226results about How to "Reduce storage" patented technology

The invention relates to the technical field of computer storage, and discloses a computer memory data processing system, which comprises the following modules: an intelligent cache prefetching module, which is used for predicting a future memory access address based on deep learning and reinforcement learning technologies and loading data into a cache in advance; the heterogeneous storage management module is used for dynamically classifying the data based on the data popularity value and realizing migration of the data among different storage media; and the storage consistency guarantee module is used for managing the data consistency among different storage layers through the improved MES I protocol. Cache prefetching is optimized by introducing deep learning and reinforcement learning technologies, and by predicting a future memory access address in real time and loading data in advance, the system effectively reduces the cache missing rate, improves the cache hit rate, remarkably reduces the memory access delay, and improves the memory access efficiency. And the system can keep more efficient data access performance in a large-scale data processing scene.

The invention provides a multi-path embedded video coding method and system based on generative artificial intelligence. The multi-path embedded video coding method comprises the following steps: firstly, coding input cue words and demand parameters, and outputting cue word vectors and demand parameter code streams; then decoding the cue word vector and the demand parameter code stream, and outputting a feature vector and a feature map; extracting the feature vector, the feature map and the representation guide vector to obtain multi-embedded feature information; reconstructing a picture by using the obtained multi-embedded feature information through a generative AI and obtaining a video to be iterated; performing video quality evaluation on the video to be iterated; and generating a binary code stream for transmission through the current cue word vector, the demand parameter code stream and the multi-embedded feature information input. According to the method, important information characteristics are transmitted in a highly abstracted manner, and a high-definition video is reconstructed at a receiving end, so that the storage space required by video coding and the size of a transmission information stream are greatly reduced, and higher-definition transmission under a narrower bandwidth is realized or video coding under an extremely low bandwidth becomes possible.

The invention belongs to the technical field of artificial intelligence and information retrieval, and provides a multi-modal retrieval method and system based on a lightweight knowledge graph and an index table, and the method comprises the steps: obtaining multi-modal source data, and extracting a structured semantic tag set; constructing a lightweight knowledge graph and a metadata index table; analyzing a natural language query input by a user to obtain a semantic query vector and a keyword set, executing semantic retrieval in the knowledge graph to obtain a text candidate result, and executing keyword matching in the metadata index table to obtain a non-text candidate result; for each non-text meta record, fusing the cross-modal similarity between the non-text meta record and the text candidate result, performing index matching on an original score and a graph semantic evidence score, calculating a comprehensive correlation score, and performing reordering; and generating a natural language answer containing a non-text record link according to a reordering result. The method is suitable for efficient cross-modal knowledge retrieval in a high-security and low-resource scene.

The invention discloses a tunnel disease data extraction and calibration method, and particularly relates to the technical field of tunnel disease detection. Comprising the following steps: S1, establishing a tunnel BIM model coordinate system; s2, calibrating the positioning precision of a high-definition camera and a three-dimensional laser scanner through a tunnel BIM model coordinate system; s3, collecting tunnel disease information by using a high-definition camera and a three-dimensional laser scanner; s4, extracting feature points of the tunnel disease information; and S5, converting the two-dimensional coordinates of the feature points into three-dimensional coordinates in an actual space. By adopting the technical scheme of the invention, the problem of low positioning accuracy in a traditional tunnel disease detection method is solved, the accuracy and efficiency of tunnel disease detection are improved, and the safety and stability of a tunnel structure are ensured.

The invention discloses an automatic driving vehicle target detection system and method based on cloud edge collaboration, and relates to the technical field of computer vision and artificial intelligence. According to the target detection model YOLO-Vehile provided by the invention, effective integration of text semantic information and visual features is realized through a CLIP-ViT text feature extractor and a YOLOV8 image feature extractor, and the understanding ability of the model for complex traffic scenes is remarkably improved. According to the target detection model YOLO-Vehile, high detection precision is maintained, meanwhile, light weight of the model is achieved, possibility is provided for deployment of the model on edge equipment with limited resources, and the problem that an existing large model is difficult to deploy rapidly in an actual scene is effectively solved. Through cooperative work of the edge server and the cloud server, flexible scheduling of computing resources and dynamic allocation of tasks are realized. The problem that an existing single calculation mode is unbalanced in calculation resource distribution in the face of complex and changeable traffic scenes is effectively solved, and the overall performance and reliability of the system are remarkably improved.

