148results about How to "Low resolution" patented technology

The invention relates to a method for improving satellite data inversion soil salinity by using hyperspectral data of an unmanned aerial vehicle. The method comprises the following steps: collecting a soil sample in a test area, and calculating soil salt content; acquiring and preprocessing satellite remote sensing image data of a test area; storing data, outputting a single-band satellite image, and calculating a spectral index; obtaining and preprocessing hyperspectral image data of the unmanned aerial vehicle in the test area; reconstructing and upscaling unmanned aerial vehicle image data pixels to a spatial resolution of 10m; reconstructing the narrow-band spectral data of the unmanned aerial vehicle into broadband spectral data consistent with a satellite image, storing and outputting a single band, and calculating a spectral index; screening a soil salt content characteristic variable, and constructing a soil salt content inversion model by taking the screened characteristic variable as an independent variable and the actually measured soil salt content as a dependent variable; and fitting a function relationship between the upscaling unmanned aerial vehicle soil salt content inversion model and the satellite inversion model by adopting a numerical regression function method to realize correction of the satellite inversion model.

The invention discloses a vehicle GNSS positioning method based on multi-motion model interaction, and the method comprises the steps: building a PCV model and a PCSAV model of a carrier according to the linear driving posture and the turning driving posture of the carrier, so as to obtain a state estimation vector and a state transition matrix of the carrier at the last moment based on the PCV model and the PCSAV model; an interactive multi-model is introduced to establish a heuristic position-speed filtering HPV-IMM model based on the interactive multi-model, information filtering interaction between a PCV model and a PCSAV model is realized, and a state estimation vector and an error covariance matrix of a carrier at the current moment are obtained, so that the position and the speed of the carrier at the current moment are obtained. The problem that a traditional single kinematic model is low in precision in multi-motion-attitude vehicle positioning is solved, and the method is suitable for multi-attitude vehicle motion dynamic navigation in an urban environment.

The invention relates to the technical field of image processing, and provides an underwater image enhancement method and system based on multi-modal degradation feature learning. The method comprises the following steps: collecting underwater image data of different modes to construct an underwater image data set, and dividing the underwater image data set into a training set and a test set; preprocessing the training set to obtain an image text degradation pair data set; constructing a multi-modal visual language MD-CLIP network framework, and training the MD-CLIP network framework by using the image text degradation pair data set; the MD-CLIP network framework comprises an image coding controller ContNet module, a pre-trained CLIP module and a comparative learning module; integrating the trained image coding controller ContNet module into an NAFNet network structure, generating an image enhancement network, and training by using the test set to obtain an underwater image enhancement model; and inputting a to-be-enhanced underwater degraded image into the underwater image enhancement model to generate an enhancement result graph of the to-be-enhanced underwater degraded image.

The invention discloses a potential field data downward continuation method and device, equipment and a medium, and relates to the technical field of geophysical geological resource exploration, and the method comprises the steps: learning a rule in downward continuation through employing a physical information neural network containing a total loss function, and training a better downward continuation model; wherein the total loss function comprises partial differential structure loss, boundary value loss and true value loss; the partial differential structure loss in the gravity field is an error of a product of a Laplacian operator of prediction data and gravity; the partial differential structure loss in the magnetic field is an error of a product of a Laplacian operator of prediction data and magnetic force; the boundary value loss is obtained by predicting data calculation after downward continuation; the true value loss is the error of the data after the downward continuation and the data after the downward continuation of the label, and the trained physical information neural network is utilized to carry out the downward continuation prediction on the actually measured potential field data, so that the high-precision downward continuation of the potential field data is realized.

The invention discloses a drainage basin rainfall runoff prediction method and device fusing a mechanism model and a machine learning model, and a program product. The method comprises the following steps: firstly, constructing a seasonal Markov chain model by taking a seasonal rainfall state transition probability as a conditional transition probability; simulating the rainfall state of each day in a preset simulation period by adopting a seasonal Markov chain model, simulating the rainfall capacity of each day according to the distribution obeyed by the rainfall capacity of each seasonal characteristic time period, and generating a rainfall sequence in the preset simulation period; predicting the runoff yield of each day under a specified rainfall sequence in a preset simulation period by adopting a drainage basin mechanism model, and counting the earlier-stage accumulated rainfall capacity and seasonal characteristics as the input characteristics of runoff yield prediction; and finally, taking the input features of all runoff prediction in a preset simulation period and the corresponding runoff simulation values as a data set, training a machine learning model, and predicting the runoff in the future by adopting the trained machine learning model. The method is high in prediction efficiency and high in prediction precision.

