67results about How to "Reduce computational cost" patented technology

The invention discloses a DNA binding residue prediction method based on a multi-modal protein language model in the technical field of bioinformatics. The DNA binding residue prediction method based on the multi-modal protein language model comprises the following steps: sequentially using tools to respectively obtain feature files of a protein sequence P to be subjected to DNA binding residue prediction; inputting all the feature files into the ESM3 to obtain an embedded matrix M; the method comprises the following steps: processing a protein sequence into a residue sample, constructing an isovariant graph neural network model according to a protein structure diagram, constructing a data set by utilizing the protein sequence of known DNA binding residues, and training the constructed network; and inputting a residue sample of the protein sequence into the trained model to obtain a DNA binding residue of the protein sequence. According to the DNA binding residue prediction method based on the multi-modal protein language model, the efficiency and accuracy of DNA binding residue prediction are improved.

The invention belongs to an electromagnetic simulation technology, and provides an integral equation grid adaptive encryption method based on non-conformal grid current discontinuity estimation, which is used for efficiently obtaining a high-precision simulation result. The method comprises the following steps: firstly, using a conformal or non-conformal triangular mesh discrete model surface, defining a MultiBranch-RWG primary function on a discrete mesh unit, establishing an electric field integral equation, and solving to obtain surface current distribution; then, according to the surface current distribution, a current discontinuity error estimation method is utilized to calculate and obtain a current discontinuity error of each grid unit; and finally, traversing current discontinuity errors of all grid units, when the current discontinuity error of any grid unit is greater than a preset threshold value, executing adaptive encryption, and executing iteration by taking the adaptively encrypted grid as input. Otherwise, outputting the grid information. According to the method, higher precision can be realized with lower calculation cost, grid generation is more flexible, and the method is more suitable for complex targets.

The invention provides a sliding bearing oil film lubrication reliability evaluation method, and relates to the field of oil film lubrication reliability diagnosis, and the method comprises the steps: building a minimum film thickness limit state function according to a bearing minimum oil film thickness calculation equation and a preset minimum oil film thickness threshold value, and determining a reliability domain, iteratively solving the minimum oil film thickness through an elastohydrodynamic lubrication equation, and solving a non-probabilistic reliability index and designing a checking point through a numerical optimization algorithm; according to a non-probability reliability index, designing a checking point and a relation between an uncertain domain and a reliable domain to judge a joint focal element needing extreme value analysis, inheriting maximum and minimum values of the joint focal element by using an intelligent genetic method, and obtaining credibility and likelihood under a threshold value; and according to the above steps, obtaining the credibility and the likelihood under each minimum oil film thickness threshold value, and determining an accumulated credibility function curve and an accumulated likelihood function curve used for evaluating the reliability degree. In this way, focal elements needing extreme value analysis are reduced, and the efficiency of evaluating the lubricating reliability of the oil film of the sliding bearing is improved.

The present invention belongs to the technical field of gas field exploitation, and discloses a numerical simulation method for shale condensate gas reservoir development based on a universal pEDFM, comprising the following steps: S1, using the universal pEDFM to calculate the conductivity of the connection between various grids; S2, constructing a commercial numerical simulation software CMG equivalent model based on the universal pEDFM; S3, calling the nonlinear solver in CMG to solve the shale condensate gas reservoir component model. The present invention adopts the above-mentioned numerical simulation method for shale condensate gas reservoir development based on the universal pEDFM, which not only ensures the advantages of the universal pEDFM in calculation accuracy and processing of complex fracture networks, but also avoids the difficulty of developing additional high-performance nonlinear solvers, and provides a numerical simulation tool with the best theoretical comprehensive calculation performance for the development of fractured shale condensate gas reservoirs.

The present invention provides a method and system for constructing a knowledge graph for mathematics courses, and relates to the technical field of knowledge graphs. The present invention solves the technical problem that the current relationship extraction method cannot accurately mine the entity relationship of mathematics courses, and realizes a relationship classification method by integrating sparse biaffine attention and adaptive threshold loss, overcoming the difficulties of extracting complex dependencies and overlapping relationships. At the same time, a hyperbolic space word embedding sparse projection model is used to predict the upper and lower relationships. The hyperbolic space has the ability to express hierarchical structures, can better predict the upper and lower relationships, and reduce the computational cost.

