819 results about "Data redundancy" patented technology

The invention relates to the field of channel management, in particular to an intelligent channel management method and system based on multi-source data fusion. The invention discloses an intelligent navigation channel management system based on multi-source data fusion. The system comprises a combined ship identification module, a positioning frequency updating module, a collision risk assessment module and an intervention management module. According to the invention, the system can sense multi-dimensional environmental parameters such as illumination intensity, visibility, AIS signal quality, radar echo signal-to-noise ratio and the like in real time by constructing a combined ship identification model containing AIS, radar, vision and infrared, and dynamically adjust the fusion weight of each sensor to compensate for the deficiency of a single sensor; according to the method, the robustness of ship dynamic monitoring is improved, high-precision tracking can be kept in severe weather, the data redundancy contradiction of a traditional multi-sensor system is avoided, and therefore continuous and stable target state input is provided for follow-up risk analysis.

The invention discloses an edge-cloud collaborative civil aviation equipment intelligent detection platform, which relates to the technical field of intelligent detection and comprises an edge computing layer, a cloud computing layer, a collaborative scheduling layer, a data collaborative channel and an image processing layer. According to the method, a dynamic edge-cloud task scheduling mechanism is adopted, a traditional static task allocation mode is broken through, millisecond-level elastic task migration is achieved through real-time network state monitoring and computing power perception, incremental federated learning collaborative modeling and a gradient aggregation mechanism based on differential privacy are adopted, collaborative optimization of a multi-edge-node model is supported, and the task migration efficiency is improved. Multi-modal sensor fusion and lightweight anomaly detection are fused, the composite fault detection precision is improved, adaptive sampling and an edge knowledge graph are adopted, the sampling rate is dynamically adjusted based on the equipment risk level, offline rapid diagnosis can be achieved in combination with a graph database, and the problems that data redundancy is caused by fixed sampling, and the detection accuracy is high are solved. And the traditional system loses the diagnosis capability when the network is interrupted.

The invention relates to the technical field of three-dimensional model reconstruction, in particular to an existing building three-dimensional model reconstruction system based on point cloud data, and the system comprises a point cloud data processing module which carries out the octree recursive space subdivision of input point cloud data, builds a hierarchical index structure, and generates a multi-view fusion point set. In the method, octree recursive space subdivision is performed on the input point cloud data to establish a hierarchical index structure, so that the key area of the building surface can be positioned more accurately in subsequent data processing, data redundancy is reduced, and the precision and efficiency of three-dimensional model construction are improved; then voxel depth field data is obtained by calculating a truncation symbol distance function value of voxels, the description accuracy of a building surface structure is improved, and an initial surface grid is formed through contour surface extraction; meanwhile, plane slicing is conducted on the multi-view fusion point set in the preset building main axis direction, and contour details of all building structure sections are captured.

The invention discloses a flexible surgical instrument-oriented multi-source sensing data adaptive fusion method and a related device. The method comprises the following steps: acquiring optical, mechanical and pose multi-mode sensing data based on a timestamp synchronization technology in a working environment; clock compensation and space coordinate transformation based on an instrument kinematics model are carried out on the multi-modal data, and space-time registration is completed; inputting the registered data into a neural radiation field model to generate continuous space implicit scene representation; inputting the implicit representation into a pre-trained RWKV fusion network to obtain a fusion feature; and superposing the fusion feature and the original low-frequency component through residual connection, and integrating to generate three-dimensional voxelization environment state data. According to the method, multi-source information high-fidelity fusion is achieved through a single path, data redundancy is remarkably reduced, and the real-time performance and precision of intra-operative environment perception are improved.

The invention relates to the field of data processing, in particular to a data acquisition method and system based on a multi-source water affair sensor, and the method comprises the steps: calculating period relevance and trend relevance, and respectively obtaining abnormal probability predicted values based on a period rule and a trend rule, taking the period relevance and the trend relevance as weights, and carrying out weighted summation on the two abnormal probability predicted values to obtain a comprehensive abnormal probability predicted value; calculating the abnormal relevance of each kind of historical influence factor data, and obtaining the abnormal possibility of the water affair data according to the abnormal relevance; correcting the comprehensive abnormal probability predicted value according to the abnormal possibility of the water affair data to obtain a final abnormal probability predicted value; setting an acquisition frequency according to the final abnormal probability predicted value; and performing water affair data acquisition control based on the acquisition frequency. And the water affair data acquisition frequency is adaptively set, so that abnormal information can be grasped in time while data redundancy is reduced.

