502 results about "Extraction algorithm" patented technology

The invention discloses a VLM model intelligent decision-making-based driving method and device and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: extracting key visual features from continuous multi-frame driving scene images based on a preset visual feature extraction algorithm; inputting a user instruction and a time sequence visual Token corresponding to the key visual features into a preset VLM model for multi-modal alignment, and generating a target planning Token; inputting the target planning Token and the time sequence vision Token into a preset trajectory generation model to obtain a predicted trajectory; and converting the predicted trajectory into a control signal, and controlling the mobile device to complete a moving action based on the control signal. The problem of modal difference between a semantic space and an action space is solved.

The invention provides an aluminum film sealing defect real-time detection method and system based on multi-algorithm fusion, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: triggering an industrial camera at a detection station to collect an original image of a pesticide aluminum film sealing on a conveyor belt; performing adaptive equalization operation on the original image through pixel brightness distribution data, eliminating light fluctuation and surface reflection interference, and outputting a standardized image; three types of defect detection are synchronously executed based on the standardized image, a dynamic threshold segmentation algorithm is combined with local area brightness analysis to detect edge damage, a contour extraction algorithm is adopted to calculate bottleneck center offset to recognize seal offset, wrinkle defects are recognized based on a surface texture feature analysis algorithm, and a primary detection result is output. The aluminum film sealing defect detection method is based on multi-algorithm fusion, has strong anti-interference capability, real-time detection performance and data traceability, and provides an efficient and reliable automatic solution for aluminum film sealing quality management and control.

The invention relates to the technical field of data processing, in particular to an accelerometer vibration rectification error analysis method, which comprises the steps of receiving multiple paths of accelerometer original current signals, processing acquired data and outputting a feature point coordinate sequence with confidence; according to the confidence and the optimization algorithm combination in the frequency band distribution characteristic dynamic scheduling knowledge base, calculating a local statistical data set; inputting the data set into a mechanical dynamics model to execute trajectory simulation, and inputting a control model reconstruction signal to compare a dual-path positioning drift distance difference to generate an error correction coefficient matrix; outputting a knowledge base updating instruction and a parameter resetting instruction by adopting a matrix reconstruction vibration rectification error quantized value; a quantized value is imported to calculate theoretical angle deviation and verify convergence, and the neural network is triggered to be retrained when the deviation exceeds a threshold value. Through a non-smooth feature extraction algorithm and a closed-loop feedback mechanism, the problem of signal distortion caused by smooth operation in the prior art is solved.

The invention discloses a writing brush calligraphy practice correction system based on real-time handwriting analysis. The system comprises a sensing layer used for collecting handwriting tracks, physiological signals, environmental parameters and ink mark characteristics; the edge calculation layer is used for executing noise filtering, coordinate system normalization, multi-modal data alignment and feature primary extraction through an Apache Kafka data pipeline; and the algorithm analysis layer is used for carrying out super-long calligraphy stroke sequence modeling by adopting an S4 architecture time sequence model, introducing a Neural ODE module for modeling, capturing dynamic characteristics in a continuous pen wielding process, constructing a multi-physics-field coupled PINN framework, constraining neural network prediction through a physical loss function, deploying a dual-stage characteristic extractor to extract high-order characteristics, and extracting the high-order characteristics. Book style features are extracted, and the current practicing book of the user is classified in real time; the intelligent correction layer is used for generating a dynamic correction suggestion, constructing a personalized learning path, realizing self-adaptive scoring and providing aesthetic dimension feedback at the same time; and the user interaction layer is used for providing an AR correction interface.

The invention discloses a data output method and device based on fixed-point number extraction and a storage medium, and relates to the technical field of computers, and the method comprises the steps: carrying out the calculation and normalization processing of a non-zero positive number of an input value through a precision formula, and generating a valid number corresponding to the non-zero positive number; querying in a compression lookup table according to a compression index corresponding to the effective number to obtain a reciprocal square root approximate value corresponding to the effective number; performing iterative optimization, index parity adjustment, remainder feedback correction and boundary specialization processing on the basis of a reciprocal square root approximate value corresponding to the effective number to generate an effective result; and carrying out reverse normalization processing on the effective result, generating a fixed point number corresponding to the effective result, and outputting the fixed point number. According to the invention, through conversion between the fixed-point number form and the effective number form, the technical problem of distributed environment amplification errors in a fixed-point number extraction algorithm in related technologies is solved, and the performance and accuracy of fixed-point number extraction are improved.

