association list; dynamic clustering parsing of the list to generate a time-series evolution chain reflecting state changes and remote control history; constructing a device differentiation identification model based on the time-series evolution chain, determining abnormal metering factors and outputting candidate correction events; labeling the corresponding areas of the candidate events as local anomalies, and generating a correction reference group by fusing multi-source information such as meter readings and sensor data; dynamically iteratively correcting the global metering results based on the correction reference group, and generating intelligent management instructions by combining the corrected load sharing ratio and branch authorization strategy. The system includes corresponding functional modules. This invention can systematically solve the problems of unclear base station power consumption topology, inaccurate metering, and extensive management, achieving high-precision metering and automated intelligent control.

The application discloses a water pump performance curve correction method based on a similarity principle, relates to the technical field of water pump working condition analysis, and comprises the following steps: acquiring a conversion working condition point corresponding to a model pump test working condition point converted to a prototype pump and a theoretical performance curve of the prototype pump; confirming a similarity parabola passing through the field measured working condition point and mapping the similarity parabola to the theoretical performance curve of the prototype pump to obtain an intersection point as a similarity working condition point of the field measured working condition point according to a similarity parabola relationship and field measured working condition point data; obtaining a corresponding point under a theoretical rotating speed according to the similarity relationship and combining the working condition point on the theoretical curve of the prototype pump; replacing the theoretical rotating speed with a measured rotating speed, converting to a corresponding point under a rated rotating speed, and fitting to obtain an actual flow-head curve; and acquiring a theoretical efficiency value and an optimal efficiency value corresponding to the similarity working condition point to obtain an actual flow-efficiency curve, so that the actual performance curve of the prototype pump can be efficiently and accurately corrected.

The invention provides a rock mass structural surface strength prediction method driven by a ground vibration load, and relates to the technical field of rock mass mechanics. The method comprises the following steps: acquiring and integrating geometric and mechanical parameters, seismic oscillation parameters and test and numerical simulation data of a structural plane to form a standardized database; cleaning and standardizing database data, constructing a dynamic attenuation factor, and screening and enhancing key features to obtain initial input features; constructing a physical experience reference model and a machine learning predictor, and training a hybrid prediction model by combining prediction results of the physical experience reference model and the machine learning predictor; verifying the model and quantizing the output uncertainty; inputting the basic data calling model to complete calculation and outputting a result. According to the method, the reliability and adaptability of rock mass structural surface strength prediction are improved, and support is provided for seismic design of geotechnical engineering.

The invention discloses a gene sequencing data management method for whole genome methylation sequencing, and relates to the technical field of gene sequencing data management.The method comprises the steps that original gene sequencing data are obtained and preprocessed, and a regional methylation matrix is generated; calculating a technical difference measure and a phenotype difference measure of each region unit based on the region methylation matrix and the sample metadata, identifying a technical methylation bias domain, and grading the technical methylation bias domain into a steady-state technical methylation bias domain and an event-driven technical methylation bias domain according to the occurrence frequency of the technical methylation bias domain in multiple batches; based on a linear adjacency relation and a co-bias relation, constructing a technical methylation bias super-domain and classifying the technical methylation bias super-domain; performing regional shielding on the methylation bias super-domain of the steady state technology, performing normalization correction on the methylation bias super-domain of the event-driven technology, and generating a corrected regional methylation matrix; calculating a sample quality index based on the corrected matrix and performing quality grading; according to the method, technical bias can be identified and corrected, and the methylation data quality and analysis reliability are improved.

A broadband voltage transformer error compensation method is proposed. First, a broadband test platform is constructed using a signal generator, power amplifier, standard voltage transformer, and high-bandwidth data acquisition system. The ratio and phase errors of the transformer at multiple frequency points are acquired through primary-side excitation and secondary-side synchronous acquisition. Based on the slope, first derivative, and second derivative characteristics of the error curve, the entire frequency band is adaptively divided into segments. Within each segment, multiple candidate models are fitted using a polynomial or exponential-linear combination model. The optimal model is selected based on the coefficient of determination, mean absolute error, and root mean square error. Ratio and phase error compensators are constructed based on the fitting functions of each segment, and error compensation is achieved using a feedforward approach. This method can significantly reduce amplitude and phase errors over a wide frequency range, improving the dynamic measurement accuracy of the voltage transformer.

