493 results about "Data redundancy" patented technology

The invention relates to the technical field of data recording, in particular to a data recording system for an automatic driving vehicle, and the system comprises a multi-source signal synchronization module, a behavior stage recognition module, a track abnormity elimination module, a dynamic compression adjustment module and a segmented index marking module. According to the method, a unified time alignment mechanism is constructed in a signal synchronization processing link, a strict corresponding relation between acquisition sequences is ensured, key time sequence characteristics of driving behavior switching are extracted by adopting a multi-behavior parameter comprehensive judgment method, segmented expression of a complex driving process is realized, and the accuracy of driving behavior switching is improved. According to track anomaly identification, through collaborative screening of inertial data and a positioning track, abnormal fragments which do not conform to physical rules are effectively eliminated, data compression dynamically sets interval length according to a parameter combined fluctuation trend, the fidelity of a key period is remarkably improved, non-key data redundancy is inhibited, and after a segmented index structure is established, the data compression efficiency is improved. And the data retrieval process is clearer.

The invention provides a wireless sensing platform with an edge computing function, and relates to the technical field of wireless sensors. In order to solve the problems that in the prior art, a wireless sensor is large in data redundancy, high in transmission delay and weak in emergency recognition and response capacity in a complex scene, a joint processing mechanism combining a causal event recognition assembly, a causal data fusion assembly, an event separation assembly and a data distribution assembly is adopted. By detecting a causal change event in an environment and dynamically extracting and fusing sensor data with a strong causal relationship, redundant data transmission caused by emergencies is reduced, transmission delay is reduced, the robustness of causal data fusion in a concurrent event scene is improved, and the reliability of data fusion is improved. According to the invention, efficient and accurate sensing and response to emergencies in a complex environment are realized.

The invention relates to the technical field of chip design, and provides a critical path screening method based on a chip time sequence report, which comprises the following steps: collecting chip time sequence report data, screening out first N critical paths from the data based on time sequence index data, and establishing a path ID-delay type-scene three-dimensional data model; and calculating the delay ratio of the unit delay to the interconnection line delay in each critical path. Performing single-type delay, type relevance and RC condition influence comparison on different scenes to obtain delay characteristic data. The invention discloses a system for executing the method, which can avoid full data redundancy and improve the analysis efficiency by screening the first N critical paths. And the delay change rule in different scenes is quantified. The delay characteristic data reflects the actual influence of the key path delay on the time sequence, so that a designer can quickly position the delay bottleneck of the key path, and a basis and a reference are provided for the targeted optimization of the total delay of the chip in the later period.

The invention discloses a power grid big data closed-loop monitoring system and a risk assessment method, and relates to the technical field of industrial data management. The power grid big data closed-loop monitoring system and risk assessment method comprises the following steps: S1, preprocessing physical quantity time sequence data, operation state data and static configuration data; s2, performing complete fidelity of the physical quantity time series data and allocation of cache resources according to the health measurement and the cache evaluation result; s3, redundant data screening, abnormal fragment repairing and high-confidence data reconstruction are executed according to an index confidence fusion analysis result; s4, carrying out multi-level risk and clustering analysis, judging a risk state, and carrying out linkage regulation and control response; and S5, integrating to health visualization and operation and maintenance linkage processing according to a global health situation analysis result. The problems of monitoring result distortion and risk closed-loop chain scission caused by the problems of data redundancy superposition and risk time period information loss due to imbalance of time slot scheduling and a cache mechanism are solved.

A tunnel lining development view-based method and apparatus for identifying spatial distribution characteristics of cracks, relating to the technical field of tunnel engineering. On the basis of a tunnel lining crack section analysis-intended area panoramic development view, in combination with a crack defect deep learning model and a conventional image processing method, the present invention can effectively reduce data redundancy, and efficiently and accurately implement multi-scale crack identification. Deep learning implements crack defect positioning and extraction at a large scale, conventional image processing implements crack defect parameter extraction at a small scale, and then a kilometer-scale tunnel crack spatial distribution and parameter display model is constructed. The present invention can intuitively obtain overall spatial distribution characteristics and related quantitative parameters of tunnel lining crack defects in a three-dimensional space at a kilometer level, such as a crack length, a crack width, a mileage position where a crack is present, and a cross-sectional position where a crack is present, thereby providing an important basis for subsequent specific analysis of the crack defects.