The invention discloses a pose estimation method, computer equipment and a storage medium. The method comprises the following steps: acquiring point cloud data of a current frame, and performing feature point extraction on the point cloud data to obtain at least two types of feature points; performing region division on the feature points of each category to obtain at least two categories of feature points corresponding to each region; clustering processing is carried out on the feature points, corresponding to the areas, of the categories to obtain centroid point sets corresponding to at least two categories; and performing pose estimation by using the centroid point sets corresponding to the at least two categories to obtain the current pose of the target equipment. According to the scheme, the accuracy of pose estimation can be improved.

The invention relates to the technical field of electric power data management, and provides a database table classification management method and system based on multi-modal fusion. The method comprises the following steps: extracting table information in a table in data, including metadata information and historical sql information of a table; on the basis of the extracted metadata information and historical sql information, constructing a feature vector of multi-modal fusion; for the existing database tables in the initial data table, based on the feature vectors of the database tables, calculating the distance between the feature vectors of each table, and adopting a hierarchical clustering method to perform clustering analysis on the data tables; specifying a storage scheme for each type of data table in the clustering result, and forming a storage scheme result of the existing database table; constructing an attention-based classification model based on a storage scheme result of the existing database table; and classifying the tables newly added into the database of the data table by adopting the classification model, and determining a storage scheme result of the newly added tables. According to the invention, the most reasonable storage scheme can be automatically allocated to the table.

The invention discloses a vehicle environment data uploading method and device, equipment and a storage medium, and relates to the technical field of data processing, and the vehicle environment data uploading method comprises the steps: obtaining sensor data and an environment change flag bit; when the change frame number of the environment change flag bit reaches a preset frame number threshold value, a yaw angle change value and a pose change value are obtained according to the sensor data; when the system performance, the yaw angle change value and the pose change value all meet preset conditions, caching the sensor data; and when the sensor data reaches a preset data threshold value, uploading the sensor data. According to the method, efficient acquisition and uploading of the environment data are realized through judgment of the environment change mark monitored by the continuous frame vehicle in real time and modification of the key frame screening threshold value by the system resource information. The data acquisition efficiency and the system resource use efficiency are improved, the acquired data transmission and storage pressure is reduced, and the resource processing consumption of the cloud server is reduced.

The invention is suitable for the technical field of phased array antenna wave control, and provides a phased array high-precision beam pointing calibration method, a controller and a memory. The method comprises the following steps: acquiring initial phase shift values of K channels of a phased array; performing step-by-step calibration on the initial phase shift values of the K channels of the phased array, and performing accumulation calculation on calibration data obtained by step-by-step calibration through a built-in data mode to obtain forced calibration data; the step-by-step calibration comprises array element calibration, temperature calibration, formula calibration, subarray calibration and forced calibration; the forced calibration data is data after the beam pointing offset error is corrected; obtaining a final phase shift value based on the forced calibration data; the final phase shift value is used for calibrating beam pointing. According to the method provided by the invention, the data storage amount can be obviously reduced under the condition of high-precision beam pointing.

The invention relates to the technical field of mobile terminals, and discloses a data acquisition method, system and device based on a mobile terminal and a storage medium, the method comprises the following steps: acquiring initial data, and classifying the initial data to obtain first data; comparing the first data based on a data demand condition input by a user, and deleting data which do not accord with the data demand condition to obtain second data; performing error analysis on the second data to obtain third data; performing precision detection on the third data, and performing storage operation on the data according to a precision detection result; wherein when the data after precision detection accords with a preset precision range, data storage operation is carried out according to the data after precision detection; and when the data after precision detection does not accord with a preset precision range, carrying out data correction operation to obtain corrected data, and carrying out storage operation on the corrected data. The method can improve the data acquisition efficiency of the mobile terminal and improve the utilization rate of the data storage space.

