50results about How to "Reduce data storage" patented technology

The invention provides a regional target characterization method and device based on a global discrete grid, and relates to the technical field of global discrete grids. The method comprises the following steps: under the condition that a to-be-represented region target is a circular region target, determining three first grid codes used for identifying the circular region target based on the central latitude and longitude of the circular region target, the physical radius of the circular region target and a preset grid level; and under the condition that the to-be-represented region target is a polygonal region target, vertex extraction is performed on a polygonal region of the polygonal region target to obtain a plurality of extracted target vertexes, and second grid codes at the plurality of target vertexes are determined based on latitudes and longitudes of the plurality of target vertexes and a preset grid level. According to the invention, the number of grid codes for identifying the area target can be reduced, the data storage amount and the transmission data amount of the equipment are reduced, and the grid code transmission efficiency of the equipment is improved.

The invention provides a lithium battery SOH universal prediction method under a false data injection background, belongs to the technical field of lithium battery management, and solves the problems that BMS measurement data is easily attacked by a network and SOH prediction universality is poor under an actual working condition, and the method specifically comprises the steps of training a GPR model and an FDIA detection model by using lithium battery charging data in a normal operation state, establishing a data driving model based on the trained GPR model; detecting and correcting the charging data of the lithium battery by using the FDIA detection model to obtain sub-normal charging data of the lithium battery; extracting health features of the sub-normal charging data of the lithium battery through a dynamic health feature extraction method; and inputting the health features into the trained data driving model to obtain a predicted value of the SOH at the initial running stage of the lithium battery, fitting the predicted value output by the data driving model by adopting an empirical model to obtain an overall degradation trend of the lithium battery, and fusing a UPF filter to realize online updating of the SOH of the lithium battery and parameters of the empirical model.

A field programmable gate array includes a configurable interconnect structure connecting logic blocks that implement circuitry to: receive input data including data values organized into rows and columns, each row having N data values; selecting R [i] unmasked data values of a row of the input data according to the mask and the index i of the row; selecting N-R [i] unmasked data values of the other line of the input data according to the mask and the index of the other line; merging R [i] unmasked data values of the row and N-R [i] data values of the other row into a combined data vector of N data values; and calculating R [i] normalized values based on the R [i] unmasked data values of the combined data vector and calculating N-R [i] normalized values based on the N-R [i] data values of the combined data vector to generate N normalized data values.

The invention provides an autonomous navigation method and device for an inspection robot, the robot and a storage medium, and the method comprises the steps: obtaining image information, determining the visibility of an inspection environment based on the image information, determining an inspection working condition according to a comparison result of the visibility and a preset visibility threshold value, and carrying out the autonomous navigation of the inspection robot. If the inspection working condition is a daytime inspection working condition, determining a selectable path and first pose information of the inspection robot based on the image information, calculating the driving direction of the inspection robot in combination with the selectable path and the first pose information, and if the inspection working condition is a night inspection working condition, calculating the driving direction of the inspection robot based on a preset path and second pose information obtained in real time; calculating the driving direction of the inspection robot, taking the driving direction as a navigation result, and controlling the inspection robot to autonomously drive according to the navigation result; different driving direction determination modes are adopted under different inspection working conditions, and the flexibility of the autonomous navigation method of the inspection robot is embodied.

The invention discloses a ClickHouse-based fused CDN (Content Delivery Network) business data processing method, device and equipment, and the method comprises the steps: obtaining business data of a corresponding fused CDN manufacturer through calling a data interface of each fused CDN manufacturer in real time; performing preprocessing including data format standardization, field expansion and invalid data filtering on the service data by using Spark, packaging the preprocessed service data and sending the packaged service data to a Kafka cluster; the method comprises the following steps: creating a distributed data table based on a ReplacingMergeTree engine in a ClickHouse database, consuming service data in a Kafka cluster in real time by utilizing Spark, and writing the service data into the distributed data table of the ClickHouse database in batches; and retrieving target business data from the ClickHouse database according to a data query request of a user, and converting the target business data into a JSON format to serve as a query result to be returned. The query performance, the storage efficiency, the expandability and the write-in performance can be improved.