The invention provides an automatic report generation method and system based on multi-source data integration and a medium, and relates to the technical field of data processing, cross-service-end redundancy check integration of a plurality of standardized data streams of a plurality of source service ends is received through a data integration cloud end, a uniform resource pool is output, and the report generation efficiency is improved. Dynamically extracting matched data in the uniform resource pool according to a real-time association label combination output by a user, and generating a temporary data set; and converting the temporary data set into a permission filtering report by taking the consulting permission of the user as a field filtering strategy. The technical problems that in the prior art, the mixed data storage precision is low, data redundancy is caused by lack of a conflict resolution mechanism, and then cross-system data calling response is delayed and the report error rate is high in subsequent practical application are solved. The technical effects of improving the data storage precision of multi-source heterogeneous data mixed storage, and improving the data calling speed and report output accuracy are achieved.

The invention discloses a point cloud intelligent cutting and dynamic optimization method based on edge computing, and belongs to the technical field of intelligent traffic systems and edge computing, and the method comprises the steps: collecting three-dimensional point cloud data through a multi-line laser radar disposed on an RSU; generating a three-dimensional bounding box through a three-dimensional target detection network in an RSU edge calculation unit, generating an ROI mask, expanding a buffer region, and performing adaptive region cutting on the point cloud to form an ROI point set and a background point set; a perception-driven cutting decision is realized, and ROI extraction is converted into a self-learning process based on semantic features from fixed threshold judgment; voxelizing the non-uniform resolution point cloud, generating a unique hash index for each voxel unit, comparing a hash set of a current frame with a hash set of a previous frame, extracting newly added and disappeared point sets, and constructing a differential data packet according to a change ratio; while high timeliness is kept, data redundancy is reduced, and the point cloud updating rate and the incremental transmission efficiency are improved; and dynamic optimization of communication-calculation cooperation is realized.

The invention relates to the technical field of optical fiber sensing, in particular to a tunable laser parameter optimization method for fiber bragg grating demodulation, which comprises the following steps of: setting scanning ranges of currents of a left Bragg reflector and a right Bragg reflector, generating a current combination sequence according to arc radius parameters, inputting the current combination into a tunable laser, collecting an output wavelength, and identifying a wavelength tuning channel and a central point thereof; and arranging the center points of the channels in sequence, and generating smooth and continuous channel center lines on the left current dimension and the right current dimension by adopting cubic spline interpolation so as to obtain a continuous and executable tuning path. According to the method, a smooth and continuous channel center line is constructed through a segmented arc scanning path and cubic spline interpolation, and an initial lookup table is formed by combining phase current fine scanning and ordered splicing, so that high-quality wavelength coverage under low sampling amount is realized, and the problems of data redundancy and channel boundary mode hopping in a traditional method are solved.

The invention discloses an efficient double-link block chain consensus method and device, equipment and a storage medium, and belongs to the field of information security. The method comprises an initialization stage, an input stage, a coding stage, a broadcasting stage and a consensus stage. According to the invention, the parallel design of a coding broadcast link and a consensus confirmation link is adopted, so that the occurrence frequency of transaction congestion is reduced; the integrity required by the asynchronous Byzantine consensus process is realized through three-step broadcasting, the updating problem of the post-quantum component does not need to be considered, and the security is higher; an asynchronous network and a Byzantine opponent model are adopted, so that malicious node attacks which do not exceed the total number of nodes can be tolerated on the premise of not setting a network time delay upper limit; in the encoding stage, the generation of the redundancy share through the RS erasure code can be realized by adopting preprocessing, the data redundancy expansion multiple in the broadcasting stage is far smaller than that of the same type of protocol, and the original transaction can be recovered by collecting part of effective shares in the consensus stage, so that the system time delay is effectively reduced, and the system throughput is improved.

The invention discloses a tunnel face geological information acquisition and image processing method based on machine vision, which belongs to the technical field of image processing, and comprises the following steps: resolving the pose of a terminal in real time through a visual inertial odometer, generating augmented reality guide information, and guiding the terminal to move to a standard acquisition point; at each acquisition point, performing multi-stage verification of scene semantic compliance, motion blur and defocus and illumination uniformity on the image flow to ensure the acquisition quality; for tunnel high-dynamic illumination, adopting an adaptive brightness segmentation and multi-resolution fusion technology based on an improved Otsu algorithm to perform high-dynamic range reconstruction on a single-frame image so as to recover details; and finally, performing clustering optimization based on the feature matching similarity, and screening out an optimal image subset for three-dimensional reconstruction. According to the method, the problems of subjective and random acquisition process, many image quality defects, detail loss under extreme illumination and large image data redundancy are solved.