The invention relates to a business process standard dynamic optimization method and system based on a large language model, and belongs to the technical field of computer application, and the method comprises the following steps: constructing a data collection strategy template; performing anomaly detection on the key field based on a preset three-level anomaly detection mechanism to obtain standard structured data; constructing a process execution accurate quantitative evaluation model, and constructing a process topological graph by using a process mining technology; calculating an abnormal probability of each node in the process topological graph by using a multi-task graph neural network, and obtaining a high-attention area in the process topological graph based on the abnormal probability; analyzing the multi-source heterogeneous data associated with the high-attention area through a large language model, positioning root causes, calculating root cause influence weights, and obtaining a root cause list; establishing an intelligent bidirectional feedback mechanism to generate optimization suggestions of the standard process; according to the method, the problems of serious data redundancy, insufficient standard dynamics and lack of feedback mechanisms in the traditional flow standard optimization technology in the power industry are effectively solved.

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 discloses a power distribution network fault knowledge graph construction method and system based on multi-modal data, equipment and a medium, and belongs to the technical field of intelligent power grids, and the method comprises the steps: carrying out the change matching of spatial position signals and meteorological parameters based on regional monitoring space-time records, meteorological environment parameters and equipment operation state data, dividing time periods, and obtaining a power distribution network fault knowledge graph; and analyzing according to spatial position association, and screening data overlapping to obtain a multi-modal data association distribution table. According to the invention, through matching and screening of space-time records, meteorological parameters and equipment state data, multi-modal data integration and abnormal feature extraction are realized, and in combination with cross analysis of signal fluctuation and meteorological conditions, abnormal signal classification and path association are optimized, and a fault propagation rule is accurately analyzed. The overall optimization significantly improves the efficiency of abnormal recognition, fault location and propagation analysis of the power distribution network, enhances the operation reliability and fault processing capability, reduces data redundancy and errors, and improves the safety and stability of the power grid.

The invention discloses a tangelo ecological orchard data management platform, relates to the technical field of agricultural informatization and intelligent management, is used for solving the problem that decision-making efficiency is affected by data delay, and realizes real-time acquisition and classified storage of orchard environment data by arranging a sensor node network and a distributed storage system. Through data preprocessing and feature extraction, the data redundancy is reduced, and the analysis efficiency is improved; an orchard environment change trend is predicted by using a time sequence analysis algorithm, and a scientific basis is provided for resource allocation; the generation of a dynamic resource allocation scheme is realized by introducing an adaptive weight allocation mechanism and a resource priority index; and an interaction module is deployed at the orchard management terminal, so that a user can check data and adjust resource allocation in real time, and the intelligent level of orchard management is improved.

The invention relates to the technical field of catch safety tracing, and discloses a block chain verification method for catch quality safety tracing. The method comprises the following steps: acquiring environmental parameter data of catches in the links of fishing, transportation and storage through a distributed sensor network, and encrypting according to a timestamp sequence to generate a catches environmental data chain; key environment indexes are extracted based on the data chain, an abnormal deviation degree is calculated by using a dynamic weight distribution algorithm, and a catch quality risk score is generated; dividing risk levels according to the risk scores, mapping data of different risk levels to a block chain differentiation storage layer, and constructing a fragment storage structure; performing redundancy verification on the high-risk data through an intelligent contract triggering mechanism, and calling a consensus node to perform cross verification to generate a verification result; and performing association analysis on the verification result and the risk score, updating the risk tag and synchronizing the risk tag to a block chain distributed account book. According to the method, full-link data tracing and verification of the catches are realized, and the data credibility and the quality control accuracy are improved.

The invention relates to the technical field of data transmission optimization, in particular to a low-power-consumption data transmission optimization system and method for a smart watch. According to the method, the data transmission strategy is dynamically adjusted by combining the user behavior state, the data content characteristics and the system operation parameters. The system firstly senses the wearing state, interaction frequency and equipment resource information of a user, evaluates the priority of to-be-transmitted data, predicts the short-time attention probability of the user based on the behavior trend, and calculates the cognitive perception index of the data. And further comprehensively considering energy consumption, cognitive risk and resource occupation through a scheduling cost function, selecting an optimal transmission mode, and executing instant uploading, delayed caching, compression processing or communication protocol switching operation at an equipment end. According to the scheme, the data redundancy transmission frequency can be effectively reduced, the timeliness of key data is ensured, the overall energy efficiency of the system is remarkably improved, and the method is suitable for the smart watches in various dynamic use scenes.

