81 results about "Mathematical relation" patented technology

The invention relates to the technical field of offshore wind power, and discloses a bearing performance degradation evaluation method for an offshore wind power foundation structure, which effectively solves the problem of neglecting corrosion heterogeneity in the prior art by acquiring initial three-dimensional geometric point cloud data and historical service data and constructing a first finite element model. The model better fits the actual structure state; the local damage evolution and cumulative effect are simulated by means of a second finite element model, meanwhile, material degradation evaluation is combined, the interaction of three key factors including corrosion thinning, local damage and material mechanical property degradation is comprehensively considered, and the defect that the multi-factor coupling influence is not fully solved in the prior art is overcome; the extreme environment load condition is introduced to construct the third finite element model, the performance degradation law of the structure in the extreme environment can be analyzed, and then the reliability and applicability of evaluation are improved by establishing a mathematical relation model and predicting the full life cycle bearing performance.

Embodiments of the present application relate to the field of computer vision. Disclosed are a target six-degree-of-freedom pose estimation method and apparatus based on binocular vision and particle swarm optimization. The method comprises: obtaining a binocular image of a target, wherein the binocular image at least comprises a rotation matrix, a displacement vector and geometric features of the target; decomposing the rotation matrix into a rotation vector and a rotation angle by means of a rotation matrix decomposition method, and performing six-degree-of-freedom uniform sampling on the rotation vector and the rotation angle to obtain a data point set; establishing a mathematical relationship between the rotation matrix and the displacement vector on the basis of the geometric property of the geometric features and the data point set; and performing target pose estimation on the binocular image of the target by designing an objective function and an application optimization algorithm to obtain a pose estimation result. The present application solves the problems in the related art of low accuracy, poor universality and poor robustness.

Systems and methods for machine-learned generation of data insight summaries are provided. A computing system can obtain numerical time series data comprising a plurality of numerical values associated with a plurality of times. The computing system can identify, based on the numerical time series data, one or more first mathematical relationships in the numerical time series data. The computing system can generate, based at least in part on the mathematical relationships, a first input context comprising first natural language data indicative of the mathematical relationships. The computing system can provide the first input context to a first machine-learned sequence processing model. The first machine-learned sequence processing model can generate, based at least in part on the first input context, one or more outputs describing the one or more first mathematical relationships. The computing system can output the one or more outputs.

The present invention relates to a concrete surface crack detection method and system based on a machine learning algorithm, which obtains concrete wave velocity, frequency, amplitude, strength, elastic modulus, temperature, and linear expansion coefficient, analyzes characteristic parameters, establishes a mathematical relationship model, fits data using a machine learning algorithm, determines model parameters, inputs relevant parameters of the concrete to be detected into a multi-parameter digital quantity plate model, and the model calculates the potential risk and predicted value of the crack. The multi-parameter digital quantity plate model is applied to real-time detection of concrete structures to realize quality assessment and crack prediction, and the concrete structure is regularly monitored to update model data. The system includes data acquisition, data preprocessing, feature extraction, crack measurement, multi-parameter digital quantity plate model establishment, multi-parameter comprehensive analysis, real-time monitoring and early warning, database management, and user interface and interaction modules.

The invention discloses a novel structure construction method using a gradient triple-period minimal surface lattice. The novel structure construction method comprises the following steps: defining a mathematical relationship of an entity part of a TPMS lattice according to a selected scene; the standard TPMS lattice in the original cube is deformed and matched into the pyramid space, a transition structure is formed, and a final TPMS distortion structure is output; connecting the upper-layer small unit lattices with the lower-layer large unit lattices by the TPMS twisted structure to generate gradient lattices without geometric defects; the gradient between different unit numbers is controlled by controlling the size of the top plane of the pyramid; the size of the transition area is adjusted, the transformation completion distance of the substructure is changed, and then the elastic modulus is adjusted and controlled; the number of layers of the base structure in the transition area is increased, and the buffering distance of structure transformation is prolonged. The problems that in the prior art, a gradient lattice transition area has geometric defects, the adjustable range of elastic modulus is small, and stress is concentrated are solved, and the stability and durability of the structure are improved.