The invention provides a campus emotion risk individual assessment method and system based on expression recognition, and the method comprises the steps: collecting monitoring image data for recognition, and obtaining recognized individual expression data and related information data; performing processing according to the individual expression data to obtain group emotion data; correcting the individual expression data according to the group emotion data to obtain individual negative emotion data; and evaluating an emotion risk target individual according to historical statistics of the individual negative emotion data. According to the method, group emotional abnormity can be accurately eliminated in a group scene, the individual emotional risk degree is objectively judged, and the accuracy of identifying the campus emotional risk target is improved.

The invention discloses an online monitoring method for a voltage transformer, belongs to the technical field of power systems, and is used for solving the problems of low online monitoring efficiency, low data processing speed and high storage cost of the voltage transformer in the prior art. Comprising an acquisition module, a data processing module, a fault diagnosis module, a database and a personnel terminal module. The acquisition frequency is dynamically adjusted through the acquisition module, unnecessary data acquisition is reduced through intelligent data acquisition, the storage cost is reduced, the data processing module sends an operation parameter set which may diagnose a fault to the fault diagnosis module, and the fault type and grade are accurately predicted in combination with the fault diagnosis module. According to the method, the fault diagnosis module is helped to perform fault analysis efficiently and specifically, the storage duration of the data in the database is intelligently adjusted according to the analysis result of the data diagnosis change line, storage resources are helped to be optimized, unnecessary data storage is reduced, and therefore the storage cost is reduced.

The invention discloses a high-efficiency solid state disk master control chip, which relates to the technical field of solid state disk master control chips, and comprises a central processing unit and a flash memory controller, the flash memory controller comprises a communication module, a flash memory control circuit and a comparison circuit; in the invention, in performance, data bus and instruction processing are optimized, read-write is accelerated, delay is reduced, and high-speed storage requirements are met; in the aspect of data, the flash memory control circuit accurately reads and writes, the comparison circuit corrects errors in real time, data reliability is guaranteed, and the problem of flash memory management is avoided; during bad block detection, factory leaving and newly-added bad blocks are accurately judged by means of multiple means such as specific verification data after all pieces are erased, and the central processing unit intelligently judges, saves time and improves efficiency; in hardware collaboration, a data selector intelligently switches paths, static random access and a memory guarantee data stability, a main state machine accurately controls a time sequence, stability is enhanced, and a high-quality storage scheme is provided for the solid state disk.

The application belongs to the technical field of radar signal processing and imaging, and provides a differential coupling single-frequency time-varying low-quantization imaging method, which comprises receiving a SAR echo signal, constructing a single-frequency time-varying threshold signal, single-bit quantization processing of a reference channel, single-bit quantization processing of a phase shift channel, double-channel data fusion and harmonic cancellation, and imaging processing based on a synthesized signal. The application effectively reduces the quantization noise base and improves the detection capability of a SAR system on weak targets under the premise of not significantly increasing the hardware cost.

The invention relates to the technical field of data processing, in particular to a warehouse material data acquisition method and system based on RFID, and the method comprises the steps: calculating the feature value of each piece of data in a historical temperature data sequence collected at a preset initial frequency, carrying out the differential iteration of a feature value sequence, and in the iteration process, calculating the feature value of each piece of data in the historical temperature data sequence; calculating the stability of each difference result, if the stability of the difference result is greater than a preset threshold value, stopping iteration to obtain a difference order, inputting the characteristic value sequence and the difference order into a pre-trained ARIMA model, and predicting to obtain a target characteristic value sequence of a next sampling period; and calculating the sampling frequency of the next sampling time period based on the initial frequency and the data mean value in the target characteristic value sequence, so as to collect the temperature data of the warehouse materials in the next sampling time period based on the sampling frequency. According to the invention, the sampling frequency of the temperature data of the warehouse materials can be accurately adjusted, and the acquisition efficiency of the temperature data is improved.