The invention discloses a method and device for recognizing an abnormal region in a CT image, a medium and a product, and relates to the technical field of abnormal region recognition, and the method comprises the steps: decomposing a to-be-detected CT image into a plurality of to-be-detected super voxels through employing a linear iteration clustering algorithm; determining any to-be-tested super voxel as a current to-be-tested super voxel; determining an abnormal value of the current to-be-detected super voxel based on a spectral space function principle and a healthy super voxel library; the abnormal value is used for expressing the probability that the super voxel region in the CT image to be detected is an abnormal region. According to the method, the abnormal region in the CT image is identified based on the spectral-space function, so that the accuracy and efficiency of identifying the abnormal region of the CT image can be improved.

The present invention discloses a grid adaptive turbulence simulation method based on Vreman dynamic coefficient coupled SA series model, and the main process of its implementation includes: step 1, judging whether to apply shielding function; step 2, calculating Vreman dynamic coefficient; step 3, determining local dynamic grid length scale; step 4, constructing scale-related adjustment function; step 5, reconstructing turbulent viscosity of SA series model; step 6, using reconstructed turbulent viscosity to perform turbulence simulation. The present invention constructs Vreman dynamic coefficient based on local velocity gradient tensor, identifies local grid size, and determines local grid length scale Δ * , the turbulent viscosity is reconstructed by constructing scale-related functions through turbulent energy spectrum integration, and a new algebraic empirical formula is constructed to characterize the turbulent kinetic energy and turbulent length scale. This overcomes the problem of high dependence of existing turbulent mixing models on grid experience, broadens grid adaptability, improves calculation accuracy, reduces calculation cost, and significantly accelerates the turbulence simulation process.

The present invention discloses a hyperspectral image compression and reconstruction method, device and equipment based on an autoencoder, which comprises: obtaining a Houston image data set and performing preprocessing including random cropping to generate an image block of a preset size as an original hyperspectral image; inputting the original hyperspectral image into an autoencoder model including an encoder and a decoder and training based on a preset loss function to obtain a trained hyperspectral compression model, wherein the network structure of the encoder includes a thumbnail generation branch and a potential encoding branch, and the network structure of the decoder includes a thumbnail decoding branch and a potential representation decoding branch; inputting a hyperspectral image to be compressed into the hyperspectral compression model, encoding the hyperspectral image to be compressed by the encoder to obtain compressed data, and decoding the compressed data by the decoder to obtain a reconstructed hyperspectral image.

The present invention provides a cloud-edge collaborative task scheduling method and device, wherein the method includes: determining the pending tasks of each terminal node and the business priority of each pending task; respectively determining the first total computing delay and the first unloading cost of each pending task unloaded to each edge node, and the second total computing delay and the second unloading cost of each pending task unloaded to the cloud center; establishing an optimization target according to the business priority of each pending task, the first total computing delay corresponding to unloading to each edge node, the first unloading cost corresponding to unloading to each edge node, the second total computing delay, and the second unloading cost; solving the optimization target to obtain the scheduling scheme of each pending task; and respectively allocating each pending task to the corresponding edge node or cloud center according to the scheduling scheme. By executing the present invention, both actual business needs are met and computing efficiency is improved and computing cost is reduced.

The present invention discloses a method for eliminating the influence of BLE direction finding carrier frequency deviation to obtain phase delay, so that the phase delay only related to the direction of the incident signal source can be obtained with extremely low computing resources without estimating the carrier frequency deviation, thereby improving the accuracy of the BLE direction finding system in the presence of carrier frequency deviation and improving the stability of the BLE direction finding system under various operating temperature conditions.

