49results about How to "Reduce redundant data" patented technology

The invention discloses an automatic labeling and acquisition standardization method and system based on video content, and the method comprises the steps: decoding a video file into a processable video frame sequence, carrying out the standardization processing, unifying the format and the coding standard, and adjusting the resolution and the frame rate; performing content analysis on the standardized video frame sequence, extracting key frames, identifying objects, scenes and actions, and generating a video analysis result and a preliminary label; according to a video content analysis result, separating an audio stream from the video to carry out noise reduction processing and voice-to-text analysis, and combining the processed text with the video content analysis result to generate multi-modal comprehensive annotation information; based on the comprehensive labeling information, taking feedback of the user in video retrieval, label clicking and watching behaviors as an adjustment basis of label weights, and updating weight distribution of labels by analyzing user behavior data; labeling information and weight distribution are integrated into an index database, and indexes are updated in real time according to user behaviors and content changes.

The invention discloses a human face sensitive data protection method based on an elastic anti-interference privacy protection mechanism, belongs to the field of information processing, and provides a dynamic self-adaptive sampling strategy based on information entropy in a data preprocessing stage, and the sampling rate is adjusted according to the distribution characteristics of a human face data set and the weight of sensitive information. And the model performance is maximized on the premise of ensuring user privacy. In order to obtain a better privacy protection effect, an entropy adjustment differential privacy mechanism is designed, and Gaussian noise of different degrees is adaptively added according to an information entropy value of face data after a face data set is dynamically and adaptively sampled, so that refined privacy protection of face sensitive data under differential privacy constraint is realized. In a model training stage, an elastic anti-interference training method is provided, diversified adversarial samples are dynamically generated in different stages of model training, and the over-fitting risk of the model is reduced while the robustness of the model to various types of adversarial attacks is enhanced.

The invention discloses a three-dimensional reconstruction method and system applied to 3D vision, two-dimensional image data used for three-dimensional reconstruction are obtained through shooting of a camera with multiple visual angles, more comprehensive scene information can be provided through the multi-angle image data, the accuracy and integrity of subsequent three-dimensional reconstruction are facilitated, and meanwhile, the three-dimensional reconstruction method and system applied to 3D vision can be applied to 3D reconstruction. The feature points are key information points in the images, association between the images of different view angles can be established through matching of the feature points, the accuracy of three-dimensional reconstruction is improved, in addition, structural information of a three-dimensional space can be effectively recovered from the images of multiple view angles according to the multi-view geometric principle, dense point cloud data are generated, and the accuracy of three-dimensional reconstruction is improved. The point cloud data is an important basis for subsequent three-dimensional reconstruction, the overall quality of the point cloud data can be improved by optimizing the preliminarily recovered point cloud data, and in addition, a three-dimensional model can be presented to a user in a visual and vivid manner through visual operation, so that the user can conveniently observe, analyze and interact.

The invention belongs to the technical field of road disease detection, and particularly relates to a visual image and radar image fused road disease detection method, which comprises the following steps: S1, a camera and a laser radar collect road data; s2, preprocessing the collected data of the laser radar; s3, fusing the image information and the radar information; s4, redundant results are removed; and S5, uploading the road disease information. Redundant data in a radar image are effectively reduced by optimizing a laser radar data processing flow, so that the storage pressure of mobile equipment is relieved, and by means of an innovative mode of deep fusion of visual image data and laser radar point cloud data, not only can the road disease type be accurately identified, but also the depth information of the disease can be acquired, and the accuracy of the road disease is improved. And the accuracy and efficiency of road disease detection are greatly improved.

The invention discloses a mixed sampling network traffic sample balancing method, which comprises the following steps of: firstly, carrying out data preprocessing on network traffic in a CIC IDS-2017 data set, and dividing the network traffic into a training set and a test set; secondly, the training set is subjected to SMOTE oversampling and ENN undersampling processing, Gaussian noise processing is introduced after SMOTE oversampling, and a mixed sampling sample is obtained; and finally, carrying out CNN training on the mixed sampling sample and verifying a training result through a test set to obtain a classification result. According to the method, Gaussian noise is added after SMOTE processing, so that the complexity of data is increased, the generated sample can better reflect various characteristics in real network traffic, and the risk of overfitting is remarkably reduced. According to the method, most of important information is reserved in the ENN, and unnecessary redundant data is reduced, so that the problem of information loss is avoided, and the adaptability of the model to complex network traffic data is improved. And the function of balancing the network flow data is realized.