This invention discloses a planetary landing guidance method based on rapid search for optimal control, belonging to the field of spacecraft guidance and control technology. The implementation method is as follows: The lander's dynamic model is decoupled into a controlled dynamic model and an uncontrolled dynamic model; an optimization model of the reachable region is constructed using thrust amplitude and thrust direction constraints as constraints, solving for the reachable region of controlled motion in a prediction time domain; a fuel consumption optimization model is constructed using different positions within the reachable region as virtual endpoints, and offline optimization is performed to obtain the optimal fuel consumption control for the lander to reach the specified virtual endpoint position. The optimal control is stored as an optimal control database, and the corresponding virtual endpoint state is stored as an endpoint state database; during landing, the uncontrolled motion is superimposed with the endpoint state database to obtain the lander's state prediction space, the optimal terminal state is searched, and the corresponding optimal control command in the optimal control database is obtained. Planetary landing guidance is then implemented based on the optimal control command.

The invention discloses a downsampling wind driven generator transmission chain vibration test analysis method. The method specifically comprises the following steps: collecting vibration data at a selected test point; carrying out downsampling processing on the vibration data of the high-rotating-speed component; according to the VDI3834 standard, carrying out band-pass filtering processing on the vibration data collected by each measuring point; carrying out characteristic index calculation on the filtered vibration signal; and if the calculated numerical value exceeds the VDI3834 standard reference numerical value, carrying out time domain analysis and frequency domain analysis in the next step.

The invention provides a method for identifying fires in electric equipment based on a multi-parameter recursive graph, and relates to the technical field of electrical fire detection. The method comprises: using a smoothing filtering technology to pre-process monitoring video images, and creating a fire identification algorithm model based on artificial intelligence to detect and identify fire images in suspicious areas; the fire identification algorithm model identifies the hidden danger electrical monitoring data of the detection position, calculates the target feature vector corresponding to the trace features of the target video image, and builds a deep neural network sub-model and a power heterogeneous multi-parameter fusion model; then using a recursive algorithm module to arrange the power time series parameters into a recursive matrix, and constructing a graph convolutional neural network based on a multi-parameter recursive graph to extract features; using a fire warning judgment module to segment the suspicious unknown areas of the image sequence where the fire occurs, and timely predict and process them, and after identification and confirmation, automatically start a multi-parameter warning judgment program, and accelerate the issuance of warning signals by a background warning system.

The present application relates to a target tracking method, a target tracking system and a storage medium, wherein the target tracking method includes: determining the predicted state value of the tracking target and updating the predicted covariance matrix; performing measurement traversal to obtain the measurement points associated with the tracking target; determining the number of target measurement points and the distribution probability of the measurement points falling into the tracking target wave gate by calculating the new information vector obtained by the observation conversion value of the measurement points associated with the tracking target and the current predicted state value of the tracking target; when it is determined that the measurement covariance matrix of the tracking target needs to be updated; the target covariance matrix of the tracking target is obtained by comparing the first covariance matrix of the new information vector with the second covariance matrix of each type of target in the offline acquisition module, and updating the measurement covariance matrix of the tracking target; wherein the second covariance matrix is ​​obtained by pre-collecting the data statistics of each type of target. Through the present application, the calculation amount of the algorithm is reduced and the accuracy of the tracking algorithm is improved.

The invention belongs to the technical field of server data processing, and particularly relates to a server data processing method based on a deep learning model, which comprises the following steps: firstly, segmenting a data set into a plurality of data blocks, and respectively transmitting the plurality of data blocks to each primary processing node of a primary processing node network for primary processing; respectively transmitting the data processed by each group of primary processing nodes to corresponding secondary processing nodes for secondary processing; respectively transmitting the data processed by each group of secondary processing nodes to corresponding re-level processing nodes for re-level processing; and finally, transmitting the data processed by the re-level processing node to a cloud server for ultimate processing to complete all data processing. By establishing the pyramid server processing node network, the step-by-step processing of data is realized, the transmission quantity in the data processing process and the data storage quantity of each stage of processing node are effectively reduced, and the burden of each node in the whole server processing node network is reduced.

