773 results about "Data combination" patented technology

The invention provides a fragment data cross-modal label generation system and method based on deep transfer learning. The method comprises the following steps: extracting first high-dimensional feature vectors in different modes; mapping the first high-dimensional feature vectors of different modals into the same semantic space through a cross-modal comparison loss function to realize multi-modal alignment and fusion to obtain second high-dimensional feature vectors; labeling semantic tags corresponding to the second high-dimensional feature vectors based on the fragmented data components by adopting a small sample transfer learning algorithm; a multi-channel Hash encoder is adopted, a self-adaptive encoding strategy is called according to different modal data combinations, and the second high-dimensional feature vector is encoded into a multi-channel binary Hash code; in combination with an incremental graph neural network, the binary hash codes and the corresponding semantic tags are dynamically expanded into the historical knowledge graph; matched fine-grained tags are established for semantic differentiation features of different entity combinations in the target knowledge graph, and a cross-modal tag tree is obtained by combining three-matrix hierarchical construction.

The invention provides an urban green land coverage rate dynamic monitoring method and device. The method comprises the following steps: acquiring multispectral satellite remote sensing data, and preprocessing the multispectral satellite remote sensing data; carrying out cloud detection and shadow detection on the preprocessed remote sensing image, and calculating an effective pixel proportion index, a spectrum quality index, a time phase matching degree index and a spatial integrity index; based on the four indexes and the corresponding weights, constructing a fuzzy comprehensive evaluation matrix, and selecting an optimal data combination; calculating a normalized difference vegetation index (NDVI) based on the near infrared band and the red light band in the optimal data combination, and performing illumination angle correction and time sequence smoothing processing on the NDVI to obtain an optimized NDVI; performing green space identification on the optimized NDVI by adopting a first deep learning model, and outputting a green space distribution map; and calculating a green land area change rate, an NDVI difference value and connectivity index change based on the green land distribution map. According to the invention, high-precision dynamic monitoring of the green land coverage rate is realized, and the monitoring efficiency and accuracy are improved.

The invention relates to a vehicle suspension control method and device, computer equipment and a readable storage medium. The method comprises the following steps: combining obtained vehicle body dynamics time series data, vehicle control time series data and environment perception time series data in a vehicle driving process into time series data of multiple channels, determining a self-adaptive modal number of the time series data under each channel according to the time series data under each channel, and determining the self-adaptive modal number of the time series data under each channel according to the self-adaptive modal number of the time series data under each channel; and performing modal decomposition on the time sequence data based on the adaptive modal number, extracting frequency domain characteristics of the time sequence data, integrating the frequency domain characteristics of the time sequence data of the multiple channels, inputting the frequency domain characteristics into a trained suspension control parameter prediction model to obtain target time sequence characteristics for predicting suspension control parameters, and performing prediction on the suspension control parameters based on the target time sequence characteristics. Vehicle suspension control parameters are obtained through prediction, and a suspension system of the vehicle is controlled based on the vehicle suspension control parameters. By adopting the method, the risk of rollover of the vehicle can be obviously reduced.

Provided are system, method, and device for automatically enhancing future prediction using a reservoir transformer in a machine learning model. According to example embodiments, the system may include: a memory storage storing computer-executable instructions; and at least one processor communicatively coupled to the memory storage, wherein the at least one processor may be configured to execute the instructions to: obtain current input data representing a current state of a complex system; determine a plurality of readout data based on previous input data representing a previous state of the complex system using a plurality of reservoirs; combine the plurality of readout data to form an ensemble reservoir data; and determine predicted output data representing a predicted state of the complex system based on the ensemble reservoir data and the current input data using a transformer.

The invention belongs to the technical field of underground engineering, and discloses an omnibearing intelligent monitoring system, method and device for an underground anchoring pull-out test. According to the method, electromagnetic signals generated in the stress process of the anchor rod are captured in real time through an electromagnetic sensor, and internal stress changes and energy distribution are reflected; the deformation and stress states of the anchor rod in the dynamic environment are evaluated through a strain gauge and accelerometer combined data combination method, the deformation condition of the anchor rod is monitored through the strain gauge, and the stress-strain relation is recorded; an accelerometer is used for detecting the dynamic response of surrounding rock mass so as to evaluate the overall stability of the rock mass; a coupling medium is injected, the computer terminal stores the collected signals locally or in a cloud end and analyzes the signals, stress and deformation parameters in an anchor rod pull-out test are extracted, and an anchor rod stress cloud picture is generated in real time. According to the method, the safety and reliability of underground engineering are remarkably improved, and a new technical support is provided for evaluation of anchoring quality.

