16results about How to "Improve association accuracy" patented technology

A flight path association method based on local and global similarity fusion belongs to the technical field of flight path association, and comprises the following steps: S1, preprocessing obtained flight path pair data, including abnormal value elimination and time coincidence part interpolation alignment; s2, respectively extracting local similarity features and global similarity features of the track pairs; s3, performing weighted fusion on the local and global similarity features, inputting the local and global similarity features into a random forest classifier model for training, and determining an optimal weight combination of different similarity features; and S4, carrying out weighted fusion on the local and global similarity features of the unknown track pair according to the optimal weight combination, and inputting the local and global similarity features into the trained random forest classifier model to carry out association judgment. According to the method, the influence caused by misjudgment of local similarity features can be reduced under the condition that the overlap ratio of the track to time is low, and the method still has good association accuracy.

The invention provides an obstacle association method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining measurement data from at least one sensor, and enabling the measurement data to comprise a plurality of obstacle measurements and detection information thereof; for each obstacle measurement to be associated, selecting a target reference point type from a plurality of predefined candidate reference point types according to detection information of the obstacle measurement to be associated; determining a measurement reference point from the detection information of the obstacle measurement based on a target reference point type selected for each obstacle measurement; determining a matching measure between a measurement reference point and a track reference point based on the measurement reference point measured by the obstacle and the track reference point determined for the fused track; according to the calculation result of the matching measurement between all the obstacle measurements and all the fused tracks, the association relationship between the obstacle measurements and the fused tracks is determined, and the accuracy and the reliability of obstacle association can be remarkably improved by adopting the method.

This application provides a data association method, apparatus, electronic device, and storage medium. The method includes acquiring a set of measurement tracks and a set of system tracks at the current time. The strategy track set includes a visual track subset, a forward radar track subset, and a side-rear radar track subset. The method involves matching the visual track subset and the system track set according to a first association rule to determine system-visual data pairs. It also involves matching the forward radar track subset and the system track set according to a second association rule to determine system-forward radar data pairs. Furthermore, it involves matching the side-rear radar track subset and the system track set according to a third association rule to determine system-side-rear radar data pairs. Finally, it involves updating the system track set based on the determined system-visual data pairs, system-forward radar data pairs, and system-side-rear radar data pairs.

The application relates to a power distribution network multi-source heterogeneous operation data association method and device, and belongs to the technical field of power system operation data processing. According to multi-source heterogeneous operation data of a target device node, a candidate device node set is determined, an association relationship between device nodes is determined through joint similarity of the target device node and each candidate device node in the candidate device node set, finally, multi-source heterogeneous operation data of the target device node and associated candidate device nodes are fused based on the association relationship between the target device node and the candidate device nodes, a reliable asynchronous, multi-source heterogeneous data unified fusion result can be provided, the accuracy and engineering usability of power distribution network unified semantic object construction are improved, and the problem that current multi-source heterogeneous data objects are difficult to unify, the same object has different names and different places have the same name, and association errors occur frequently is solved.

The invention relates to the technical field of rosewood waste management. The invention provides a rosewood waste intelligent management method, system and equipment and a medium. The method comprises the steps that point cloud collection is conducted on waste through a preset three-dimensional vision device to obtain point cloud data; performing feature extraction on the spectral data to obtain a spectral feature vector, and performing morphological reconstruction on the point cloud data to obtain a morphological feature vector; performing similarity matching processing on the spectral feature vector, the morphological feature vector and a pre-constructed wood feature library to obtain wood type probability distribution; searching a plurality of paths in a pre-constructed waste-process-value knowledge graph based on the space-time correlation factor and the wood type probability distribution to obtain a target process node set; and performing process parameter optimization on the target process node set to obtain a collaborative process parameter packet so as to achieve the technical effects of improving the correlation accuracy of the waste and the production equipment, enhancing the distinguishing capability of similar rosewood materials and optimizing the self-adaptive adjustment efficiency of a high-value process path.