The embodiment of the invention provides an archive management method and system based on big data analysis, and belongs to the technical field of data processing. The method comprises the steps of obtaining initial text data and initial image data corresponding to an initial user; performing deduplication processing on the initial text data to obtain target text data, and performing deduplication processing on the initial image data to obtain target image data; performing association relationship analysis on the target text data and the target image data to obtain a target relationship corresponding to the target text data and the target image data; performing text classification on the target text data to obtain a target type corresponding to the target text data; and performing data storage on the target text data and the target image data according to the target type and the target relationship to obtain a target storage result. The problems that in the prior art, the occupied storage space is increased and the data access and retrieval efficiency is reduced due to the fact that texts and images are often used as independent information units to be processed and an effective integration mechanism is lacked are solved.

The invention provides an NFC chip intelligent data loading method and system, and the method comprises the steps: effectively extracting and modeling historical use data, so as to predict a future use scene; generating a priority list of the loaded data according to the scene prediction result; the loading strategy is adjusted according to the real-time signal intensity, and the loading reliability of the high-priority data is ensured; the loaded data is quickly analyzed, and it is ensured that the data can be used by a user in time; and optimizing the scene prediction model and the loading strategy based on feedback data in the loading process to form closed-loop optimization. According to the method, intelligent scene prediction, signal intensity perception and hierarchical loading strategies are introduced, so that the data loading efficiency and reliability are comprehensively 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.

A combustion improver continuous feeding method for methane in-situ burning explosion fracturing comprises the steps that a fracturing well section is determined, a well is drilled, and a drillable bridge plug is put in; assembling and installing a putting tool; the putting tool is put down, and the oil pipe through pipe and the shaft through pipe are connected in a sealed mode; high-pressure nitrogen is injected through nitrogen injection equipment, and the packer is set; a combustion improver is injected into the gas storage space of the throwing tool main body through the high-pressure hose by using oxygen injection equipment, and oxygen is injected into the sealed target section at a certain initial pressure; nitrogen with increased pressure is used for pushing a piston in the feeding tool main body downwards, so that the residual combustion improver in the gas storage space is discharged; the packing of the combustion improver is completed; finally, plugging the tool and performing compression ignition; according to the method, the continuous throwing of the methane in-situ burning explosion fracturing combustion improver and the packing technology of the combustion improver are combined, the packing problem of the combustion improver during the continuous throwing process of the combustion improver can be effectively solved, and the safety of the continuous conveying of the combustion improver along the way can be obviously improved.

The invention discloses a hydraulic station with a dustproof structure, and relates to the technical field of hydraulic stations, the hydraulic station with the dustproof structure comprises a hydraulic station main body, and a control panel is fixedly mounted on the outer wall of the hydraulic station main body. Dust drifting away from the top of the hydraulic station body can be adsorbed through cooperation of a fan, a hose and a dust collecting cover, the dust is prevented from falling on the surface of the hydraulic station body to affect normal operation of the hydraulic station body, and then the dust can be filtered and stored through a collecting box and a filter screen; the dust collection cover can be driven to swing back and forth above the hydraulic station main body through reciprocating rotation of the swing rod on the outer wall of the stand column, and the dust collection range of the dust collection cover can be expanded through reciprocating swing of the dust collection cover, so that dust drifting away from the top of the hydraulic station main body can be fully adsorbed by the dust collection cover, and the dust removal effect can be improved; a large amount of dust is prevented from being attached to the hydraulic station body in the operation process.

The invention discloses a rose picking robot and belongs to the field of agricultural automatic picking equipment. The device is composed of a crawler, a collecting device, a picking device, an unmanned aerial vehicle and an autonomous endurance device. According to the device, an unmanned aerial vehicle is used for collecting rose data and driving a picking mechanism to pick roses, an end effector on the picking mechanism is a cutting container with eight adjustable cutting blades, opening and closing of the cutting blades are controlled through a motor to achieve swallowing of the roses and cutting of pedicels, a draught fan is installed on a crawler, and the unmanned aerial vehicle drives the picking mechanism to pick the roses. According to the rose cutting unmanned aerial vehicle, the rotating blades in the draught fan are driven by the motor to rotate, the blades rotate to generate airflow, roses in the cutting container are sucked into the storage box on the crawler, the load of the unmanned aerial vehicle is reduced in the process, and the cruising ability of the unmanned aerial vehicle is improved; the unmanned aerial vehicle can automatically return to be charged when the electric quantity is insufficient, and the crash risk caused by electric quantity exhaustion is reduced.