The invention discloses a radiation source open set identification method and device based on a dynamic group isovariant network and a generative adversarial network. The method comprises the following steps: preprocessing a plurality of radiation source individual signals of known categories to obtain a data set for closed set identification; a dynamic group isovariant network is constructed, the network is trained on the obtained known category data set, and optimal model parameters are found; simulating virtual unknown category data on the basis of the known category data by using the generative adversarial network, and retraining the constructed recognition model by combining the unknown category data with the known category data to obtain a model capable of being used for open set recognition; and combining actually acquired unknown category data with known category data to serve as a test data set, extracting features through a retrained model, inputting the features into an OpenMax layer, judging whether the category is a known category or an unknown category according to threshold distribution, and detecting the recognition performance of the retrained network open set. The training efficiency is high in small sample recognition task, the classification recognition performance is good, and the robustness is high in open set recognition task.

A radar and camera sensor combined detection method, comprising: obtaining a plurality of image perception data groups and a plurality of raw radar perception data groups of a target object, the target object comprising a moving target object and a stationary target object (S11); according to a first data processing method, processing the plurality of raw radar perception data groups to obtain a plurality of radar sparse tensor information groups (S12); and forming a stereoscopic multi-dimensional depth perception data combination and outputting same, the stereoscopic multi-dimensional depth perception data combination being formed in a multi-dimensional matrix structure, and the stereoscopic multi-dimensional depth perception data combination comprising a plurality of radar sparse tensor information groups assigned to the multi-dimensional matrix structure and a corresponding plurality of image perception data groups (S13). The method solves the problems of how to effectively identify static objects, occluded objects, etc. and process a large amount of radar raw data.

Certain aspects of the present disclosure provide techniques for wireless communication by a user equipment. The techniques include receiving, from a backscatter device, first backscatter data during one or more network entity downlink slots; receiving, from the backscatter device, during a network entity uplink slot immediately following the one or more network entity downlink slots, second backscatter data; and combining, at the user equipment, the first backscatter data and the second backscatter data to generate a backscatter data package.

The invention provides a multi-mode PDF (Portable Document Format) document analysis method, device and equipment and a medium, and the method comprises the following steps: loading a PDF document and carrying out preprocessing, including page splitting and content cleaning, to generate standardized document data; dynamically extracting contents in the standardized document data, wherein the contents comprise paragraphs, pictures and table elements; processing the dynamically extracted pictures, including shielding meaningless pictures based on a preset rule, and analyzing picture contents by using a multi-modal model to generate readable picture information; processing the dynamically extracted table, including optimizing and merging the table structure into a single element format to generate structured table data; combining paragraphs, readable picture information and structured table data, converting the paragraphs, the readable picture information and the structured table data into a complete structured document format, and inserting in key positions to enhance coherence context description; and outputting the complete structured document format as a final analysis result. According to the method, the analysis precision and the knowledge extraction efficiency of the complex PDF document can be remarkably improved.

The present application provides a method and apparatus for batch generation of test cases, and a computing device. The method comprises: acquiring a function to be tested of a target software product; acquiring an action factor and a data factor corresponding to said function; the action factor is used to describe at least one of the following types of information in a test case: a pre-condition, a test step, and an expected result; the data factor comprises at least one test input parameter corresponding to said function, and multiple values of each test input parameter; combining the plurality of values of the at least one test input parameter, and obtaining multiple data combination results; on the basis of the action factor and the multiple data combination results, generating a plurality of corresponding test cases in batches; and displaying the generated multiple test cases to a user. The described method can not only increase the efficiency of generating test cases, but also avoid the problem of missed testing.