The application provides a multi-source target fusion method based on multi-modal detection information, which comprises the following steps: firstly, acquiring multi-source heterogeneous sensing data and performing space-time unified pretreatment; judging the dynamic and static state according to the target position change; performing single-source double-time cross calibration on the static target, using the azimuth angle, the pitch angle and the sensor displacement of the adjacent two time points, obtaining the calibrated position through triangulation and confidence weighting, and performing cross-source association with type constraint; for the dynamic target, the correlation matrix is generated by using recursive trajectory similarity calculation and density hierarchical clustering; the association cluster is identified by depth-first search, and the robust fusion is performed by using the random sample consensus method, and high-precision target information is output. The application is suitable for multi-source heterogeneous detection information fusion of air / ground manned / unmanned detection platforms in land confrontation environment, and can generate high-confidence and anti-deception target intelligence to support command decision.

The application relates to the technical field of intelligent traffic perception, in particular to a traffic multi-target correlation method based on spatial grouping and feature matching. First, a spatial position grouping algorithm is used to pre-classify detection targets, a dynamic grouping model is established based on the spatial density distribution and grouping relationship of the targets, and the target search space is effectively reduced; then, multi-dimensional correlation metrics are calculated, a correlation matrix is constructed, the motion consistency and feature similarity of the targets are comprehensively evaluated, the optimal matching result of all the matching successful track records in the same group is recorded, the matching result is output in a delay mode, when more than 50% of the targets in the same group are all matched successfully or the target matching times exceed 10 times, the point record with the most matching times in the track is output as the current matching result. The traffic multi-target correlation method based on spatial position grouping and feature matching can significantly improve the accuracy and real-time performance of multi-target correlation by fusing the spatial distribution characteristics, motion features and delay output of the targets.

The invention discloses a correlation analysis method for multi-source defect data of a semiconductor wafer, and relates to the technical field of wafer defect detection in semiconductor manufacturing. Aiming at the pain points of non-uniform types of multi-source defect data, difficulty in cross-parameter association, improper processing of repeated records and single similarity algorithm in the prior art, the method disclosed by the invention realizes uniform adaptation of the multi-type defect data, complete retention of repeated data, differential algorithm configuration, cross-batch parameter adaptation and deep association of a time sequence and a process; according to the method, a standardized report containing process optimization suggestions is generated through template loading, dynamic parameter adaptive calibration, multi-dimensional feature weighted fusion, configurable spatial similarity algorithm, cross-batch time sequence correlation analysis, defect-process parameter mapping correlation key technologies, output numerical values, space and comprehensive correlation coefficients; the method is high in compatibility, complete in analysis dimension and high in decision value, can be seamlessly integrated to an existing wafer quality control system, and provides comprehensive support for optimization of a semiconductor manufacturing process.

The present application provides a test method, device, medium and program product for battery storage gas production, which relates to the field of battery performance testing technology. The method includes: receiving the oil bath temperature setting information from the user end to heat the silicone oil in the oil bath pot, and then receiving test parameter data including sampling time. Then, after determining the association between the target battery to be tested and the oil bath pot and the balance in a preset manner, receive a command to start the test. Then control the balance to sample weight data according to the sampling time, determine the volume change of the battery to be tested based on multiple weight data, and determine the test results in combination with the corresponding information of the oil bath pot. Finally, the test results are stored according to the data storage path and sent to the user end for display, providing an accurate, convenient, efficient and traceable solution for battery gas production testing.

This application discloses a hierarchical adaptive intelligent association and alignment method for multimodal flight data, comprising: acquiring multi-source airborne flight data and preprocessing it; mapping data of different modes in the preprocessed multi-source airborne flight data to a common feature space to obtain cross-modal alignment features, wherein an aircraft physical dynamic model is introduced as a constraint during the mapping process; performing local precise association and global semantic association in parallel based on the cross-modal alignment features; wherein the local precise association is used to obtain pixel-level or point-level matching results, and the global semantic association is used to obtain globally consistent semantic association results; and fusing the results of the local precise association and the global semantic association to obtain association information of corresponding granularity. This invention effectively solves the "data silo" problem caused by differences in format, spatiotemporal reference, and modality of multi-source flight data, significantly improving association accuracy, robustness, and system adaptability.