The invention provides an Internet of Things data acquisition and abnormity early warning method and system, and relates to the technical field of Internet of Things data processing, and the method comprises the steps: constructing a digital twin model for a physical entity at a cloud end, and issuing prediction data to an edge node; acquiring sensor data by an edge computing node at a basic frequency, and computing a residual error between real-time data and predicted data and an information entropy of a residual error sequence; dynamically adjusting the data acquisition frequency of the edge node based on a two-dimensional decision space formed by the residual error and the information entropy; and when the residual error and the information entropy both exceed the threshold values, potential abnormity is judged, an edge side abnormity analysis program is triggered, and abnormal event information is uploaded. According to the invention, through cloud edge cooperation and intelligent decision making, the acquisition frequency is reduced in a stationary period, resources are saved, the frequency is improved when an abnormal symptom appears, monitoring is enhanced, and optimal balance between resource consumption and monitoring precision is realized; double criteria are adopted to effectively filter noise interference, and the false alarm rate and the missing report rate are reduced.

A wind turbine blade generative optimization design method based on a generative adversarial network comprises the steps that detailed data of existing wind turbine blades are obtained through a simulation algorithm, and a training data set is formed; and generating a new fan blade three-dimensional structure through the encoder and the generative adversarial network. And evaluating the power generation efficiency of the newly designed fan blade by using the generative adversarial network. And optimizing the fan blade design through adversarial training of the generative neural network and the predictive neural network, and finally outputting the optimized fan blade design. According to the method, the problems that multiple design targets such as aerodynamic performance, structural strength and cost are difficult to consider at the same time during fan blade optimization, and local optimal solutions are likely to be caught in are solved; through multi-target training, optimization design meeting multiple targets at the same time is generated, through feedback of a discriminator, the generated design is improved step by step, and the blade shape with the optimal comprehensive performance is found.

The invention provides a micro-seismic event identification method and device based on distributed optical fibers. The micro-seismic event identification method based on the distributed optical fibers comprises the following steps: acquiring energy channel data of distributed optical fiber sound wave data according to the channel number of the distributed optical fiber sound wave data of a target reservoir; determining a microseism event identification threshold according to the energy channel data; and recognizing the micro-seismic event of the target reservoir according to the energy channel data and the micro-seismic event recognition threshold. According to the micro-seismic event identification method based on the distributed optical fibers, the micro-seismic event identification curve can be obtained without channel-by-channel calculation, the micro-seismic event identification efficiency is improved, the storage amount of the acoustic wave data of the distributed optical fibers is reduced, and therefore the requirement for field real-time monitoring during fracturing is met, and the application prospect is wide. Moreover, the calculation process is simple and efficient, and technical support can be provided for microseism monitoring event identification and subsequent positioning during distributed optical fiber fracturing monitoring.

The embodiment of the invention discloses a value data processing method and device, electronic equipment and a computer readable medium. A specific embodiment of the method comprises the following steps: determining a similar value product information set and user group information; for each piece of similar value product information, executing a first generation step: determining a first matching information set; performing data screening on the similar risk circulation data set to generate a screened data set; for each piece of user information, determining second matching information; screening out at least one piece of user information, and generating an extended value transfer data sequence set; generating an extended data set; generating value risk information; and storing various data or information, and sending information links and link instructions to the value monitoring terminal. According to the embodiment, the value risk information for the target user can be accurately generated, and the value loss for the target user is avoided by executing the corresponding value operation on the target user.