The invention relates to the technical field of flight management, in particular to a flight departure guarantee resource allocation scheduling optimization management method, which comprises the following steps: according to a runway, a running sequence and a time interval corresponding to a flight shift list, constructing a flight state diagram for the flight shift list in a preorder and postorder manner; determining the transfer cost of each state in the flight state transfer process by taking the maximum capacity and the minimum adjustment time interval of each node in the flight state diagram in a unit time period as constraint conditions, and sequencing the flights into a time-adjusted flight list according to the transfer cost; performing iterative solution by using the timing flight list, determining an optimal adjustment time interval in unit time, and determining an adjustment quantity in a unit time period according to the adjustment time interval in combination with time slot allocation; when the adjustment quantity accords with a preset constraint rule, performing data combination on the corresponding flights, and regarding the combined data as an output adjustment scheme; the efficiency and the performability of flight scheduling processing are realized.

The invention discloses a hierarchical processing system for energy storage data, and the system comprises an optimization feature extraction module which collects initial energy storage data, builds a unified time sequence on the time dimension of the initial energy storage data through a specified time interval # imgabs0 #, carries out the alignment sampling, introduces the initial energy storage data after the alignment sampling into an auto-encoder, and carries out the noise reduction processing, performing feature extraction on the data subjected to noise reduction processing to obtain optimized energy storage feature data; the dynamic grading adjustment module is used for standardizing the optimized energy storage characteristic data, calculating the information entropy of the standardized characteristic to obtain an initial weight, introducing a Q reinforcement learning algorithm to carry out online optimization on the weight, designing a third-order grading model based on the optimized initial weight, and carrying out dynamic grading adjustment on the optimal energy storage characteristic data; and performing data classification on different optimization energy storage characteristic data combinations and dynamic weights according to a third-order classification model, dividing the data into an emergency level, an important level and a common level based on the third-order classification model, and providing targeted processing and management strategies for different levels of data.

Proposed is a multi-sensor fusion device for performing extrinsic calibration in real time. The multi-sensor fusion device may include a camera unit including at least one camera and configured to produce image information of a surrounding environment by using the camera. The multi-sensor fusion device may also include a light detection and ranging (LiDAR) unit including at least one LiDAR and configured to produce a point cloud of the surrounding environment by using the LiDAR; a data combination unit configured to produce combined data in which the image information is combined with the point cloud. The multi-sensor fusion device may further include a data computation unit configured to derive an extrinsic calibration parameter by inputting the combined data into an extrinsic calibration model and perform extrinsic calibration of the camera and the LiDAR based on the derived extrinsic calibration parameter.

The invention provides a control method of storage equipment, the storage equipment and a computer storage medium. The method comprises the following steps: taking data read under initial threshold voltage as original data; symmetrically shifting the initial threshold voltage by taking the initial threshold voltage as a center, and reading data again as bias data; the mutually symmetrical bias data are logically processed, and judgment data are obtained; the original data and the judgment data are combined, a partition array is obtained, the credibility of the partition array is obtained through a lookup table, and the credibility and the accuracy of the judgment data are in positive correlation; the judgment data with high confidence reliability are preferentially judged, and the judgment data are decoded and processed; when decoding processing is started, the number of times of symmetrical offset initial threshold voltage is increased, and data are read again to serve as bias voltage data of the next round; and the step of obtaining the judgment data, the step of obtaining the credibility and the decoding process are cycled until decoding succeeds or the number of symmetric offset times reaches an offset time threshold value.

The invention discloses an electrical equipment defect detection method, system and equipment based on multi-modal alignment, and particularly relates to the technical field of electrical equipment detection. Obtaining a spatial feature difference and a time feature difference between every two pieces of modal data; performing logistic regression processing on the spatial feature difference and the time feature difference to obtain an alignable coefficient between each two modal data, and determining a to-be-aligned data combination for electrical equipment defect detection based on the alignable coefficients; and performing data alignment on the to-be-aligned data combination to obtain aligned modal data, and performing defect detection analysis on the power equipment by using the time sequence model according to the aligned modal data to obtain a defect detection result of the power equipment.

