2256results about How to "Avoid loss" patented technology

The invention discloses a coal mine dust diffusion intelligent monitoring method based on a distributed sensing network, and relates to the technical field of coal mine dust diffusion intelligent monitoring, and the method comprises the following steps: deploying a plurality of sensing nodes in a coal mine, each node having a function of collecting environmental data, a distributed sensing network is formed through a wireless or wired communication module, and when all nodes are started, a unique node identifier is issued through a central controller, and self-inspection is carried out. Through bidirectional delay calculation and a progressive fine tuning strategy, high-precision time synchronization is achieved, and it is ensured that timestamps of data such as dust concentration and airflow are accurate and reliable. And data jump caused by large correction is avoided through gradual fine adjustment, data continuity is guaranteed, and the precision of dust diffusion analysis and an early warning model is improved. A redundant node management and automatic switching mechanism improves the fault processing capability of the system, avoids a monitoring blind area, accelerates maintenance response through fault information reporting, and ensures stable operation of a monitoring network.

The invention discloses a method and a system for realizing data synchronization between heterogeneous databases based on logs. The method comprises the following steps: acquiring connection information of a source database and a target database; querying a table structure of the source end database and generating an SQL statement to establish a data mapping rule; matching a corresponding rule model from a mapping rule model library; if the matching is not successful, the system abnormally quits and prompts the user to create a new mapping model and add the new mapping model to the mapping rule model base; converting a table structure of the source end database into a JSON character string based on a standard field type; an SQL statement required by the target database is output through the data mapping engine module; the message monitoring program reads the data in the temporary storage area and converts the data into data meeting the requirements of the target database; formatting the converted data into SQL statements supported by a target database; and sending the generated SQL statement to a target database to complete data migration. Accurate and controllable synchronization of the data and the model in a heterogeneous environment is realized.

The invention relates to the technical field of geological disaster intelligent monitoring, in particular to a geological disaster intelligent monitoring system and method based on multiple sensors. The method comprises the following steps: covering a monitoring area through multiple sensors, and collecting and summarizing environment data; separating effective signals and noise by adopting a space-time adaptive decomposition method, completing data denoising by combining a signal-to-noise ratio dynamic threshold and a local weighted regression smoothing trend residual error, then aligning time and dimensions through linear interpolation and standardization, and integrating multi-sensor data based on an adaptive weighted fusion method; the fused data is subjected to generation and review of a to-be-reviewed mark, so that the data integrity is ensured; key features are extracted, a mixed model based on LSTM and a random forest is constructed to evaluate disaster risks, and a risk grade division rule is set; and finally, outputting fusion data and an analysis result, and generating an early warning signal and a decision suggestion for potential geological disasters. According to the invention, accurate monitoring and risk assessment of geological environment changes are realized.

The invention discloses a human body behavior recognition method and system based on a space-time fusion network, and relates to the technical field of behavior recognition, and the method comprises the steps: obtaining a human body skeleton sequence, carrying out the joint position coding of the sequence, taking the coded sequence as an input feature, inputting the input feature into the space-time fusion network based on graph convolution and Transform, and outputting a human body behavior recognition result; wherein the space-time fusion network comprises a plurality of groups of graph convolution Transform space fusion modules and multi-scale time fusion modules which are connected in sequence, after input features are input, local structure features and global space features are extracted through the parallel adaptive graph convolution module and Transform module respectively, feature fusion is carried out through the correlation fusion module, and the local structure features and the global space features are fused through the correlation fusion module. And the fused features are input into a multi-scale time fusion module, and are subjected to feature extraction through a multi-scale convolution branch and a Transform branch and then spliced, so that a more accurate human behavior recognition result is output.