This invention relates to a visual inspection device for elevator pit buffers, belonging to the technical field of buffer visual inspection devices. It includes: multiple robotic arms, multiple joint drive mechanisms, and multiple camera mechanisms. The robotic arms are sequentially rotatably connected, with the arm closer to the first end being the first robotic arm and the arm closer to the last end being the last robotic arm. Multiple joint drive mechanisms are respectively arranged between adjacent robotic arms, with their fixed parts fixed to the first robotic arms and their rotating parts fixed to the corresponding last robotic arms. Multiple camera mechanisms are circumferentially spaced and fixed to the last robotic arm. This invention designs the last robotic arm as a ring-shaped mechanism, giving it the dual functions of a robotic arm end effector and a multi-camera mounting support structure. It can be fitted over the buffer from above, achieving a wraparound multi-view image acquisition, effectively solving the problem of difficulty in observing the buffer in narrow pit spaces.

This invention belongs to the field of heat exchanger technology, specifically a tubular air conditioning heat exchanger, comprising: a heat exchanger body, which internally defines a shell side for refrigerant phase change heat transfer and heat exchange tubes penetrating the shell side; an acoustic vibration excitation assembly, including a sound wave generator disposed within the heat exchanger body and a low-frequency vibrator coupled to a tube bundle support frame, for applying multi-physics field coupled excitation energy to the shell side fluid and the liquid film on the inner wall of the heat exchange tubes; a multi-mode sensing network, including a differential pressure sensor installed at the shell side inlet and outlet, an optical fiber displacement sensor embedded in the cross section of the heat exchange tubes, an electrical conductivity probe array closely attached to the gas-liquid interface, a capacitance tomography sensor surrounding the shell side periphery, and a broadband hydrophone for capturing the sound pressure at the antinode of the standing wave in the cavity; and a real-time closed-loop drive for the low-frequency vibrator, enabling the heat exchanger to maintain adaptive phase-locked operation between breaking through thermal resistance boundaries and overload protection, significantly improving the system's energy conversion extreme value and long-term disturbance rejection life.

The invention discloses a vehicle time history response calculation method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a mass matrix, a damping matrix and a stiffness matrix of a to-be-tested vehicle; inputting the mass matrix, the damping matrix and the stiffness matrix into a pre-constructed time history response calculation model; according to the mass matrix, the damping matrix and the stiffness matrix, the time history response of the to-be-tested vehicle is calculated through a time history response calculation model; wherein the time history response comprises a displacement vector, a speed vector and an acceleration vector. According to the embodiment of the invention, the calculation efficiency can be remarkably improved while the calculation precision is ensured, and the method is suitable for time-history response analysis of the key structure of the commercial vehicle under the complex dynamic load.

The utility model relates to traffic detection technical field in intelligent traffic system especially, more particularly to a kind of traffic detection device based on millimeter wave radar, comprising: the picture acquisition mechanism of being set to device front end;First trigger radar and picture acquisition mechanism are sequentially arranged, detect vehicle entry condition;Second trigger radar and first trigger radar are sequentially arranged, confirm vehicle existence state;Transaction identification antenna is set after second trigger radar;Third trigger radar and transaction identification antenna are installed in same position, identify vehicle transaction success and send signal;Flow radar and third trigger radar are sequentially arranged;Gate lever and third trigger radar are communicatively connected, perform lifting action;Anti-smashing radar and gate lever are set in same position, detect vehicle after passing through and carry out relay attraction to complete gate lever pole falling action;Display is set in whole device end. The utility model greatly reduces installation complexity and long-term maintenance cost, and improves the comprehensive detection capability of the system.

