20 results about "Trajectory compression" patented technology

The invention discloses a public security data collaborative analysis method based on differential privacy and a block chain, and the method comprises the following steps: S1, constructing a disturbance configuration table by a first collaborative party, and configuring a disturbance trajectory compression Laplacian mechanism for trajectory data; s2, a second collaboration party formulates an analysis task request and determines a data type; s3, the third collaboration party writes the disturbance configuration table and the task request into the block chain through the task declaration smart contract; s4, each data owner performs differential disturbance on local data according to the disturbance configuration table, and writes the local data into the block chain through a data declaration smart contract; s5, the second collaboration party collects disturbance data and constructs a joint model, and submits a training intermediate result abstract to the block chain; and S6, the second collaboration party sends an encryption result to the first collaboration party after completing joint model training, and a third collaboration party records a result abstract and supports initiation of an on-chain correction request. According to the invention, differential privacy and block chain technologies are combined, and a cooperative analysis method oriented to public security data is provided.

The invention relates to the technical field of medical data processing, in particular to a multi-modal body feeling information processing method and system for physiological state assessment, and the method comprises the steps: collecting multi-modal parameters; constructing a consistency feature; carrying out individual normal behavior zone modeling; compressing and generating a consistent track; extracting deviation path features; carrying out similar matching on physiological trajectories; state type and stage judgment; carrying out self-adaptive correction on the behavior belt; and outputting a health intervention prompt. According to the method, the multi-source physiological signals are introduced at the same time, the time sequence cooperation relation of different types of parameters is utilized, and the cross-modal consistency characteristic matrix is constructed, so that the evaluation process does not depend on isolated change of a single signal any more, and comprehensive judgment is carried out based on linkage evolution of multi-dimensional information; the problems that due to the fact that a single physiological signal and a static evaluation benchmark are relied on, physiological state evaluation is prone to being influenced by individual differences and long-term state changes, and misjudgment and drifting are generated are effectively solved.

The application relates to a trajectory data compression and decompression method, device, equipment and medium, and is applied to the field of maps. The method comprises the following steps: acquiring a trajectory point representing an object trajectory; matching the trajectory point to a road network of an electronic map to obtain at least one object trajectory section; the object trajectory section is a section passed by the object in the road network; performing thinning processing on the at least one object trajectory section according to section characteristic information corresponding to each object trajectory section to obtain a key trajectory section; the key trajectory section is a part of the object trajectory section and is used for restoring the object trajectory; and the key trajectory section is compressed and stored to obtain trajectory compression data. The method can save storage resources.

The present invention discloses a method for compressing big data of mobile object trajectories oriented to indoor scene semantics, including: obtaining indoor scene data, modeling important spatial entity elements in the scenes within a floor and across floors to obtain topological components of indoor scene elements; modeling indoor pedestrian sub-trajectories and indoor geographical space elements in sequence, respectively establishing local overlapping semantic models for sub-trajectory - local corridor elements and sub-trajectory - regions to obtain their semantic relationships; introducing an encoding method to obtain overlapping semantic information between sub-trajectory tuples and indoor local corridors and regions; reconstructing the sub-trajectory semantics into semantic trajectories for different indoor geographical space elements, while compressing and segmenting the original trajectories, performing a direct spatial connection between the trajectory data and indoor scene elements to enhance the expression ability of the trajectories.

The embodiment of the application provides a trajectory data processing method and device, system and storage medium, and relates to the technical field of data processing. The original trajectory point sequence is divided into multiple trajectory subsegments with overlapping areas, and independent trajectory compression processing is performed on each subsegment in parallel based on a global tolerance threshold, so that the serial computing task of a long trajectory is converted into multiple subtasks which can be processed simultaneously, multi-core computing resources are fully utilized, and the computing load is dispersed, thereby significantly shortening the overall processing time. Subsequently, the key points of adjacent subsegments in the overlapping area are corrected for consistency, boundary inconsistency and redundancy caused by parallel processing are eliminated, and the geometric continuity of the compressed trajectory is ensured. Finally, the corrected key points are spliced in sequence to obtain a complete compression result. The mode of point-by-point serial compression is changed, the throughput and efficiency of large-scale trajectory data processing are effectively improved, and the demand for high-concurrency real-time processing is better met.

