130 results about "Eigenvalue computation" patented technology

The invention relates to the technical field of energy storage battery diagnosis, and discloses an energy storage battery fault diagnosis method and system based on a data fusion algorithm. The method comprises the following steps: acquiring historical operation data and real-time operation data of an energy storage battery in a preset operation period, and generating a historical fault data set according to the historical operation data; performing multi-source feature fusion processing on the historical fault data set to generate a fusion feature parameter set integrating voltage, current, temperature and impedance parameter joint change features; a multi-dimensional fault space is constructed based on the parameter set, coordinate axes of the multi-dimensional fault space correspond to different parameter dimensions, and spatial position coordinates represent parameter change characteristic values; calculating the fault correlation degree of the historical fault event in the multi-dimensional fault space, and determining a fault early warning index set; and extracting real-time characteristic parameters based on the real-time operation data to form a state vector, carrying out space mapping correlation calculation on the state vector and the fault early warning index set in a multi-dimensional fault space, and outputting a real-time fault correlation factor.

The present invention discloses a method for diagnosing micro-short circuit fault of a battery, comprising: acquiring real-time voltage data of each battery in battery module of electric vehicle, obtaining voltage matrix after noise elimination; calculating dynamic reference voltage sequence of the each battery in the voltage matrix within sliding window length, extracting eigenvalues of the each battery at different times; calculating correlation coefficients between voltages of the each battery at different times and reference voltage within the sliding window; forming feature point matrix of the battery module based on the eigenvalues and the correlation coefficients; calculating dynamic reference feature point sequence based on the feature point matrix, and calculating improved Frechet distances between feature point sequences of the each battery and the dynamic reference feature point sequence, using Frechet distance values as anomaly scores of the each battery; thus, judging whether a short-circuit fault occurs in the each battery.

The invention discloses a signal acquisition and processing method and system based on a multifunctional radar, and relates to the technical field of signal acquisition and processing, and the method comprises the steps: employing a quantum genetic algorithm to optimize radar transmission waveform parameters, including center frequency, bandwidth and frequency modulation slope, and generating a nonlinear frequency modulation waveform through FPGA hardware; a non-uniform sparse array is adopted to receive a target echo signal, time domain random interval sampling and frequency domain pseudo-random frequency point selection are synchronously implemented, and time-space-frequency three-dimensional compressed sensing observation data are formed; performing trilinear tensor joint sparse reconstruction on time-space-frequency three-dimensional compressed sensing observation data, decomposing a polarization scattering matrix eigenvalue from a reconstructed signal, and calculating a coupling characteristic quantity of eigenvalue entropy and micro-Doppler frequency; inputting the coupling characteristic quantity into a deep reinforcement learning model, and dynamically outputting a constant false alarm detection threshold, a moving target display filter order and a resource allocation weight; and detecting a threshold based on a constant false alarm rate.

The invention relates to a data processing method and device, computer equipment, a computer readable storage medium and a computer program product. After first matrix data is obtained, tensor data to be processed are obtained, and the tensor data are distributed to a plurality of calculation units; calculating a local feature value corresponding to the target calculation unit according to the local tensor data allocated to the target calculation unit; dividing the target calculation unit into data blocks corresponding to the number of the plurality of calculation units; according to the serial number of the data block, mapping the local feature value recorded in each data block to a target storage block of a target memory until the local feature value in each calculation unit is distributed to the corresponding storage block in the target memory, and obtaining a global feature value table; according to the global feature value table, feature value calculation is carried out to determine the target feature value, conflict-free parallel calculation is achieved, the utilization rate of calculation resources is increased, and the data processing efficiency is improved.

The invention discloses a deep foundation bearing capacity characteristic value calculation method and system based on data fusion. The method comprises the steps that data collection is completed through a deep load test, and a load settlement curve and a settlement time logarithmic curve are formed; generating a smooth curve; identifying a slope mutation point of the curve by adopting a first-order derivative mutation method, wherein a load corresponding to the mutation point is a proportional limit load; if the slope abrupt change point is not recognized, the curve is set as a slowly changing curve; extracting the slope and the slope change rate of the curve under each level of load, and completing the judgment and correction of the limit load by fusing the multi-dimensional data of the two curves and combining preset thresholds including the settlement amount and the settlement stabilization time; and further calculating the bearing capacity characteristic value of the deep foundation by combining the numerical relationship between the ultimate load and the proportional limit load.