The invention discloses an LLM capability enhancement method of a hybrid enhancement strategy, and particularly relates to the technical field of natural language processing. The method comprises the following steps: obtaining corpus data and external literature data of a target application field, carrying out denoising, word segmentation and semantic indexing processing, and establishing a searchable heterogeneous knowledge base; secondly, classifying the corpus data by adopting a topic analysis and word vector clustering method to form a basic corpus set; performing multi-round parameter updating on the large language model by using a domain fine tuning algorithm based on the basic corpus set, and setting a fusion threshold value between a new parameter and an original parameter; and retrieving related literature fragments from the heterogeneous knowledge base through a matching algorithm, screening content conforming to field correlation by utilizing semantic similarity evaluation, fusing the content with the fine-tuned model, processing a fusion result by adopting a context fusion algorithm and an entity linking method, completing syntactic analysis, and generating a precise solution oriented to the target application field.

The invention provides an electric power multi-modal data hierarchical routing feature processing and fusion method and system, and relates to the technical field of electric power multi-modal data process.The method comprises the steps that multi-modal data are obtained and preprocessed; performing feature extraction on the preprocessed multi-modal data; selecting the most suitable expert model through a hybrid expert model, and carrying out feature extraction on the preprocessed multi-modal data; performing feature alignment and fusion on features of different modes after feature extraction; aligning the features of each modal with the features of other modals through a cross-modal attention mechanism, and fusing the features of other modals during alignment to obtain middle-level features of the corresponding modals; and carrying out weighted fusion on the middle-level features of each mode to obtain a fused global feature. According to the invention, efficient processing and fusion of multi-modal data can be realized.

The invention relates to a multi-article identification and distinguishing method, system and device and a medium. The method comprises the steps of obtaining multi-view image data of articles in a target area; performing object detection and instance segmentation on the multi-view image data by using a deep learning algorithm to obtain preprocessed image data; acquiring a first spatial position of the object in the target area by using a depth sensor, and performing spatial correction by combining the preprocessed image data to obtain depth information of the object in the target area; in combination with the preprocessed image data and depth information, performing secondary positioning on articles in the target area through a multi-modal data fusion algorithm, optimizing the distinguishing between the articles in the target area by using spatial correlation, confirming the identity information of each article in the target area, and generating an identification result; and matching the identification result with the article state in a preset database to generate a matching result, and feeding back the matching result to the user side. The method has the effect of improving the recognition precision of the borrowing machine.

The invention provides a lightweight underwater image enhancement method based on adaptive feature fusion, and the method comprises the steps: decomposing an underwater image enhancement task into a strong prior enhancement task and a fine-grained enhancement task through employing a lightweight underwater image enhancement network based on adaptive feature fusion, so as to make full use of the multi-scale information in an image; the strong prior enhancement task is used for extracting global and low-layer features of the image; the fine-grained enhancement task is used for improving local details and color recovery of the image so as to improve the visual quality; the lightweight underwater image enhancement network comprises a multi-scale feature aggregation module and a global channel attention module, the multi-scale feature aggregation module is composed of an adaptive feature fusion module and a segmented convolution feed-forward network, and the lightweight underwater image enhancement network is used for combining global and local information of features and adaptively adjusting importance of each channel through information association between channels.

The invention relates to the technical field of image data processing, in particular to a wood composite board furniture surface defect identification method and system. And wood board images are collected from the surface of the wood composite board furniture. Adaptive grid division is carried out on the wood board image according to different scales, texture direction information and high and low frequency texture components are extracted, and a texture feature map is obtained; and extracting structure edge information of the wood board image to obtain a structure information graph. Matching the texture feature map with the structure information map, and calculating local mutual information of the texture feature map and the structure information map in each region; the method comprises the following steps: acquiring resolution images of a wood board image under different scales, and calculating a mutual information matrix of the resolution images; distributing an abnormal score for each matrix element based on the mutual information matrix; and dividing grids according to the abnormal score, calculating a correlation value of adjacent grids, and obtaining a defect area based on the correlation value. And carrying out defect classification on the defect area. According to the invention, interference of natural textures can be reduced, and defect identification of small defects and tiny changes can be improved.

