94 results about "Eigenvalue computation" patented technology

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.

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 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 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.

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.

The invention discloses a table name dynamic matching method after table division of a database, which comprises the following steps of: in response to a data operation request, extracting a fragment key field and an operation type identifier contained in the request; a sub-table state information set pre-configured by the system is obtained, the sub-table state information set comprises the total number of sub-tables and the real-time state index of each sub-table, and the real-time state indexes at least comprise a current load factor, a data distribution characteristic value and a disaster recovery state value; wherein the data distribution characteristic value is used for representing a data distribution concentration trend of each sub-table on a fragmentation key dimension; inputting the fragment key field, the operation type identifier and the sub-table state information set into a dynamic matching model; the dynamic matching model is configured to execute basic mapping and dynamic tuning two-stage processing; in the basic mapping stage, based on the fragment key field and the data distribution characteristic value of each sub-table, calculating to obtain a preliminary sub-table identifier; according to the invention, load balancing, dynamic adaptation, stability and reliability can be realized.

The application discloses a kind of operation data feature analysis methods suitable for distribution network operating state judgment, it includes: obtaining multiple scene dimensions under the data to be analyzed and pre-processing;Characteristic value calculation is carried out based on the data after pre-processing, and the characteristic value data of each scene dimension in different time section is obtained;According to the preset characteristic dimension, the characteristic value data is grouped, and multiple characteristic value data slices under each time section are generated, and different characteristic value data slices are associated according to the correlation between data;Characteristic value data slice with associated relationship is combined to generate characteristic value data block;Characteristic value data block under different time section is used as the domain of two-dimensional table, and two-dimensional characteristic value snapshot table is generated;Operation data feature analysis is carried out based on two-dimensional characteristic value snapshot table.The present application can organically comb the disordered and massive data, and be normalized into feature snapshot, facilitate the operation abnormal analysis of distribution network and distribution equipment.

The application relates to the technical field of image denoising, and discloses an intelligent detection method, device and equipment for a field wiring scheme, which comprises the following steps: collecting wiring videos from different visual angles, and extracting wiring images of different visual angles frame by frame; marking target pixel points, extracting linear eigenvalues, bending eigenvalues and scattering eigenvalues of the target pixel points, calculating abnormal wiring eigenvalues of the target pixel points, and identifying significant pixel points in the wiring images; calculating the complexity of the significant pixel points and an adjustment threshold value, assigning values to the adjustment threshold value of pixel points which are not significant pixel points, denoising the wiring images according to the adjustment threshold value, and obtaining denoised wiring images of all visual angles; performing image repair, reflection suppression and motion compensation on the denoised wiring images, extracting features of the denoised wiring images, and completing detection of the field wiring scheme. The application can improve the accuracy and stability of wiring scheme detection.

The invention relates to the technical field of power failure identification, and discloses a power failure risk identification method and system based on big data, and the method comprises the steps: training a first model and a second model, and the outputs of the first model and the second model are power failure probabilities; obtaining a typical characteristic value of any node in the historical topological structure; obtaining a typical characteristic value of the to-be-detected node, and calculating a first integrating degree based on the typical characteristic value of the to-be-detected node; calculating a second integrating degree of the to-be-detected node; and inputting the to-be-detected topological structure and the working condition data into the first model to obtain a first predicted value, inputting the working condition data sequence of the to-be-detected stage in the preset time period into the second model to obtain a second predicted value, and calculating the comprehensive power failure probability of the to-be-detected node based on the first predicted value, the second predicted value, the first integrating degree and the second integrating degree. According to the technical scheme, the accuracy of the power failure risk identification result can be improved.

The invention provides a video repeated frame detection method and device. The method comprises the following steps: storing a video signal which is output by a video source and follows an HDMI protocol specification to obtain a target video; each video frame of the target video is read, for each video frame, the difference feature value of the video frame is calculated, and the difference feature value represents the difference degree between the video frame and the adjacent previous video frame; and for every two different characteristic values with adjacent sizes, calculating the difference value between the larger value and the smaller value in the two different characteristic values with adjacent sizes, determining the maximum value in all the difference values, if the maximum value is greater than a preset threshold value, determining a target threshold value according to the maximum value, and if the target threshold value is greater than the preset threshold value, determining the target threshold value according to the target threshold value. And determining the video frame with the difference characteristic value smaller than the target threshold value in each video frame as the repeated frame. According to the scheme, a real picture seen by human eyes can be tested, the threshold value does not need to be manually set, multi-step verification is not needed, the detection is accurate, and the processing is rapid.