The invention provides a multi-unmanned aerial vehicle adaptive migration method for a sudden no-fly zone, and relates to the technical field of unmanned aerial vehicles. And acquiring fusion environment data through cooperative sensing of multiple unmanned aerial vehicles, processing the data by adopting a semantic boundary extraction algorithm based on deep learning, and outputting a sudden no-fly zone map and unmanned aerial vehicle safety state codes. On the basis, the unmanned aerial vehicles are shunted according to the safety state codes and the sudden no-fly zone map, real-time obstacle avoidance tracks are generated for the non-intruding unmanned aerial vehicles, and cluster collaborative separation is executed for the intruding unmanned aerial vehicles. For unmanned aerial vehicles whose tasks are interrupted due to obstacle avoidance or separation, task decomposition is realized based on the sudden no-fly zone map and space-time isolation, and continuous execution of cluster tasks is realized through an energy consumption constraint auction mechanism, so that a plurality of unmanned aerial vehicles can better cope with a complex and changeable sudden no-fly zone environment.

The invention discloses an intelligent pressure regulation and control method and system for artery compression, and the method comprises the steps: collecting the arterial blood pressure waveform and vascular elasticity parameters of a puncture part of a patient in real time, and the multi-dimensional pressure data of a pressure sensor; constructing an initial pressure parameter set containing a pressure distribution gradient and a vascular response coefficient through a dynamic feature extraction algorithm; carrying out collaborative analysis on the initial pressure parameter set and blood flow characteristic parameters obtained through blood flow monitoring, and generating a regulation and control matrix containing an optimal compression force range and a time decay function; performing multi-target optimization processing on the regulation and control matrix through a parallel optimization algorithm to generate a real-time pressure control instruction set; based on the deviation value of the real-time pressure execution data fed back by the user interface and the blood flow monitoring data, a grading alarm mechanism is triggered, and a compression strategy is automatically corrected. By utilizing the embodiment of the invention, accurate regulation and control of the artery compression process can be realized, and the hemostatic effect is optimized and the medical quality is improved on the premise of ensuring the safety of a patient.

The invention belongs to the technical field of strip mine step line automatic extraction, and discloses a strip mine step line automatic extraction algorithm based on double RANSAC optimization shape detection, and the method comprises the steps: introducing a block processing strategy, and controlling the scale of each sub-point cloud through dividing sub-regions; samples are randomly extracted from the disordered point cloud according to plane sample data, shapes are extracted through probability selection and connection of the multiple samples, and finally optimization and storage of the shapes are completed; and setting a normal vector threshold value, a point set number threshold value and a point cloud density threshold value so as to effectively filter out an inclined plane, a curved surface and a residual point set and reserve a target shape area. According to the invention, full-automatic extraction of the step line is realized by using the point cloud data, and the extraction can be completed within half a workday. And large subjective errors exist in manual extraction, the precision and efficiency of the extraction result can be greatly improved by utilizing a computer technology based on the high-precision point cloud data, the labor and time cost is reduced, and the engineering benefit and the economic benefit are remarkable.

The invention belongs to the field of customs affair auditing, and particularly relates to a customs affair compliance intelligent auditing system and method, and the method comprises the steps: a data collection module synchronously generates an auditing rule updating table and a customs declaration service information table in real time through a multi-mode extraction algorithm; the compliance auditing module completes multi-dimensional compliance verification based on a transverse service auditing node network, a longitudinal service transmission node chain and a declaration node network in combination with a dynamic rule priority index and reinforcement learning algorithm; the evaluation optimization module calculates a service transmission risk probability, an auditing delay probability and an error probability in real time through a bidirectional risk evaluation model, and feeds back the probability to an auditing space to optimize a rule weight and a flow path; through a composite auditing architecture of'declaration clustering + longitudinal encryption transmission + transverse topological association ', dynamic adaptation of auditing rules, accurate risk traceability and intelligent process optimization are realized, and the problems of low efficiency in integration of multi-source heterogeneous data and lagging auditing of complex business scenes in cross-border trade are effectively solved.