The invention discloses a storage and identification method and system based on multi-dimensional personnel identification data, and the method comprises the steps: obtaining the effective feature data of a to-be-identified personnel, determining the feature value, the initial weight value and the interference quantized value corresponding to each dimension feature vector in the effective feature data, calculating a target weight value corresponding to each dimension feature vector based on the initial weight value of each dimension feature vector and the corresponding interference quantized value, calculating a fusion value corresponding to the effective feature data based on the target weight value and the feature value of each dimension feature vector, and performing feature vector mapping on the fusion value to obtain the effective feature data. And obtaining and storing a target feature description vector corresponding to each dimension feature vector, inputting the target feature description vector into a pre-training recognition model, and outputting a recognition result of the to-be-recognized person. Through the above method, the dimension feature vectors of different dimensions are fused, the accuracy of the target feature description vector is ensured, and the accuracy of the recognition result is improved.

The invention discloses a target knowledge data recognition and extraction method optimized by a depth algorithm, relates to the technical field of knowledge extraction, and solves the problem of overlong subsequent content output time caused by relatively slow target data comparison process. Text semantics are converted into quantifiable geometric features, compared with traditional keyword matching, implicit association (such as synonyms and context dependence) between semantics can be captured, recognition features are mapped to a two-dimensional coordinate system, a target object is rapidly matched through the Euclidean distance, the efficiency is improved by 10-20 times compared with full-amount traversal query, clustering analysis based on historical extracted data is achieved, and the query efficiency is improved. Through calculation of a mean value time column and a feature value TT, the optimal computing power configuration is automatically identified, and for time feature sorting of different extraction objects, tasks which are short in time consumption are preferentially processed, so that the queue is prevented from being blocked by long tasks.

For maintaining precision in processing of generating information relating to an object by using point cloud data, an image processing apparatus includes a data acquisition unit, a feature value computation unit, and an area correction unit. The data acquisition unit acquires point cloud data generated by scanning a space to be measured. The feature value computation unit sets a plurality of partial areas with a first boundary of a processing target area in the point cloud data as a reference, and computes a feature value for each of the plurality of partial areas. The area correction unit corrects the processing target area by setting a new second boundary, based on a position of a partial area satisfying a reference relating to the feature value.

The invention belongs to the technical field of data acquisition, and particularly relates to a signal sampling method and system for dynamic frequency optimization. The method comprises the following steps: presetting a basic sampling frequency based on target signal characteristics, sampling an analog signal at a frequency f1 through an analog-to-digital converter (ADC) to generate a first digital signal sequence, executing a feature extraction algorithm on the first digital signal sequence in an MCU to obtain an actual signal feature value, and calculating a difference value between the actual feature value and a preset target feature value, according to the method, signal features are extracted in real time, the sampling frequency is adjusted in a closed-loop mode through PI D, power consumption and resource occupation are reduced, meanwhile, it is ensured that transient events are completely captured, the power consumption is reduced in a self-adaptive mode along with signal complexity, the capture rate of the transient events is high, and the method is suitable for large-scale popularization and application. Missing acquisition of a traditional fixed low-frequency scheme is avoided; the method is suitable for wearable, industrial monitoring, automotive electronics and other scenes.

A water meter bearing wear state monitoring method and system based on a flow rate working condition relates to the field of electric digital data processing, and the method comprises the following steps: monitoring the generation frequency of a metering pulse on a target water meter, and determining whether the current metering working condition of the target water meter is a high flow rate working condition or a low flow rate working condition based on the generation frequency; counting a first pulse time interval sequence under the low flow speed working condition, and calculating a dispersion characteristic value of the first pulse time interval sequence as a first state characteristic value; counting a second pulse time interval sequence under the high flow rate working condition, and calculating a dispersion characteristic value of the second pulse time interval sequence as a second state characteristic value; based on the first state characteristic value and the second state characteristic value, calculating a working condition response index representing the wear degradation effect of the water meter bearing on the target water meter; when the working condition response index is larger than a preset abrasion judgment threshold value, abrasion early warning information corresponding to the target water meter is generated. By implementing the application, the water meter maintenance cost can be reduced.