The invention discloses a rail vehicle fault diagnosis information acquisition method. An important information acquisition and storage strategy is adopted. The method comprises the following steps: setting an important information acquisition starting condition of a monitoring point and a single acquisition total duration T0; meanwhile, setting a time-division frequency-division acquisition scheme in the total time period T0; the time-phased frequency-division acquisition scheme comprises the steps that the total acquisition time T0 is divided into T1, T2,..., Ti,... and TN, the sampling frequency corresponding to each time period is F1, F2,..., Fi,... and FN, and T0 is equal to T1 + T2 +... + Ti +... + TN and F1gt; f2gt; gt,... gt; figt; gt,... gt; i is greater than or equal to 1 and less than or equal to N, and N is greater than or equal to 2, namely, the acquisition frequencies in all time periods are different and are sequentially reduced. According to the invention, all effective information of railway vehicle daily fault diagnosis can be collected, invalid information is little or even no information exists, and the problems that the number of monitoring points for railway vehicle fault diagnosis is large, the information collection amount is large, and the calculation processing amount is large can be solved.

The application discloses a data compression method and device of a database, electronic equipment and a storage medium, wherein the database is used for storing multiple pieces of data, the method comprises the following steps: acquiring a data table of the database, wherein the data table comprises multiple field names and field values of each piece of data under each field name; determining a to-be-analyzed set from the data table; performing deduplication statistics on the field values of each field name in the to-be-analyzed set to obtain the deduplicated number of the field values of each field name; determining at least one reference field name from the multiple field names by using the deduplicated number; dividing the data corresponding to the to-be-analyzed set into at least two to-be-compressed sets based on the at least one reference field name, wherein the field values of at least one reference field name in each to-be-compressed set are the same; and performing compression processing on the to-be-compressed sets respectively. Through the above method, the data compression rate of the database can be reduced.

The application discloses a multi-path camera image frame extraction method and system, relates to the technical field of data processing, and extracts frames through a two-stage frame extraction strategy combining low-frequency monitoring and high-frequency tracking. When no event occurs, monitoring is performed at a very low first preset data sampling frequency, and subsequent processing is triggered only when a scene change is detected, so that data storage under a static scene is greatly reduced. When a key target is recognized, high-frequency sampling is immediately switched to, so that the integrity of key information is ensured. Compared with fixed-frequency frame extraction, the on-demand resource allocation manner can more effectively save storage and bandwidth.

The application relates to a log storage method and device, computer equipment, a storage medium and a computer program product, and relates to the technical field of storage. The method comprises the following steps: in the case that the used service life of an EMMC is less than a preset service life threshold, storing logs into an intermediate storage space; compressing the logs stored in the intermediate storage space to obtain compressed logs; storing the compressed logs into the EMMC; and in the case that the space occupied by the stored logs in the EMMC is greater than a first preset space threshold, deleting the stored logs in the EMMC. Overall, although the log switch is in an open state, the corresponding preset service life threshold and the first preset space threshold of the EMMC can be used for judgment, and the judgment result is used for determining how to store the logs, so that the loss rate of the remaining service life of the EMMC is reduced. Since the log switch is continuously in the open state, manual temporary adjustment is not needed, and the operation cost is low.