The invention relates to the technical field of medicine information management, in particular to a traceability method and system based on a medicine consumable traceability code. The method comprises the following steps: acquiring medicine purchasing information data; generating a unique code for the number of medicine purchasing information so as to generate a medicine warehousing traceability code; performing drug information association on the drug purchase information data according to the drug warehousing traceability code to generate a drug warehousing associated traceability code; storing the medicine warehousing associated traceability code in an information base to generate a medicine warehousing traceability code information base; performing scanning access mode division on the medicine warehousing traceability code, and generating a medicine dispensing scanning request mode and a medicine returning scanning request mode; and performing drug splitting tracing on the drug warehousing tracing code in a drug dispensing scanning request mode to generate drug splitting tracing data. According to the invention, through full-process closed-loop management, data association enhancement, intelligent scheduling optimization and drug return tracing improvement, the efficiency and precision of the drug consumable traceability code tracing method are improved.

The invention provides a multi-modal protein function prediction method and system, and the method comprises the following steps: carrying out the processing of a one-dimensional convolution layer and a pooling layer of protein sequence data, extracting the local dependence features in a protein sequence, and capturing the mode and key sequence features in the protein; using protein sequence data as input, and using a large-scale pre-trained protein language model to extract residue-level embedded information in a protein sequence as node features to be applied to the contact graph; the method comprises the following steps: taking protein sequence data as input, predicting the probability of contact between protein residues based on a Cb-Cb distance, and constructing a contact graph; and performing joint processing on features of different modes by using a multi-head attention mechanism and a residual structure, integrating data from different modes through feature fusion, and fusing the extracted key sequence features, the residue level embedded information and the contact graph. The prediction method is accurate in prediction and good in performance.

The invention discloses a server power supply system and a server, relates to the field of server power supply, and is used for solving the problem of high energy loss in a traditional alternating current power supply architecture. The power conversion module converts the power supply into high-voltage direct current with the voltage not lower than the preset voltage, the high-voltage direct current is transmitted to the power distribution board of the server through the bus copper bar, and the bus copper bar serves as a transmission medium of the high-voltage direct current and has low resistance and high current-carrying capacity. Extra loss caused by electromagnetic induction and a capacitance effect in alternating current transmission is avoided; high-voltage direct current is directly input to the power distribution board, so that multiple times of conversion in an intermediate link are avoided, and energy loss in the conversion process is reduced; and the power distribution board directly converts the high-voltage direct current into the voltage required by the load, so that the loss of multi-stage conversion in the traditional architecture is reduced. According to the invention, the power transmission efficiency is improved, the energy loss is reduced, and the requirement of a modern high-computing-power and high-energy-consumption server for efficient power supply is met.

The invention provides a long-distance pipeline magnetic flux leakage defect detection method and system based on improved YOLOv8, and belongs to the field of pipeline safety detection.The method comprises the steps that magnetic flux leakage image data of a long-distance pipeline are obtained, defect labeling is conducted on the image data, and a data set is obtained. A lightweight SPD-Conv module is introduced into a backbone network of an original YOLOv8 network model, a CBAM attention module is added in a neck structure, an ECIOULOSS loss function is used for replacing a loss function in an original model, an improved YOLOv8 model is formed, part of data is selected from a data set according to the proportion to be input into the model for training, and a pipeline detection model is obtained. And inputting a to-be-detected image into the pipeline detection model, and outputting defect information and defect positions in the image. Through the improved YOLOv8 network model, the influence of human factors on magnetic flux leakage defect detection is effectively reduced, and the accuracy and reliability of long-distance pipeline magnetic flux leakage defect detection are remarkably improved.

The invention discloses a low-dew-point double-rotating-wheel dehumidifier energy-saving prediction method based on time-space diagram attention. The invention provides a time-space diagram attention neural network based on an attention mechanism. According to the time-space diagram attention neural network based on the attention mechanism, a diagram attention network, a bidirectional gating circulation unit and a time sequence Transform model are combined. The method comprises the following steps: firstly, constructing a graph structure according to equipment topological structure information, vectorizing a time slice input matrix, introducing an attention mechanism, and adaptively distributing node weights to update node features; adopting two forward and reverse unidirectional GRUs structures, and fusing past information and future information for feature prediction; and finally, through position embedding and a time sequence Transform structure, hidden information between sliding window inputs is mined, loss of effective features is reduced, and the long-term prediction capability of the model is improved. According to the method, the spatial-temporal correlation of original data can be better captured, the equipment operation state parameters are effectively predicted, and the energy-saving optimization control process is guided.