The invention discloses a spatial data version processing method and device based on Postgres and a readable medium, and the method comprises the steps: importing vector data of a spatial layer into a Postgres database, and generating a layer data table; creating an extended version management table and a branch extension table corresponding to the extended version management table; creating a current user, creating a corresponding local architecture and a local version management table, updating the local version management table and creating a difference table corresponding to the branch extension table, a local version view and a view trigger if the vector data of the space layer is migrated to the local architecture corresponding to the current user; if the current user edits the vector data of the space layer in the local architecture or the branch extension table is updated, and no conflict exists or the conflict is solved, updating the version number of the corresponding layer in the local version management table, and submitting version record information corresponding to the edited content of the current user to the branch extension table; universal version management functions can be realized.

The invention discloses a geographic information data acquisition vehicle, relates to the technical field of geographic information data acquisition vehicles, and aims to solve the problem that data acquisition is incomplete when a data acquisition vehicle performs geographic information acquisition. According to the method, data from different sensors are fused by using a particle filtering method, advantages of various sensors can be comprehensively considered, uncertainty and errors of single sensor data are reduced, scene model construction is performed on fused feature information by using a neural network model algorithm, and the accuracy of scene modeling is improved. The standard geographic data model of the data acquisition area can be quickly and accurately generated. The system can reflect the change of an actual scene in real time, can adapt to various complex environments and scenes, and combines a satellite navigation sensor, a laser radar sensor, a camera, a millimeter wave radar and inertial navigation. Data including geographic positions, environment information, images and videos, dynamic states of target objects, body motion states and the like can be obtained.

The invention discloses a nonlinear multi-scale dynamic system control mechanism inference method based on a Koopman operator theory, and the method comprises the steps: building an order reduction mechanism model of a dynamic system from observed time series data based on the Koopman operator theory; firstly, dynamic sampling is carried out on observation time sequence data based on a self-adaptive sampling strategy designed by the method, the sampling rate is adjusted according to data changes, and information of different time scales is effectively separated. Thirdly, performing local data smoothing and numerical differentiation on the discrete sampling points by applying cubic B-spline interpolation; on the basis, a Block-Hankel matrix is innovatively constructed by using the nonlinear modeling capability of a Koopman operator theory so as to capture the multivariate dynamic characteristics of the system; and a main dynamic mode is extracted by using a kernel singular value decomposition technology, and system identification is expanded to a nonlinear power system. And finally, the SINDy algorithm is used for identifying the reduced-order model, and accurate modeling of the complex nonlinear multi-scale system is realized. According to the method, the advantages of Koopman embedding and sparse regression are fused on the basis of the multi-scale data processing capability of adaptive sampling, so that the method has relatively high robustness and universality, and has theoretical and practical significance for mechanism modeling of a nonlinear and multi-scale system.

The invention discloses a lossless compression and decompression method based on time series data, which is characterized in that a sampled numerical value, a timestamp and a quality code are written into a cache when one piece of data is sampled, and direct write-in operation on a time series database can be reduced through a cache mechanism, so that the pressure of the database is reduced, the response speed of a system is improved, and the cache is subjected to local processing; the preprocessing step may comprise operations such as data cleaning, deduplication and sorting, the operations are beneficial to reducing redundant data, improving compression efficiency and ensuring the quality of compressed data, the preprocessed data are compressed according to the sequence of ID, starting time, ending time, quantity, timestamp, quality code and numerical value, the data are compressed according to a specific sequence, and the compression efficiency is improved. And the decompression process is a process of recovering the compressed data to an original state, so that the problems of high read-write pressure and high storage pressure of an upper computer of a traditional wind power control system are solved.