The invention relates to the technical field of hydrological data processing, in particular to a water conservancy intelligent internet-of-things sensing method and system, and the method comprises the following steps: combining statistical data into a feature set based on hydrological monitoring data including flow, water level and rainfall information, judging whether the data accords with normal distribution, and generating a data distribution feature vector set. According to the method, the change trend of the data is captured through multi-level data distribution characteristic analysis, and the monitoring precision of the data exception is enhanced. In the prediction process, new data and weight adjustment are gradually fused, so that the model can adapt to environmental changes in real time, and the hydrological state in a future short period is accurately predicted. Besides, abnormal data can be identified in real time based on analysis of quantiles, potential problems can be quickly found, and state division of different levels can be carried out according to abnormal degrees. In sampling scheduling, the priority of data acquisition is adjusted according to a probability model of state transition, so that monitoring resources are efficiently configured.

The invention provides a foundation pit excavation deformation prediction method and device based on finite elements, and relates to the technical field of foundation pit construction.The method comprises the steps that firstly, a digital geologic model is built based on geological survey drilling data, and a foundation pit excavation finite element numerical simulation model is built in combination with a foundation pit supporting design drawing of a design institute; orthogonal design parameters are combined to obtain multiple groups of simulation data, and a twinborn database is established; constructing an initial prediction model and training by using a twin database to obtain a target prediction model; obtaining foundation pit monitoring data through a sensor, extracting data corresponding to the twin database to form an effective parameter combination, and inputting a target model to predict a foundation pit excavation deformation result in the next stage; and judging whether a foundation is excavated or not according to a deformation result, and if not, continuing to predict until the foundation is reached. According to the method, modeling is performed by combining geology and design data, the model is trained by using a twin database, deformation can be accurately predicted, dynamic monitoring and real-time prediction are realized by combining monitoring data, and construction safety is guaranteed.

The invention provides a method and a device for generating test cases in batches and computing equipment. The method comprises the following steps: acquiring a to-be-tested function of a target software product; an action factor and a data factor corresponding to the to-be-tested function are obtained, the action factor is used for describing at least one of the following information in a test case: a precondition, a test step and an expected result, and the data factor comprises at least one test input parameter corresponding to the to-be-tested function and a plurality of values of each test input parameter; combining the plurality of values of the at least one test input parameter to obtain a plurality of data combination results; generating a plurality of corresponding test cases in batches according to the action factors and the plurality of data combination results; and displaying the plurality of generated test cases to the user. The method not only can improve the generation efficiency of the test case, but also can avoid the problem of missing test.

The invention discloses an individual health risk dynamic assessment system based on multi-modal biological feature fusion, and relates to the technical field of medical health information processing. The method comprises the following steps: constructing a high / low-dimension data combination rule; dynamically adjusting the feature fusion weight according to the clinical feature importance weight and the medical efficiency value calculated by the detection time interval; a cost difference value formula is introduced to quantify the economical efficiency of combined evaluation and independent evaluation; calculating a health risk comprehensive coefficient of the monitoring scene by combining the health data credibility score and the index type / amplitude / duration weighted risk value; the risk level is rechecked through expert assessment or a secondary model, so that the assessment accuracy is ensured; updating the individual credibility score according to the evaluation result; and for the result exceeding the confidence interval, classifying and optimizing a feature fusion algorithm, a model parameter or a data combination strategy. The system realizes accurate, economic and real-time individual health risk dynamic assessment, and is suitable for scenes of chronic disease management, health intervention and the like.

The invention discloses an active and passive safety fusion evaluation weight coefficient determination method and system, and relates to the technical field of automobile safety evaluation, and the method comprises the steps: obtaining active safety data and passive safety data; combining the active and passive data to obtain total sample data; constructing an analytic hierarchy model and a judgment matrix of each hierarchy; calculating a total level sequence of all indexes in the scheme layer to obtain a weight value of each index; performing weighted scoring on each data sample to obtain a total score; constructing a multi-layer perceptron model; and extracting the final weight of each index. According to the method, the vehicle speed after braking of the vehicle active safety system is taken as the collision speed of human-vehicle collision simulation in the passive safety system, and an active safety index and a passive safety index are associated to carry out active and passive safety fusion evaluation; and the index weight coefficient value for fusion evaluation is extracted through an analytic hierarchy process and construction of a multi-layer perceptron model, so that the data accuracy is high, the objectivity is high, and the comprehensiveness is high.