The present application relates to the technical field of fabric detection, and particularly relates to a fabric flaw intelligent detection method and system based on machine vision, which comprises the following steps: performing gray-scale processing, geometric correction and denoising, illumination compensation and texture enhancement processing on the fabric image collected under controlled illumination, extracting edges and tracking contour point sequences; calculating outer angles, directionality and signed curvatures based on local point sets, combining concave-convex discrimination, key point reservation, thinning and anti-aliasing smoothing, and reconstructing continuous defect boundary trajectories; and then matching with a defect feature interval library to output defect types and boundary positioning information. Through the present application, the problems of missing detection / mis-detection and unstable positioning of defect boundaries under complex texture and illumination fluctuation conditions in the prior art are solved.

The present invention discloses a data encoding method for three-dimensional point cloud target detection, comprising: dividing the real space area covered by the three-dimensional point cloud data into a plurality of voxel grids; performing data encoding on the plurality of voxel grids to form a first pseudo image including five channel features, the five channel features including the maximum value of the coordinates of the points in the current voxel grid along the vertical direction, the minimum value of the coordinates along the vertical direction, the maximum reflectivity, the number of points in the voxel grid, and the distribution description coefficient of the point cloud in the voxel grid; splitting the first pseudo image into a second pseudo image including three channel features of the five channel features and a third pseudo image including two channel features of the five channel features; performing feature learning on the second pseudo image and the third pseudo image using a trained convolutional neural network model and obtaining the border and category of the three-dimensional point cloud data. The method of the present invention significantly reduces the amount of data storage and the amount of calculation, and can effectively improve the detection frame rate.

This invention discloses a method and system for epilepsy monitoring and early warning, relating to the field of medical signal processing. It solves the problems of high false alarms and false negatives, and low energy efficiency in existing monitoring technologies due to physiological artifact interference, individual differences, and continuous high power consumption. The key technical points are: first, achieving deep artifact removal of the signal through multi-angle collaborative filtering; second, constructing a personalized lightweight prediction model based on historical data similarity matching and transfer learning; and finally, employing a dynamic signal filtering and graded power adjustment mechanism. This achieves the technical effect of significantly improving the accuracy of early warning for patients with specific lesions while greatly reducing system redundancy and energy consumption.

Embodiments of the present application provide a picture search method, an electronic device, a storage medium and a program product, comprising: in response to receiving a picture search request, obtaining a text semantic feature and a keyword of a search text, the search text being carried in the picture search request; in the case of obtaining the keyword, searching a picture information set respectively matched with the text semantic feature and at least one keyword from a picture information library to obtain a candidate picture information set; and selecting a candidate picture information satisfying a preset condition from the candidate picture information set as a picture search result according to a similarity corresponding to each candidate picture information in the candidate picture information set, the similarity being a similarity between a picture global feature of the corresponding candidate picture information and the text semantic feature. The scheme can improve the picture search efficiency, reduce the calculation overhead, and also take into account the recall rate and accuracy of the picture search, and reduce the index persistence cost of the semantic vector.

The invention relates to a heterogeneous distributed digital identity-oriented aggregation service method, and belongs to the technical field of block chain digital identity aggregation. The method comprises the steps that 1, a user uses a tag generation algorithm to generate a random tag alpha and sends the random tag alpha to a verifiable certificate issuing mechanism, and the user requests to issue a verifiable certificate; 2, the verifiable certificate issuing mechanism signs and issues a verifiable certificate which contains the distributed digital identity identifier and has an attribute authentication result to the user; 3, the user calls an aggregation function Aggregate () in the smart contract to aggregate the signature of the attribute; 4, the user randomizes the aggregated signature and label alpha, and sends the randomized signature and label alpha and the aggregated public key to a verification mechanism for authentication of the aggregated signature; and 5, the verification mechanism calls a verification function Verify () in the smart contract to verify the aggregation signature by using the property of bilinear pairing so as to prove the legality of the aggregation signature. Compared with the traditional heterogeneous distributed digital identity verification, the signature verification efficiency is improved.