The invention discloses a federated learning privacy protection method based on client grouping, and relates to the technical field of data security, the method provides privacy protection of different levels for different clients in an environment in which client data is not independently and identically distributed, and a central server groups the clients with similar data distribution based on Jensen-Shannon divergence; quantizing the balance degree of a client local data set through a balance data set, determining privacy protection levels among different groups, adding interference noise of a specific level to model updating of clients in the groups, and then performing intra-group aggregation; according to the method, for different privacy protection requirements between the clients, the clients are grouped, interference noise of different sizes is added to different groups, the requirement for privacy heterogeneity is met, meanwhile, the noise interference degree in the whole system is reduced, the global model precision is improved, and the method has the advantages of being high in practicability and easy to popularize. The problem of low global model precision caused by traditional differential privacy is solved.

The invention relates to the technical field of interference reflection remote sensing, in particular to a tide level monitoring method and device, electronic equipment, a storage medium and a program product. The method comprises the following steps: acquiring SNR data of a low elevation angle of a reflection region on a sea surface; carrying out atmospheric refraction correction and trend term removal operation on the data to obtain a high-frequency component of the data; performing wavelet transformation on the high-frequency component to obtain an instantaneous frequency time sequence and a peak power time sequence at different moments, screening the instantaneous frequency time sequence and the peak power time sequence respectively, and finally performing average value calculation on frequency screening results to obtain a main frequency of the high-frequency component; the antenna height is calculated based on the dominant frequency to obtain an initial tide level sequence, the initial tide level sequence is decomposed into an astronomical tide sequence and a residual water level sequence which are arranged at equal intervals through quasi-tide harmonic analysis, and the astronomical tide sequence is corrected through local weighted linear regression to obtain a final tide level sequence. Therefore, the problem that the precision of a tide level monitoring result obtained in the prior art is low is solved.

The invention provides a method and a system for predicting the health state of a battery under a small amount of data, belongs to the technical field of battery health management, and aims to solve the problem that the health state of the battery is not accurately predicted due to narrow use range and low use quantity of part of batteries with special purposes. If a battery model with a narrow application scene under the same type is an A-type battery, and otherwise, the battery model is a B-type battery, the A-type battery health state prediction method comprises the following steps: step 1, training with source domain sample data to obtain a structure and parameters of a source domain task model; 2, the source domain task model serves as a target domain task model, and weight parameters of the target domain task model are directly migrated or randomly initialized according to different migration schemes; 3, according to the migration scheme, performing fine tuning and freezing operation on each layer of parameters of the target domain task model by using the target domain sample data; and 4, predicting and outputting an A-type battery health state test sample by using the trained target domain task model.

The embodiment of the invention discloses a cable fault prediction system and method based on cable full life cycle data, and the method comprises the steps: determining the fault type and production type batch of a first cable when the fault of the first cable is monitored, first full life cycle data of the first cable and second full life cycle data of a second cable with the same batch as the production type are obtained, the first full life cycle data and the second full life cycle data comprise a plurality of sub life cycles and corresponding cycle parameter data, and the associated life cycle of the fault type is determined; and respectively determining cycle parameter data corresponding to the sub-life cycle same as the associated life cycle in the first full life cycle data and the second full life cycle data as first comparison data and second comparison data, performing similarity comparison on the first comparison data and the second comparison data, and predicting the second cable according to a comparison result to obtain a predicted fault cable. According to the scheme, multi-dimensional cable fault prediction under more possibilities can be realized by monitoring the cable in the whole life cycle.

The invention discloses a preparation method of a bentonite-containing composite material and an application of the bentonite-containing composite material in desertification control, and relates to the technical field of composite material preparation, the preparation method comprises the following specific steps: step 1, raw material preparation and pretreatment: selecting a high-purity bentonite mineral source as a basic material, and preparing a raw material; compared with the prior art, the preparation method has the advantages that bentonite, chitosan and other natural high-molecular polymers are synergistically compounded, and plant fibers are added for modification, so that multiple remarkable advantages are brought, firstly, chitosan has numerous active functional groups and can interact with the surface of bentonite and water molecules through hydrogen bonds, electrostatic interaction and the like, and the surface of bentonite can be modified; the plant fibers contain rich cellulose, the interlaced structure of the plant fibers can be embedded into a composite system, long-term effectiveness of the material in the desert environment is guaranteed when the material encounters external force, and moreover, chitosan can be converted into organic nutrients to nourish soil and continuously improve soil fertility after being degraded, so that the soil fertility is improved; the durability of the soil improvement effect is greatly improved.