The invention discloses a power distribution network small current grounding fault diagnosis positioning method and system based on edge calculation, and belongs to the field of power system automation, and the method comprises the steps: extracting and recognizing transient features, if a suspected grounding fault is detected, carrying out the multi-source feature fusion analysis through a regional main node, and constructing a transient response time sequence map; a weighted fuzzy clustering algorithm is combined with a multi-channel criterion to determine a fault channel, and a specific fault branch is further positioned through a topology tracking algorithm; introducing a multi-scale wavelet packet energy analysis method into the regional main node, performing energy inversion calculation on the transient current signal under multiple frequency bands, and identifying an energy concentration region near the grounding point through an energy distribution gradient; and constructing a robustness data redundancy model based on the historical data of the edge nodes and the topological relation, and identifying and eliminating abnormal feature data containing noise or distortion. According to the method, transient characteristics are rapidly extracted and preliminary judgment is made after a fault occurs, so that the real-time performance of fault response is greatly improved.

The application is suitable for the technical field of computers, and provides a named entity recognition method, including: acquiring a to-be-recognized text, and converting the to-be-recognized text into a first matrix of n*k dimensions; performing multi-layer convolution layer convolution on the first matrix, wherein the last convolution layer of a convolution kernel in the multi-layer convolution layer has a channel number of m, four convolution operations are performed on the last convolution layer, and four parallel second matrices of n*m dimensions are obtained; performing attention weight self-adaption on three second matrices of the four second matrices to obtain a third matrix of n*m dimensions, performing matrix addition on the third matrix and the remaining one second matrix, and outputting a fourth matrix of n*m dimensions; performing classification on the fourth matrix, and outputting an entity label corresponding to the to-be-recognized text; and outputting a named entity corresponding to the to-be-recognized text according to the entity label. By introducing an attention mechanism in the convolution layer, data redundancy is effectively reduced, the number of model parameters is reduced, and the recognition speed of the named entity recognition model is accelerated.

The application provides a road slope stability prediction method and device, a storage medium and an electronic equipment, which comprises the following steps: collecting multi-dimensional environmental parameters of a road slope in real time to form an original parameter set; analyzing the original parameter set based on a Granger causality test algorithm to determine the causal influence relationship between parameters, construct a parameter causal network, and screen out key parameters; calculating the similarity of each key parameter in the time and space dimensions through a dynamic time warping algorithm to generate a space-time correlation matrix; weighting and fusing the key parameters based on the space-time correlation matrix to obtain a fused parameter; inputting the fused parameter into a pre-trained slope stability prediction model to obtain a predicted value of slope stability; and outputting a slope instability warning information when the predicted value exceeds a dynamically updated warning threshold. In the application, the defects of inaccurate prediction and data redundancy of the current road slope stability prediction method are overcome.

The embodiment of the invention discloses a parameter file management method, and the method comprises the steps: reading a parameter file needed by each application node in a vehicle from a persistent storage region of the vehicle after a starting instruction of the vehicle is received, and writing the parameter file into a volatile storage region of the vehicle; and for any first application node in the application nodes, if a parameter reading instruction sent by the first application node is received, returning a required first parameter file to the first application node from the volatile storage area, so that the first application node is started and operated based on the first parameter file. According to the embodiment of the invention, various parameter files originally dispersed in a persistent storage area are read into a high-speed volatile storage area at one time through a centralized loading strategy during starting, and a uniform parameter access center is established. The mechanism fundamentally solves the problems of data redundancy and consistency caused by parameter decentralized management, and lays a high-performance foundation for subsequent access.

The invention discloses a map construction method of a three-dimensional Gaussian SLAM (Simultaneous Localization and Mapping) algorithm based on depth information fusion, which comprises the following steps of: firstly, adopting a dynamic point cloud downsampling method based on depth information, so that the defect that structural information is easy to lose due to the fact that point cloud spatial distribution and geometric characteristics caused by the depth information are not considered in traditional random downsampling can be overcome; the sampling rate is dynamically adjusted through depth information, a key geometric structure is reserved while data redundancy is reduced, good geometric priori is provided for generation of Gaussian point clouds, and the generated Gaussian point clouds are more fit with the surface of an object; moreover, according to the method, the optimization efficiency in the map reconstruction process is improved through the perspective principle of depth information adaptive point size and simulation of human eye observation, and the geometric error of luminosity rendering of the reconstructed map is smaller; in addition, the depth information is optimized by utilizing multi-frame depth fusion, so that the geometric position generated by the Gaussian point cloud is more accurate, and the subsequent optimization time is reduced.