The invention relates to the technical field of remote sensing image on-orbit real-time processing, in particular to a low-redundancy-rate image segmentation method and system based on an FPGA, and the method comprises the steps: determining the maximum physical size of a target object; calculating a first maximum pixel value occupied by the target type object in the remote sensing image according to the maximum physical size of the target type object; performing correction calculation on the maximum physical size according to the side-sway angle during satellite imaging to obtain the maximum physical size of the target object after side-sway distortion is considered; calculating a second maximum pixel value occupied by the target type object in the remote sensing image according to the maximum physical size of the target type object after side-sway distortion is considered; taking the maximum value from the first maximum pixel value and the second maximum pixel value as an overlapping region setting value; setting an overlapping region setting value as a sliding window segmentation step length; and carrying out sliding window segmentation on the segmented sub-graphs according to the sliding window segmentation step length. According to the method and the device, the minimization of the overlapped region of the segmented sub-images is realized, the segmentation redundancy rate is reduced, and the data storage and transmission quantity is reduced.

The invention relates to the technical field of industrial production state monitoring, and discloses a production state audit playback method and device, and the method comprises the steps: recognizing a measuring instrument and a valve as targets based on an upper process configuration image through employing a target detection technology, and binding the targets as an integral target; performing information identification on each overall target, and determining detailed information of each overall target in the upper process configuration picture; based on a video compression technology, determining a complete frame and a difference frame of a production state according to detailed information of the overall target; and obtaining a request playback time, constructing an initial picture of the production state according to the initial complete frame, and playing back the production state according to the time sequence of the subsequent complete frame and the difference frame. According to the invention, real-time monitoring and auditing playback of key equipment and parameters in the production process can be realized, and the efficiency and accuracy of production management are improved.

The invention discloses an embedded firmware camera data modification method and device, a medium and equipment, and relates to the technical field of computer vision, and the method comprises the steps: collecting image data in real time, and carrying out the primary processing; a frame difference method and a background modeling algorithm are adopted to carry out region division and priority distribution on the image data after primary processing, and a region priority mapping table is generated; extracting feature points of a high-priority motion area in the optimized image data through an optical flow method, and calculating a motion vector between continuous frames; according to the calculated motion vector between the continuous frames, an event-driven frame processing mechanism is triggered, and the frame rate and the processing precision are automatically adjusted; through the motion vector extracted through the optical flow method, the motion speed of the object can be calculated in real time, the frame rate and the processing precision are dynamically adjusted according to the motion speed, it is ensured that enough details are captured in the rapid motion process, and processing resources are saved in the static process.

The invention discloses a multi-cluster container scheduling method based on a mirror image layer in an edge computing environment, which comprises the following steps of: establishing a system model of a multi-cluster edge computing environment, and comprehensively describing communication modes inside and outside clusters, resource constraints of nodes, a dependency relationship of a container mirror image layer and the like; analyzing task scheduling requirements and optimization requirements of mirror image layer downloading, establishing a corresponding mathematical model by combining node computing resource limiting conditions, task scheduling requirements and mirror image downloading time delay which need to be met in an edge computing environment, and proposing a method for optimizing the task scheduling requirements and the mirror image downloading time delay by minimizing container starting delay. A scheduling optimization problem aiming at ensuring resource load balancing between clusters is solved; using a heuristic method to quickly screen out candidate edge nodes, and using linear programming to optimize task allocation; calculating the load balance degree between clusters in the edge computing environment, and adjusting the task allocation condition according to the resource load degree of the edge cluster; and for the mirror image layer downloaded by the edge node, the downloading sequence is optimized by using dynamic planning.

The invention relates to the field of numerical control machining and computer-aided manufacturing software, and particularly discloses a honeycomb core six-axis ultrasonic cutting track digital control and interpolation optimization system which comprises a track preprocessing module, a parameterized spline interpolation module, a six-axis linkage inverse solution module, a dynamic feed-forward compensation module, an ultrasonic energy adaptive control module and a multi-source information fusion and decision module. By the adoption of the technical scheme, smooth and efficient interpolation of a track, dynamic feed-forward compensation of motion errors and self-adaptive adjustment of cutting energy can be achieved, and therefore the machining precision, the surface quality and the system stability are improved. According to the invention, ultrasonic energy control is originally incorporated into an overall trajectory control closed loop, and real-time accurate regulation and control of cutting energy are realized through the ultrasonic energy adaptive control module. The module is based on force, amplitude, temperature and other multi-sensor information, adopts fuzzy reasoning to intelligently decide output power, and ensures that cutting energy is constant and optimal under different processing conditions.