Methods and systems include, in at least one aspect: determining an optical model of an object in flight using two dimensional image data obtained from a camera, determining a radar model of the object in flight using radar data obtained from a radar device, connecting the optical model and the radar model in time using a timestamp derived for the camera and the radar device based on a difference between a duration required by the camera to produce an image frame and a duration required by the radar device to produce a radar reading, and producing three dimensional location information of the object in flight in three dimensional space using the optical model and the radar model connected in time using the timestamp.

The invention provides a component-level collaborative editing control method, which is applied to a multi-user collaborative editing control system, and the system comprises a plurality of front-end engines and a back-end server. A front-end engine obtains user editing operation data and a rear-end real-time version snapshot, component structure difference comparison is carried out, and a component operation list is generated; and performing component conflict judgment based on the operation list, merging and editing operation data according to a conflict judgment result and a preset conflict resolution strategy, generating processed version snapshots, sending the processed version snapshots to a rear end, storing the processed version snapshots by a rear-end server, and sending the processed version snapshots to each front-end engine. Compared with the prior art, the method has the advantages that the data transmission quantity and the load of the rear-end server are reduced by a lightweight architecture through the front-end component level difference comparison and the preset conflict resolution strategy, the rear-end server does not need to be frequently requested for conflict resolution and data merging, the response speed and the collaborative editing efficiency are remarkably improved, and the user experience is improved. And better user experience is brought to the user. The system has the same beneficial effects.

The invention relates to a PLC-bridging gateway internet-of-things automatic identification method, system and device and a medium. The method comprises the following steps: sequentially sending handshake detection signals to a plurality of PLC devices and receiving response messages; forming a candidate handshake data combination set, and completing handshake matching through a priority algorithm and an adaptive polling mechanism; verifying the response frame, analyzing the equipment information, and confirming the active state and communication parameters of the PLC equipment; generating a drive binding strategy according to a matching result, and distributing a session identifier for the drive binding strategy; generating a session identifier of the target PLC equipment, and updating a session routing table in the bridging gateway; and translating the industrial data collected by the target PLC device into an Internet of Things protocol data packet and uploading the Internet of Things protocol data packet to a cloud data center. According to the invention, automatic identification and rapid adaptation of various interfaces and protocols can be realized; the communication stability and real-time performance are improved through dynamic scheduling and route updating; uniform data uploading is supported, and efficient deployment of cloud analysis and the industrial Internet of Things is facilitated.

The invention discloses a multi-stage wind power abnormal data combination cleaning method, and belongs to the field of new energy power generation data processing. Aiming at the problems of various types of abnormal values of wind power original data, serious interference and low cleaning precision, and single anomaly detection means, easy missing detection and misjudgment and the like in the prior art, the invention provides a staged combined cleaning strategy, which comprises the following steps of: firstly, dividing equal interval sections of wind speed and power, and eliminating isolated point type anomalies in distribution by using double quartile analysis; a CFSFDP density peak value clustering algorithm is introduced, low-density anomaly clusters are mined according to a density-distance joint criterion, and the recognition capability of structural aggregation anomaly is improved through two rounds of clustering; performing segmentation modeling on a wind speed-power relation by using upper and lower envelope line fitting based on a function, and removing envelope outer drift type noise; and finally, complementing edge missing data by using an interpolation algorithm. According to the method, the systematicness, precision and adaptability of the wind power data cleaning process are remarkably enhanced, high-quality data are provided for subsequent power prediction and energy optimization scheduling, and the application prospect is wide.

The invention discloses a multi-granularity generation type video compression method, which comprises the following steps of: acquiring a video frame sequence, performing spatial-temporal characteristic decomposition, decomposing and mapping a video signal to three potential subspaces to obtain a first potential subspace representation, a second potential subspace representation and a third potential subspace representation, the second potential subspace represents space-time motion features in the video, and the third potential subspace represents high-frequency detail information in the video; and encoding the first, second and third encoded data to generate corresponding first, second and third encoded data, and combining the first, second and third encoded data to generate a compressed code stream. According to the method, the problem of unbalanced code rate distribution is effectively solved, and content self-adaptive efficient compression is realized; the multi-scale feature extraction capability is enhanced, the problem of sub-band matching mismatching is avoided, and the compression performance is improved.