The invention is suitable for the technical field of visual inspection, and provides an automobile interior injection molding part defect detection method and system based on visual inspection, and the method comprises the steps: collecting an original image set under a preset light source condition, carrying out the defect feature extraction of the original image set through employing a preset defect feature extraction algorithm, and obtaining a defect feature set, performing similarity matching on the defect feature set and a preset defect template by using a preset defect matching algorithm to obtain a target similarity; when the target similarity meets a preset similarity threshold value, determining the defect type of the preset defect template as the defect type of the defect feature set; performing defect feature extraction processing on the internal defect features of the transparent and / or semitransparent injection molded part by using a transparency analysis algorithm to obtain an optimized defect feature tag set; and training the to-be-trained defect detection model based on the original image set and the optimized defect feature tag set to obtain a target defect detection model, so that accurate identification of the injection molding part defect can be realized.

The invention relates to the technical field of wind power road geological analysis, in particular to a three-dimensional geological modeling and section visualization method and system for wind power road construction. The method comprises the following steps: establishing a geological drilling data matrix through geological drilling data; extracting geological attribute data through lithology analysis, calibrating the relationship of each stratum through sequence analysis, constructing a standardized geological drilling data set, constructing a spatial continuous geological attribute scalar value distribution model through a Gaussian process interpolation algorithm, and constructing an implicit function by taking the geological attribute scalar value distribution model as the implicit function. And generating a three-dimensional geologic model through a contour surface extraction algorithm, carrying out geologic attribute scalar value extraction on the three-dimensional geologic model based on the road route data, generating wind power road profile data, and visualizing a data result. An efficient and low-cost solution is provided for geological judgment and optimization in wind power plant road design, the requirements for accuracy and high efficiency of engineering projects are met, and reliable guarantee is provided for wind power road construction.

The invention discloses a self-adaptive multi-level digital watermarking method based on coding process optimization. The method comprises the following steps: firstly, performing deep preprocessing on an input video stream through a pre-trained multi-modal deep learning model, extracting space complexity, time dynamics and content significance features, and generating a uniform feature vector; and constructing a dynamic decision model based on the feature vectors, adaptively determining the watermark intensity, the embedding position and the type, and realizing accurate matching of watermark parameters and video content characteristics. A multi-level watermark hierarchical embedding mechanism is adopted, a robust invisible watermark, a fragile invisible watermark and a dynamic visible watermark are embedded into a video, and the security is enhanced by combining chaotic encryption or Hash chain encryption. During copyright verification, watermark information of each layer is recovered through an adaptive extraction algorithm, and data verification and infringement traceability are completed in combination with a block chain evidence storage system. According to the method, a full-process copyright protection system of embedding, coding, extraction and verification is constructed.

The invention discloses a bridge safety early warning method and system, and the method comprises the steps: collecting bridge structure response and environment parameter data, and carrying out the preprocessing of the data, and obtaining a distributed multi-source sensing information matrix; constructing a distributed neuron cell automaton network, interacting state information through unit local communication, operating a lightweight feature extraction algorithm, and generating a multi-dimensional feature vector; carrying out distributed modeling by utilizing a pre-trained distributed LSTM-Transform hybrid model, and carrying out parallel calculation on a local prediction result by each unit to obtain a global health state evaluation result; a local gradient change anomaly detection mechanism is established, data anomaly is identified by comparing prediction results of adjacent units, and a potential risk area is positioned by combining a related algorithm; and constructing a hierarchical early warning threshold system, activating a corresponding early warning response by means of local decision logic, executing an alarm operation, generating recovery guidance information and monitoring uploaded data. According to the invention, two types of network models are combined, so that the system reliability and early warning accuracy can be remarkably improved.

