13 results about "Pooling data" patented technology

A data anonymization pipeline system for managing holding and pooling data is disclosed. The data anonymization pipeline system transforms personal data at a source and then stores the transformed data in a safe environment. Furthermore, a re-identification risk assessment is performed before providing access to a user to fetch the de-identified data for secondary purposes.

The invention discloses a technical trade measure data automatic sharing method and system based on artificial intelligence, and belongs to the technical field of data sharing, and the method comprises the steps: a control center receives a technical trade measure new version data entry signal, carries out the judgment of a target data sharing reliability decision pool, and carries out the data sharing; if the judgment result of the target data sharing reliability decision pool is a secondary judgment pool, re-judgment of the target data sharing reliability decision pool is carried out, and if the re-judgment result of the target data sharing reliability decision pool is an experimental sharing pool, the formal sharing qualification of the target data is confirmed, and the automatic sharing process of the target data is executed. The method can guarantee the safety and stability of data sharing, accurately judges the actual application value of the data through experimental sharing and sharing value scoring analysis, ensures that the data entering a wide sharing link can provide an accurate basis, continuously monitors the data of the observation pool, reasonably cleans invalid data, improves the data management efficiency, and is suitable for popularization and application. And the accuracy, reliability and practicability of data sharing are improved.

The present invention discloses a method for real-time monitoring and calculation of urban regional human flow, which divides the acquired boundary information of any urban area into production end, consumption end and task supplementary recording, wherein: the production end mainly realizes the real-time access function of spatiotemporal information data, completes data cleaning, judges data quality, generates schedules for high-quality data in real time, extracts features for low-quality data to generate intermediate tables, and stores them in data cache pool; the consumption end reads cache pool data, judges data quality, generates schedules for high-quality data in real time, and performs task scheduling according to the task status, execution order and restart times of each task marked by the schedule, and the task scheduling mainly includes calculation tasks and prediction tasks; task supplementary recording reads offline data, judges data quality, and calculates regional human flow. It solves the problem of real-time calculation of tens of millions of moving objects based on spatiotemporal big data, has the advantages of highly flexible streaming time window, high throughput, etc., the calculation results are more accurate, and offline supplementary recording results are supported.

A data anonymization pipeline system for managing holding and pooling data is disclosed. The data anonymization pipeline system transforms personal data at a source and then stores the transformed data in a safe environment. Furthermore, a re-identification risk assessment is performed before providing access to a user to fetch the de-identified data for secondary purposes.

The invention discloses a video frame efficient interception control method and device and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: obtaining and analyzing video data according to a resource locator and an initial interaction instruction, and obtaining metadata and a key frame list; positioning a nearest key frame from the metadata and the key frame list through a specified target time point to obtain a data fragment corresponding to the nearest key frame; after continuous frame data are obtained through decoding in the webpage working thread according to the data fragments, the continuous frame data are stored in the developed cache pool, and current frame data are obtained; storing a pre-loading frame predicted according to the user behavior into a cache pool, and generating cache pool data in combination with current frame data and a cache pool rule; and after determining target frame data in the cache pool data based on a user request, processing the target frame data through a canvas image drawing method, and outputting a rendered target video frame. The problem of low efficiency is solved, and the video editing speed is improved.

A data anonymization pipeline system for managing holding and pooling data is disclosed. The data anonymization pipeline system transforms personal data at a source and then stores the transformed data in a safe environment. Furthermore, a re-identification risk assessment is performed before providing access to a user to fetch the de-identified data for secondary purposes.

The invention provides a railway point cloud-oriented active learning weak supervision semantic segmentation method and system, and the method comprises the steps: obtaining a training data set of a semantic segmentation network model, and initializing the training data set into marked pool data and unmarked pool data; constructing a semantic segmentation network model; performing iterative training on the semantic segmentation network model by using the marked pool data based on an active learning strategy, updating the marked pool data by using the unmarked pool data based on a cyclic output difference and an information entropy theory in the training process, and outputting the trained semantic segmentation network model; and inputting a to-be-processed railway scene point cloud to the trained semantic segmentation network model, and outputting a railway point cloud semantic segmentation result.

