39results about How to "Expand the scope of the data" patented technology

The invention discloses a Bayesian reliability comprehensive estimation method based on multisource degraded data, which comprises the following steps of: 1. collecting multisource degraded data; 2. establishing a degraded statistic model and confirming a reliability function; 3. preprocessing the degraded data; 4. confirming and fusing prior distribution and posterior distribution; and 5. estimating the reliability of a product. The invention provides a method for establishing the degraded statistic model, which converts a form of the degraded data by carrying out transformation processing on the performance degrading model of the product and leads the degraded data which is not submitted to probability distribution to submit to the probability distribution, thereby leading the Bayesian method for commonly processing life data to be capable of processing the degraded data and realizing the Bayesian statistic estimation based on the degraded data; accordingly, the Bayesian method is introduced into the estimation method of an degradation accelerating test, and the estimation method of the degradation accelerating test is expanded.

A voice search and analysis system comprises a voice transcription module, an index establishing module, a voice search module and a test analysis module. Keywords contained in voice, positions where the keywords appear and other characteristic information are searched for. Analysis can be effectively performed on massive voice recording files and audio files through voice analysis, knowledge exploration in the voice and rapid search on the voice files are achieved, and the problems that voice data are difficult to search for and analyze are solved.

A storage controller changes a block size to carry out a shredding process. A data shredder uses a large block size BSZ1 set by a block size setting part to write shredding data in a storage area of a disk drive and erase data stored therein. An error arising during the writing operation of the shredding data is detected by an error detecting part. When the error is detected, the block size setting part sets the block size smaller by one stage than the initial block size to the data shredder. Every time the error arises, the block size used in the shredding process is diminished. Thus, the number of times of writings of the shredding data is reduced as much as possible to improve a processing speed and erase the data of a wide range as much as possible.

The invention discloses a liquid viscosity transmission tester with a hydraulic load device. The liquid viscosity transmission tester with the hydraulic load device comprises an oil supplying system and a simulation load loading system; an oil supplying system loop is connected with an oil supplying oil path coarse filter, a gear oil pump, an overflow valve, a speed regulating valve, a cooler, a liquid viscosity host, a flowmeter and an oil tank in sequence by a hydraulic pipeline. According to the simulation load loading system, a vane pump is adopted to replace the load device to load; the liquid viscosity host drives the load vane pump to work by the connection of a coupler; a simulation load loading system loop is connected with a load oil path coarse filter, the vane pump, a pilot operated compound relief valve and the oil tank in sequence by a hydraulic pipeline. The liquid viscosity transmission tester with the hydraulic load device is suitable for the test research of a liquid viscosity transmission technology, the system is greatly simplified while the basic demands of the test are met, so the system structure is deeply optimized and the design cost and the manufacturing cost are saved; the load loading system is closer to an actual working condition, so the system is more practical and reliable and has broad use value.

A storage controller changes a block size to carry out a shredding process. A data shredder uses a large block size set by a block size setting part to write shredding data in a storage area of a disk drive and erase data stored therein. An error arising during the writing operation of the shredding data is detected by an error detecting part. When the error is detected, the block size setting part sets the block size smaller by one stage than the initial block size to the data shredder. Every time the error arises, the block size used in the shredding process is diminished. Thus, the number of times of writings of the shredding data is reduced as much as possible to improve a processing speed and erase the data of a wide range as much as possible.

The invention discloses a multi-RGB-D full face material recovery method based on deep learning. The method comprises two steps of face material information estimation based on an image and gradient optimization based on differential rendering. The method comprises the following steps: step 1, firstly preprocessing geometric and texture data to generate a mask containing a full-face skin part; then constructing a texture estimation module and an illumination estimation module, and generating a simulation training data set; and finally, obtaining initial values of texture information and illumination coefficients by using a material texture and illumination estimation module and the simulation training data set. step 2, firstly processing and scanning geometric data, and then expanding to realize a full-face rendering equation; improving the loss function to obtain an optimization result; and finally, performing detail optimization on the special region. According to the invention, the data range of the face material recovery technology can be expanded, and the optimization effect of the material recovery technology is improved.

The invention discloses an improved association rule report data mining method based on mutual exclusion expression, relates to a knowledge discovery and data mining method in the field of data science, and solves the problems of high memory consumption and low efficiency when a traditional association rule algorithm processes massive data. The method comprises the steps of 1, converting data intotransaction data based on a data threshold range, and obtaining a binary sparse matrix with grouping labels based on data logic; 2, obtaining a set in which all frequent items are 1, and removing a non-frequent item set to obtain a new grouping result; and 3, performing self-connection iterative search on the frequent item set to search the frequent item set, and cutting and iterating the candidate item set until a new frequent item set cannot be generated, thereby obtaining an association rule mining result. The basic idea of the method is to convert structured data into transaction data, generate groups based on a mutual exclusion relationship, and perform rule mining, thereby reducing the computing memory and improving the computing efficiency. The application scene is wide, and the social and economic values are very high.