This invention discloses a high-resolution corner detection method and system for images based on the second-order Gaussian directional derivative, belonging to the field of image detection. The method includes the following steps: acquiring raw image data and performing second-order Gaussian directional derivative filtering at different directional angles using a preset response separability scale to obtain the directional derivative response value of each pixel in each direction; constructing a directional response vector for each pixel based on the directional derivative response value, and generating an autocorrelation matrix based on the directional response vector; calculating the corner response metric value of each pixel based on the eigenvalues ​​of the autocorrelation matrix; performing local nonmaximum suppression processing based on the corner response metric value, and filtering pixels that meet a preset response threshold to obtain a set of corner coordinates. This invention can solve the problem in the prior art where corner response functions are difficult to effectively separate in structurally similar edge regions.

The invention discloses a multi-sensor network topological structure vulnerability detection method and system, and relates to the technical field of sensors, and the method comprises the steps: solving a characteristic value [lambda] i (A) of a state matrix; constructing a weighted directed topological graph, establishing a Laplacian matrix, and performing characteristic decomposition to obtain a characteristic value mu j (L); calculating the state estimator of each sensor, and stacking to obtain an overall system error; establishing a state matrix phi of a unified consensus estimation error system, and solving a joint characteristic value of phi; constructing a deviation system, and forming an accessibility matrix R; and if the characteristic value muj (L) and the characteristic value lambdai (A) exist, and meanwhile, the joint characteristic vector vij belongs to the reachable subspace span (R), the topological structure of the current multi-sensor network and the configuration of the consensus gain are fragile under the Byzantine attack. According to the method, the vulnerability of the topological structure can be judged without a large number of time domain simulation or physical attack experiments.

This application discloses a method and system for detecting the vulnerability of multi-sensor network topology, relating to the field of sensor technology. The method includes: calculating eigenvalues ​​of the state matrix. l i ( A Construct a weighted directed topological graph, establish the Laplace matrix, and obtain eigenvalues ​​through eigenvalue decomposition. m j ( L ); Calculate the state estimates of each sensor and stack them to obtain the overall system error; Establish the state matrix Φ of the unified consensus estimation error system and find the joint eigenvalues ​​of Φ; Construct the deviation system to form the reachability matrix. R If eigenvalues ​​exist m j ( L ) and eigenvalues l i ( A This allows for the simultaneous joint eigenvectors. v ij Belongs to reachable subspace span ( R If the current multi-sensor network topology and consensus gain configuration are vulnerable to Byzantine attacks, then the present application's method can determine the vulnerability of the topology without extensive time-domain simulations or physical attack experiments.

This invention relates to the field of electrical equipment condition monitoring technology, specifically disclosing a method and system for identifying the online status of electrical equipment. The method includes: first, establishing a parameterized feature model calibrated using measured data from the equipment's initial commissioning; then, substituting real-time operating parameters to generate dynamic benchmark feature values; calculating the deviation between the measured feature values ​​and the dynamic benchmark; determining the equipment status level and locating physical parameter deviation information based on a physical rule base; and finally, outputting standardized diagnostic results. This invention abandons the fixed threshold mode, achieving adaptive operating conditions for the benchmark value, eliminating the influence of operating condition fluctuations, significantly reducing the false alarm rate, and enabling interpretable alarms through physical parameter deviation location, providing precise guidance for maintenance. Deviation pattern recognition offsets individual equipment differences, enabling cross-equipment reuse of the same model. It employs lightweight computational logic, adapting to substation edge computing devices, resulting in low computational power consumption and meeting the low-cost, high-reliability application requirements of the power industry.

The embodiment of the present application discloses a kind of to the method and system of the multi-spectral comprehensive analysis of smoke screen masking interference effect, it is related to civil air defense engineering protection technical field, can automatically evaluate the interference camouflage effect of smoke screen masking.The present application includes: obtaining the image of protected target, and the feature extraction of significant area in image is carried out, then the eigenvalue of significant area is obtained;According to the obtained eigenvalue, the interference detection probability of the protected target under different smoke screen conditions is calculated, wherein the smoke screen condition at least one smoke screen type, smoke screen type corresponds to guidance mode, the guidance mode includes: optical, laser, thermal infrared and radar guidance;The calculation result of interference detection probability is sent to designer terminal.

The invention discloses a small interference stability analysis method and system considering an electromagnetic transient state of a power system, and belongs to the technical field of power system stability analysis. The method comprises the following steps: performing steady-state load flow calculation by adopting a Newton-Raphson method, and establishing a distribution parameter line model; initializing a dynamic element based on the power flow result; linearizing the dynamic phasor model at the balance point to obtain a matrix bundle; and solving the generalized eigenvalue by adopting a partial eigenvalue calculation method, judging the modal stability, and analyzing the participation degree and energy distribution of the oscillation modal in combination with the eigenvector. The system comprises corresponding modules. The problems that balance point calculation is not convergent and a distribution parameter model is insufficient in small interference analysis in the electromagnetic transient state are solved, quantitative analysis, stability judgment and source positioning of broadband oscillation are achieved, and the method is suitable for a modern electric power system.