The invention relates to the technical field of trajectory tracking, in particular to a campus personnel trajectory tracking system based on big data, which comprises a multi-channel video acquisition module, a target personnel identification module, a preliminary trajectory construction module, a blind area trajectory prediction module and a display terminal, according to the invention, the monitoring video stream of the campus key area is called in real time and analyzed, the image sequence of the target person is constructed, the image sequence is analyzed, and if the face image cannot be extracted, the identity similarity value is calculated based on the clothing color feature value and the body feature value to determine the target person. The method comprises the following steps: constructing a preliminary track according to spatio-temporal information of a target person at different monitoring terminals, identifying passing paths corresponding to missing segments of the preliminary track, if the passing paths are not unique, calculating a comprehensive confidence evaluation value of each passing path, selecting the passing path with the maximum value as a complemented path, connecting front and back nodes to generate a complete track, and completing the complemented path. Therefore, high-precision identification and continuous trajectory tracking of non-school personnel are realized.

The purpose of the present invention is to provide a technique for diagnosing, early, efficiently, and with minimal false alarms, an abnormality caused by aging deterioration of a device in a semiconductor manufacturing apparatus, on the basis of a change in a feature quantity trend obtained from a signal waveform acquired by a sensor mounted on the device.　To this end, a diagnostic apparatus for a semiconductor manufacturing apparatus according to the present invention comprises: a feature quantity computation unit that computes feature quantity from sensor waveform data corresponding to a process ID associated with a sensor item indicating a state of a device to produce a feature quantity trend in a time series; a trend type classification unit that classifies the feature quantity trend by trend type; a trend change amount computation unit that classifies a change in the feature quantity trend by trend change type, applies a trend change amount computation method corresponding to the trend change type on the basis of the trend type, and calculates a trend change amount; and a trend change abnormality diagnosis unit that determines an abnormality of the feature quantity trend in diagnosis from the feature quantity trend in diagnosis and the feature quantity trend in normal condition.

The invention discloses a video key frame extraction method based on cover frame difference and a related device, and the method comprises the steps: obtaining cover frames of a to-be-processed video clip, calculating the frame feature value of each cover frame in a cover frame set, calculating the similarity between the cover frames based on the frame feature values, and obtaining the similarity between the cover frames; dividing video clips into a plurality of scene groups according to cover inter-frame similarity, on this basis, calculating inter-frame change degrees of the clips in the scene groups, adaptively determining the number of key frames in each group according to the inter-frame change degrees, and finally selecting key frames which can best represent content change in the scene groups. The calculation cost of the key frame extraction process is obviously reduced, and the frame extraction efficiency is improved.

The invention discloses a lossless compression method and device for estimating a compression ratio of a compression algorithm based on a time sequence data characteristic value in a database. The method comprises the following steps: acquiring time sequence data blocks grouped according to columns; extracting characteristic values of the data blocks; aiming at a plurality of lossless compression algorithms, calculating each algorithm estimation compression ratio based on a preset compression ratio estimation formula and the characteristic value; selecting the algorithm with the highest estimation compression ratio as a target algorithm; and performing lossless compression on the data block by utilizing the target algorithm. According to the invention, through a lightweight estimation mechanism, high cost and high overhead of an AI model are avoided, and adaptive compression is realized with low calculation overhead; the time sequence data compression ratio is effectively improved (the time sequence data is close to the theoretical limit when the potential data is high and reaches the highest compression ratio of 75% when the potential is low), the compression potential is fully explored, the storage cost is remarkably reduced, and high efficiency and practicability are achieved.

The present application is applicable to the technical field of big data, and particularly relates to an attribute value extraction method and apparatus across data structures, a device, and a medium. The method comprises: calculating, on the basis of attribute values of target data in a target dataset and feature values of graph data, first similarity values representing the grammatical similarities between nodes in the graph data and the target data, and determining first candidate nodes; matching existing attributes in the target data with first feature paths of the first candidate nodes to determine first candidate matching nodes; calculating, on the basis of feature vectors of the first candidate matching nodes and attribute vectors of the target data, second similarity values representing the semantic similarities between the first candidate matching nodes and the target data, and determining first matching nodes representing a same entity as the target data; and matching missing attributes in the target data with second feature paths of the first matching nodes to determine first candidate attribute values, sorting the first candidate attribute values, and determining batch attribute values corresponding to the missing attributes. The speed of attribute value extraction is improved.