The invention discloses a data transmission method based on logstash + kafka, which comprises operation of service data producing end and operation of service data consuming end. 1) automatically collecting the service data in the service system database through the service data production terminal logstash, and transmitting the service data to the middleware kafka; 2) the middleware kafka collects the data transmitted by each service data producing end and uniformly sends the data to the service data consuming end logstash; 3) the service data consuming terminal logstash receives the messageof the middleware kafka, converts the message into a data file, and stores the message in the target database. Compared with the prior art, the invention has the advantages of simple configuration andoperation, high processing speed, and can effectively realize the operation of collecting, processing, storing and the like of the dispersed service data, and can quickly and conveniently build the transmission channel between the service data production end and the service data consumption end, and has good expansibility, and reduces the difficulty of development and operation and maintenance. The invention has the advantages of simple configuration and operation, high processing speed, and effectively realizes the operations of collecting, processing, storing, etc.

The invention belongs to the field of biomedical cell tissue culture and experiments, and discloses a stimulation culture device and a stimulation culture method for applying two stresses including atraction force and a pressure to cells, controllable time sequences are realized for a culture chamber to respectively realize pressurization and pressure extraction, and the same-frequency stretchingand retraction of an elastic culture cavity are matched to realize the joint stimulation of any pressure and tensile strain on the cells in a controllable range, and a simple and effective device anda technical scheme for forming strain and pressure on the cells by the tensile force and carrying out linkage adjustable stimulation culture on the cells at the same time are provided.

A data processing apparatus comprises a holding unit configured to hold filter coefficients of a transferred filter, a generating unit configured to generate an extended filter by extending a size of the transferred filter while sequentially reading out the filter coefficients held in the holding unit, and an arithmetic unit configured to perform convolution by using filter coefficients of the extended filter.

The invention discloses a Bayesian reliability comprehensive estimation method based on multisource degraded data, which comprises the following steps of: 1. collecting multisource degraded data; 2. establishing a degraded statistic model and confirming a reliability function; 3. preprocessing the degraded data; 4. confirming and fusing prior distribution and posterior distribution; and 5. estimating the reliability of a product. The invention provides a method for establishing the degraded statistic model, which converts a form of the degraded data by carrying out transformation processing on the performance degrading model of the product and leads the degraded data which is not submitted to probability distribution to submit to the probability distribution, thereby leading the Bayesian method for commonly processing life data to be capable of processing the degraded data and realizing the Bayesian statistic estimation based on the degraded data; accordingly, the Bayesian method is introduced into the estimation method of an degradation accelerating test, and the estimation method of the degradation accelerating test is expanded.

The invention discloses a model construction method for repeated incoming call analysis and recognition. The model construction method comprises the steps: translating voice into text contents, recording the text contents in a single work order table, and judging repeated incoming calls through the analysis of the call contents between a seat person and a customer; judging in combination with theacceptance content and the processing condition in the work order content, and judging that the incoming call is a repeated incoming call when a specified character is contained; judging the main workorder and the associated work order with the same telephone number in the associated work order table as a group of repeated incoming calls, marking the work order as a mother work order, and markingother work orders as child work orders; and extracting the work order of which the information recorded in the work order acceptance content is that ' the associated work order number is ' but is notincluded in the associated work order table. The model construction method introduces the idea of processing the structured work order information and the idea of processing the unstructured text content to repeated incoming call recognition, expands the data range for repeated incoming call recognition, and guarantees the improvement of the model coverage rate and accuracy.

The invention discloses a multi-RGB‑D full face material restoration method based on deep learning. The invention includes two steps of image-based human face material information estimation and gradient optimization based on differentiable rendering. Step 1: First, preprocess the geometry and texture data to generate a mask containing the skin part of the whole face; then build the texture estimation module and illumination estimation module to generate a simulation training data set; finally use the material texture and illumination estimation module to combine with the simulation training The data set obtains the initial value of the texture information and the illumination coefficient. Step 2 first processes the scanned geometric data, and then expands to realize the full-face rendering equation; then improves the loss function to obtain the optimization result; finally, optimizes the details for special areas. The invention can expand the data range of the face material recovery technology, and improve the optimization effect of the material recovery technology.

The invention relates to a big-data analysis system. According to the system, various data collection manners can be utilized to collect data; an encrypted data signal is formed by the collected datathrough encryption processing; the encrypted data signal is transmitted to a data storage module through a data transmission module; the encrypted data signal is transmitted to a data analysis serverby the data storage module; and in the data analysis server, firstly, the encrypted data signal is decrypted through a data decryption module, detection, marking, classification and analysis are sequentially carried out on a decrypted data signal through a data detection module, a data marking module, a data classification module and a data analysis module, then a data analysis result is obtained,the data analysis result obtained by the data analysis module is evaluated through an evaluation module, and finally, deviation correction is carried out on an unqualified data analysis result through a correction module. Accuracy of the big-data analysis system is improved.

The disclosure provides an interface prototype data generation method and computer equipment. The method comprises following steps: loading business model data of an enterprise, wherein the business model data comprises a data model, a product model, a process model and an interface experience model; establishing a first constraint relationship between the data model and the process model by checking the data entities establishing a second constraint relationship between the product model and the process model by checking the customer-product-channel CPC adaptation model; generating interface prototype data according to the data model, the product model, the process model, the first constraint relationship, the second constraint relationship and the interface experience model. According to the method and equipment of the disclosure, flow model data is introduced during the process of interface prototype data generation, and constraint relationships are built up between the flow model and the product model and the data model, which substantially improve the accuracy of data expression of interface prototype for relative business model data.