The invention relates to the technical field of oxytetracycline hydrochloride conveying, in particular to an oxytetracycline hydrochloride airflow conveyor which comprises an airflow feeding pump, an inner pipe discharging mechanism arranged on the airflow feeding pump, an outer pipe resonance mechanism arranged in the inner pipe discharging mechanism and a powder distributing and conveying mechanism arranged on the outer pipe resonance mechanism. And the air flow feeding pump is used for providing air flow kinetic energy for transferring the powdery oxytetracycline hydrochloride. An independent airflow feeding pump is arranged on a path for preparing oxytetracycline hydrochloride of a powder rotating roller, when the airflow feeding pump operates, constant-pressure airflow released outwards passes through an inner pipe discharging mechanism, thrust applied by part of air pressure can bear a pressure gasket to vibrate at high frequency along with transmission of part of airflow into a vibration bin assembly, and therefore the effect that oxytetracycline hydrochloride is subjected to high-frequency vibration is achieved. And finally, the inner pipe clamped by the plurality of pressure-bearing gaskets can continuously release the airflow outwards in a corrugated state, so that the loss of the powdery oxytetracycline hydrochloride on the pipe wall due to standing can be avoided.

The invention relates to the technical field of circuit breaker service life evaluation, in particular to a high-voltage circuit breaker residual service life intelligent evaluation system, which comprises an operation data acquisition module for acquiring operation data of a high-voltage circuit breaker; the degradation degree feature extraction module is used for extracting degradation degree features from the processed operation data; the residual life calculation module is used for constructing a residual life prediction model by utilizing the degradation degree characteristics and adopting a generalized regression neural network algorithm, and calculating the residual life of the residual life prediction model; the early warning and maintenance strategy module is used for formulating a predictive maintenance strategy of the high-voltage circuit breaker based on the residual life calculation result in combination with the residual life probability density function and the expected loss rate of the circuit breaker, and balancing the economical efficiency and the reliability; according to the method, the degradation process of the circuit breaker is reflected in real time, potential faults of equipment are predicted in time, and delay and inaccuracy caused by traditional experience judgment and regular inspection are avoided.

The invention discloses a trigeminal structure spot size converter for a proton exchange waveguide and a preparation method. The trigeminal structure spot size converter sequentially comprises a substrate, an insulating layer and a lithium niobate film layer, the lithium niobate film layer comprises a first waveguide part which is used as an input end of the converter; the second waveguide part is connected with one end of the first waveguide part and is positioned on the same plane as the first waveguide part; a third waveguide portion on the second waveguide portion; the proton exchange waveguide is arranged in the third waveguide part, light is transmitted in the first waveguide part, the second waveguide part, the third waveguide part and the proton exchange waveguide in sequence, and the transverse sizes of the second waveguide part, the third waveguide part and the proton exchange waveguide are sequentially increased in the light transmission direction. The silicon oxynitride waveguide guides light waves in the optical fiber to enter the chip, the light waves in the optical fiber are efficiently guided, the light waves are limited in the silicon oxynitride waveguide and enter the lithium niobate waveguide, and loss caused by light wave diffusion is avoided; a conical waveguide structure is adopted, mode mutation is avoided, and coupling loss is reduced.