The invention provides a ship trajectory compression method based on shape and space-time motion characteristics. The ship trajectory compression method comprises the following steps: cleaning and repairing original AIS data; performing compression processing on the shape data of each track by using a DP algorithm; performing compression processing on the spatio-temporal data of each track by utilizing a TS (Transport Stream) algorithm; carrying out compression processing on the navigational speed data of the ship track by utilizing an SAC algorithm; performing compression processing on the course of the ship in the sailing process by using an HAC algorithm; combining the compression results after the compression processing by using an SSMC algorithm to obtain a final compression result; and selecting an adaptive threshold, and verifying and evaluating a final compression result. According to the method, adaptive determination of SSMC algorithm parameters is realized according to the compression rate and the length loss rate, the compressed trajectory retains a complete trajectory shape and rich navigational speed and course change trends, the smaller length loss rate and the minimum distance between the trajectories can be obtained, and better trajectory reconstruction capability is shown under the higher compression rate.

The invention discloses a vehicle track data compression method, device and equipment and a storage medium, and relates to the technical field of vehicles, and the method comprises the steps: carrying out the working condition division of to-be-compressed track data according to a mean value clustering strategy and a working condition division parameter, and obtaining the vehicle track data corresponding to a target working condition type; optimizing a compression threshold value corresponding to the target working condition type according to a Bayesian optimization strategy to obtain a track compression threshold value; and performing data compression on the vehicle track data corresponding to the target working condition type according to a preset compression strategy and a track compression threshold. The method comprises the following steps: clustering trajectory data, dividing roads into four working condition types of highways, national roads, urban areas and village and town roads, adopting a Bayesian optimization algorithm for different categories, selecting an average error and a compression ratio to measure a compression effect, and iteratively searching an optimal threshold value of the category in a parameter space. And then the trajectory data of each category is compressed based on the optimal threshold, so that the consumption of operation resources is reduced.

The application discloses a trajectory similarity analysis method based on trajectory compression and clustering. Firstly, N reference trajectories are compressed, and some trajectory data is removed within a certain error range, so as to facilitate subsequent processing and analysis. Secondly, the range of trajectory segments participating in calculation is reduced by intercepting common trajectories and angle pre-judgment, and the reference trajectory data is clustered offline. When calculating the similarity matrix, the similarity of each two trajectories only needs to be calculated once within the error range to reduce the complexity. Finally, the representative trajectory of each cluster is found, the similarity of the to-be-matched trajectory and the representative trajectory of each cluster is calculated, and whether the to-be-matched trajectory exists in the reference trajectory database is judged. If the to-be-matched trajectory exists in the reference trajectory database, the trajectory similarity of the to-be-matched trajectory and each reference trajectory of the corresponding cluster is calculated, so that it is determined that the actual trajectory is similar to which reference trajectory, and a similarity result is obtained.

The application discloses a trajectory compression method. In view of the deficiency of the prior art, the solution idea of the application is that, based on the global optimization idea, the feature trajectory sub-chain obtained by simplifying and compressing a line segment is globally optimized, and multiple influence factors such as an offset angle, a perpendicular Euclidean distance, a trajectory segmentation number and the like are comprehensively considered, an evaluation index capable of comprehensively reflecting space and time characteristics of a compression result is established, and therefore the contradiction between compression efficiency and effective information reservation and between the original trajectory and the possibility of being as similar as possible is well balanced. The application also discloses a trajectory compression device. Compared with the prior art, the application can realize offline compression of all trajectories containing more effective information and having higher accuracy, and the overall operation accuracy and efficiency of trajectory compression is improved.

The invention provides a vehicle track compression method and device, a vehicle and a storage medium, and the method relates to the field of data compression, and comprises the steps: obtaining the feature information of each track point in a plurality of track points of a vehicle driving track, the feature information comprising first feature information and second feature information; determining a first track point of which the first feature information meets a first preset condition in the plurality of track points, wherein the first track point represents a reserved track point and the driving track is not segmented at the first track point; determining a second track point of which the second feature information meets a second preset condition in the plurality of track points, wherein the second track point represents a reserved track point and the driving track is segmented at the second track point; and compressing the driving track based on the first track point and the second track point to obtain a compressed track. According to the method, when the driving track is compressed, mutual influence between track characteristics can be avoided, and the track compression effect is improved.