The invention discloses a distributed data encryption transmission method and system, a storage medium and a program product, and the method comprises the steps: carrying out the layering and fragmentation processing of to-be-transmitted data, and obtaining a plurality of main fragments and a plurality of main fragment index tables; dividing each main fragment into a preset number of sub-fragments and generating a sub-fragment mapping table; generating verification data for each sub-fragment according to the data redundancy; the verification data and the corresponding sub-fragments are combined to generate redundant fragments; constructing a transmission control tree for each main fragment based on a network link state; generating node affinity according to the transmission control tree, and converting the node affinity into a time sequence key seed; performing encryption processing on the redundant fragments by using the time sequence key seed to generate a verification feature sequence; combining the redundant fragments with the verification feature sequence to form a transmission data packet; and transmitting the transmission data packet according to the node hierarchical relationship of the transmission control tree. According to the invention, the integrity and consistency of data fragmentation are improved, and the reliability and security of data transmission are further improved.

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 multi-edge-node collaborative industrial energy consumption data compression and storage method and system, particularly relates to the technical field of data compression and storage, and realizes hierarchical compression processing by layering industrial energy consumption data acquisition equipment into a main node, a core slave node and a terminal slave node. According to the method, various compression algorithms are used for carrying out lossy or lossless compression on different types of data, and cooperative compression and data redundancy elimination between edge nodes are realized under the coordination of a main node by introducing a distance calculation mechanism of'node hierarchy + auxiliary parameters'. The system constructs a node correlation graph through data abstracts, optimizes a data transmission path based on reinforcement learning, and adopts a cold and hot data classification strategy to improve the storage efficiency. The data compression efficiency and reliability are improved, and the method is suitable for real-time processing and long-term storage of large-scale industrial energy consumption data.

The invention relates to the technical field of building operation and maintenance management, in particular to a building operation and maintenance management lightweight method based on BIM data, and the method comprises the steps: generating a structured demand list through constructing a multi-dimensional operation and maintenance scene demand matrix, and carrying out the geometric simplification and attribute screening of a BIM model based on the demand list through employing a Revit secondary development technology, and storing the processed geometric data and attribute data in a classified manner through a data separation technology, and establishing an associated index, thereby finally realizing dynamic display of the lightweight model at a Web end. The problem of BIM operation and maintenance data redundancy and the problem of insufficient scene adaptation proposed in the background technology are effectively solved, and the operation and maintenance decision efficiency and the information interaction experience are remarkably improved through accurate attribute information matching.

The invention provides an intelligent micro-grid panoramic sensing system based on digital twinning, which relates to the technical field of micro-grid panoramic sensing and comprises a data acquisition layer, a data processing layer, a digital twinning model layer, an application service layer and a user interaction layer. A sensor is divided into a core layer, key equipment / nodes, an edge layer and general equipment / non-key nodes, a self-adaptive sampling frequency adjustment mechanism is introduced, the sampling frequency is dynamically adjusted according to the running state of the micro-grid, the frequency is reduced when the micro-grid is stable, and the frequency is improved when the micro-grid is abnormal, so that the sampling efficiency is improved. The problems of data redundancy and insufficient real-time performance of a traditional sensor network are solved, high-precision collection of key data, efficiency optimization of non-key data, collaborative data processing of edge calculation and federated learning are considered, a lightweight deep learning model is deployed at edge calculation nodes, and real-time data preprocessing, filtering, denoising and feature extraction are achieved.