The invention relates to the technical field of data processing, and provides a BIM model continuous grid bottom surface extraction method, device and equipment and a storage medium, according to the scheme of the invention, grid bottom surface extraction is carried out based on a user-defined data structure after BIM model triangularization, the influence of a professional design software production environment and a multi-source heterogeneous storage format is eliminated, and universality is achieved; according to the method, the cleaned and corrected triangularized grid model is subjected to space division and an elevation extraction algorithm, so that the grid bottom surface extraction efficiency is greatly improved; according to the method, curved surface reconstruction is carried out according to the spatial point cloud normal set and the point cloud data set, so that the construction accuracy of grid bottom surface extraction in the projection direction is ensured, a finer and more continuous geometric expression is provided, and the problem of model bottom surface image missing is solved.

The invention discloses an electronic document tracing method and device based on dynamic encryption and multi-modal watermarking, and relates to the technical field of information security and digital rights management. The method comprises the following steps: acquiring a PDF document uploaded by a user, encrypting the PDF document by adopting a randomly generated main encryption key, and acquiring and storing the encrypted PDF document; the content of the encrypted PDF document is analyzed, secret information is embedded into a picture in the document based on a two-dimensional discrete cosine transform algorithm, and picture watermark embedding is completed; a self-adaptive watermark embedding algorithm is adopted to embed secret information into a text in the document, and text watermark embedding is completed; related information of the checking operation is written into a block chain database; dynamically extracting watermark information from the divulged PDF document by adopting a watermark extraction algorithm; and querying a traceability database according to the extracted watermark information, and outputting a traceability result by comparing the extracted watermark information with information in a database storing various watermark information. According to the invention, the survival rate and the concealment of the watermark can be improved.

The invention discloses a book data processing and intelligent service system based on artificial intelligence, and relates to the technical field of artificial intelligence and digital library crossing, and the technical scheme is characterized in that a multi-format document intelligent conversion and regular cleaning mechanism is adopted, a multi-level metadata extraction algorithm is combined, and a structured knowledge base is accurately constructed; vector semantic retrieval, BM25 keyword retrieval and a cross encoder reordering model are creatively fused, and high-precision context retrieval is achieved through dynamic weight configuration and locality sensitive hash de-duplication. A parallel data processing architecture supporting GPU acceleration is constructed based on a Chroma vector database, the knowledge response capability of a local large model is enhanced in combination with an RAG technology, and verifiable standardized content is generated. The system is compatible with a Linux / Windows platform and containerized deployment, has hundred million-level data throughput efficiency, can be widely applied to the fields of intelligent libraries, knowledge questions and answers and personalized recommendation, and remarkably improves the retrieval accuracy of book data and the intellectualization level of knowledge services.

The invention discloses a road and bridge expansion joint defect detection system based on image recognition. Real-time image data of a road and a bridge are acquired based on a cruise image acquisition sensor; the method comprises the following steps: performing coordinate system matching on real-time image data and a standard image, eliminating a visual angle difference by adopting an SIFT (Scale Invariant Feature Transform) feature point extraction algorithm and homography matrix transformation, comparing and aligning the real-time image data and the standard image through a double-encoder-decoder network, and judging whether a defect exists in a suspicious defect area or not by using a MobileNetV3 classification model. If the defects exist, a ResNet-50 model is adopted to classify the types and severity of the defects, and cross validation is carried out on defect detection results and vibration characteristics of the bridge and the road. The method can have high classification precision, provides an accurate decision basis for maintenance and management of roads and bridges, guarantees the detection efficiency, and improves the detection accuracy.

