12 results about "Regularization algorithm" patented technology

The invention provides a multi-source data fusion industrial energy-saving management system based on big data, and the system comprises a data collection and edge processing module which is used for collecting on-site multi-source heterogeneous data in parallel and executing filtering and time calibration; the industrial big data storage center is used for storing data by adopting a cold and hot data layered architecture; the spatio-temporal data deep fusion engine is configured to be used for constructing a virtual production takt axis, mapping a continuous energy consumption waveform to the axis by using a nonlinear time warping algorithm, eliminating time lag interference and generating a standard energy consumption fingerprint in combination with a thermal response model; the dynamic energy-saving decision control module is used for calculating a transient energy consumption balance index covering recovery compensation and life loss based on the fingerprint vector, and generating a control instruction when an energy-saving gain threshold value is met; and the terminal execution feedback module is used for instruction issuing and feedback. According to the method, the problem of spatial-temporal dislocation of discrete work orders and continuous energy consumption data is solved, and refined dynamic energy-saving control of equipment is realized.

The application discloses a kind of based on neural network's semi-batch reaction thermal behavior identification method, first define thermal behavior label, solve dimensionless mathematical model;Again, extract core numerical features from traditional criterion, and match with NI, TR and QFS thermal behavior to constitute data set;Again, data set is preprocessed, and is divided;Again, the recognition accuracy and generalization ability of different machine learning algorithms to reaction thermal behavior are analyzed and compared, and double-layer BP neural network is selected;Then determine the structure of double-layer BP neural network;Again, the weight and threshold of double-layer BP neural network are optimized using genetic algorithm and bayesian regularization algorithm;Again, confusion matrix is drawn to evaluate the classification performance of double-layer BP neural network;Finally, dimensionless mathematical model and optimized double-layer BP neural network are deployed, simulate and thermal behavior prediction for semi-batch reaction, effectively solve the problem that reaction system thermal behavior identification angle is not comprehensive, with stronger generalization ability and universality.

The invention is suitable for the technical field of smart cities, and provides a road inspection disease deduplication method, which comprises the following steps: collecting road disease data from different mobile inspection devices; performing space-time fusion processing on the road disease data through a preset space-time fusion model to obtain road disease fusion features; calculating an optimal transmission distance between the road disease fusion features through a preset entropy regularization algorithm, and generating a cost matrix; and based on the cost matrix, determining target road disease data, and performing de-duplication processing on the target road disease data. The method solves the problems that an existing disease duplicate removal method depends on static visual features of diseases, the essential similarity of the diseases under geometric deformation cannot be effectively captured, and the same disease is easily and repeatedly detected.

The application provides a power distribution room dynamic temperature control method and system based on heat tracing, a fine thermodynamic model of the power distribution room is constructed, and inverse problem solving is performed on multi-point temperature monitoring data based on a total variation regularization algorithm, so that accurate tracing and visual positioning of hidden and non-uniformly distributed heat sources are realized; on this basis, the system dynamically solves an optimal control strategy according to the heat source distribution obtained through real-time inversion, in combination with a preset cooling efficiency matrix, so as to drive each cooling device to perform directional, quantitative and collaborative precise cooling. The method and system effectively overcome the inherent defects of the traditional feedback regulation mode based on average temperature, such as difficulty in identifying local hot spots and mismatch of cooling resource allocation, achieve the technical effects of significantly improving temperature control accuracy and equipment operation reliability while greatly reducing the energy consumption of the cooling system, and provide an innovative solution for intelligent and efficient temperature management of closed electrical spaces such as power distribution rooms.

The application discloses a coseismic dislocation amount calculation method and system based on monocular monitoring video images, belongs to the technical field of earthquake emergency monitoring and space geodetic survey, and sequentially completes video acquisition preprocessing, feature point partition, camera vibration correction and relative displacement calculation, sub-pixel displacement optimization, pixel-physical displacement conversion, coseismic dislocation parameter solving and sliding track reconstruction and result verification output. Image sequences are extracted from monocular monitoring video streams, tracking and correction windows are selected after distortion correction and scale calibration, the influence of camera shaking is eliminated, sub-pixel level displacement measurement is realized by combining a regularization algorithm, a mapping model is established by recalibrating objects to complete displacement conversion and component decomposition, sliding parameters are solved, and a track curve is drawn. The coseismic dislocation amount measurement is low in cost, high in precision and real-time, can capture complex fault sliding dynamics, and supplements a traditional measurement method, thereby providing key data support for earthquake emergency.

The invention discloses an EIT image reconstruction quality evaluation-based dike termite nest identification method, and relates to the technical field of dike termite nest identification, and the method comprises the following steps: multi-channel EIT data collection, ant nest multi-physical field positive problem solving, image reconstruction based on a Tikhonov regularization algorithm, image reconstruction quality function calculation and image reconstruction quality evaluation. By designing a multi-channel EIT data acquisition scheme, full-coverage acquisition of electrical signals of the whole dike is realized, weak conductivity abnormal signals caused by ant caves are effectively reserved, comprehensive and pure original data support is provided for subsequent positive problem solving and image reconstruction, and the method has the advantages of being high in accuracy and high in reliability. The method ensures that signals of deep or tiny ant caves are not covered by noise, and solves the problems of limited data acquisition range, large noise interference and difficulty in capturing weak electrical characteristics of the ant caves in the dike in the existing detection technology.