The present invention discloses a cross-device association method based on mobile smart terminal sensor data fusion analysis, which includes the following steps: collecting multiple sensor data of two types of devices as model training data; performing pre-processing operations such as splitting, equalization, and normalization on the data; designing a neural network model. Since the present invention needs to compare the similarity of sensor data collected by the two types of devices, a twin neural network is used as a discriminant model, and the cosine distance is used to calculate the similarity of the output vectors of the two convolutional neural networks, and the loss function is further calculated based on the similarity; the designed twin neural network is used to train the corresponding model for each scenario; a similarity threshold is set, and if the threshold is exceeded, the data matching is successful, otherwise it fails, thereby realizing cross-device association. The method of the present invention is the first method for associating multiple mobile smart terminal devices of a user, and the amount of user data collected is small, the association speed is fast, and the association accuracy is high.

A bioinformation processing device includes a signal reception unit configured to receive a signal related to bioinformation reflected from at least one person to be measured, a candidate region identification unit configured to calculate an arrival direction of the signal and / or a distance to the at least one person to be measured from the signal received and identify a candidate region of the at least one person to be measured using the arrival direction and / or the distance calculated, an information generation unit configured to generate bioinformation corresponding to the candidate region of the at least one person to be measured from the signal received, a position information acquisition unit configured to acquire position information of the at least one person to be measured, and a bioinformation association unit configured to associate the at least one person to be measured with the bioinformation generated, based on the position information acquired.

An embodiment of the present application provides a method, apparatus, device and storage medium for associating a human body with a human hand, wherein the method comprises: acquiring an image to be detected whose content includes a human body and a human hand; determining a human body detection frame of the human body and a hand detection frame of the human hand in the image to be detected; determining an association probability between the human body detection frame and the hand detection frame; determining an external frame of the human body detection frame and the hand detection frame; and determining a degree of association between the human body and the human hand in the external frame based on key points of the hand in the external frame and the association probability.

This invention belongs to the field of large-scale power language models and discloses a method and related apparatus for recommending test cases for large-scale power models. It constructs state data including attack success rate and recommendation latency, utilizes reinforcement learning pre-trained depth-determining models to adaptively select the current knowledge graph exploration depth, dynamically adjusts the knowledge graph propagation range based on historical recommendation results, and employs a relation-aware attention mechanism in recursive propagation to weighted aggregate neighbor tail entities. Entity features under different relations are projected onto the corresponding relation space for semantic matching. A bidirectional interactive aggregator is combined to fuse its own features with neighboring features through addition and multiplication, enhancing feature representation capabilities. A layered aggregation mechanism concatenates features from various depths into a fusion vector and sorts them by inner product to select test cases, enabling the recommendation results to incorporate hierarchical semantic information from multi-hop propagation. This achieves synergistic optimization of recommendation accuracy and response speed, effectively supporting rapid evaluation of the security performance of large-scale power models.

The present invention relates to the technical field of circuit board welding, and more specifically, to a flexible fixed LED street lamp circuit board welding device and method. It is inflated after the circuit board is in place, and the following process is formed: a circuit board with a larger area is squeezed, causing the distance of the blocking piece to change. At this time, after inflation, the change in the distance of the blocking piece causes a change in the inflation time, that is, the larger the area of ​​the circuit board, the longer its inflation time. At this time, the length of time for squeezing the squeezing block will not cause a change in the inflation time, thereby solving the problem that it is difficult for staff to grasp the force of pressing down the circuit board due to the influence of this variable, so that the expansion size of the airbag is only positively correlated with the size of the circuit board area. In the present invention, the gas source starts to supply gas to the gas pipeline after the blocking piece is opened to the maximum extent.

This invention proposes a method and apparatus for maintaining a space target catalog database by fusing post-hoc multi-source observation data, belonging to the field of space situational awareness. The method includes: acquiring and preprocessing post-hoc observation data of the observed space target; calculating observation arc segments using the preprocessed observation data, using the obtained initial orbit determination results as the orbital features of the observation arc segments, and then matching the observation arc segments with the orbital data of targets in the catalog database, using the successfully matched observation arc segments as the associated arc segments of the target; fusing multiple associated arc segments of the target for precise orbit determination, and updating and maintaining the catalog database using the results of the precise orbit determination. This invention can quickly preprocess the input multi-source observation data and complete the association matching between the data and space targets, and finally fuse the multi-source observation data for precise orbit determination, ensuring computational efficiency and process integrity, and realizing the maintenance of the space target catalog database.