The present invention belongs to the technical field of autonomous driving. Disclosed are a method and apparatus for evaluating the compliance of autonomous driving. A specific embodiment of the method comprises: acquiring current traveling data generated by an autonomous driving target vehicle during traveling; acquiring parsing processing corresponding to a preset compliance engine system configuration file; and on the basis of a parsing result, calling a mathematical function library to perform compliance evaluation on the current traveling data, so as to generate a compliance evaluation result. In the present embodiment, a legal language can be converted into logical descriptions and mathematical equivalence relations; and software codes of an embedded language are generated on the basis of the logic and mathematical relations, current traveling data of a target vehicle is then evaluated on the basis of the software codes, and a compliance evaluation result is automatically generated. Thus, the compliance of autonomous driving can be automatically evaluated on the basis of the current traveling data of the target vehicle, and the solution meets the requirements of autonomous driving of the target vehicle, and is also applicable to different scenarios, thereby improving the application universality of the solution.

The invention relates to a scene modeling-based primary school mathematics application question intelligent solving guiding method and system. The method comprises the steps of obtaining a question description text of a question; the question description text is used for representing the content of the question; based on the question description text, analyzing condition parameters of the questions to obtain a question description feature map; graph nodes in the question description feature graph are subject texts and object texts of the questions, and connecting edges among the graph nodes are constructed based on relation texts of the questions; determining a scene type of the question based on a preset scene template library and the question description feature map, and obtaining a comprehensive representation vector of the question based on the scene type; the comprehensive representation vector comprises a scene type, a basic mathematical relationship template and a problem description feature map; and based on the comprehensive representation vector, predicting the solving difficulty of the question, and based on the solving difficulty, generating a question solving guide scheme of the question. By adopting the method, the problem solving guide efficiency can be improved.

The invention discloses a dynamic SQL (Structured Query Language) optimization method based on consanguinity dependency, and relates to the technical field of database query optimization. The method comprises the following steps: analyzing all indexes in a database, and dividing the indexes into atomic indexes and composite indexes; a blood relationship dependency tree is constructed according to the mathematical relationship between the indexes, each node represents one index, and each directed edge represents the dependency relationship; receiving a to-be-queried index set, and recursively searching all dependent target atomic indexes based on the graph structure; grouping the target atomic indexes according to data sources, and generating corresponding SQL query statements; and executing the SQL statement, and calculating a final query index result after obtaining an atomic index value. According to the invention, through unified management of the index dependence structure, repeated calculation across indexes is eliminated, the query performance is improved, the resource consumption is reduced, and the index development and maintenance efficiency is improved.

The invention relates to a target detection-based iris rapid and accurate segmentation method, which comprises the following steps of: obtaining an eye image for training, establishing a three-dimensional coordinate system and obtaining parameters of an iris ellipse by utilizing a three-dimensional mathematical relationship; constructing a directional bounding box fitted with the contour of the iris ellipse, the four edges of the directional bounding box being tangent to the iris ellipse, and solving the coordinates of the four vertexes of the directional bounding box; carrying out model training by taking the coordinates of the four vertexes of the directional bounding box and the parameters of the iris ellipse as model training labels so as to obtain a network model; the network model is used for detecting a directional bounding box surrounding an iris ellipse of the target eye image, and calculating and segmenting the iris ellipse according to the directional bounding box surrounding the iris. According to the method, a traditional iris segmentation mode depending on edge detection or pixel classification is abandoned, a brand new technical route based on deep learning target detection is adopted, and the limitation of a traditional method in processing a low-quality iris image is effectively overcome.

The invention belongs to the technical field of crucible material proportion optimization, and particularly relates to a quartz crucible material proportion optimization method based on a genetic algorithm. Crucible performance data under different proportions are collected and sorted, data quality is ensured by means of data cleaning and standardization, the data are divided into a training set and a test set for subsequent model training and evaluation, a mathematical relationship between the proportion and crucible performance is established by adopting a regression model, and the performance of the crucible is evaluated. The prediction precision of the model is ensured through training and testing, so that an accurate fitness function is provided for a genetic algorithm; the genetic algorithm is introduced, so that the method can efficiently search an optimal ratio in a global range, and is prevented from falling into a local optimal solution; according to the method, the effectiveness of the optimal ratio is ensured, the research and development period is greatly shortened, the cost is reduced, and the global optimal ratio is quickly determined through systematic data processing, accurate model construction and efficient algorithm application.