The present invention discloses an HBase optimization scheme based on row-column hybrid storage, comprising the following steps: S1, constructing a new data table and index table, wherein the table structure of the data table and index is as follows: storing multiple values ​​of the same field continuously, and recording the maximum value, minimum value, and starting offset of the same field of the relevant columns in the data table and index table; S2, constructing a new row-column hybrid storage query scheme based on the new table structure; S3, converting Get to Scan on the data table; S4, rewriting Scan on the index table. This HBase optimization scheme based on row-column hybrid storage performs data modeling based on a row-column hybrid storage mode, can make full use of data similarity, significantly reduce data storage, and is verified by actual measurement to occupy 50% less storage than the original scheme; based on the new data aggregation storage mode, it can reduce the number of random data reads and improve overall query performance.

The invention discloses a multimedia file generation method and device, equipment and a medium, in response to a calling request for a target interface, the target interface obtains an initial multimedia file, the initial multimedia file comprises multimedia image data and multimedia description data, and the multimedia image data and the multimedia description data are transmitted to the target interface; the multimedia description data comprises an initial storage address of the multimedia image data; storing the multimedia image data to a target server, and obtaining a first target storage address of the multimedia image data in the target server; replacing the initial storage address in the initial multimedia file by using the first target storage address to obtain a target multimedia file; the target multimedia file comprises the replaced multimedia description data, and the volume of the multimedia file can be effectively reduced by utilizing server storage.

The application discloses an MES-based production data management system and method, relates to the technical field of data calculation, and comprises the following steps: obtaining production data of each production manufacturing stage in a manufacturing execution system; calculating data values of the production data based on the outlying characteristics of each production data; when the data values meet preset conditions, screening first storage data which needs full storage and second storage data which needs partial storage from the production data; matching each first storage data and second storage data with a storage server based on the data values and the storage cost of the storage server, determining the corresponding relationship between the production data and the storage server, and storing the production data in each storage server based on the corresponding relationship. The application achieves the technical effects of reducing the difficulty and cost of local maintenance of a super large data table in the later stage.

The invention discloses an automatic management system and method for in-vitro diagnostic equipment, and the system comprises the in-vitro diagnostic equipment which is used for intercepting and transmitting a current equipment display image; the local control end is used for receiving the current equipment display image and identifying and sending the current working state of the in-vitro diagnosis equipment according to the current equipment display image; the server side is used for receiving the current working state and generating and sending a corresponding control strategy according to the current working state; and the execution end is used for receiving the control strategy and executing corresponding automatic control operation according to the control strategy after automatic calibration. According to the invention, automatic control of in-vitro diagnosis equipment can be realized.

The present invention provides a stacked pixel array particle detection system and a control method thereof, which belongs to the field of particle detection, and includes a plurality of particle detectors, a pixel array detector, a signal acquisition module and a main control module, wherein the particle detector and the pixel array detector are stacked in the particle incident direction, and the particle detector is used to receive the incident particles; the signal acquisition module is used to process and transmit the electrical signal detected by the particle detector to the main control module, and the main control module is used to generate the timing signal required for the pixel array detector to work according to the electrical signal of the particle detector, so as to obtain the image data of the pixel array detector, and store the image data with reference to the particle event count value of the particle detector; the pixel array detector is used to obtain and store the image data of the incident particles under the control of the generated timing signal. The present invention can reduce the amount of data collected while ensuring that the space particles are detected, and alleviate the pressure of data storage and downlink.

The invention provides an electrical transient model data processing method and device, equipment and a storage medium. Storage of target model data is achieved through a transient model classification library, when data corresponding to a target model needs to be applied, model attributes and model parameters of the target model can be directly called, or after the model attributes and the model parameters of the target model are called, a first model is obtained through further processing, and the first model is used for applying the data corresponding to the target model. Therefore, the target model and / or the first model are / is used for a specific application scene, for example, corresponding model data can be obtained through calling or processing in a power system operation mode configuration process. Therefore, under the condition of realizing standardized storage and flexible storage of the transient model, the data storage and data processing cost is reduced, and the effectiveness and practicability of data processing of the electrical transient model are also improved.