The invention provides a data recording method and device, a storage medium and electronic equipment, and the method comprises the steps: continuously recording and caching a group of data frames of a specified data stream; continuously monitoring each specified data stream to judge whether a preset triggering condition corresponding to each specified data stream is met or not based on the data frame of each specified data stream; the preset trigger condition corresponding to each specified data stream indicates that each specified data stream contains an abnormal data event; under the condition that a preset triggering condition corresponding to a target data stream is determined to be satisfied based on a first data frame of the target data stream in the group of specified data streams, determining a target time interval in which the target data stream is about to fall into a disk; the data frames of the target data stream in the target time interval are extracted from the cached data frames of the set of specified data streams, the to-be-stored data are obtained, the to-be-stored data are stored in the database, and the problem that the IO resource overhead cannot be reduced while the data integrity cannot be guaranteed in the prior art is solved.

The present application relates to coke oven operation control technical field, especially to a kind of applied to electric heating coke oven expansion pressure detection method, comprising: based on the coal material state change in the historical coking cycle of electric heating coke oven division coking stage, and set reference collection frequency for each coking stage;For each coking stage, based on reference collection frequency, real-time data acquisition is carried out, and expansion pressure detection data are obtained;Expansion pressure detection data are calculated for pressure anomaly, and expansion pressure change rate and expansion pressure distribution characteristics are obtained;And according to this, reference collection frequency is revised, and the dynamic collection frequency corresponding to the coking stage is obtained;In response to at least one of expansion pressure detection data, change rate, distribution characteristics and dynamic collection frequency exceeds corresponding preset warning threshold, expansion pressure detection alarm information is generated.It can make collection frequency dynamic adjustment with coal material expansion characteristics, ensure the timeliness of pressure peak capture, reduce redundant data in stable stage.

The invention discloses a data file transmission method and device, a storage medium and a program product, and relates to the technical field of data processing.The data file transmission method comprises the steps that a data file and file configuration information of the data file are obtained, and a data conversion task of the data file is generated according to the file configuration information; performing data conversion on the data file according to the data conversion task to obtain a conversion file; and according to the file configuration information, sending a ready notification of the conversion file to a receiver, so that the receiver obtains the conversion file according to the ready notification. According to the method, data conversion is preposed, and data conversion and file transmission are processed in parallel, so that the data file transmission efficiency is improved.

The invention discloses a three-dimensional terrain reconstruction method based on an elevation image, electronic equipment and a storage medium. The method comprises the following steps: (1) loading data; (2) edge detection; (3) concurrent mapping; and (4) performing three-dimensional reconstruction. According to the method, the edge detection technology is adopted, different areas on the topographic map are divided, adaptive down-sampling is carried out according to the categories of the different areas, and the elevation values of the sampling points are converted into height information, so that the elevation image of the terrain is reconstructed into a visual three-dimensional image; on the premise that the visualization effect is maintained to be equivalent to that of the existing elevation image, a large amount of redundant data is reduced, so that the time overhead is greatly reduced.

The invention discloses a 3D printing real model rendering method based on variable granularity, and belongs to the technical field of 3D rendering. The method comprises the steps that 3D printing parameters and the number of layers of a printed object are obtained, and the 3D printing parameters comprise 3D printing head parameters and 3D printer parameters; carrying out single-layer modeling on a printed object on the basis of 3D printing parameters and an interval adjusting strategy; based on a multi-layer fusion strategy, surface elements of each layer of the printed object are obtained; obtaining a body element set of the printed object based on the surface element of each layer of the printed object and the same region principle; based on the body element set of the printed object, constructing a 3D model library; based on the 3D model library and the secondary rendering strategy, the real model of the printed object is rendered, and the method improves the speed and authenticity of 3D rendering.

The invention discloses a calibration method and device for a salient pole type permanent magnet synchronous motor. The calibration method comprises the following steps: determining a plurality of to-be-calibrated rotating speeds of a to-be-calibrated motor; determining a calibration table corresponding to the rotating speed to be calibrated, wherein the calibration table comprises a calibration starting point; and according to the calibration sequence and the calibration table of the plurality of to-be-calibrated rotating speeds, calibrating the to-be-calibrated rotating speeds in sequence, and determining an optimal calibration result. Compared with the prior art, the problems of low calibration efficiency, long calibration time and more redundant data in the traditional motor calibration process are solved.