The invention relates to the technical field of train aerodynamics optimization, in particular to a train surface bionic microstructure partition arrangement and drag reduction optimization method and system.The method comprises the steps that the train surface is divided into design partitions which are geometrically continuous and relatively consistent in near-wall flow form according to train appearance geometric parameters and external flow field characteristics; resistance mechanisms in the same subarea tend to be the same; a turbulent flow correction model is adopted for the design partition where the bionic microstructure is arranged, geometric parameters of the bionic microstructure are mapped into near-wall dissipation correction, and the wall surface shear stress is equivalently changed; an initial turbulence model is adopted for the non-arranged partition, and physical quantity continuity is kept at the junction through data exchange of a virtual grid; and a stable and comparable aerodynamic resistance evaluation result is obtained, and a target partition arrangement scheme is screened from candidate arrangement schemes, so that the reliability of resistance reduction optimization is improved at relatively low calculation cost.

The present invention discloses a grid-adaptive turbulence simulation method based on turbulence energy spectrum coupling v2-f series models, and the main process of its implementation includes: step one, judging whether to apply a shielding function; step two, identifying the local grid scale; step three, constructing a scale-related adjustment function based on the turbulence energy spectrum integral coupling v2-f series model; step four, using the adjustment function to reconstruct the turbulent viscosity of the v2-f series model; step five, based on the v2-f series model, using the reconstructed turbulent viscosity to perform turbulence simulation. The present invention determines the local grid length scale Δ by identifying the local grid size. * , and then reconstruct the turbulent viscosity by constructing scale-related functions through turbulent energy spectrum integration to achieve grid adaptive simulation, which effectively overcomes the problem of high empirical dependence of existing RANS / LES hybrid models on grids, greatly reduces computational costs, and significantly accelerates the turbulence simulation process, providing an efficient numerical simulation method for solving fast and high-precision simulation of complex engineering flow problems.

The invention relates to helicopter design, in particular to a power cabin fire extinguishing agent concentration distribution calculation method. Numerical calculation is adopted to directly simulate the concentration distribution of the fire extinguishing agent in the power cabin, the calculation period is long, the concentration distribution of the fire extinguishing agent in the power cabin is rapidly obtained, numerical calculation and a theoretical analysis method are combined, the method is used for calculating the concentration distribution of the fire extinguishing agent, an instantaneous point source concentration equation when wind exists is constructed through Matlab, and if other fire extinguishing agents are replaced, the calculation period is short. A new fire extinguishing agent concentration diffusion solution can be established by modifying a diffusion system lamp associated with the fire extinguishing agent. For different models, power cabins and engine accessories are simplified, and after a power cabin model is obtained, fire extinguishing agent concentration distribution can be solved according to the method. The method can be applied to rotor wing or fixed wing fire extinguishing agent concentration solving.

The present application relates to the field of underwater vehicle flow field analysis, and discloses a variable reconstruction underwater vehicle flow field mutual interference analysis method, comprising: modeling a single underwater vehicle; based on the single underwater vehicle, constructing a variable reconstruction underwater vehicle; converting the variable reconstruction process into a series of transient processes parameterized by the axial spacing between the head end face and the tail end face of adjacent single underwater vehicles; building a fluid simulation calculation framework, and batch calculating the fluid dynamic parameters of different parts of the variable reconstruction underwater vehicle under different axial spacings corresponding to each transient process; based on the variation law of the fluid dynamic parameters with the axial spacing, determining a characteristic spacing from the axial spacing; using the velocity nephogram and the pressure nephogram, and combining the characteristic spacing and the fluid dynamic parameters of different parts of the variable reconstruction underwater vehicle, the flow field mutual interference is analyzed. The present application effectively reduces the calculation cost and improves the efficiency of flow field analysis.

The invention discloses a variable cycle combination method for multiple oil displacement systems based on a gradient boosting tree algorithm. The method comprises the following steps: S1, constructing a variable cycle combination large-scale learning sample of the oil displacement system; and S2, establishing a gradient boosting tree prediction model. According to the gradient boosting tree algorithm, a plurality of weak decision trees are integrated to gradually optimize model errors, flexibility and interpretability are kept, meanwhile, high robustness and over-fitting resistance are achieved, a plurality of weak learners can be weighted and combined in each step, and high prediction capacity and precision are achieved. According to the invention, on the basis of the gradient boosting tree algorithm, mining learning is carried out on large-scale learning samples by designing an appropriate deep neural network, the internal law of the numerical calculation process can be captured, and then time-consuming large-scale matrix iteration in numerical calculation is replaced; accurate prediction solving of the variable circulation oil displacement process of multiple oil displacement systems is achieved while calculation consumption is remarkably reduced.