This application relates to the field of model training technology, specifically to a semi-real-time prediction model training method based on hierarchical triggering of data distribution drift. The method includes: extracting feature values ​​from transmission current signals to train a fault prediction model for real-time prediction of fault feature values ​​at all upcoming data collection times within a preset time period; calculating distribution drift evaluation values ​​and persistence evaluation values ​​to assess the significance of data distribution drift persistence within a preset adjacent time period before the current data collection time; statistically analyzing the quartiles of historical persistence evaluation values ​​to perform semi-real-time update training of the fault prediction model using hierarchical triggering; and using the real-time updated fault prediction model to predict transmission line faults. This application aims to improve the real-time accuracy of fault prediction models in predicting transmission line faults, thereby more effectively ensuring the safety and reliability of transmission lines.

The application discloses a process device controlled state evaluation method and device and a storage medium. Firstly, time series data of each monitoring point is acquired, and a sliding window method is used to calculate a correlation coefficient matrix. Then, eigenvalue calculation is performed on the correlation coefficient matrix to obtain an eigenvalue vector at a current moment. Then, clustering analysis is performed on historical eigenvalues to determine the number of cluster centers, and clustering judgment is performed on the current eigenvalue vector. Finally, the eigenvalue vector of the current window, the clustering change vector of the trend window and the clustering count of the historical window are taken as inputs, an isolation forest algorithm is used for abnormal value judgment, and the current controlled state of the device is output. The application realizes comprehensive analysis and evaluation of real-time, trend and historical information, can more accurately judge the controlled state of the device compared with a traditional method, and improves the accuracy of state evaluation.

The invention belongs to the technical field of image processing and automatic counting, and particularly relates to an interactive correction counting method and system. The counting method comprises the following steps: collecting a counting video, and carrying out preliminary counting through an identification model or algorithm based on the counting video; processing the counting video to obtain a multi-dimensional feature value of a target image in the counting video; and calculating an abnormal value according to the multi-dimensional feature value of the target image, performing interactive correction through the abnormal value, and obtaining a final count value in combination with a correction result and a preliminary count result. According to the invention, efficient, accurate and low-cost counting can be realized.

The invention relates to the technical field of earthquake monitoring, in particular to an intelligent earthquake monitoring system based on the Internet of Things. The adaptive optimization module is used for dynamically generating an aftershock probability density field; the adaptive optimization module is used for configuring a stress release relaxation factor based on a sequence active modal index calculated by a kurtosis characteristic value and a skewness characteristic value of probability space distribution in a current time slice to obtain a primary iteration aftershock probability density field; the structure-oriented constraint adjusting module is used for adjusting the structure-oriented constraint weight based on the deviation degree of the main axis direction of the high-probability aftershock cluster and the fault trend of the known region to obtain a secondary iteration aftershock probability density field; and the aftershock risk assessment module is used for determining whether the reliability of the secondary iteration aftershock probability density field is qualified or not based on the width of the confidence interval of the aftershock occurrence rate, and optimizing the preset active index based on an unqualified condition. The aftershock probability prediction precision is improved.

The invention relates to the technical field of trajectory prediction, in particular to an AI-based target motion trajectory prediction and servo control optimization method, which comprises the following steps of: calculating a motion trajectory rule representation parameter by determining a target speed influence characteristic value, a target trajectory influence characteristic value and a target visible characteristic value; the motion trail regularity of an unmanned aerial vehicle load equipment tracking target is determined to control the tracking parameter adjustment mode of the unmanned aerial vehicle load equipment, and in response to the motion trail abnormity of the unmanned aerial vehicle load equipment tracking target, after the tracking target is covered by an object, a cloud layer and the like, according to the motion parameters of the tracking target, the road condition feature value is combined to adjust the tracking parameter of the unmanned aerial vehicle load equipment. And calculating and predicting the motion trail of the tracking target, and determining that the tracking target can be identified and tracked when appearing again within a certain time. According to the invention, trajectory analysis is carried out on the tracked target, the motion trajectory is predicted when the tracked target is shielded, the unmanned aerial vehicle load equipment is controlled to continuously identify and track, and the tracking efficiency and the tracking accuracy are improved.