The invention relates to the technical field of hard alloy, in particular to a hard alloy and a machining method. The machining method comprises the following steps that tungsten carbide, cobalt and titanium carbide are weighed according to a preset proportion; tungsten carbide, cobalt and titanium carbide are put into a ball mill to be mixed, and a mixture is obtained; performing granulation on the mixture; the granulated raw materials are put into a mold to be shaped, and a green body is obtained; sintering the green body to obtain a semi-finished product; and cutting, grinding and polishing the semi-finished product to obtain the hard alloy. By optimizing the raw material ratio, the hardness, toughness and bending strength of the hard alloy are remarkably improved, and the hard alloy can still keep good use performance under various severe working conditions. The production efficiency is improved, and the production period is shortened. Raw material inspection and finished product detection are enhanced, stable and reliable product quality is ensured, diversified requirements of customers are met, and therefore the problem that existing hard alloy is low in comprehensive performance is solved.

The invention relates to a data monitoring technology, and discloses an energy consumption equipment data monitoring and energy optimization method and device based on the Internet of Things, and the method comprises the steps: obtaining an operation parameter set of energy consumption equipment in a preset time period, carrying out the denoising processing of the operation parameter set, obtaining a denoised operation parameter set, and carrying out the denoising processing of the denoised operation parameter set; calculating an operation stability coefficient according to the de-noising operation parameter set, if the operation stability coefficient is smaller than a preset stability threshold value, calculating an adjustment parameter and adjusting the operation voltage and the operation current, otherwise, calculating an operation efficiency coefficient of the energy consumption equipment, and if the operation efficiency coefficient is larger than or equal to a preset efficiency coefficient threshold value, adjusting the operation voltage and the operation current. And if yes, returning to the step of acquiring the operating parameter set, otherwise, calculating an optimization parameter according to the de-noising operating parameter set, and adjusting the operating voltage and the operating current of the energy consumption equipment according to the optimization parameter. According to the invention, the efficiency of energy consumption equipment data monitoring and energy optimization can be improved.

The invention belongs to the technical field of fluid machinery, and particularly relates to a method for evaluating the operation condition of a multistage centrifugal water injection pump. In order to solve the problem that in the prior art, the prediction precision of the operation condition of the multi-stage centrifugal pump is low, operation condition factor data of the multi-stage centrifugal water injection pump are obtained, wheel level factor data of the multi-stage centrifugal water injection pump are calculated, and the operation condition of the multi-stage centrifugal water injection pump is predicted according to various factor data influencing the operation condition of the water injection pump and result data. According to the method, the working condition early warning of the pump at a certain moment can be predicted through the method, the pump can be located on a certain stage of impeller, the problem can be found and solved before the impeller breaks down through maintenance and replacement in advance, the working condition of the water injection pump can be predicted, and the service life of the water injection pump can be prolonged. The maintenance cost and the failure rate are reduced, and the reliability and the stability of the multi-stage centrifugal pump are improved.

The invention belongs to the technical field of temperature measurement, and provides a multi-point temperature measurement system and method in a rotating state, and the system comprises a temperature difference measurement subsystem, a compensation temperature measurement subsystem, and a power supply subsystem. The temperature difference measurement subsystem comprises two module integration boxes, an ADAM-4520 signal conversion module and a computer; the compensation temperature measurement subsystem comprises two thermocouple junction boxes; the power supply subsystem comprises a 24V DC power supply and a slip ring. According to the invention, the three platinum resistors are arranged for temperature data acquisition, so that the measurement precision of the cold junction compensation temperature is improved; by introducing a free wiring terminal, the experiment efficiency is improved; by arranging the two module integration boxes, the total installation number of the ADAM-4118 analog quantity input modules is expanded to 32, the number of temperature measuring points is expanded to 256, and the requirement for measuring the temperature of multiple points under the rotating condition is met.

The invention relates to a virtual character model generation method and device, a storage medium and electronic equipment, and relates to the technical field of computers.The method comprises the steps that target image generation parameters needed for generating a two-dimensional original picture of a virtual character are determined, and the two-dimensional original picture of the virtual character is generated based on the target image generation parameters; constructing an original three-dimensional model corresponding to the virtual character according to the two-dimensional original picture, and extracting a model wiring grid and a model map in the original three-dimensional model; adding a skin pore texture map for the original three-dimensional model according to the model wiring grid and the model map; and generating a target model map according to the skin pore texture map and a basic model map corresponding to the two-dimensional original picture, and rendering the original three-dimensional model based on the target model map to obtain a target three-dimensional model of the virtual character. According to the invention, the generation efficiency of the three-dimensional head model is improved.