The invention relates to the technical field of intelligent supply chain management, in particular to an intelligent procurement source searching method and system based on multi-target dynamic optimization and a real-time feedback mechanism, and the method comprises the steps: collecting full-link data of a supply chain in real time, and enabling the data to comprise enterprise internal ERP system data, supplier database data, market public opinion platform data and Internet of Things equipment data; the integrity and credibility of the data are ensured through an API interface and a block chain technology; carrying out denoising and normalization processing on the collected multi-source data, eliminating data redundancy and contradiction, and constructing a unified data model; the method has the beneficial effects that operational research, machine learning, a dynamic game theory and a real-time data analysis technology are fused, and an intelligent source searching decision system is constructed through a multi-target dynamic optimization model, a real-time feedback closed-loop system and a multi-source heterogeneous data fusion framework. The core technology covers a multi-target weight dynamic allocation model based on reinforcement learning, and collaborative optimization of dimensions such as cost, quality, delivery cycle, risk and the like is realized.

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 discloses a programmable hyperspectral laser radar device and a real-time registration method thereof.The programmable hyperspectral laser radar device comprises an optical module and a real-time signal processing module, the real-time signal processing module carries out real-time processing on received laser echo signals, and selects a windowing position and a spectral wavelength width based on converted non-laser echo signals; acquiring a plurality of arbitrary programmable spectrum channels, and reconstructing the acquired image information of the plurality of spectrum channels through a spectrum separation method to obtain different spectrum images corresponding to the target scanning area; and the data integration module is used for real-time registration of the laser point cloud and the hyperspectral image and assigning the three-dimensional coordinate information and the spectral information needing to be reserved to the laser point cloud. According to the technical scheme, a user is supported to define the windowing position and the spectral wavelength width, only the specific wave band is extracted, full-wave band data redundancy is avoided, the processing efficiency can be improved, meanwhile, key spectral features are reserved, and the method is suitable for specific scenes.

The invention provides a multi-modal data feature extraction and optimization method based on teleoperation robot tasks, and relates to the technical field of robot imitation learning, and the method comprises the steps: firstly carrying out the data optimization of multi-modal data obtained by a robot through setting the truncation proportion of time steps, and removing meaningless operation data; then, depth information is introduced into the RGB color image in the multi-modal data needing to be processed, and the environment perception capability is enhanced; and finally, performing multi-modal feature mining by applying a DINO technology, wherein the multi-modal feature mining is specifically realized through a pre-training DINOv2 model of a ViT architecture. Through an innovative data optimization and multi-modal feature extraction method, the problems of data redundancy, insufficient feature extraction and low multi-modal information fusion efficiency in imitation learning are solved, and a remarkable actual value is brought to a user.

The invention discloses an optical network mutual backup video data encryption method and system based on a distributed system, and belongs to the field of data encryption. The method comprises the following steps: generating a node trust evaluation value by acquiring optical network topology data including link state parameters and edge node dynamic load parameters; and adopting a distributed consensus protocol to screen edge nodes meeting the dynamic load fluctuation range and the trust evaluation value threshold as encrypted cooperative nodes. And generating a master key segmentation factor based on the node trust value, splitting the master key into sub-key fragments, partitioning optical network mutual backup video data, and binding and encrypting the partitioned optical network mutual backup video data with the sub-keys to form mutual backup encrypted data blocks, thereby realizing redundant storage of heterogeneous nodes across physical locations. According to the method, the initial encryption path is constructed according to the link state parameters, the transmission priority is dynamically disturbed by using the chaotic mapping sequence, the target encryption path adaptive to the storage requirement is generated, and finally the encrypted data blocks are distributed to the heterogeneous nodes through the path, so that the encryption efficiency and the data redundancy storage reliability can be improved.

The embodiment of the invention discloses a three-dimensional tennis track real-time reconstruction method and system based on multiple cameras, internal and external parameters and calibration results of the multiple cameras are utilized, respective two-dimensional pixel coordinates are converted into three-dimensional rays, and nodes are positioned in combination with a minimum distance algorithm. According to the embodiment of the invention, the real-time reconstruction of the three-dimensional track of the tennis ball is realized by virtue of the synchronous multi-camera two-dimensional image through the steps of camera accurate calibration, two-dimensional to three-dimensional ray conversion, intersection point calculation and screening, data output and the like. Compared with the prior art, the reconstruction precision is improved through accurate calibration and ray intersection screening; by means of multi-camera data synchronization and simple calculation process, data delay is reduced, and real-time performance is prominent; by means of multi-camera data redundancy and a placeholder mechanism, the robustness in a complex environment is enhanced; the output three-dimensional point data can directly support the functions of ball speed calculation and the like, and the expansibility is good.