The invention provides a target positioning method and system based on a distributed networking radar system, and belongs to the technical field of radars, and the method comprises the steps: controlling a plurality of radar nodes to transmit stepping linear frequency modulation continuous wave pulse signals, and collecting target echoes; carrying out band-pass filtering, pulse compression and fast time synchronization processing on the echoes, and generating interference-free data through a moving target extraction algorithm; realizing cross-node clock synchronization based on a moving target behavior cognition model and a template matching algorithm; constructing a multi-node distance estimation equation set, generating an accurate distance value through calibration broadband synthesis and FFT, and determining a target three-dimensional coordinate; and generating a motion track and speed prediction through Kalman filtering based on the target position sequence. The system comprises a signal emission and acquisition module, a signal processing module, a clock synchronization module, a distance estimation and coordinate fusion module and a trajectory prediction module. The method supports multi-target tracking, improves positioning precision and time synchronization precision, has few errors, and is compatible with any node deployment form.

The invention discloses a tunnel back break analysis method based on a dynamic center line and an incremental convex hull, and belongs to the technical field of tunnel engineering detection. According to the system, the problems that a traditional detection method is low in efficiency and large in error are solved by fusing dynamic slice projection, robust registration and intelligent contour generation technologies. The method specifically comprises the following steps: carrying out configurable downsampling (the density is reduced to 0.2%-0.5% at an interval of 50-1000 points) on an original point cloud, constructing a tunnel three-dimensional model (the side length of a grid is less than or equal to 0.1 m) by adopting a dynamic center line extraction algorithm, and carrying out ICP (Inductively Coupled Plasma) registration (error lt; the method comprises the following steps of: synchronously extracting a design / actual measurement section contour by using a combined slicing technology, generating an actual measurement polygon based on an incremental convex hull algorithm (a filtering threshold value is 20 points), and calculating an over-break volume (an error is less than or equal to 1.5%) by using a spatial Boolean operation. A dynamic center line extraction and incremental convex hull algorithm is innovatively introduced, automatic generation of a color gradation analysis atlas (over-excavation RGB (255, 69, 0) and under-excavation RGB (30, 144, 255)) is supported, and full-process closed-loop processing is realized.

The invention discloses a soil organic matter inversion method based on satellite image multi-pixel combination. The method comprises the following steps: acquiring satellite remote sensing data in a research area; preprocessing to obtain a data set; classifying the whole research area to obtain a plurality of subclasses; analyzing the obtained spectral features of each subclass, and performing end member extraction on each class based on an end member extraction algorithm to obtain geographic coordinates corresponding to the end members; arranging training sampling points and adding a plurality of verification points based on geographic coordinates obtained by end member extraction; actually measuring the soil organic matter content of the sampling point; performing data enhancement based on a multi-pixel mixing method to generate an expanded data set; establishing nonlinear mapping of the spectral characteristics and the organic matters; and performing spatial interpolation on the model predicted value, and drawing to obtain a soil organic matter distribution diagram. According to the method, inversion of the soil organic matter content is achieved through satellite remote sensing image features, the sampling frequency is greatly reduced, the laboratory detection cost is saved, pollution caused by chemical method detection is reduced, and meanwhile the transportability of the method is guaranteed.

The invention discloses an AI simulation method and system for facial cosmetic plastic surgery, and the method comprises the steps: for a specific region with high feature complexity, generating a feature subset containing fine feature distribution through high-performance node distribution processing, combination with an adaptive feature extraction algorithm, and dynamic adjustment of extraction frequency and segmentation granularity; on the basis, a three-dimensional reconstruction algorithm is adopted to dynamically adjust the splicing weight, particularly, the splicing precision is improved and a high-precision three-dimensional model is generated for an area with relatively thin tissue thickness, and finally, a vivid effect simulation diagram is obtained through rendering parameter adjustment driven by a user demand and preferentially rendering a height modification area. According to the method, through adaptive parameter adjustment and fine processing, the modeling precision and rendering efficiency of the complex biological characteristic data are remarkably improved, and the method is suitable for medical images, biological recognition and other scenes.