The invention discloses a water quality prediction method based on generative adversarial network data enhancement. The method comprises the steps of data acquisition and data processing, water quality data enhancement, generated data analysis and evaluation, water quality prediction, and result discussion and analysis. According to the method, the mixed deep learning model based on the Bi T imeGAN module and the GRU module is established, so that the water quality data volume is increased, and the prediction precision of the deep learning model is improved. The method has the advantages that the developed Bi T imeGAN model considers global and local features of time series data, the limitation that a traditional generative adversarial network and a time series generation model cannot capture global and local feature information of the time series data is solved, extraction and screening of the global and local time series features of the time series information are achieved, and the time series information extraction efficiency is improved. The generated data is close to real data in the aspect of static characteristics, and meanwhile, the dependency relationship of time sequence data in the aspect of time is reserved; the model established by the method has higher precision in the aspects of time sequence data generation and water quality prediction.

The utility model discloses a permeation wall structure of a variable-diameter pore channel and belongs to the technical field of permeation spray pipes. The structure comprises an upper-layer pore channel, a middle-layer pore channel, a lower-layer pore channel and a circular sheet, the upper-layer pore channel is a circular pore channel; the middle-layer pore channel is a round pore channel, grooves are formed in the inner wall surface of the middle-layer pore channel in a manner of reversely extending along two ends of the diameter, and the diameter of the round pore channel of the middle-layer pore channel is larger than that of the round pore channel of the upper-layer pore channel; the lower-layer pore channel is an arc cross-shaped pore channel, and the arc part is consistent with the middle-layer pore channel in diameter; a round sheet is embedded in the middle layer hole channel, a protrusion extends out of the round sheet in the diameter direction, and the protrusion of the round sheet is matched with the groove of the middle layer hole channel. According to the structure, through accurate size matching, when the circular sheet moves to the position of the upper-layer hole channel, the hole channel can be completely sealed, and gas leakage is prevented. The problem that the permeation spray pipe is low in performance under the high-altitude working condition is solved by effectively controlling air flow exchange inside and outside the spray pipe, and the whole ballistic thrust performance of the permeation spray pipe is improved.

The utility model relates to the technical field of medicine inspection, in particular to a medicine inspection dissolving device which comprises a dissolving box, a medicine adding opening, a dissolving mechanism and a protection mechanism. The protection mechanism comprises a surrounding plate, a motor, a screw rod, an adapter ring and a guide plate; tablets are added into the dissolving box from the medicine adding opening, the dissolving mechanism crushes the tablets in the dissolving box and then dissolves the tablets, during medicine adding, if the distance between the top of the medicine adding opening and the medicine adding end is too large, the motor drives the screw to rotate, the screw drives the adapter ring to move upwards in a threaded fit mode, and the adapter ring is driven by the motor to move upwards. The adapter ring drives the surrounding plate to move upwards, so that the height of the medicine adding opening is increased, the distance is reduced, tablets accurately fall into the medicine adding opening, the guiding plate is used for guiding the tablets remaining on the top of the medicine adding opening into the dissolving box, and the problem that the precision of the tablets falling into the medicine adding opening is low is solved.

The invention relates to a data monitoring technology, and discloses a financial market data intelligent monitoring method, which comprises the following steps: carrying out data cleaning on multi-path price source price information of a financial market to obtain preprocessed price information, carrying out same-type data multi-price source comparison on the preprocessed price information to obtain a price difference value set, if a price difference value greater than a difference value threshold value does not exist, information is acquired again, otherwise, early warning information is sent out, latest price verification is performed on the preprocessed price information, if the latest price verification is passed, early warning information is sent out, and if the latest price verification is passed, financial market data is acquired and classified, and after financial market classification data is obtained, features are extracted to obtain financial parameter time domain features; and analyzing a risk coefficient according to the financial parameter time domain characteristics by using a financial market logistic regression model, and judging whether a risk exists or not according to the risk coefficient. The invention further provides a financial market data intelligent monitoring device, electronic equipment and a storage medium. According to the invention, timeliness and accuracy of financial market data monitoring can be improved.