The application belongs to the technical field of expressway, and particularly relates to an expressway traffic congestion prediction method and system based on deep learning. The method comprises the following steps: acquiring traffic flow characteristic information, identifying a traffic bottleneck node based on the traffic flow characteristic information, summarizing reference characteristic values of the traffic bottleneck node to form an overall traffic flow characteristic data set, performing discrete degree analysis on the data set to obtain a characteristic index, extracting a traffic congestion concentrated interval according to the characteristic index, identifying a risk time point in the interval, and outputting a predicted expressway congestion period and a corresponding congestion area according to the risk time point and the traffic congestion concentrated interval. The application can improve the accuracy of congestion root identification by identifying a traffic bottleneck node and quantitatively analyzing the evolution trend of the bottleneck state, and can output a region to be regulated and a regulation period, thereby providing decision support for traffic relief.

This invention relates to the interdisciplinary field of mechanical engineering and optical inspection, and discloses a method and system for quality sorting of automotive stamping parts combined with optical inspection. The method includes: synchronous imaging by a high-speed linear array camera and a structured light projector via an encoder and photoelectric triggering device; fusing 3D point clouds and 2D images, and using a deep convolutional neural network for defect identification and quality grading; calculating the six-degree-of-freedom pose of the workpiece using an iterative nearest-point algorithm and principal component analysis; calculating the spatiotemporal parameters of sorting based on a predictive instruction generation module, and issuing dynamic control commands to multi-degree-of-freedom pneumatic push rods via a hard real-time communication bus to execute sorting. The system includes a synchronous triggering unit, an imaging unit, a defect identification unit, a pose calculation unit, a predictive instruction generation module, and a dynamic sorting execution unit. This invention achieves millisecond-level accurate identification and adaptive sorting, significantly improving detection accuracy, sorting efficiency, and system robustness.

The application discloses an IBCU plunger cavity rigidity self-learning algorithm and relates to the technical field of hydraulic brake-by-wire systems, and comprises the following steps: triggering a plunger cavity sealing detection state in the IBCU power-on process; when the plunger cavity pressure and the plunger position meet the credible condition, acquiring the pressure data of the plunger cavity in the pressure building process and the corresponding plunger cavity liquid consumption volume data. The application acquires the plunger cavity rigidity parameter through self-learning, realizes individual modeling, and improves the parameter accuracy; optimizes the liquid quantity estimation based on the corrected rigidity parameter, improves the pressure building speed and the control accuracy, especially improves the single round pressure building performance; simultaneously compensates the individual differences, enhances the system consistency, makes the theoretical model and the actuality more matched, and thus improves the overall braking performance and stability.

The application relates to the technical field of electronic reconnaissance and signal processing, and discloses a radiation source individual fine identification method and system based on multi-modal feature fusion, which comprises the following steps: performing preprocessing on obtained radiation source original signals to obtain signal pulse units; extracting time domain sequences of the signal pulse units and performing multi-domain transformation to generate a first time-frequency image, a second time-frequency image and a bispectrum image; inputting the first time-frequency image, the second time-frequency image, the bispectrum image and the time domain sequence into a multi-modal fusion neural network; extracting corresponding image depth features and comprehensive sequence features through three image processing branches and a sequence processing branch; performing cross-modal fusion on the three image depth features and the comprehensive sequence features through a fusion module to output a radiation source individual identification result. The application improves the identification accuracy of radiation source individuals in a complex electromagnetic environment.

This invention relates to the field of tunnel construction technology, and more particularly to a system and method for dynamically optimizing tunnel construction parameters based on geological information from point cloud data. At multiple monitoring time points, point cloud data is collected from the tunnel segment to be constructed, obtaining multiple point cloud datasets. The tunnel segment is divided into multiple construction areas, and multiple regional point cloud datasets are obtained from these datasets. Point cloud change analysis is performed on each regional dataset to obtain multiple point cloud change rates. Based on these regional point cloud datasets, reflection intensity analysis is conducted to obtain multiple reflection intensity coefficients. Combined with the multiple point cloud change rates, geological analysis is performed to obtain the distribution of concealed geological information. Based on this distribution, tunnel construction parameters are dynamically optimized to obtain optimized tunnel construction parameters for tunnel construction. This achieves the technical effect of improving the efficiency and safety of tunnel construction while reducing construction risks and costs.