The invention provides a multi-source heterogeneous data fusion driven bridge tower construction scene digital twin modeling system and method. The system comprises a knowledge graph construction module, a multi-source heterogeneous data association fusion module and a bridge tower construction scene modeling module. According to the method, accurate fusion of multi-source heterogeneous data is realized through semantic guidance of the knowledge graph, the problems of data redundancy and semantic fracture are solved, and time-space consistency of a data set is guaranteed. And on the basis of a verification standard and a quantization threshold value, constructing and updating a control scene in the whole process, and ensuring that a virtual scene is highly matched with a physical construction scene. And a static framework building, dynamic local increment or alternative updating mode is adopted, so that the efficiency is greatly improved, and real-time change of construction is adapted. A guiding mechanism and a verification standard are automatically generated, only a special scene needs a small amount of manual calibration, manual dependence and errors are reduced, and modeling semi-automation is achieved. The digital twinborn model provides a reliable decision basis for construction management and control, helps to reduce cost and increase efficiency, and improves construction safety and quality stability.

The invention discloses a frequency converter abnormal state detection method based on time series data analysis, which belongs to the technical field of electric power, and comprises the steps of: finally generating a structured abnormal event through acquisition, preprocessing, feature extraction, anomaly detection, anomaly level judgment and trend analysis of data of a multi-source sensor; accurate prediction and early warning of the operation state of a complex system are achieved, the technical problems of high real-time performance, low data redundancy and multi-source feature fusion of abnormal monitoring of the frequency converter are solved, redundant data are reduced, the data volume is reduced, the communication bandwidth pressure is reduced, it is ensured that high-frequency, low-frequency and event data are aligned on the same time axis, and the accuracy of abnormal monitoring of the frequency converter is improved. Abnormal data are transmitted preferentially, abnormal information is guaranteed to be complete and reliable, suspicious anomalies are screened rapidly, local and remote alarms are generated according to the abnormal levels, an abnormal database and a periodic summary report are stored, and operation and maintenance and remote monitoring are facilitated.

The invention discloses a multi-source heterogeneous scheduling behavior data integration method and system, and the method comprises the steps: extracting data from SCADA, WAMS, OMS and GIS systems, analyzing a data mode, and constructing a metadata dictionary; a conversion rule is defined to map the relation mode into an ontology class, conversion from the data mode to the ontology is achieved through a mapping tool, and instance data is introduced; performing preprocessing based on attribute names, dividing subsets according to data types, calculating an attribute similarity matrix, and matching similar concepts; merging data according to calculation results of the similarity matrix, performing power dispatching behavior analysis, identifying abnormity and providing early warning; according to the method, real-time acquisition, cleaning and conversion of multi-source heterogeneous data of SCADA, WAMS, OMS, GIS and the like are realized, the problems of data redundancy, format errors, semantic conflicts and the like in a traditional method are effectively solved, and the consistency and accuracy of data integration are remarkably improved.

The invention discloses a non-invasive blood pressure simulator fault intelligent diagnosis method, and relates to the technical field of medical equipment fault detection, and the method comprises the steps: preprocessing the operation data of a non-invasive blood pressure simulator to obtain a data matrix; performing principal component analysis on the data matrix to obtain a plurality of principal components, constructing a principal component projection matrix based on the principal components, and performing dimension reduction on the data matrix based on the principal component projection matrix to obtain principal component data; obtaining score data based on the principal component data and the principal component projection matrix, and weighting the score data to obtain an abnormal score; if the abnormal score is larger than or equal to a preset abnormal threshold value, it is judged that the non-invasive blood pressure simulator is abnormal, and the problem that an existing intelligent fault diagnosis method for the non-invasive blood pressure simulator has high-dimensional data redundancy and feature coupling and cannot adapt to abnormal mode diversity, and consequently fault recognition accuracy is low is solved.

The invention provides an Internet of Vehicles redundant data supplementary transmission method and system based on dynamic priority scheduling, and relates to the technical field of new energy vehicle Internet of Vehicles. The method comprises the following steps: acquiring multi-mode vehicle state time sequence data; analyzing the multi-modal vehicle state time sequence data through an early warning prediction model, and predicting the early warning probability of the data; determining a multi-modal data supplementary transmission priority based on the timeliness, the data volume and the early warning probability of the multi-modal data; performing hierarchical compression processing on different types of data based on the multi-modal data supplementary transmission priority to obtain compressed data; and storing the compressed data into the emergency cache region and / or the main storage region according to a preset rule, and carrying out supplementary transmission according to the supplementary transmission priority after the network is recovered. The method and the device are used in the process of complementary transmission of redundant data of the Internet of Vehicles based on dynamic priority scheduling, and solve the technical problems of high redundancy overhead and low complementary transmission efficiency of vehicle-mounted complementary transmission data in the prior art.