The invention relates to a method for producing an electrode from an electrode-continuous web (9), comprising: a coating process in which the electrode-continuous web (9) is coated with an electrode coating (5); and a cutting process in which the uncoated region (11) is cut, in particular while forming the arrester flag (7). The method has a marking process in which an uncoated region (11) of the electrode-continuous web (9) is marked for product tracking in the case of origin certification or quality complaints. According to the invention, the marking process is an imprint process in which a continuous imprint pattern (19), which is void-free and varied in the production direction (F), is imprint as a mark into the uncoated region (11) of the electrode-continuous web (9).

The application discloses an optimization method of a rolling wing blade connecting rod mechanism, and belongs to the field of rolling wing aircrafts.The blade swing angle is represented by structural parameters of the blade connecting rod mechanism, and the structural parameters of the blade connecting rod mechanism, including the lengths of the connecting rods, the coordinates of key hinge points and configuration bias, are defined as a variable vector; multiple target functions are constructed for multiple key working conditions such as hovering, forward flight and transition, the multiple target functions not only quantify the performance of the connecting rod mechanism itself, such as kinematic fitting error with a target motion law, dynamic stability index and force transmission efficiency index, but also cover aerodynamic efficiency index and aerodynamic acoustic index which are directly related to the performance of the whole machine; and the optimal structural parameters of the blade connecting rod mechanism are obtained by solving the multiple target functions under constraints, so that the global optimization of the structural parameters of the blade connecting rod mechanism under multiple working conditions, multiple targets and multiple constraints can be realized.

The present invention discloses an adaptive measurement method, device and system for broadband signals of a power grid, the method comprising: obtaining a signal spectrum and a main lobe interference condition of a broadband signal of a power grid; according to the main lobe interference condition of the signal, adaptively using a dual-spectrum interpolation algorithm or a complex modulation refinement spectrum analysis method to estimate signal parameters of the signal spectrum, and obtaining all parameters of the broadband signal of the power grid. The present invention uses the phase characteristics of the spectrum lines of the signal spectrum to adaptively determine the main lobe interference of the signal, uses a complex modulation refinement spectrum analysis method to estimate parameters for the signal spectrum with main lobe interference, and uses a dual-spectrum interpolation algorithm to estimate the signal spectrum without main lobe interference. The present invention effectively realizes high-precision adaptive measurement of harmonics within a wide frequency range.

The present invention proposes a structured lyrics-melody generation method and system based on generative AI, including two key modules: Theme-to-Lyric (TTL) and Structure Identification and Optimization (SIO). The TTL module uses a large-scale language model to generate high-quality Chinese and English lyrics, and continuously optimizes the lyrics content through interaction with users to meet the personalized needs of users. The SIO module ensures that the generated melody conforms to the basic rules of music theory and enhances the sense of hierarchy and emotional expression of the music work by analyzing and extracting the structural features in the music. The advantage of the present invention is that by introducing the collaborative work of the TTL module and the SIO module, efficient generation and optimization of lyrics and melodies are achieved. The generated music works not only have a reasonable structure and coherence, but also can achieve higher quality standards in practical applications, promoting the intelligent and automated process in the field of music creation.

The embodiment of the present invention discloses a vascular imaging method, device, equipment and storage medium, wherein the method comprises: obtaining an original filling image and at least two original mask images of a target acquisition part of a target object; determining an original mask image in the same heart beating state as the original filling image from the at least two original mask images as a mask image to be selected; determining a target mask image corresponding to the original filling image according to the mask image to be selected; and determining a vascular subtraction image of the target acquisition part according to the original filling image and the target mask image. The technical solution of the embodiment of the present invention improves the imaging quality of the vascular subtraction image by determining a target mask image that highly matches the original filling image from multiple original mask images.