The present invention discloses a method, apparatus, device, and computer-readable medium for trajectory compression, and relates to the field of computer technology. A specific implementation of the method includes: among the trajectory points of a driving trajectory, screening out the retained trajectory points of the driving trajectory according to the degree of deviation of the trajectory points from the road; determining the retained trajectory points as key trajectory points based on the driving duration of the retained trajectory points, where the driving duration is the time interval from the previous trajectory point of the retained trajectory point to the next trajectory point of the retained trajectory point; and adding the starting point of the driving trajectory, the key trajectory points, and the ending point of the driving trajectory to the compressed trajectory to compress the driving trajectory. This implementation can reduce the time complexity of compressed trajectory data, thereby reducing compression time.

The invention provides a machine learning adaptive trajectory compression method fusing traffic mode coding. The method comprises the following steps: S1, preprocessing original trajectory data; s2, respectively outputting a synchronous Euclidean distance SED threshold value and an angle threshold value by using a single XGBoost model, and adaptively adjusting compression parameters through geometrical characteristics and motion characteristics of a track; s3, taking the synchronous Euclidean distance SED and the angular deviation as compression criteria, and adopting a sliding window algorithm to detect key points; s4, optimizing a compression result by using a post-processing technology, marking missing high-error points as key points and inserting the key points into a compression track by checking the maximum deviation between the key points for the second time so as to balance the compression rate and precision; and S5, outputting a result of the compressed trajectory through a quality evaluation module, calculating a compression ratio, a position error and a direction consistency index, and ensuring the geometric and motion feature integrity of the trajectory. According to the invention, the balance between high compression ratio and high fidelity is realized.

The present invention discloses a method for detecting abnormal behaviors of ships based on distance measurement and isolation mechanism. This method analyzes based on ship AIS data. First, the AIS data is preprocessed and trajectory compressed. Then, a position information model of the ship is extracted from the trajectory clusters after multi-dimensional density clustering. By comparing the difference between the ship trajectory points and the ship position information model, real-time abnormal detection of the ship's position information is achieved. Next, the isolation forest algorithm is used to detect abnormal ship speeds, and a functional relationship between the speed weight and the score value for determining abnormal speeds is established. The abnormal speed values are detected and eliminated to obtain an accurate ship speed set. Finally, the speed to be detected is added to the speed set, and whether there is a speed abnormality is judged by calculating the abnormal score value of the speed to be detected. The present invention can improve the driving safety of ships at sea and timely detect and warn against abnormal behaviors of ships at sea.

This invention provides a trajectory compression method, apparatus, device, medium, and product, relating to the field of information technology. The method includes: acquiring trajectory data to be compressed; determining a trajectory point sequence based on the trajectory data; determining envelope feature points and turning feature points based on the trajectory point sequence; and determining compressed data of the trajectory data based on the envelope feature points and the turning feature points. This invention extracts envelope feature points and turning feature points from trajectory data. Since these feature points contain the geometric features of the trajectory data, efficient compression of the trajectory data can be achieved while ensuring that the data shape remains unchanged, effectively reducing the time complexity of the operation.

The application discloses a kind of heterogeneous ship multi-track subsection general feature extraction method, the heterogeneous ship is the multiple ships of different ship type C and ship length L, the method comprises the following steps: determining AIS data acquisition sea area;Classify heterogeneous ship;General subsection is carried out to multi-track;Extract general subsection track feature;In turn select the track in track set, according to MMSI select the general subsection of belonging ship classification and its general subsection track feature.The application can simultaneously process multiple tracks of different ships, and can be used for trajectory compression or trajectory feature extraction.The application determines track feature points by scoring, ensuring the effectiveness of track feature extraction.In addition, other tracks can also be directly applied to the application, ensuring the generality of the track feature extraction algorithm.Compared with traditional feature extraction algorithms, the iteration and calculation time are reduced.