The invention provides a landslide susceptibility evaluation method based on a deep neural network and considering time sequence InSAR and time sequence rainfall, belongs to the technical field of geological disasters and geographic information, and particularly relates to a landslide susceptibility prediction method based on the deep neural network. The method comprises the following steps: establishing a buffer area through landslide points, selecting a research area to randomly generate non-landslide points, screening related static characteristic factors by using a Pearson's correlation coefficient matrix and a VIF method, obtaining GCP points by using PS-InSAR, introducing the generated points as SBAS-InSAR processing parameters, generating dynamic characteristic factors of surface deformation, and calculating the deformation of the surface deformation. The time sequence average rainfall of the region is obtained through spatial interpolation, the rainfall before earth surface deformation is obtained through data processing codes to serve as another dynamic characteristic factor, finally evaluation is conducted through the constructed ResNetconvLSTMUnet, and the susceptibility index is divided into five grades; according to the method, the dual-time-sequence dynamic factors are creatively fused, the spatial-temporal feature extraction capability is enhanced, the data redundancy is reduced, the sample selection is reasonable, the evaluation precision is high, and scientific support can be provided for landslide disaster prevention and reduction.

The utility model relates to ultrasonic water meter manufacturing technical field especially a kind of multi-transducer integrated measurement assembly and multi-acoustic-path fluid measuring system.The multi-transducer integrated measurement assembly includes carrier and multiple transducers, and transducer is fixed with carrier as basis.Carrier is injection molded part, and transducer can be pre-placed in injection mold cavity molding;Or carrier is equipped with multiple installation cavities, cooperate pressing plate and locking unit to fix transducer, limit its radial and axial displacement degree of freedom.In this way, on the one hand, the same carrier carries multiple transducers simultaneously, integrated design greatly saves space, breaks the limit of traditional dispersed layout;On the other hand, transducer is fixed with carrier as unified reference, reduces the cumulative error when installing, guarantees the accuracy of acoustic path;In addition, transducer is asymmetrically distributed, when multiple assemblies measure together, the formed fluid channel is staggered and projection is not overlapped, avoids the interference between acoustic path, is conducive to eliminating data redundancy and other problems.

The invention discloses a three-dimensional environment reconstruction and robot attitude synchronization method based on vision, and aims to solve the problems of high cost, high power consumption, data redundancy and asynchronization of pose and map space-time reference caused by dependence on laser radar in the prior art. The method comprises the following steps: controlling a robot carrying a depth camera module to collect multi-angle image data at a stationary point by adopting a stationary point scanning mode triggered as required, and generating local point cloud; and aligning the plurality of local point clouds to a global coordinate system through coordinate transformation according to the precise pose parameters of the robot, and fusing to generate a global three-dimensional map which is strongly coupled and bound with the pose of the robot. According to the invention, only a low-cost depth camera module is needed, the system cost, the power consumption and the data load are remarkably reduced, and accurate synchronization of the map and the pose is ensured.

The invention discloses an edge cloud collaborative multi-modal data real-time processing method and system. The method mainly comprises the steps that 1) a data sensing system directionally collects multi-modal data; 2) the edge device preprocesses the multi-modal data and uploads the multi-modal data to the cloud computing center; 3) the cloud computing center constructs a multi-modal data model to extract high-order feature vectors of the multi-modal data; 4) the cloud computing center performs data fusion processing on the extracted feature data to obtain fusion feature data with unified dimensions, and transmits the fusion feature data to the edge device; and 5) the edge device performs post-processing and application output on the fusion data. According to the invention, multi-modal data is processed through edge cloud, multi-source information is fused and real-time response is carried out to improve processing efficiency and accuracy, bandwidth pressure and cost are reduced through local preprocessing and edge resource scheduling, and system reliability is enhanced through a distributed architecture and data redundancy.