The invention relates to the field of unmanned aerial vehicle communication security, and provides an unmanned aerial vehicle identity authentication method and system, and electronic equipment. The authentication request data packet comprises an identity label of the target unmanned aerial vehicle and first timestamp information when the authentication request data packet is generated; analyzing the authentication request data packet to obtain the identity label and the first timestamp information; verifying the identity label through a block chain network, and if the identity label passes verification, verifying the first timestamp information; and generating an identity authentication result and feeding back the identity authentication result to the target unmanned aerial vehicle. The method and the device are used for solving the defects of heavy calculation burden, data redundancy and high energy consumption in the scheme of the related technology, dual identity authentication can be performed through an identity identifier and timestamp information, the security is improved, and decentration can be realized by using a block chain network to avoid a single-point fault.

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 an equipment health assessment method based on artificial intelligence and electronic equipment, and belongs to the technical field of equipment assessment. The method comprises the steps that firstly, heterogeneous data such as sensor data, historical maintenance records and environmental parameters in the equipment operation process are acquired through a multi-source data acquisition system, and performance index data of equipment manufacturers and user feedback information are integrated; then, a multi-step preprocessing process including data cleaning, feature extraction, normalization processing, feature dimension reduction based on principal component analysis and the like is adopted, and the problems of data redundancy and noise interference are effectively solved; a deep learning model based on a convolutional neural network or a recurrent neural network is constructed, and a dynamic weight adjustment mechanism is introduced, so that the model can adapt to running state changes of different devices; and finally, outputting an evaluation report containing health grade division and future state prediction, and generating corresponding early warning information and maintenance suggestions.

The invention relates to the technical field of cloud computing, and discloses a data processing method and system based on cloud computing, and the method comprises the steps: dividing a cloud computing system into a data storage unit, a computing resource scheduling unit and a task distribution unit, determining the parameters of each unit, carrying out the modeling, constructing a distributed mathematical simulation model, and solving to obtain an initial simulation result. By establishing a dynamic programming model and a genetic algorithm model, computing resource scheduling and task allocation are optimized respectively, and a data redundancy strategy and a fragmentation rule are determined at the same time. The minimum system total energy consumption, the minimum task average time delay or the maximum resource utilization rate serves as a global optimization objective function, and the convergence condition is judged to optimize system configuration. According to the method, the data storage reliability, resource scheduling reasonability and task allocation efficiency of the cloud computing system are effectively improved, global optimization of the system is realized, and the method has a good application prospect.

The invention provides a winter wheat soil moisture inversion method and system based on an improved water cloud model, storage equipment and electronic equipment, and the method comprises the following steps: obtaining total backscattering characteristics VV and VH, an incident angle and a plurality of polarization characteristic parameters through employing a single-view complex image and a geocoding radiation correction image; calculating a vegetation index from the preprocessed optical image by applying a wave band calculation mode; an improved water cloud model method is applied to correct the radar backscattering coefficient affected by the winter wheat plants, the backscattering coefficient of the earth surface is obtained, and data redundancy is reduced; performing hyper-parameter optimization of the CNN network by applying a CPO algorithm; and constructing a CNN-CPO-RF model by using CNN and RF models to carry out soil moisture estimation. According to the invention, a more accurate soil moisture result can be obtained. According to the method, the robustness of farmland surface soil moisture inversion is remarkably improved, and a new technical means is provided for performing soil moisture inversion by using a satellite-borne SAR image.

The invention aims to provide an abnormal access detection method and system based on data analysis, and relates to the technical field of network data communication, user behavior classification is carried out based on a support vector machine, only normal behavior data is needed to establish a detection model, abnormal data is not needed, the model training complexity is highly simplified, and the user experience is improved. Compared with a traditional method, the OCSVM has distinct advantages in the aspects of single-class data processing and anomaly detection; according to the method, log data are analyzed by using an LEX lexical analyzer and a YACC syntactic analyzer, high-precision description of user behaviors is realized, feature selection is performed on original logs, sensitive information such as user names, operation time and client IPs is extracted, data redundancy is reduced, and high efficiency of model training and recognition is ensured; according to the anomaly detection method provided by the invention, the user behavior anomaly information in the database can be efficiently analyzed, and an alarm can be given in time and necessary countermeasures can be taken, so that potential safety risks or data leakage can be prevented.