The application discloses a spatiotemporal semantic constraint trajectory data compression method, which comprises the following steps: S1, setting a time-synchronized Euclidean distance SED threshold and a trajectory point semantic similarity threshold; S2, acquiring a trajectory T={P1, P2, L, P n} and an approximate trajectory simtr={P1, P n}; S3, compressing the trajectory based on a trajectory compression criterion; and S4, outputting a compressed trajectory. The application solves the technical problem that a trajectory compression result lacks interpretability in a conventional trajectory data compression method, and can be used for spatiotemporal trajectory data semantic representation and deep data mining, such as semantic information mining in human activity trajectory big data, hurricane activity feature mining, and animal migration rule and habit analysis.

The invention discloses a radar trajectory simulation method and system based on interactive editing and modal compression, and relates to the field of radar trajectory simulation. According to the method, the technical threshold of trajectory design is remarkably reduced through user interactive trajectory construction, and non-technical personnel can also efficiently generate simulation tasks. And the construction and management efficiency of the multi-target trajectory is greatly improved through a trajectory template multiplexing mechanism. Particularly, a trajectory compression algorithm based on multi-feature keeping still retains turning, speed change and other key flight features while reducing trajectory data points, ensures continuity and authenticity of a playback trajectory, can control a trajectory compression ratio within 10: 1, and effectively improves simulator performance and experience.

The present application relates to the technical field of medical data processing, and particularly relates to a multi-modal somatosensory information processing method and system for physiological state evaluation, which comprises: multi-modal parameter acquisition; consistency feature construction; individual normal behavior band modeling; consistency trajectory compression generation; deviation path feature extraction; physiological trajectory similarity matching; state type and stage determination; behavior band adaptive correction; and health intervention prompt output. By simultaneously introducing multi-source physiological signals, using the time sequence collaborative relationship of different types of parameters, and constructing a cross-modal consistency feature matrix, the evaluation process is no longer dependent on the isolated changes of a single signal, but is based on the comprehensive determination of the linked evolution of multi-dimensional information, effectively solving the problem that the physiological state evaluation is easily affected by individual differences and long-term state changes, resulting in misjudgment and drift due to the dependence on a single physiological signal and static evaluation benchmark.

The invention provides a double-base-station gantry crane positioning method and system based on event records, and relates to the technical field of industrial equipment positioning and navigation. According to the method, the distance measurement values of the two base stations in the same straight line direction are received, a state estimation algorithm including speed entropy decision, longitudinal check and noise self-adaptive adjustment is adopted, more reliable observation values can be intelligently recognized and screened out, motion state changes such as equipment starting and stopping and speed changing can be dynamically adapted, and the method is more reliable. And the direct influence of the non-line-of-sight error and the abnormal jump on the positioning result is obviously inhibited. According to the method, state judgment and track compression based on event recognition are combined, a more accurate real-time position is output, and the movement continuity and reasonability of a positioning result are ensured. According to the method, the positioning track of the gantry crane is smoother and more stable and conforms to the actual motion law, the overall precision and reliability of a positioning system are remarkably improved, and the problem that large hoisting equipment such as the gantry crane is inaccurate in equipment positioning is solved.

This invention discloses a trajectory compression method combining dynamic windows and the Newton-Raphson iteration method. An initial dynamic window size is set, and each sampling point within the dynamic window is treated as a whole input to generate a Bézier curve algorithm. After reading a new sampling point, the Bézier curve within the current dynamic window is calculated. It is then determined whether the number of sampling points exceeds the size of the dynamic window. If the number of sampling points is greater than or equal to the size of the dynamic window, a new dynamic window is generated; otherwise, the new sampling point is processed. The new dynamic window is generated while ensuring that the starting point of the new dynamic window maintains first-order continuity with the ending point of the previous dynamic window. It is then determined whether there are new sampling points. If new sampling points exist, the process returns to step 2 to process them; otherwise, the path fitting calculation process ends. The resulting Bézier curve equation is the final output. This invention reduces the computational load required for curve compression while maintaining the first-order continuity of the entire curve.