The application discloses an Excel data analysis and extraction method, which comprises the following steps: receiving an Excel uploading request sent by a client by a server, performing eigenvalue calculation and data extraction on the uploaded Excel as a task and adding the task to a task queue, executing the task to be executed in the task queue by a data extraction and analysis center in the server, saving the extracted data, and feeding back the execution result to the task queue to provide the client with the execution result when the client queries the execution result. The technical effect of the application is to provide a method for analyzing and extracting Excel data, and the application can automatically identify the Excel structure to perform data extraction and conversion, greatly reducing the conversion cost and improving the conversion efficiency.

The invention relates to a sheave detection method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring wire groove morphology data of a to-be-detected grooved wheel; obtaining a statistical characteristic value according to the wire groove morphology data, wherein the statistical characteristic value is used for representing the wire groove depth of the to-be-measured grooved wheel; based on the statistical characteristic value and a preset operation rule, calculating a trunking abnormity threshold value of the to-be-detected sheave; and performing anomaly detection on the to-be-detected sheave according to the trunking anomaly threshold and the trunking morphology data. According to the method, the trunking anomaly threshold value is calculated based on the statistical characteristic value of the trunking morphology data of the sheave, and the trunking anomaly detection efficiency and accuracy are improved.

The invention discloses a method for calculating an approximate circle diameter in a touch algorithm, which comprises the following steps of: 1, calculating a central physical coordinate according to original data obtained by touch, and taking the central physical coordinate as an expected value; step 2, calculating a covariance; step 3, constructing a covariance matrix; 4, performing eigenvalue decomposition on the constructed covariance matrix to obtain eigenvalues; step 5, calculating an initial ellipse semi-axis length through the characteristic value; step 6, calculating an equivalent diameter; step 7, simulating a curve equation by using a plurality of samples; and step 8, when actual touch occurs, obtaining the equivalent diameter through the steps 1-6, and calculating to obtain the diameter of the touch object. According to the method, eigenvalue calculation processing is carried out on the original covariance matrix, the contour of a real touch area can be accurately fitted, coordinate distribution characteristics in a theoretical model are considered, original data and physical size factors of a sensor are combined, and therefore the accuracy of touch size calculation is remarkably improved.

This operation analysis system comprises: a production efficiency information map creation unit (231) that creates a production efficiency information map, which is an information map indicating the correspondence relationship between production efficiency information and operation-related information; a production efficiency feature amount calculation unit (233) that extracts, as elements, individual information related to the production efficiency information of a region of interest from a plurality of pieces of the operation-related information, and calculates, for all combinations of the operation-related information and the production efficiency information, the aggregate value of the production efficiency information for each element as a production efficiency feature amount; and a factor candidate extraction unit (234) that calculates, for all combinations of the operation-related information and the production efficiency information, the degree of deviation, which is an index for evaluating the difference in the production efficiency feature amount between the elements for each type of the operation-related information, and extracts the type of the operation-related information in descending order of the degree of deviation from all the combinations of the operation-related information and the production efficiency information of the region of interest. According to the operation analysis system, it is possible to reduce the burden on a user during introduction and analyze the cause of a decrease in the production efficiency of a facility.

The present application relates to the technical field of powder metering, and discloses a dry powder denitration agent steady flow anti-fluctuation metering system and method, which comprises a pretreatment module, a feeding module, a conveying module, a detection module and a control module. The pretreatment module breaks material agglomeration through a fluidized bed and a vibrating loosening device; the improved rotary valve feeder prevents material bridging through surface treatment and axial vibration; a microwave density meter and a near-infrared particle size instrument collect multi-source sensing data. The control module aligns the data in time series, reconstructs a two-dimensional sequence input matrix, and inputs the matrix into a memory neural network model to output a predicted effective mass flow. The control module combines physical feedforward and predicted deviation feedback to calculate a target rotating speed instruction, and synchronously calculates a fan frequency instruction according to a median diameter characteristic value. The present application eliminates the phenomena of dry powder material moisture absorption and agglomeration and jamming, overcomes the time lag interference of gas-solid flow, realizes dynamic closed-loop regulation of the conveying system, and improves the metering accuracy.