The invention discloses an electric power engineering defect diagnosis method based on AI image recognition and a block chain technology, and relates to the technical field of electric power engineering defect diagnosis, and the method comprises the following steps: setting the initial flight rate of an unmanned plane according to the monitoring demands and field conditions of an electric power line, it is ensured that the unmanned aerial vehicle can cover all key areas of the power line in the inspection process, and meanwhile the inspection efficiency is kept. According to the invention, by intelligently adjusting the flight rate of the unmanned aerial vehicle, the unmanned aerial vehicle can stay in a potential abnormal area for a longer time, and detailed acquisition of temperature data is ensured, so that tiny temperature changes, especially tiny temperature anomalies caused by electric arcs and electrical overload, can be captured more accurately. According to the intelligent adjusting mechanism, in the inspection process, the inspection efficiency of the whole process can be guaranteed, accurate monitoring of the key area can be guaranteed, and the risk of detail loss or temperature anomaly missing detection caused by the too high flight speed is reduced.

The invention belongs to the technical field of yarn production detection and image processing, and discloses a lightweight yarn defect detection method based on an improved YOLOv8n network, which comprises the following steps: collecting yarn pictures on a production line, preprocessing the collected pictures, inputting the preprocessed pictures into a DGL-YOLO network trained offline, and outputting yarn defect detection pictures with abnormal positions and types. The DGL-YOLO network is based on a YOLOv8n network, a CBS module except the first two CBS modules in the Backbone and a CBS module in the Neck are all replaced by a DMAConv module, and a C2f module in the Backbone and a C2f module in the Neck are all replaced by a C2f-GhostDynamicConv module. The detection method provided by the invention has high confidence coefficient in yarn abnormity identification, and is suitable for being deployed on mobile equipment of a yarn production line to complete a task of real-time yarn detection.

The invention discloses a high-stability optical fiber connecting device and a using method, and relates to the technical field of optical fiber communication accessories, the high-stability optical fiber connecting device comprises a connector, plug-in assemblies are installed on the two sides of the connector in a plug-in mode, locking heads are installed on the side edges of the plug-in assemblies in a threaded mode, and the two sides of the connector are connected with optical fibers to be connected. The plug-in assembly and the locking head are movably sleeved on the outer surface of the optical fiber to be connected, the outer surface of the connector is fixedly connected with a protective sleeve, and sealing sleeves are installed on two sides of the protective sleeve in a threaded mode. According to the high-stability optical fiber connecting device and the using method, the fiber cores of the to-be-connected optical fibers are extruded and fixed through the spring pieces, the auxiliary balls move on the two sides, and the two sides of the fiber cores of the to-be-connected optical fibers are fixed, so that the connecting stability of the two to-be-connected optical fibers is kept; the outer surface of the to-be-connected optical fiber is sealed, and the connection part of the to-be-connected optical fiber is kept clean, so that the optical fiber connector can adapt to multiple scenes, is flexible and convenient to disassemble and assemble, and is convenient to maintain and replace.

The invention discloses a defect identification method and system based on an efficient multi-path feature fusion network, relates to the technical field of cloth defect detection, and solves the problems that deep-layer, shallow-layer, large-scale and small-scale feature information cannot be effectively fused in the prior art, and an existing model is high in calculation complexity and large in model parameter quantity. The method comprises the steps that a cloth flaw data set is constructed, an improved YOLOv5s model is constructed, and model training is carried out, and the improvement content comprises the steps that GFPN is fused into a Neck network of YOLOv5s, cross-scale connection layers between interval levels and cross-layer jump connection layers between the same scales are introduced into a backbone network, in the Neck network, C3Ghost lightweight feature fusion nodes are adopted, and the cross-scale jump connection layers between the interval levels and the cross-scale jump connection layers between the interval levels are fused into the backbone network. According to the method, the deep feature map is subjected to up-sampling by adopting a Dysample method, deep and shallow feature information and large-scale and small-scale feature information are effectively fused, the performance of a YOLOv5s model in multi-scale and multi-proportion target detection is improved, and meanwhile, calculation parameters and complexity of the model are reduced.