The application discloses a real-time communication and data interaction method for coordinated operation of energy storage power stations and power grid dispatching, and relates to the technical field of data processing.The application solves the technical problems that transmission rate control is only dependent on simple indexes and fixed adjustment mode, cannot adapt to dynamic changes of network load, and has data redundancy and low transmission efficiency in the transmission process.The application can more flexibly and accurately adjust the data transmission rate according to the real-time changes of the network load, improve the data transmission efficiency and network utilization, and guarantee the stability and smoothness of data transmission by constructing a multi-dimensional load monitoring system, comprehensively considering indexes such as CPU usage, memory usage, network bandwidth utilization and packet loss rate, and processing the data through binary conversion, segmentation, recombination and encryption, classifying and recombining the data according to the total sum of binary numbers, combining parity encryption, and enhancing data security; and adding meta information header to integrate data and optimize the transmission structure.

The invention discloses a privacy data desensitization method based on a big data algorithm, and relates to the technical field of data desensitization. In the analysis process, all dynamic desensitization departments of different static features are determined based on the access quantity and access time of business personnel of different business departments to each static feature, and feature data of static features which are not frequently accessed by different departments are inquired and desensitized in a dynamic desensitization mode. And synchronously deleting the desensitized data which is inquired and desensitized by adopting a dynamic desensitization mode from the storage management module, so that on one hand, the situation that the storage space occupation amount is unreasonably increased due to a processing mode of one department and one desensitized copy is avoided; and on the other hand, when the original data is updated or the desensitization rule is adjusted, all copies do not need to be synchronously updated, so that the problems of data redundancy, version chaos and the like are avoided.

The application discloses a resource allocation method and system for C-RSMA auxiliary digital twin network joint device pairing, and belongs to the technical field of wireless communication and digital twin fusion. In view of the problems that the channel difference of a digital twin network is large, data demand is unbalanced, and data redundancy and high system time delay are caused by the fact that the prior art does not combine modeling accuracy constraints, a C-RSMA digital twin uplink transmission system model is first constructed, near-end-far-end device pairing is completed through a two-stage strategy, the minimum transmission data amount of the device is determined in combination with the modeling accuracy constraint, and then the joint optimization of power allocation, time allocation factor and computing resource is completed through an alternating optimization framework, so that the overall generation time delay of the digital twin system is minimized. The application can significantly improve the transmission reliability of edge devices and the system resource utilization rate, and is suitable for a large-scale multi-device access digital twin commercial scene.

The invention relates to the technical field of computers, and particularly discloses a bill information storage method and device, equipment, a storage medium and a program product. The method comprises the following steps: acquiring first bill information of a first sub-bill and first complete circulation information of the first sub-bill, wherein the first complete circulation information comprises circulation information corresponding to each node in the circulation process of the first sub-bill; obtaining second complete circulation information according to the first bill information; deleting circulation information the same as the second complete circulation information from the first complete circulation information to obtain a first part of circulation information; acquiring first splitting process information of the first sub-bill based on the first bill information; and correspondingly storing the first splitting process information, the first bill information and the first part of circulation information into a circulation record database. By adopting the method, the query efficiency of the bill circulation process can be improved while data redundancy is avoided.

A kind of frame extraction method for road test vehicle image information, comprising: obtaining road test vehicle image information, and marking candidate key frame in road test vehicle image information;According to the marked candidate key frame, based on actual demand, relevant threshold setting and index calculation;According to the set threshold and calculated index, key frame is marked;According to the marked key frame and set threshold and calculated index, similar frame is marked;According to neural network model, no key target frame is marked;According to the marked key frame, similar frame and no key target frame, image information is frame extracted.The present application is highly adaptable to the business scene of automatic driving, and can more effectively solve the data redundancy problem of storage end.Free limited human resources, and then improve the execution efficiency of the entire automatic driving tool chain.And different content section image scene is processed by divide and conquer, and the frame extraction result is more reliable.

The application discloses a kind of real-time acquisition method and system of key index of fuel oil production, it is related to data acquisition technical field, by determining all key indexes of fuel oil production, different index set is grouped based on index aggregation degree, establishes associated archive tree, establishes data lock window, each index set is set as data key, according to the whole data key of data lock window adaptation associated archive tree, set acquisition target in data lock window, establish the relationship grid of acquisition target and data key, finally the target information of acquisition target is searched along relationship grid, obtain all search points satisfying search condition, integrate the search data of each search point as the key index of fuel oil production.This application realizes hierarchical archiving according to process by index aggregation degree, avoids undifferentiated acquisition of total amount, reduces data redundancy, with the aid of data lock window and relationship grid, realize the dynamic accurate mapping of acquisition target and key index, improve the efficiency and accuracy of data acquisition.