The invention discloses a P4-based early flow type identification and flow scheduling method, system and device, and the method comprises the steps: S1, extracting a quintuple from a data packet arriving at a programmable switch, and carrying out the bidirectional flow identification, so as to determine the flow ID of the data packet; synchronously compressing the length of the data packet to obtain a compressed packet length characteristic value, and executing a multi-stage dynamic priority decision process based on a packet counter associated with the flow ID and a sliding window for storing a historical compressed packet length characteristic value, and determining a current priority for the data packet; and S2, calculating a scheduling HVT Rank value according to the determined current priority, the virtual completion time of the previous data packet associated with the flow ID and the compressed packet length characteristic value, and queuing and scheduling the data packets according to the scheduling HVT Rank value. The method can adapt to a dynamic self-adaptive data packet scheduling mechanism in a highly dynamic SAGINs environment.

The application relates to the technical field of virtual reality and artificial intelligence data processing, and discloses a museum ecological system based on a meta-universe joint AI double engine and an implementation method, which comprises an immersive near-eye display terminal, a cloud distributed processing center and a distributed ledger network.The terminal collects multi-source sensing data streams; the processing center separates physiological tremor energy characteristic values through a multi-source biological signal analysis module and synthesizes a comprehensive stress index, generates rendering pipeline intervention instructions according to the comprehensive stress index by using a nonlinear semantic reconstruction module, calculates an effective collision radius by using a physical simulation and rendering engine to comprehensively equip attribute metadata, the comprehensive stress index and the physiological tremor energy characteristic values, and generates a drill evaluation report; and the distributed ledger network encrypts and stores the report.The application realizes self-adaptive adjustment of virtual environment visual rendering and physical interaction difficulty according to the physiological and psychological state of a user, and solves the problems of lack of real-time physiological response and low data reliability in the existing drill system.

This characteristic change detection system includes a characteristic change detection device that detects, using vehicle information related to travel of a vehicle, a change in characteristics of a driver who drives the vehicle. The characteristic change detection device includes: a driving characteristic value calculation unit that calculates a driving characteristic value representing the characteristics of the driver from a normal distribution indicating normal characteristics prepared in advance and an evaluation distribution generated from the vehicle information; and a detection unit that detects the presence or absence of a change in characteristics by using the driving characteristic value and a predetermined reference value. The reference value is calculated on the basis of the normal distribution and a danger distribution indicating dangerous characteristics.

This invention belongs to the field of lithium-ion battery thermal runaway early warning technology, specifically disclosing a graded early warning method and system for lithium-ion battery thermal runaway based on bulging sound signals. The method includes: acquiring a lithium-ion battery sound signal sequence and performing denoising processing; performing an inverse Fourier transform on the denoised sound signal to transmit the signal back to the time domain, obtaining a denoised continuous signal; extracting six features based on the denoised continuous signal: signal peak value, zero crossover rate, energy change rate, high-frequency energy proportion, frequency entropy, and low-frequency persistence; calculating the importance distance of each feature according to its corresponding eigenvalue in the correlation coefficient matrix; selecting the five features with the largest importance distance as the final features, and using a weighted combination number Bagging model to obtain the battery thermal runaway risk early warning result. This invention has high sensitivity and can detect lithium-ion battery thermal runaway before significant changes in current, voltage, or temperature occur.

The invention discloses a steel frame node identification method and system based on a data center, and belongs to the technical field of computer aided design, the steel frame node identification method based on the data center comprises the following steps: S1, intelligent classification and standardization processing of steel structure node members; s2, setting the connection mode and the connection mode of the steel structure node components; s3, the connection relation between the steel structure node components is calculated; s4, calculating a 3D node constructed by the steel structural member; s5, calculation of steel structure component splitting 2D nodes; and S6, calculating, matching and identifying the characteristic value of the basic node of the steel structural member. The method is used for realizing automatic and high-precision identification and classification of various nodes in the steel structure model, the technical bottlenecks of high human error rate, long modeling period and difficulty in adapting to complex node types in the traditional node identification process are solved, and the efficiency and quality of steel structure node design and construction are improved.