The present application relates to image analysis technology, disclose a kind of water rare biological detection method and system based on underwater image analysis, comprising: monitoring target water area in preset time period in light signal data stream and spectrum data stream, identify the light signal data stream light emitting event, obtain light emitting event timestamp sequence, based on light emitting event timestamp sequence obtain light emitting image sequence in light signal data stream and spectrum data sequence in spectrum data stream, to light emitting image sequence and spectrum data sequence is carried out denoising correction and space-time synchronization processing, obtain after alignment image block sequence and after alignment spectrum curve, according to after alignment spectrum curve constructs spectrum attention weight, to after alignment image block sequence is carried out multi-scale morphological feature extraction, obtain multi-scale morphological feature map, to multi-scale morphological feature map is carried out rare biological target detection, obtain target detection result.The present application can improve the accuracy of water rare biological detection.

The embodiment of the invention provides a temperature determination method and device, a storage medium and electronic equipment, and the method comprises the steps: periodically transmitting a first sound wave through a sound wave transmitter, and determining target delay time corresponding to the first sound wave under the condition of determining that a sound wave receiver receives the first sound wave, the sound wave emitter and the sound wave receiver are both arranged in a fan cabin, and the target delay time is the propagation time of the first sound wave from the sound wave emitter to the sound wave receiver; determining a first distance between the sound wave emitter and the sound wave receiver; and the average temperature in the fan cabin is determined according to the target delay time and the first distance. According to the method, the problem of low precision of temperature values obtained through temperature measurement technologies such as thermocouples, thermistors and infrared temperature measurement in related technologies can be solved.

The present invention relates to the field of logistics and transportation technology, and discloses a cargo transportation optimization method and system based on an overseas warehouse WMS, including: extracting order demand characteristics of target transported goods; determining an inventory call factor through the cargo inventory, calculating the shortest transportation time of the target transported goods according to the inventory call factor, and generating a warehouse cargo call path of the target transported goods according to the shortest transportation time; calculating an order urgency factor of the target transported goods, and determining a transportation mode of the target transported goods according to the order urgency factor; generating an initial cargo transportation route of the target transported goods according to a dynamic transportation factor, a warehouse cargo call path and a transportation mode; generating a cargo transportation optimization model of the target transported goods according to the dynamic transportation factor, dynamically optimizing the initial cargo transportation route through the cargo transportation optimization model, and transporting the target transported goods according to the optimized initial cargo transportation route. The present invention can improve the accuracy of cargo transportation optimization.

The invention discloses a parking space determination method and device, equipment, a medium and a product. The parking space determination method comprises the following steps: acquiring an environment image, track information and ultrasonic information of a vehicle; performing semantic segmentation processing on the environment image to obtain a to-be-processed image containing semantic information; determining obstacle points according to the semantic information and the track information to obtain an obstacle point set; and performing parking space fitting calculation according to the obstacle point set and the ultrasonic information to obtain a target parking space. According to the technical characteristics, the accuracy of parking space construction is improved, so that the smooth proceeding of subsequent automatic parking is guaranteed, and the use experience of a user is improved.

The invention relates to the technical field of photoresist compositions, in particular to a photoresist composition as well as a preparation method and application thereof. The photoresist composition is prepared from the following raw materials: (A) a photo-initiation multifunctional group resin polymer, (B) a cationic photoinitiator, (C) a film-forming agent, (D) a silicon-containing auxiliary agent and (E) a fluorine-containing compound, the photo-initiated multifunctional group resin polymer comprises a multifunctional group epoxy resin and a hydroxyl-containing oligomer. The photoresist composition is applied to the technical field of photoetching, and is applied to a soft / hard substrate to prepare a permanent photoresist pattern. And the ultraviolet light exposure energy can be reduced, and better resolution and attachment effect can be obtained.

The invention relates to the artificial intelligence technology, and provides a pilotless automobile visual identification method and device and a medium, and the method comprises the steps: carrying out the environment adaptability enhancement processing of a collected initial environment image of a target automobile, and obtaining an environment enhancement image; extracting image features of the environment enhanced image, and then performing feature classification of the image features to obtain classification features of the image features; generating a movement track of a change feature in the classification features; generating a traffic scene of the target automobile according to fixed features in the classification features and a preset energy function; and generating a path plan of the target automobile according to the movement track and the traffic scene, performing instruction conversion on the path plan to obtain a control instruction of the target automobile, and performing driving operation on the target automobile by using the control instruction. According to the invention, the visual identification accuracy of the pilotless automobile can be improved.