The present invention proposes an attribute-based online / offline signature method and system based on commercial secret SM9, the system includes an attribute authorization end, a signature end and a verification end; the attribute authorization end generates a master private key and public parameters according to the security parameters during signing; and further generates a signature based on the master private key, public parameters, signature end attribute set and access structure. A , generating a public-private key pair of the signing end; the signing end generates an offline signature based on the public parameters generated by the attribute authorization end and the signing end private key; and also generates an online signature based on the public parameters, the signing end attribute set, the offline signature, the signing end private key and the message; the verification end verifies the message based on the public parameters generated by the attribute authorization end, the signing end public key, M And the online signature generated by the signing end is used to verify the validity of the signature; the present invention can reduce the signature calculation cost of the lightweight device in the online stage and comply with the commercial SM9 identification signature standard.

The invention belongs to the technical field of domain adaptation of ultra-high-definition images, and provides a multi-granularity perception and prototype driven ultra-high-definition image domain self-adaptive recognition method, which comprises the following steps of: firstly, acquiring a conventional image data set and an ultra-high-definition image data set across image domains, and preprocessing the conventional image data set and the ultra-high-definition image data set to obtain an ultra-high-definition image data set; obtaining a preprocessed batch training image data set; secondly, constructing and initializing a multi-granularity feature extractor and a feature classifier; then, based on the batch training image data set, training is carried out in combination with a multi-granularity feature extractor and a feature classifier to obtain an ultra-high-definition image unsupervised domain self-adaptive recognition model; and finally, inputting a to-be-recognized ultra-high-definition image into the ultra-high-definition image-based unsupervised domain adaptive recognition model for classification to obtain a recognition result of the ultra-high-definition image. According to the method, the problems that the fine-grained features of the ultra-high-definition image are ignored, the ultra-high-definition image field is difficult to mark, the number is small, and the computing resource consumption is large are solved.

The present application discloses a log template extraction method and device, which belongs to the field of computer technology. The method comprises: deleting specified characters in each line of log records in multiple lines of log records of a log to obtain each line of log records after preprocessing; determining at least one first log record group based on each line of log records after preprocessing, and different first log record groups include different lines of log records in the log; extracting log templates in each of the at least one first log record group, and determining the log template of the log based on the extracted log template. The present application solves the problem of low accuracy of the currently determined log templates, and the present application is applied to log template extraction of logs.

The invention discloses a hyperspectral image compression and reconstruction method, device and equipment based on an auto-encoder, and the method comprises the steps: obtaining a Houston image data set, carrying out the preprocessing including random cutting, and generating an image block with a preset size as an original hyperspectral image; the original hyperspectral image is input into a self-encoding model comprising an encoder and a decoder, training is carried out based on a preset loss function, a trained hyperspectral compression model is obtained, a network structure of the encoder comprises a thumbnail generation branch and a potential encoding branch, and the thumbnail generation branch is used for generating a thumbnail image; the network structure of the decoder comprises a thumbnail decoding branch and a potential representation decoding branch; and inputting a to-be-compressed hyperspectral image into the hyperspectral compression model, encoding the to-be-compressed hyperspectral image through the encoder to obtain compressed data, and decoding the compressed data through the decoder to obtain a reconstructed hyperspectral image.

The invention provides a data integrity detection method for an edge computing and cloud computing collaborative environment by taking practical application as a background, and belongs to the technical field of cloud computing security. In the scheme, an edge server is close to a client, assists a client in generating a data verification tag, and stores and manages frequently used data. The cloud client generates a'partial label 'for the data, and a complete label is generated by the edge server. And when the verifier challenges the data integrity, the edge server is responsible for generating integrity evidence of the stored data, and the cloud server is responsible for generating complete evidence and is responsible for the overall integrity of the data. Through the introduction of the edge server, the calculation cost of the cloud client is reduced, the data access efficiency of the client is improved, the data storage and access pressure of the cloud server is shared, the cloud server is assisted to safely and efficiently complete data integrity detection, and the operation efficiency of the system is improved on the whole.