The application discloses a kind of high-altitude operation personnel rule violation identification method based on gesture capture, including the following steps: generating high-altitude operation image frame sequence;Scene reference reconstruction is carried out, and high-altitude operation scene reference data is generated;Personnel region anchoring is generated by carrying out human contour segmentation;Personnel gesture observation sequence is generated;Human posture capture is carried out, and operation personnel gesture joint sequence is generated;Gesture trajectory condenses, and personnel gesture trajectory data is generated;Coupling data of gesture boundary is obtained by gesture-boundary coupling projection;Using improved CleverCatch model, candidate rule violation data is generated;Temporal continuity verification and gesture stability check are carried out, and effective rule violation data is generated, and high-altitude operation personnel rule violation identification result is obtained, improve the accuracy and stability of high-altitude operation personnel rule violation identification, reduce artificial patrol omission and ordinary video identification false alarm.

The present application relates to big data platform security operation and maintenance and task scheduling technical field, specifically for the security operation and maintenance task scheduling method for big data intelligent platform, including: collecting business task dependency data, service level agreement data, historical business throughput time sequence data, real-time business load state data and the occupation consumption characteristic data of the to-be-scheduled security operation and maintenance task;Build the ideal business processing benchmark under the state of no operation and maintenance interference;Characteristic injection generates the theoretically interfered business state;Align to generate real deviation data and theoretical deviation data, evaluate the goodness of fit and generate the predicted business load state;According to the business capacity boundary, output the execution permission, suspended execution or forced termination instruction, and update the task disposal priority in the subsequent management period according to the actual execution business feedback data;The present application further improves the stability of long-term scheduling.

This invention belongs to the field of new energy meteorological monitoring technology, specifically relating to an adaptive correction method and system for new energy meteorological monitoring data. The method includes: preprocessing multi-source meteorological monitoring data collected from target monitoring stations to obtain processed data; determining the path code corresponding to the processed data using a pre-established multi-dimensional decision tree model; and correcting the processed data according to the path code and the corresponding model to obtain the final corrected meteorological monitoring data. The multi-dimensional decision tree model is constructed using multi-source meteorological monitoring data from historical monitoring stations. The technical solution provided by this invention achieves automatic matching of optimal correction strategies under different scenarios, significantly improving data quality and providing a more reliable data foundation for new energy power prediction.

This invention relates to the field of intelligent sensing and detection technology, and provides a quality inspection device for cardboard box printing, including an adsorption module and an intelligent sensing quality inspection module. The intelligent sensing quality inspection module includes a detection industrial control computer, which internally includes a multispectral image acquisition unit, a deformation compensation unit, a multi-dimensional feature extraction unit, and an adaptive stitching unit. The adaptive stitching unit includes a multi-pose fusion subunit, configured to fuse images acquired and processed under at least two different adsorption poses, and utilize the complementary information of the occluded area under different poses to generate an unobstructed full-page inspection image. This device, by setting a positioning frame with a hollow plate, uses negative pressure adsorption to flatten each side panel of the cardboard box to the same horizontal reference plane, effectively solving the problem of bending and inconsistent imaging of adjacent side panels due to residual stress from creases after the cardboard box is unfolded, and providing a unified imaging benchmark for subsequent inspection.

This invention discloses a real-time monitoring method for vehicle lane-crossing behavior based on image semantic segmentation, comprising the following steps: acquiring traffic video data to obtain a road image sequence and performing preprocessing; inputting the image semantic segmentation model for semantic segmentation processing to obtain a semantic segmentation result image; performing morphological processing and connected component analysis to obtain the set of pixel boundaries of the vehicle region and the set of pixel boundaries of the lane lines; calculating the spatial position data of the lane lines and performing perspective correction processing to obtain perspective-corrected lane line spatial position data; generating the position region of the vehicle region in the image coordinate system; performing target association matching to generate vehicle motion trajectory data; establishing spatial relationship data and forming relative position changes; performing temporal consistency analysis and real-time determination, and outputting the vehicle lane-crossing behavior detection result, thereby achieving continuous, accurate, and real-time monitoring of vehicle lane-crossing behavior.