The invention discloses a depression state prediction method and system based on voice multi-scale time domain perception, and the method comprises the steps: collecting voice signals of a participant reading a unified standardized text, and carrying out the preprocessing of the voice signals, and generating a corresponding Mel spectrogram; extracting time context features by using a spectrum-time domain feature extraction algorithm to obtain joint feature representation; carrying out multi-scale division on the combined feature along the time dimension, and fusing the features of each scale into a global feature; and obtaining depression state discrimination results of the participants based on the global features. By combining the spectrum-time domain feature extraction algorithm and the frame-level time attention, the time domain locality features of depression speech such as inter-sentence pause, hesitant pause, inter-word transition and formant blurring can be explicitly analyzed and accurately positioned on the Mel spectrum, so that the recognition accuracy and the result stability are remarkably improved.

The invention belongs to the technical field of three-dimensional surface reconstruction, and particularly relates to a robust three-dimensional surface reconstruction method based on multi-scale fusion. Acquiring three-dimensional point cloud data, fitting local implicit curved surfaces of neighborhood points by adopting a weighted least square method, and generating a geometric reconstruction grid model; extracting geometric features in different receptive fields through multi-scale convolution, introducing channel attention, space attention and a gating mechanism to perform weighted fusion on the geometric features in different receptive fields, enhancing a global dependency relationship by adopting a feature mixing operation, and generating a deep learning grid model; performing double-branch fusion on the geometric reconstruction grid model and the deep learning grid model, highlighting a geometric sensitive area, and obtaining a fused enhanced feature; inputting the fused enhanced features into an implicit function decoder, and predicting a signed distance function value of each vertex; and based on the predicted signed distance function field, a contour surface extraction algorithm is adopted to generate a triangular mesh model, so that three-dimensional surface reconstruction is completed.

The invention relates to the technical field of information security and random number generation, and particularly discloses a method and a system for generating a random number by utilizing mobile phone holding micro-vibration and touch pressure, and the random number is generated by collecting physical behavior characteristics when a user holds a mobile terminal within preset time and combining sensor signal processing and a randomness extraction algorithm. The random number generation does not need external hardware support, the randomness is higher, the operation is simple and convenient, and the problems of insufficient randomness and complex operation caused by dependence on an external noise source or specific input in the prior art are solved. According to the method, the dependence on the external environment or additional hardware is avoided, the security, unpredictability and practicability of random number generation are remarkably improved through a multi-level security protection mechanism, reliable technical support is provided for the field of modern information security, and the method has the advantages of being easy to implement, low in cost and high in security and has wide application prospects. The method is suitable for popularization and application in multiple fields of mobile payment, Internet of Things, games and the like.

The invention discloses a simulation plant intelligent light control method and device and electronic equipment, and relates to the technical field of intelligent control / Internet of Things. The method comprises the following steps: acquiring environment illumination intensity, sound intensity, human body existence state and activity state information, acquiring time information, generating a time period identifier, and receiving a user instruction and preference setting information of a mobile terminal; performing fusion processing on the data to generate a standardized environment perception data set; executing a scene feature extraction algorithm based on the data set to generate a scene feature vector, and mapping the feature vector into a scene type identifier by adopting a predefined scene recognition rule and a K-nearest neighbor hybrid algorithm; according to the scene type identifier, generating a light control strategy by adopting a corresponding strategy decision mode; and executing a strategy to drive a light-emitting unit in the simulation plant, and monitoring an actual lighting effect parameter in real time for self-adaptive correction. According to the invention, the self-adaptive adjustment of the lighting effect is realized, and the user experience is remarkably improved.

The invention relates to a lightweight acoustic feature extraction method for end-side audio and video quality inspection. The method comprises the following steps: end-side equipment separates audio and video stream data through a double-time-sequence anchor point alignment method, and acquires independent audio data; performing grading preprocessing on the independent audio data to obtain noise-reduced independent audio data; on the basis of the segmented audio, a time domain and frequency domain collaborative extraction algorithm is adopted, multi-dimensional time domain features and frequency features sensitive to audio quality difference are obtained, and a lightweight acoustic feature vector is obtained through an incremental principal component analysis method; based on the lightweight acoustic feature vector, utilizing a dual-threshold matching judgment mode to obtain similarity between features; and on the basis of the feature similarity, through feature dimension deviation verification, obtaining a quality inspection result containing a standard grade and feature anomaly positioning. Lightweight acoustic feature extraction of end side audio and video quality inspection is realized.