The invention discloses a mobile terminal offline bidirectional data synchronization method and system, and relates to the technical field of data synchronization processing, and the method comprises the following steps: initializing a whole table synchronization mechanism, and establishing a file mapping structure; a user performs data operation in an offline state, records data operation details and stores the data operation details in a local cache; when the network connection is recovered, a whole table synchronization mechanism is started, data compression and partitioning are performed, a synchronization request is sent to a server, and segmented data transmission is performed; after receiving the data, the server performs data storage and management by using a file mapping mechanism; detecting potential conflicts by utilizing a multi-version parallel management mechanism, carrying out conflict resolution, merging data with local data after the conflict resolution, and confirming that synchronization is completed to a server; after it is confirmed that all data are synchronized, local and global timestamps are updated, and meanwhile, the optimal thread count is dynamically determined by adopting a multi-thread technology. According to the invention, the efficiency, reliability and accuracy of data synchronization of the mobile terminal are remarkably improved.

The invention discloses a power-saving device and method for high-voltage power utilization, and relates to the technical field of power grid control, a self-adaptive dynamic control strategy and a load prediction function are introduced, and a reactive compensation module can adjust the compensation amount according to the real-time change of the load demand; when the load fluctuates violently, a response can be quickly made, unnecessary energy consumption is reduced, the main line and the load end are respectively provided with compensation equipment, and distributed compensation and main line compensation are dynamically matched through load fluctuation prediction and a self-adaptive control technology; when the load changes, the load end equipment preferentially and quickly responds, the coordination of compensation is effectively improved, redundant power loss is avoided, and the harmonic management module realizes accurate harmonic suppression through real-time harmonic data monitoring and parameter adaptive adjustment of the intelligent filter; the intelligent filter automatically adjusts the damping coefficient and the frequency gain according to the frequency and the power density of each harmonic component, and the harmonic component of the specific frequency is inhibited in time; and the harmonic wave fluctuation coping capability of the system is improved.

The invention is suitable for the technical field of data processing, and provides an illumination lamp data transmission system, which comprises a plurality of illumination lamps, a plurality of PLC concentrators respectively connected with the illumination lamps on an electric power circuit in a region where the illumination lamps are located, LPWAN gateways connected with the PLC concentrators, and a monitoring center communicating with the LPWAN gateways through an LPWAN network. The lighting lamp is provided with a data acquisition module and a PLC module; the PLC concentrator is provided with a modulation-demodulation module, an error detection module and a data forwarding module; the LPWAN gateway is provided with a data coding module, a data packaging module and an LPWAN communication module; the monitoring center comprises a data receiving module, a decoding processing module and a data processing module. The problem that an existing lighting lamp is poor in data transmission signal stability in a special environment where communication is inconvenient is solved.

The invention relates to a touch all-in-one machine interaction control method and device, computer equipment and a medium, and the method comprises the steps: receiving image data through a preset video stream protocol, and obtaining a corresponding image data stream; acquiring a real-time network bandwidth condition, and then performing adaptive compression processing on the image data stream based on the real-time network bandwidth condition, and dynamically adjusting image transmission parameters; comprehensively analyzing the image data stream by using a deep learning algorithm, extracting operation characteristics of a user, generating an output control instruction based on the operation characteristics, and further controlling and adjusting image display and / or an equipment state; content information is extracted from the image data stream, and content analysis is carried out by adopting a corresponding content analysis strategy according to the content information, so that real-time display and / or storage are / is carried out according to the analyzed content; and obtaining a user interaction behavior, generating a corresponding adjustment feedback instruction based on the user interaction behavior, and performing personalized adjustment on the user interface. The method has the effect of improving the image transmission quality.

The invention discloses a fumigation treatment multi-parameter comprehensive monitoring system which comprises a multi-parameter sensor monitoring module, a multi-modal data processing and analyzing module, a system intelligent monitoring and adjusting module, a fumigation environment control module, a remote maintenance and management module and a system interaction and feedback module. The multi-parameter sensor monitoring module is used for collecting parameter data. The multi-modal data processing and analyzing module is used for fusing and analyzing data. The system intelligent monitoring and adjusting module is used for intelligently monitoring and adjusting fumigation strategies. The fumigation environment control module is used for adjusting environment parameters. The remote maintenance and management module is used for system maintenance and management. And the system interaction and feedback module is used for display interaction of data and a control state. According to the fumigation treatment multi-parameter comprehensive monitoring system, a multi-modal data fusion algorithm based on information channel decomposition is provided for multi-parameter data fusion and analysis; and a multi-parameter intelligent monitoring algorithm based on a multi-layer feedforward neural network is provided to intelligently monitor the fumigation environment.