The invention provides a document synchronization method and device, a storage medium and electronic equipment. The method comprises the steps of obtaining first document content of a to-be-synchronized target local document, and obtaining a document content version of a target cloud document corresponding to the target local document in current cloud storage; second document content is determined based on the document content version, and a first document change operation set is determined according to the difference between the first document content and the second document content; generating a first document synchronization instruction according to the first document change operation set; and sending the first document synchronization instruction to the target server, so that the target server updates the content of the target cloud document based on the first document synchronization instruction. Only the change data is transmitted to the server, redundant data and meaningless time consumption generated by full-amount synchronization are reduced, the change operation is re-determined based on the document content indicated by the cloud document version, data loss or coverage errors of the cloud document are avoided, and data consistency and integrity in the data synchronization process are improved.

The invention relates to the technical field of artificial intelligence image recognition and data mining, and discloses an image data mining processing system and method combining a visual large model and a visual small model. The method aims at solving the problems that in the prior art, the image recognition recall rate and accuracy are difficult to consider, the calculation cost and the processing speed are unbalanced, the data management efficiency is low, and the model generalization ability is insufficient. The system comprises a data input module, a frame extraction deduplication module, a picture library construction module, a visual large model judgment module, a visual small model judgment module, a label preprocessing module, a manual inspection module, a training data generation module, a model training module and a data security module. According to the method, the generalization ability is guaranteed through preliminary screening of an LPM (Deic) large model, missing detection is complemented through secondary verification in cooperation with a YOLO small model, false alarms are eliminated in combination with manual verification, meanwhile, double models are optimized through targeted sample training, the pain point of missing detection or misjudgment of a single model is effectively solved, and accurate and comprehensive consideration of target recognition is achieved.

The invention relates to the technical field of data processing, in particular to a multi-dimensional automobile accessory big data processing method and system based on a vertical class model, and the method comprises the steps: obtaining multi-dimensional data information of automobile accessories at regular time; according to key text information in the multi-dimensional data information, constructing a text feature vector of the automobile accessory; determining a first similarity between each attribute of each automobile accessory and the automobile accessory with the same name; obtaining the attribute importance degree of each attribute of each automobile accessory; based on a vertical class model, evaluating the similarity degree of the text feature vectors between any two automobile accessories obtained in a single time, and classifying the automobile accessories; and determining a first similarity between each attribute of any automobile accessory in the same kind of automobile accessories and the remaining automobile accessories, and calculating the automobile accessory similarity between the any automobile accessory and the remaining automobile accessories by combining the attribute importance of each attribute of the any automobile accessory so as to perform duplicate removal processing on the same kind of automobile accessories. Therefore, the duplicate removal effect of the automobile accessory data is improved.

The invention relates to the field of water resource management, in particular to a method for establishing an electricity-to-water coefficient prediction model, which comprises the following steps: step S100, acquiring an electricity-to-water coefficient, water consumption and an influence factor data set of the electricity-to-water coefficient obtained by field test; step S200, performing correlation analysis on the electricity-to-water coefficient and the influence factor thereof by using a Pearson correlation analysis method to obtain a high-correlation influence factor data set; and step S300, preprocessing the high-correlation impact factor data set, and then inputting the high-correlation impact factor data set into a convolutional neural network prediction model for training to obtain an electricity-to-water coefficient prediction model with relatively high prediction precision. According to the method, the Pearson correlation analysis and the convolutional neural network are combined, the prediction precision of the electricity-to-water coefficient can be remarkably improved, the method has high generalization ability, high prediction accuracy can be kept in different regions and under different conditions, the prediction efficiency is greatly improved, and the method is suitable for popularization and application. The prediction model provided by the invention can efficiently and accurately predict the coefficient of water conversion by electricity.

The invention discloses a self-adaptive image transmission planning method and system based on image content and downlink transmission bandwidth, and belongs to the technical field of image processing and task planning. The method comprises the following steps: obtaining the size and resolution of an original image; a region-of-interest image and a non-region-of-interest image are generated by extracting a region-of-interest in an original image, and the position of the region-of-interest in the original image is acquired; wherein the resolution of the region-of-interest image is the resolution of the original image, and the resolution of the non-region-of-interest image is generated based on a downlink transmission bandwidth; and transmitting the region-of-interest image, the non-region-of-interest image, the size of the original image and the position of the region-of-interest in the original image to a second device via a downlink, so that the second device recovers the original image. According to the invention, the transmission pressure of the ground station can be effectively reduced, and redundant data are significantly reduced.