The application discloses an automatic counting method for the egg-laying quantity or hatching rate of Spodoptera exigua. The method comprises the following steps: collecting a multilayer focal plane image sequence of an egg mass, constructing a height field model of the egg mass, dividing egg grains into different physical layers based on the spatial position relationship in the vertical direction, and respectively counting the number of each layer. The method also introduces a shielding compensation calculation to correct the lower layer of the blocked egg grains, so as to accurately obtain the layered egg quantity data. The application solves the problem that the traditional two-dimensional image recognition cannot distinguish the hierarchy of the egg mass, which leads to a serious low count, realizes the accurate counting of the stacked egg mass, and provides a reliable technical means for entomological research and pest forecasting.

The application relates to a multi-protocol adaptive control method, system, device and medium of an LED dimming power supply, which comprises the following steps: receiving a dimming signal output by an external dimming device, and preprocessing the dimming signal to obtain a target input signal; extracting protocol identification features from the target input signal, and constructing a protocol feature vector; matching the protocol feature vector with a preset protocol template library to generate a candidate protocol set and corresponding initial confidence; dynamically correcting the initial confidence by combining historical recognition results, signal quality indexes and output response consistency to determine a target protocol type; calling corresponding protocol analysis rules and control parameter templates according to the target protocol type to generate target dimming control parameters; and adjusting LED drive output based on the target dimming control parameters. The application has the effect of improving the adaptive identification capability of the LED dimming power supply for various dimming protocols.

The application relates to a power grid data processing method, device, equipment, readable storage medium and program product. The method comprises the following steps: obtaining service data generated by interaction of each subject unit in a source grid-load-storage coordination system; distributing the service data to corresponding main processing node groups based on corresponding service scenarios of the service data; the main processing node groups comprise at least two main processing nodes, each main processing node is pre-configured with a data processing algorithm and a resource allocation strategy matched with the corresponding service scenario; the load state of each main processing node is obtained, and the service data is distributed to a target main processing node for data processing; the target main processing node refers to a main processing node whose core running load index in the load state is in a corresponding preset interval range. The method can improve the data processing efficiency of power grid data.

The invention discloses a multi-coal-source coal blending data processing method based on a WSMOTE algorithm, and belongs to the technical field of coal processing data processing, and the method comprises the steps: data collection and classification: collecting multi-dimensional data of a raw coal floating and sinking test, a coal blending test and the like, carrying out the preprocessing, dividing the data into a missing data set and a complete data set, carrying out the interpolation optimization of a missing value, and carrying out the calculation of the complete data set. And carrying out adaptive processing according to data distribution types, or carrying out weighted combination normalization according to feature importance, analyzing the number and distribution characteristics of minority class samples, dynamically adjusting the neighbor sample size and the new sample synthesis amount, and completing data enhancement and standardization. According to the method, standardized parameters are dynamically adjusted to adapt to data distribution changes, abnormal values are accurately processed, the combined interpolation model is subjected to weight optimization, errors are reduced, and data integrity is improved. WSMOTE parameters are adaptively combined with data distribution to synthesize samples, data are balanced, and the over-fitting risk is reduced. In practical application, the quality of coal blending model training data can be improved, and optimization of a multi-coal-source coal blending scheme is assisted.