The method comprises the following steps: when a scintillation crystal of the gamma-ray energy spectrometer receives gamma rays, acquiring a spectral line array of the gamma-ray energy spectrometer, extracting a maximum value in the spectral line array as a spectrum stabilization result, comparing whether the spectrum stabilization result is the same as a preset spectrum stabilization target or not, and if the spectrum stabilization result is the same as the preset spectrum stabilization target, judging whether the spectrum stabilization target is the same as the preset spectrum stabilization target or not; if so, determining that the gamma-ray spectrometer is calibrated, and executing a detection step, otherwise, executing a spectrum stabilization step; judging whether the difference value between the spectrum stabilization result and the spectrum stabilization target is smaller than a preset value, if yes, executing software spectrum stabilization processing, adjusting the spectral line array through the coefficient to calibrate the spectrum stabilization result to be the same as the spectrum stabilization target, and executing the detection step, and if not, executing hardware spectrum stabilization processing, and adjusting the power supply voltage of a photomultiplier in the gamma-ray energy spectrometer. And changing the field intensity of the internal electric field of the photomultiplier until the spectrum stabilization result is calibrated to be the same as the spectrum stabilization target, and executing the detection step.

The present disclosure is about a code classification method and device, a computer-readable storage medium, and an electronic device, and relates to the technical field of malicious code classification. The method includes: extracting a first code feature of the code to be classified, and calculating a first feature sensitivity factor of the first code feature based on a sensitive factor calculation model included in a preset sensitive function library; wherein the first code feature includes a first static feature and a first dynamic feature; constructing a first family feature to be classified corresponding to the code to be classified according to the first code feature and the first feature sensitivity factor; calculating the similarity between the first family feature to be classified and the original family feature included in a preset malicious code family feature library; determining the family category of the malicious code family to which the code to be classified belongs according to the similarity calculation result. The present disclosure improves the accuracy of the classification results.

The invention relates to the technical field of data analysis, and discloses a solid state disk remaining life analysis method based on an improved classifier model, which comprises the following steps: carrying out abnormal value screening and time sequence smooth interpolation operation on hard disk monitoring data to obtain a hard disk data sequence; performing data standardization operation on the hard disk data sequence to obtain a standard data sequence, and performing multi-channel feature extraction and mutual information dimension reduction operation on the standard data sequence to obtain a hard disk attribute feature sequence; performing bidirectional time sequence coding and global attention coding on the hard disk attribute feature sequence to obtain hard disk attribute time sequence features; variational fault classification, fault labeling and time sequence expansion convolution are carried out on the hard disk attribute time sequence features to obtain standard time sequence features, and life analysis is carried out on the standard time sequence features to obtain the remaining life of the hard disk. The invention further provides a solid state disk remaining life analysis system based on the improved classifier model. The method can improve the accuracy of life analysis.

The invention discloses an abutment edge line generation method and device, equipment and a storage medium. The abutment edge line generation method comprises the steps that oral cavity scanning data are acquired, and the oral cavity scanning data comprise point cloud data of a plurality of nodes; performing classification processing on each node based on the point cloud data of each node to obtain classification information of each node, the node types including an abutment tooth type and a background type; an undirected graph corresponding to the oral cavity scanning data is constructed, the undirected graph comprises a plurality of nodes in the oral cavity scanning data and edges among the nodes, and weights corresponding to the edges among the nodes are determined based on point cloud data of the nodes and / or classification information of the nodes; and performing graph segmentation on the undirected graph based on the weights of the edges in the undirected graph to obtain an abutment sub-graph and a background sub-graph, determining abutment edge nodes based on the abutment sub-graph and the background sub-graph, and generating abutment edge lines based on the abutment edge nodes. And the accuracy of generating the abutment edge line is improved.

The present application discloses a message transmission method and system, storage medium and electronic device in an Internet of Vehicles, wherein the method comprises: receiving packaged data broadcasted by a sending terminal through a direct communication interface of a receiving terminal, wherein the packaged data carries a wireless communication message and a first message identifier, the wireless communication message is used to provide a preset service, and the first message identifier is obtained by encoding the first dimension information of the preset service; when the first message identifier is verified, the first dimension information of the preset service is determined based on the first message identifier; when it is determined that the receiving terminal is allowed to obtain the preset service according to the first dimension information of the preset service, the preset service is obtained based on the wireless communication message. Through the present application, the problem of low accuracy of service provision caused by poor reliability of message transmission in the message transmission method in the Internet of Vehicles in the related art is solved.