The invention discloses a data acquisition method based on big data, and relates to the technical field of big data, and the data acquisition method comprises the following steps: obtaining to-be-acquired data; obtaining target collection data; marking target acquisition abnormal data; evaluating the influence degree of the target acquisition abnormal data; and judging whether the target acquisition abnormal data meets a preset data acquisition standard or not. Based on the anomaly recognition algorithm, anomaly detection and marking can be quickly performed on the target data, accurate positioning of potential problem data is realized, the data anomaly processing efficiency can be improved, the abnormal data is compared with the existing database through the evaluation algorithm, the influence degree of the abnormal data can be accurately evaluated, and the accuracy of data anomaly processing is improved. The method shortens the manual judgment time, judges whether the data meets the collection standard or not according to the evaluation result, can effectively eliminate irrelevant or low-value data, guarantees the high correlation and application value of the collected data, and reduces the processing cost of invalid data.

A data quantization method and device, a computer readable storage medium and a chip, the data quantization method comprising: obtaining a data block to be quantized, dividing the data block to be quantized into one or more data groups to be quantized according to a preset grouping method; performing quantization calculation on each data group to be quantized according to a target quantization format, to obtain quantization data corresponding to data in each data group to be quantized and quantization metadata corresponding to each data group to be quantized, wherein the quantization metadata is used for dequantization of the quantization data; and storing the quantization metadata corresponding to the data block to be quantized and the quantization data as a continuous data structure.

This invention relates to the field of photovoltaic monitoring technology, and discloses a photovoltaic monitoring adaptive sampling method, system, device, and medium based on multi-parameter coupled decision-making. The method includes: constructing a multi-dimensional environmental feature vector; dynamically setting a sampling threshold; determining the current operating mode and the corresponding baseline sampling period; introducing a hysteresis comparison mechanism and a delay confirmation mechanism to optimize the switching control of the operating mode; and introducing a correction coefficient to correct the baseline sampling period to obtain the final sampling period; performing mixed verification on the collected data and outputting data confidence labels; monitoring the operating status in real time; and judging and handling real-time operating faults based on the data verification results. This invention, by constructing a multi-dimensional environmental state vector, performing adaptive sampling based on the environmental state vector, and calculating the sampling period in real time, can output a clean dataset with confidence labels and back-optimize the threshold parameters of the decision layer.

This invention discloses a continuous synthesis apparatus and process for nitrobenzene. The synthesis apparatus includes a mixed acid preparation system, a continuous benzene nitration system, an organic phase continuous stratification system, and a continuous water washing-alkali washing-water washing system. The process includes: concentrated sulfuric acid and concentrated nitric acid are pumped into the mixing module of the mixed acid preparation system to obtain a mixed acid solution; benzene and the mixed acid solution are pumped into the microchannel reactor of the continuous benzene nitration system; the nitrated material is cooled and then pumped into the continuous organic phase stratification system; the resulting intermediate organic phase nitrate enters the next processing unit; the organic phase nitrate and water enter the continuous water washing-alkali washing-water washing system for washing to obtain the final product, nitrobenzene. The synthesis apparatus and process of this invention achieve continuous production of nitrobenzene throughout the entire process, realizing continuous and automated operation from raw material benzene to the final product, nitrobenzene.

The present application relates to the technical field of transport box, and discloses a transport box for egg circulation, which comprises a box body, a bottom plate arranged under the box body and used for supporting egg trays, a locking member movably arranged on the bottom plate and having a locking state and an unlocking state, a pushing assembly in transmission connection with the locking member, and a driving assembly used for driving the pushing assembly to move and switch the locking member between the locking state and the unlocking state. When the locking member is in the locking state, the box body is fixed relative to the bottom plate. When the locking member is in the unlocking state, the box body can be separated from the bottom plate. The transport box for egg circulation can realize overall loading and unloading through the separable design of the bottom plate and the box body. When loading, all the trays can be stacked on the bottom plate, and then the box body is buckled. When unloading, the box body can be lifted to expose the trays integrally, so that the loading and unloading time is greatly shortened, and the transport box is suitable for cooperation with automatic handling equipment and can realize rapid and batch circulation.