The invention relates to a remote sensing image space-time fusion method combining a multi-scale and multi-dimensional mixed attention mechanism, and belongs to the field of remote sensing image processing. According to the method, multi-scale feature extraction and a multi-dimensional mixed attention mechanism are combined, and feature extraction is performed by using depth separable expansion convolution, so that a good receptive field is maintained, and detailed information loss is avoided. MMNet optimizes information fusion through a homologous different-time and different-source simultaneous multi-scale feature difference injection mechanism and a multi-dimensional mixed attention mechanism, so that the accuracy and reliability of space-time fusion are improved. According to the method, global information and local details in the image can be effectively captured, relatively complete data in the remote sensing image are fully utilized, and a new solution is provided for the field of remote sensing image processing.

The invention belongs to the technical field of signal processing, and particularly relates to a health condition monitoring method based on a smart bracelet, and the method comprises the steps: 1, regarding a physiological signal collected by each sensor on the smart bracelet in a set time interval as a combination of a plurality of different frequency components, and containing band-pass noise; 2, evolving each time domain signal in a high-order tensor field to obtain a wavelet coefficient; 3, calculating the fractal dimension and singular spectrum of each sensor under each mother wavelet based on the wavelet coefficient; obtaining a chaos state between the sensors; and 4, introducing a continuously evolved global health threshold value by integrally summarizing the collaborative state and the chaotic state of the sensor, and ensuring the numerical value to be stable. According to the method, the problems of insufficient multi-sensor signal fusion, limited anti-noise capability, lack of dynamics and individuation and the like in the prior art are solved, and the accuracy, robustness and real-time performance of health monitoring are remarkably improved.

The invention discloses a deep learning-driven intelligent analysis method for automobile supply chain bill data, and relates to the technical field of data management.The deep learning-driven intelligent analysis method comprises the steps that an automobile supply chain bill is scanned to obtain scanned images of the automobile supply chain bill, and then the multiple scanned images obtained through scanning are preprocessed periodically to obtain a ciphertext conversion sequence; converting the ciphertext conversion sequence, and determining an array ciphertext interval of each pre-processing period according to the number of pre-processing times and the number characteristic of the segmentation array of the currently pre-processed scanning image in the process of pre-processing the scanning image, according to an array ciphertext interval, 16 kinds of originally existing four-bit binary numbers are compressed into 8 kinds, and ciphertext characters are used for increasing fault tolerance of a ciphertext conversion sequence after forward transmission, so that the ciphertext conversion sequence obtained through final conversion can have 16 kinds of four-bit binary numbers on the whole or in groups, complexity and difficulty are increased for three-party cracking, and the conversion efficiency of the ciphertext conversion sequence is improved. And the security of the scanned image in transmission is further ensured.

The invention relates to a vehicle abnormal position correction method, system and device based on road network position information and a medium, and the method comprises the steps: collecting and processing road network map information, tracking a vehicle position, and storing the vehicle position as a historical position point set; when the number of the historical position points exceeds a set threshold value, whether the vehicle is in an abnormal deviation state or not is judged according to the scene where the vehicle is located and the relative position relation between the vehicle and the road and road section range; if the vehicle is in the abnormal deviation state, acquiring a latest position point from a historical position point set, and predicting and covering the position of the vehicle; if not, front and back position abnormal state identification is carried out; and when the front and back positions are identified to be abnormal, obtaining the latest position point from the historical position point set, and predicting and covering the position of the vehicle. According to the invention, by fully utilizing and fusing a plurality of data sources, designing an innovative judgment rule and adopting a refined exception handling mode, the accuracy of vehicle position judgment and the fineness of exception handling are remarkably improved.