The invention relates to the technical field of model testing, in particular to a model testing method and device and electronic equipment, and is used for solving the problems of long test data combination time consumption and low coverage rate of a model testing mode in the related technology. The multiple features are classified, a core feature set, a common feature set and an edge feature set are obtained, and the model can be a deep learning model; generating a test set of the model according to a differentiated data generation strategy of feature importance on the basis of the rule set of the service type adapted to the model, the core feature set, the common feature set and the edge feature set, and performing model performance test on the model by using the test set to obtain a test report; therefore, the efficiency of model algorithm testing is improved.

The application discloses a power equipment defect detection method, system and device based on multi-modal alignment, and particularly relates to the technical field of power equipment detection, and the technical points are as follows: the method comprises the following steps: in the collected visible light image, infrared image and ultrasonic signal, the spatial feature difference and the time feature difference between every two modal data are obtained; the spatial feature difference and the time feature difference are subjected to logic regression processing, the alignable coefficient between every two modal data is obtained, the to-be-aligned data combination for power equipment defect detection is determined based on the alignable coefficient; the to-be-aligned data combination is subjected to data alignment, the aligned modal data is obtained, and according to the aligned modal data, the time sequence model is used for defect detection analysis on the power equipment, so that the power equipment defect detection result is obtained.

A method of mass spectrometry comprising: a) providing a mass spectrometer having a time of flight (TOP) mass analyser: b) performing a survey scan comprising separating a packet of precursor ion species and mass analysing ions so as to obtain first mass spectral data: c) determining a time window over which one of the precursor ion species. or fragment or product ions derived therefrom, were mass analysed: d) separating another packet of precursor ion species and mass analysing ions so as to obtain second mass spectral data. wherein the pusher of the TOP mass analyser is pulsed according to a plurality of consecutive pulse sequences during a plurality of respective pulse sequence time periods. wherein each pulse sequence consists of consecutive pushes that are arranged such that the duration between any pair of pushes in the pulse sequence is different to the duration between any other pair of pushes within the pulse sequence: f) selecting mass spectral data, from the second mass spectral data, that was obtained during a time period corresponding to said time window of the survey scan, so as to obtain selected data; g) repeating steps d) to f) at least one further time such that multiple sets of said selected data are obtained; combining said multiple sets of selected data and decoding the combined data to obtain mass spectral data representative of the mass to charge ratios of the ions detected by the TOP mass analyser.

The invention discloses a communication method and system for multiple battery modules, and relates to the technical field of communication methods, and the method comprises the steps: determining a corresponding short priority code according to first communication data and the current working state of an electric vehicle; and the target battery module and the remaining battery modules are determined based on the short priority code, the primary communication relationship and the current working states of the plurality of battery modules, so that the accuracy of the target battery module is improved, and the priority communication right of the target battery module to the first communication data is ensured. If the target battery module has the priority communication right and fails to read the first communication data, performing autonomous communication on the first communication data based on the remaining battery modules, and determining a combination of the battery module and the first communication data; the communication priority relationship of the plurality of battery modules is determined according to the previous communication events and the working states of the battery modules, so that the communication accuracy of the first communication data is improved, and the accuracy of the communication priority relationship of the plurality of battery modules is ensured.

The invention provides a multi-modal data archiving processing system based on cloud computing, and relates to the technical field of data processing, and the system is used for obtaining an archiving request and an archiving task, determining an archiving type, a processing priority and a task number according to the archiving request and the archiving task, recording a memory identifier, an execution path and a cache address of a processing node, and storing the memory identifier, the execution path and the cache address of the processing node; the method comprises the following steps: extracting a structured field group and an unstructured content block of an archiving task, mapping the structured field group according to fields to generate a structure reference table, segmenting the unstructured content block according to a time sequence to generate a content time stream, recording each field mapping and time segmentation operation, and determining a reference relationship between each field mapping and time segmentation operation and a processing node; and determining a task number, a resource state and a data fragment of each processing node to obtain state anchoring data, combining the structure reference table and the content time stream with the state anchoring data into a double-layer archiving component, and archiving the double-layer archiving component. According to the method, content backtracking and operation backtracking can be carried out on the multi-modal data archiving process.