The application relates to the technical field of data processing, and discloses a data abstract method and system based on deviation degree analysis and a fault diagnosis method, which comprises the following steps: S1. After a target algorithm, to-be-abstracted test data, the minimum register unit stored data bit number and the operation logic involved in the to-be-tested step in the target algorithm are acquired, the allocation weight corresponding to the data stored in each minimum register unit is obtained; S2. The to-be-abstracted test data is split into a plurality of groups in units of the data bit number of the minimum register unit, and each group of data is subjected to class average pooling processing according to a preset precision requirement; after each group of data is subjected to the class average pooling processing, an average pooling data is obtained; S3. The data abstract result corresponding to the to-be-abstracted test data after the to-be-tested step is obtained. The application solves the problem that the prior art cannot be used to represent high-bit-width operation results, and has the characteristics of significantly reducing the bit width of data.

A data anonymization pipeline system for managing holding and pooling data is disclosed. The data anonymization pipeline system transforms personal data at a source and then stores the transformed data in a safe environment. Furthermore, a re-identification risk assessment is performed before providing access to a user to fetch the de-identified data for secondary purposes.

An apparatus includes a processor and a memory storing instructions to cause the processor to receive (1) user data associated with a user, (2) target workforce data, (3) target role data, and (4) candidate pool data, randomly select a plurality of candidates from the candidate pool data, execute a machine learning model to generate matching scores for the user and each candidate to the target role data, to produce a matching score distribution, and generate, via a statistical model, a fit score for the user based on a number of candidates in a subset of candidates, where each candidate from the subset of candidates has a matching score lower than the user matching score.

The invention relates to the field of data processing, in particular to a down-sampling pooling method and device, equipment and a program product. The method comprises the following steps: receiving to-be-pooled data; the to-be-pooled data is updated and cached in a row-by-row comparison or accumulation mode, and a local result after row pooling is generated; and updating the local results to an RAM space in a one-by-one comparison or accumulation mode, and determining a final result of the to-be-pooled data. According to the method, pooling operation of a pooling core with a relatively large size can be realized through association and cooperation of logic devices under the condition that extra resource consumption is not increased, so that extraction of characteristics in a relatively large range is facilitated, and the dimension reduction efficiency is improved.

This application relates to a three-dimensional point cloud analysis method, apparatus, device, medium, and program product. The method comprises: obtaining point cloud embedding data of an input point cloud; performing pooling processing on the point cloud embedding data to obtain average pooling data and maximum pooling data; constructing point tokens based on a position injector, the point cloud embedding data, and the average pooling data to obtain candidate point tokens; generating prompt words for the candidate point tokens based on the average pooling data and the maximum pooling data to obtain target point tokens; fine-tuning a two-dimensional pre-trained model using the target point tokens to obtain a target point cloud analysis model; and using the target point cloud analysis model to process three-dimensional point cloud analysis tasks. This method can improve the accuracy of three-dimensional point cloud analysis while reducing the number of parameters.

The invention discloses a gas concentration prediction method based on an MEMS electronic nose. The method comprises the following steps: 1, constructing a miniature portable electronic nose system; 2, the gas sensor array collects gas characteristic data; 3, a preprocessing module carries out preprocessing operation on the gas characteristic data to obtain standard data; 4, a one-dimensional convolution layer in the gas concentration prediction network obtains standard data and carries out one-dimensional convolution operation on the standard data to obtain convolution data; 5, performing batch normalization operation on the convolution data by a batch normalization layer to obtain normalized data; 6, performing pooling operation on the normalized data by the first maximum pooling layer to obtain first pooling data; 7, the attention layer performs key feature extraction on the first pooling data to obtain channel feature data; 8, performing pooling operation on the channel feature data by the second maximum pooling layer to obtain second pooling data; and 9, performing full connection operation on the second pooling data by the full connection layer, and outputting a gas concentration prediction result. The electronic nose has the advantages that the detection precision and portability of the electronic nose are improved.