The invention discloses a regularization algorithm and neural network cascaded spectrum reconstruction method and system. The method comprises the following steps: firstly, carrying out spectrum reconstruction on obtained light current data by adopting a regularization algorithm to obtain a pre-reconstructed spectrum; then the pre-reconstructed spectrum is equally divided into a plurality of sub-band spectrums according to a set interval range, and mutual interference between sub-bands is avoided; and inputting each sub-band spectrum into an adaptive neural network, and synchronously performing spectrum reconstruction on each sub-band spectrum through each neural network to obtain a sub-band reconstruction spectrum. And finally, splicing all the sub-band reconstructed spectrums into a final reconstructed spectrum according to a set fusion strategy. Therefore, the pre-reconstruction spectrum with a certain reference is used as the input of the neural network reconstruction spectrum to carry out refined spectrum reconstruction, so that the dependence of the neural network on a data set is greatly reduced, the spectrum reconstruction problem is simplified, the parameter scale of the neural network is reduced, the calculation cost and the storage space occupation are reduced, and the reconstruction rate is also improved.

The invention relates to the technical field of semiconductor device photoelectric performance testing, and discloses a high-sensitivity heterojunction spectrum detection system and a data acquisition method thereof, and the system comprises a bias voltage control unit, a signal acquisition unit, a digital processing unit and an upper computer processing unit. The digital processing unit adopts a trapezoidal hysteresis scanning strategy, and utilizes a transient shielding window and asymmetric residence time to induce charge hysteresis; and performing synchronous demodulation on the current response through a parallel channel, extracting differential conductance, differential capacitance and second harmonic components, and correcting a system phase error by using a rotation matrix. The upper computer processing unit constructs an augmented matrix based on multi-dimensional physical parameters, combines a physical kernel function, and utilizes a mixed regularization algorithm with smooth and sparse constraints to invert and calculate an interface state density distribution spectrum. According to the method, the problems of large signal scanning interference and inversion ill-conditioned conditions in general detection are solved, and high-precision quantitative characterization of interface state defects is realized.

The present application belongs to the field of seismic exploration technology, and relates to a strong interference seismic data source function inversion method based on lateral local regularization, which applies lateral local regularization constraint in the source function waveform inversion process to ensure the lateral continuity and accuracy of the inversion result; utilizes near trace short time data to reduce the nonlinearity of the inversion and the dependence on the velocity model, and ensures the overall calculation efficiency of the algorithm; performs source function inversion under the full waveform inversion framework, and the inversion result has the advantages of high accuracy and high resolution in theory; combines the advantages of the cross-correlation target function and the lateral local regularization algorithm, can obtain high-quality source function inversion result under strong interference, utilizes the mutual constraint between adjacent source functions to enhance the lateral continuity of the inversion result, and to a large extent overcomes the initial source dependence problem. The method can stably obtain high-precision and good lateral consistency source function inversion result under the condition of strong interference of seismic data.

This invention relates to the field of highway bridge safety monitoring technology and discloses a bridge dynamic weighing algorithm based on a regularization algorithm. The invention obtains an axle load calculation formula with a regularization matrix by adding a penalty term to the error function. The regularization matrix is ​​determined by the axle load covariance matrix and the load response covariance matrix. Specifically, the process involves: obtaining the influence line distribution through calibration tests; obtaining the initial axle load given an initial regularization matrix; calculating the load response covariance matrix based on the influence line distribution, the initial axle load, and measurement noise, and iterating a stable axle load covariance matrix by combining it with the preset initial axle load covariance matrix; generating a regularization matrix based on the load response covariance matrix and the updated axle load covariance matrix, and calculating a new axle load; repeating the above iterative process until the axle load update difference is less than a preset value, and finally outputting the axle load result. This invention effectively improves the axle load identification accuracy of the bridge dynamic weighing system.

The invention relates to a mirror image type acoustic tomography step-by-step three-dimensional field inversion method based on an empirical orthogonal function. The method comprises the following steps: collecting marine environment data; performing modal extraction and dimension reduction characterization, and establishing a field characterization system; constructing an acoustic propagation forward modeling model; constructing a core observed quantity, carrying out joint inversion, and carrying out robust solution by adopting a regularization algorithm to obtain a time coefficient of a section vertical structure; parameterizing a three-dimensional sound velocity field inversion problem, and assimilating discrete section sound velocity inversion into globally optimal field estimation; parameterizing a three-dimensional flow velocity field inversion problem, and assimilating discrete section flow velocity inversion into global optimal field estimation; solving and reconstructing a three-dimensional sound velocity field inversion problem, estimating a global coefficient for describing a sound velocity abnormal field, and finally generating a quantitative ocean sound velocity field product; and solving and reconstructing a three-dimensional flow velocity field inversion problem, estimating a global coefficient for describing an east component and north component abnormal field, and finally reconstructing a three-dimensional flow velocity field.

The present application relates to the technical field of power electronic and energy storage system control, especially to a WIPI wireless power dynamic balancing method for energy storage system grid connection, comprising the following steps: constructing a fractional order digital twin model to predict the state of charge and temperature rise of the module; based on the prediction results, using a three-layer GraphSAGE and a twenty-qubit variational quantum optimizer to obtain a demand matrix and a loss matrix respectively, and fusing them into a power coupling target according to a preset weight; thresholding the target to generate a 32x32 magneto-acoustic super-pixel image, completing magnetic field shaping at the microsecond level, and simultaneously driving a GaN inverter to output matching phase and frequency; collecting power flow and temperature data through a high-precision analog-to-digital converter, and using an entropy regularization Sinkhorn algorithm to iteratively correct the power transport matrix and update the pixels in real time; finally, writing the feedback hash into a consortium chain storage to complete the millisecond-level trusted traceability. The present application realizes fast, accurate and traceable balancing of wireless energy of the energy storage module.