The application discloses an Ethernet message transmission method and device, electronic equipment and a storage medium, and relates to the technical field of Ethernet. The specific implementation scheme is as follows: in a plurality of pre-stored transmission messages, at least two transmission messages with the same content are subjected to redundant deletion, and a target message is reserved; the target message is subjected to redundant identification deletion processing, and a processed target message is obtained; and the processed target message is transmitted to a receiving port, so that the target message is transmitted to a data receiving party through the receiving port. The embodiment of the application can realize Ethernet transmission of messages to a plurality of objects, realize message transmission in a multicast or broadcast scenario, and thus improve the message transmission efficiency.

The invention relates to a distributed optical fiber bridge foundation deformation monitoring system and a construction and monitoring method, the distributed optical fiber bridge foundation deformation monitoring system comprises a top optical fiber tube unit, a middle optical fiber tube unit and a bottom optical fiber tube unit, and a connecting optical fiber and a free optical fiber penetrate out of the top of the top optical fiber unit and are respectively connected with a BOTDA (Brillouin Optical Time Domain Analysis) demodulator. A bridge foundation deformation monitoring system is pre-buried by drilling holes in the ground of a bridge foundation area, millimeter-level-precision pile foundation deformation continuous monitoring is achieved through parameter changes, geometric transformation and mechanical transformation of optical fiber signals, and data are stable and reliable; the monitoring system in an optical fiber form is buried in a soil body, so that the pile foundation structure can be prevented from being damaged; meanwhile, the method is suitable for a newly-built bridge and an existing bridge, supports the adjustment of the monitoring range as required in an optical fiber laying area, reduces redundant data, and effectively improves the monitoring efficiency.

The present invention provides a method for collecting and processing micro-blood flow of coronary arteries based on ultrasound, comprising the following steps: S1, obtaining a demodulated baseband channel data set; S2, obtaining a tissue signal and a blood flow signal; S3, executing a third processing path on the tissue signal, and simultaneously obtaining myocardial motion estimation information and a spectral Doppler image; S4, inputting the myocardial motion estimation information into a first processing path and a second processing path respectively as a feedback mechanism; S5, executing the first processing path on the tissue signal, and obtaining a grayscale tissue image in combination with the myocardial motion estimation information; S6, executing the second processing path on the blood flow signal, and obtaining a power Doppler image and a velocity Doppler image in combination with the myocardial motion estimation information; S7, outputting a grayscale tissue image, a power Doppler image, a velocity Doppler image and a spectral Doppler image as a final image. The present invention can realize high-sensitivity imaging of micro-vessels of coronary arteries without contrast agents.

The application discloses a bridge structure self-adaptive sampling monitoring method and device suitable for an extreme road environment, and belongs to the technical field of bridge structure health monitoring.The core target of the application is to solve the problems of repeated arrangement of existing monitoring equipment, road-bridge data fragmentation, and low self-adaptive sampling regulation precision.Through innovative design of a road-bridge multiplexing integrated sensing node, single-node multi-parameter and full-scene monitoring are realized, and the arrangement cost and construction difficulty are reduced; through construction of a self-adaptive sampling strategy based on fuzzy logic control, a feasible algorithm model is given, continuous dynamic regulation of sampling parameters is realized, monitoring precision and low power consumption are taken into account, deep collaborative linkage of road environment monitoring and bridge structure monitoring is realized, and the safe operation of the bridge structure under the extreme road environment is ensured.

The present invention provides a method, device and electronic device for monitoring road spike equipment abnormality, which relates to the field of monitoring technology, including: obtaining vibration detection information of road spike equipment; judging whether the road spike equipment is vibrating abnormally based on the vibration detection information of the road spike equipment; when the road spike equipment is vibrating abnormally, automatically starting the monitoring device within the preset range of the road spike equipment, monitoring the environment within the preset range of the road spike equipment, and sending an abnormal vibration reminder to the user. The present invention improves the accuracy and reliability of monitoring, and also realizes the intelligent processing and analysis of abnormal events.