The invention relates to the technical field of commodity production and marketing management, and discloses a multi-platform-based marketing and production data return system, which comprises a data acquisition module, a data processing module, a profit and loss analysis module and a data return module. According to the marketing and production data return system based on multiple platforms, the data acquisition module acquires the sales information generated by multiple sales platforms, and the data processing module processes the acquired sales information to obtain the commodity sales data in a unified format. The profit and loss analysis module obtains the corresponding profit and loss data based on the production profit and loss model and the input commodity sales data, and finally, the operation background determines the corresponding production plan based on the actual production environment, the system architecture is simple, the implementation method is convenient, and the profit and loss data is obtained through the reliable commodity sales data, so that the implementation efficiency is improved. The finally obtained profit and loss data is a result matched with an actual sales condition, and an accurate and reliable data basis is provided for a subsequent commodity production plan.

The invention provides a high-precision reconstruction method for a three-dimensional global sound field in a deep ground space, and the method comprises the following steps: S1, sound field collection: collecting sound signals through a microphone array disposed in a target space; s2, model construction: an equivalent sound source and virtual sound source mixed model is constructed based on the sound signals collected in S1, the equivalent sound source and virtual sound source mixed model performs virtual extrapolation on a physical boundary of a target space to form an equivalent sound source surface, the equivalent sound source surface is dispersed into a plurality of equivalent sound sources, and the plurality of equivalent sound sources are subjected to virtual extrapolation; arranging at least one layer of virtual sound source outside the equivalent sound source surface; s3, sound field reconstruction: reconstructing and outputting a three-dimensional sound field in the target space based on the construction of the equivalent sound source and virtual sound source mixed model; according to the method, the original sound signals are directly collected in the target space, the authenticity of sound field data used for reconstruction is ensured, a reliable data foundation is laid for subsequent high-precision physical reconstruction, and compared with a traditional method depending on a database or artificially synthesizing a sound source, the method is extremely high in reduction degree.

The present application provides an asphalt pavement apparent disease evolution deduction method, system, device and medium, which belongs to the field of road engineering, transportation infrastructure operation and maintenance and intelligent detection technology, and comprises the following steps: constructing a unified space-time reference system, aligning and normalizing multi-source data in space-time; forming section-level disease quantification characteristics based on the recognition results of the inspection images and the structural indexes; event coding the maintenance measures of the road surface and pre-processing the section-level disease quantification characteristics corresponding to the time of the maintenance measures; constructing the correlation, time lag effect and spatial consistency between the disease types, disease characteristics and structural indexes to generate the collaborative evolution characteristics of the coupling relationship between diseases; constructing a deep time series prediction model to obtain the disease development trend at a specified time scale in the future, and forming the section-level disease evolution prediction result. The present application can depict the disease development law at different road sections and different time scales, and predict the future disease expansion.

The present application relates to the technical field of park energy data management, and particularly relates to a zero-carbon smart park comprehensive energy method and system based on big data, wherein the method comprises the following steps: S1, generating a carbon equity unit record; S2, establishing a corresponding source summary for each carbon equity unit; S3, writing the identification information corresponding to the transferred carbon equity unit into the restriction identification set corresponding to the first node; S4, generating a merged source summary; inheriting the source summary before splitting; S5, performing backflow risk verification based on the source summary attached to the to-be-transferred carbon equity unit and the identification information in the restriction identification set; S6, using the registration result for comprehensive energy scheduling and carbon accounting; the method effectively avoids the loss of source information and the risk of repeated offsetting caused by merging and splitting, and facilitates linkage with comprehensive energy scheduling and carbon accounting, thereby improving the real-time performance, implementability and system autonomy of zero-carbon smart park carbon equity management.

This invention discloses an intelligent target recognition method for maritime vessels based on multimodal feature fusion, belonging to the field of radar target recognition technology. The method first acquires the target's HRRP sequence, RCS sequence, and statistical features extracted from the HRRP; then, it uses a three-channel parallel coding network to extract deep features of three modalities; furthermore, it explicitly models the intermodal correlations through a cross-attention module and dynamically learns the importance of each modality through an adaptive weight fusion layer, achieving dual-path collaborative fusion; finally, it completes target classification based on the fused features. This invention fully leverages the complementary information of multimodal radar data, significantly improving the target recognition accuracy and robustness in complex electromagnetic environments such as low signal-to-noise ratios.