The present invention relates to a method for calculating the height of road facilities based on monocular camera images, including: calibrating a monocular camera to obtain preset parameters; using the calibrated monocular camera to collect the original image of the road facilities, and obtaining the ground point coordinates and vertex coordinates; using the mapping change matrix, the ground point coordinates and the vertex coordinates to calculate the pixel distance in the plan view image, and using the projection mapping matrix and the ground point coordinates to calculate the distance from the road facilities to the monocular camera; bringing the pixel distance of the road facilities in the plan view image, the distance from the road facilities to the monocular camera, and the distance from the imaging plane to the monocular camera position into a preset height model to calculate the height of the road facilities. The present invention can quickly calculate the height information of road facilities through monocular camera images, and fill in the gaps in the height of facilities currently realized in the intelligent detection of road facilities based on monocular camera images; and has the characteristics of low implementation cost, low calculation cost, high operation efficiency, etc.

The invention discloses a low-carbon economic dispatching method and device for a mixed hydrogen-electricity combined system, and belongs to the technical field of electrical control engineering.According to the method, the lowest system operation cost under the condition of considering carbon tax is taken as an optimization target, a discretization linear dynamic energy flow equation is taken as a mixed hydrogen net operation constraint, and a low-carbon economic dispatching result is obtained; a mixed hydrogen-electricity combined system low-carbon economic dispatching model is constructed, the dynamic characteristics in the mixed hydrogen natural gas transportation process can be described, the optimization problem solving requirement is met, and the change of gas components in the transportation process can be tracked to accurately reflect the running state of the mixed hydrogen natural gas system; the calculation cost for solving the nonlinear dynamic energy flow model in real time is reduced while the model characteristics are reserved; the low-carbon economic dispatching model of the mixed hydrogen-electricity combined system is solved for dispatching, it is guaranteed that the combined system always runs within the safety boundary, the renewable energy consumption capacity of the system is improved, and the running cost and carbon emission of the system are reduced.

The invention discloses a commutation branch tower wind disaster vulnerability evaluation method considering material parameter variation, which comprises the following steps of: during calculation of a wind-induced response of a commutation branch tower, considering a variation rule of various structural material parameters by using sampling operation, and learning a relationship between a mean value and a standard deviation of a wind speed and a structural demand index by using an intelligent prediction model; and finally, predicting the surpassing probability of the structure under a series of wind speed values, and forming a comprehensive wind disaster vulnerability result considering multi-structure material parameter variation. According to the method, more accurate beyond probability assessment is provided on the basis of the existing power transmission tower wind damage vulnerability assessment, and more uncertainty risks are considered. The uncertainty of various variation parameters is considered in a random sampling mode, the calculation amount is reduced, the quantitative relation between the wind speed and the override probability and the relation between the wind speed and the various material parameters and the override probability are obtained through the intelligent prediction model, and under the specified wind speed, the calculation amount is reduced, and the calculation efficiency is improved. The override probability can be calculated more quickly through the intelligent prediction model, a vulnerability curve or curved surface under multiple wind speeds is obtained, and the analysis efficiency is greatly improved.

The present invention relates to a distributed finite-time adaptive fault-tolerant control method and system based on a vehicle queue, comprising: constructing a vehicle longitudinal kinematic equation to obtain a vehicle queue consistency error dynamic equation; designing a sliding surface based on the vehicle queue consistency error, and calculating the sliding surface approach law; setting an adaptive parameter estimation law based on the vehicle team collaborative braking consistency error equation and the error sliding surface, estimating the vehicle fault degree online based on the optimal parameter adaptive gain, and obtaining an online vehicle fault estimation value according to different vehicle fault degrees; combining the sliding surface approach law, the vehicle queue consistency error dynamic equation and the online vehicle fault estimation value to obtain a queue distributed finite-time adaptive fault-tolerant controller to achieve fault-tolerant control of the fleet in the event of a fault. The present invention achieves fleet fault-tolerant control by designing a finite-time active fault-tolerant control strategy based on online fault estimation.

The present invention discloses a multi-UAV self-organizing collaborative system and method, which realizes the communication networking and high-precision positioning of multiple UAVs through a differential satellite positioning system and Wi-Fi wireless communication technology, and designs a self-organizing collaborative control method with multiple functions such as autonomous interactive object selection, individual spacing maintenance, obstacle avoidance, target tracking, and viewpoint synthesis, thereby realizing autonomous communication and distributed autonomous control of multiple UAVs, and being able to provide a system solution for large-scale UAV self-organizing collaboration.