The invention relates to the technical field of unmanned aerial vehicles, and provides an unmanned aerial vehicle target identification and threat assessment method and application, and the method comprises the steps: obtaining an original data stream of an unmanned aerial vehicle target from a multi-source sensor, and carrying out the processing, and obtaining a unified multi-source data set; generating a static feature set by adopting a feature extraction algorithm; generating a dynamic feature set by adopting a time sequence analysis algorithm; performing feature fusion by adopting a neural network model to obtain a fused feature set; generating an unmanned aerial vehicle target knowledge graph by adopting a relation extraction algorithm; extracting association strength from the unmanned aerial vehicle target knowledge graph, and mining threat modes by adopting a graph neural network algorithm to obtain a threat mode set; generating a threat evaluation vector set by adopting a probabilistic reasoning algorithm; generating a countering strategy set by adopting a classification algorithm; and updating the unmanned aerial vehicle target knowledge graph by adopting a feedback learning algorithm to obtain an updated knowledge graph. By adopting the method provided by the invention, the unmanned aerial vehicle target can be quickly and accurately identified, a targeted countering strategy is formulated, and accurate countering is realized.

The invention discloses a watermark embedding and watermark tracing method and device for digital information, equipment and a medium, and relates to the technical field of digital watermarking, and the method comprises the steps: generating an initial watermark based on the Redis atom incremental operation when carrying out the watermark embedding of a plurality of initial sensitive digital information, and segmenting the initial watermark to obtain a plurality of initial sub-watermarks; randomly selecting an initial sub-watermark as a target watermark, and respectively embedding the target watermark into each piece of initial sensitive digital information to obtain each piece of target sensitive digital information containing the watermark; and when watermark tracing is carried out on a plurality of pieces of target sensitive digital information, the to-be-confirmed watermarks corresponding to the target sensitive digital information are analyzed based on a watermark extraction algorithm, the target watermarks are determined from the to-be-confirmed watermarks, and corresponding watermark tracing results are generated. According to the method, protection of sensitive digital information is realized through a watermark embedding and tracing scheme which is high in concealment and robustness and is adaptive to multiple types of short data.

The invention relates to the technical field of nephropathy data storage, and discloses a nephropathy person state data storage method based on image data processing. The method comprises the following steps: acquiring a continuous time sequence medical image data set of a nephropathy person; processing the continuous time sequence medical image data set by using an image feature extraction algorithm to generate a multi-dimensional feature vector set; performing iterative grouping operation on the multi-dimensional feature vector set by adopting a feature clustering algorithm to form a nephropathy state category set; in each iteration grouping operation stage, identifying a key feature set, and calculating an adjustment parameter of a feature clustering algorithm based on a historical storage data set; and updating a grouping rule of a feature clustering algorithm according to the adjustment parameter, and synchronizing the updated grouping rule to a nephropathy state data storage library. According to the method, the clustering grouping rule is dynamically adjusted, so that the storage of the nephropathy state data better fits the illness state change characteristics, and the validity and pertinence of the state data storage are improved.

The invention discloses a BIM (Building Information Modeling)-based pile support foundation pit whole-process safety management system, relates to the technical field of foundation pit safety management, and aims to solve the technical problem of frequent false alarm caused by insufficient construction interference identification capability of a fixed threshold alarm mechanism. And a decision intervention module. According to the method, the multi-source information atlas is constructed, and construction activities, equipment states, environmental factors and monitoring data are dynamically associated. When the data is abnormal, the root cause correlation analysis unit is started, and the intensity and duration characteristics of the abnormal signal are calculated by using a time sequence characteristic extraction algorithm and are compared with the historical pattern library. The mechanism can intelligently judge whether data abnormity is derived from construction interference such as substantive soil body / structure instability or heavy equipment operation, the alarm accuracy is remarkably improved, and the problem of frequent false alarm caused by insufficient construction interference identification capability of a fixed threshold value alarm mechanism is solved.