A lead screw detection device disclosed by the present invention comprises a detection cabinet body, the detection cabinet body is provided with a control host, the control host is internally provided with a driving assembly, the control host is connected with a clamping mechanism driven by the driving assembly to rotate, and the clamping mechanism is used for clamping one end of a lead screw piece and driving the lead screw piece to rotate; a supporting column is fixed in the detection cabinet body, a horizontal adjusting mechanism and a pneumatic control assembly are arranged on the supporting column, the horizontal adjusting mechanism is connected with a supporting detection block, and electric sliding rails with grating rulers are symmetrically fixed on the supporting detection block; a first sliding plate and a second sliding plate are arranged on the electric sliding rail in a sliding mode, a detection mechanism is arranged on the first sliding plate, and a fixing mechanism is arranged on the second sliding plate. The invention belongs to the technical field of lead screw detection, and particularly relates to a lead screw detection device.

The present invention provides a method and device for constructing a data set, identifying malware, and constructing an identification model. Based on new samples and samples in a seed set, if a new sample is determined to be an abnormal sample using an anomaly detection algorithm, the new sample is added to the seed set until the number of samples in the seed set meets a preset condition. The present invention determines abnormal samples based on an anomaly detection algorithm, so that the abnormal samples and the samples in the seed set have a higher degree of abnormality, and removes new samples with a lower degree of abnormality than the seed set, thereby reducing redundant data in the seed set, reducing the scale of the seed set, and thus saving data storage space. At the same time, when a seed set with a smaller scale is applied to malware identification, the training time of the malware identification model can also be shortened, and each sample in the seed set after removing redundant data has a strong representativeness, avoiding the problem of noise caused by excessive redundant data in the data set in the traditional method affecting the accuracy of the identification model.

The invention relates to a cone beam CT imaging system based on a slip ring, and belongs to the technical field of CT imaging. The width domain and the height domain of a detected object are obtained through detection of a first light curtain arranged at the inlet end of a detection table; the upper computer obtains the transverse position deviation of the detected object based on the width domain and the height domain and detects the height to control the detection table to move the center of the detected object to the detection position, and then the second light curtain detects the movement distance of the detected object to achieve more accurate positioning. In the scanning imaging process, the upper computer issues a scanning instruction to the slip ring through the first converter, the slip ring analyzes the scanning instruction through the second converter, sends the area array detector and the radiation source to start scanning and generates an image and state data, and then the image and the state data are transmitted back to the upper computer through the second converter, the slip ring and the first converter in sequence. The problems that an existing sliding ring and an area array detector are universal in adaptability, an optimal detection position is difficult to position, a large amount of imaging redundant data is caused, and one-time through-type spiral cone beam CT imaging cannot be carried out are solved.

The invention provides a fire rescue map rapid generation method and device, electronic equipment and a medium, and belongs to the field of map construction. The method comprises the following steps: acquiring scanning point cloud data of a rescue area, and performing Tai Chi algorithm disc sampling on the scanning point cloud data to obtain a point cloud sampling image; performing image graying and Gaussian blur processing on the point cloud sampling image to obtain a filtering point cloud image, performing image gradient calculation on the filtering point cloud image to obtain an initial edge image, and performing dual-threshold boundary selection on the initial edge image to obtain a rescue area map; and fusing the rescue area map and a preset global map to obtain a fire rescue map. According to the method, the processing efficiency of point cloud data is effectively improved through Tai Chi algorithm disc sampling, redundant data is reduced through image graying, data denoising is performed through Gaussian blur processing, the calculation efficiency is effectively improved, and rapid generation of a fire rescue map is realized.

This application discloses a memory management method, related devices, and program products for a multi-agent interaction system, relating to the field of artificial intelligence technology. The method includes: responding to a user-initiated task command, calling a multi-agent ensemble to infer the task result, and feeding the task result back to the user; acquiring memory data generated by each agent in the multi-agent ensemble during the inference process; and storing the memory data of each agent hierarchically according to task memory level, dialogue memory level, and state memory level. A single task memory includes one or more rounds of dialogue memory related to a single task processed by the agent, and a single dialogue memory includes one or more state memories related to one round of dialogue by the agent. The state memory is used to record the state data of the model inference process called by the agent in one round of dialogue. This application provides more flexible memory data management, offering more flexible, accurate, and efficient context management and organization capabilities in human-machine and multi-agent collaboration processes.