A system and method for processing multi-modal data using variational autoencoders with homomorphic operations is disclosed. Multi-modal input data, comprising a plurality of different data types, is encoded into a unified latent space using a multi-modal variational autoencoder. The system performs homomorphic operations on the encoded data within the unified latent space while preserving mathematical relationships between data types. The processed data is then decoded using the multi-modal variational autoencoder to generate reconstructed output. The system implements modality-specific processing layers and cross-modal mechanisms to handle diverse data types effectively. The homomorphic operations enable computations to be performed while the data is in encoded form, maintaining the validity of these operations in the decoded output. This approach provides a comprehensive framework for processing multi-modal data efficiently while preserving privacy and data integrity across different modalities.

The invention relates to an intelligent optimization method for a non-cellular station address for sensing fusion, and the method comprises the steps: S1, building a mathematical relation model between communication performance and an access point station address based on a logarithmic distance path loss model; s2, on the basis of a bistatic system path loss model, constructing a mathematical relationship model between the sensing performance and the access point site; and S3, modeling an access point site optimization problem in the cellular-free sensing fusion system as a Markov decision process, constructing a state space and an action space, and calculating a state of the access point site according to a mathematical relationship model between communication performance and the access point site and a mathematical relationship model between sensing performance and the access point site. Fusing to obtain a reward function of a Markov decision process; and S4, solving the Markov decision process by adopting a flexible action-evaluation deep reinforcement learning algorithm to obtain an optimal access point deployment strategy. Compared with the prior art, the method has the advantages of improving the site selection accuracy, widening the application range, improving the site selection effect stability and the like.

The invention relates to a requirement system, construction method and analysis synchronization method for an aircraft system oriented to axiomatic design, and relates to the technical field of system engineering. The invention solves the problem that the requirement model constructed based on natural language in the prior art has inaccuracies that affect the planning schedule and cost. The invention provides a method for constructing a requirement system, which obtains a glossary based on the terms and corresponding explanations of various fields and terms with mathematical relationships and corresponding mathematical explanations; obtains optional conditions based on universal requirements, event-driven requirements, state-driven requirements, undesirable requirements, optional feature requirements and conditional keywords; obtains a requirement main sentence based on the requirement subject, requirement modality, functional requirements or design constraints; obtains an optional requirement reason based on an obscure requirement reason; obtains a structured requirement template based on the optional conditions, requirement main sentence and requirement reason; and obtains a requirement system based on the glossary and the structured requirement template. The invention is applicable to the field of system engineering.

The present application provides data compression for neural networks. Systems and methods for generating representative values for a data set determine a first common value by first compressing a portion of values in the data set and a second common value by further compressing a subset of the portion of values. The representative value is generated by taking a difference between the first common value and the second common value, where the representative value corresponds to a mathematical relationship between the first and second common values and each value within the subset of the portion of values. The representative value requires less storage than the first and second common values.

The embodiments of the present application disclose a method and device for estimating the six-degree-of-freedom pose of a target based on binocular vision and particle swarm optimization, which relates to the field of computer vision. The method comprises: obtaining a binocular image of a target; the binocular image includes at least the rotation matrix, displacement vector, and geometric features of the target; decomposing the rotation matrix into a rotation vector and a rotation angle by a rotation matrix decomposition method, uniformly sampling the rotation vector and the rotation angle in six degrees of freedom to obtain a data point set; establishing a mathematical relationship between the rotation matrix and the displacement vector based on the geometric properties of the geometric features and the data point set; and performing target pose estimation on the binocular image of the target by designing an objective function and applying an optimization algorithm to obtain a pose estimation result. The present application solves the problems of low accuracy, poor versatility, and poor robustness existing in the related art.

The invention provides a power distribution network disturbance identification method and system based on multiple time scales, and the method comprises the following steps: S1, achieving the standardized processing and noise characteristic modeling of measurement data through building a mathematical relation among node voltage, a phase angle and a measurement quantity, and reflecting a system topological structure and parameter sensitivity through a Jacobian matrix; s2, firstly, based on the step S1, capturing dynamic feature changes in different time ranges; extracting features through feature engineering, optimizing a feature set by adopting a recursive feature elimination method, and improving the accuracy and stability of the model; s3, after the classification framework preliminarily confirms that a traditional event belongs to a large class, training and verifying feature data extracted in the previous step by adopting an XGBoost algorithm, realizing high-precision identification of multiple classes of disturbance events through hyper-parameter optimization and cross validation, and outputting an event class and a confidence result; according to the technical scheme, intelligent analysis of the operation state of the power distribution network and accurate identification of abnormal events can be realized.

The invention discloses a joint surface pressure distribution prediction method for kinetic analysis of an aluminum-steel bolt connecting piece, which comprises the following steps of: performing mathematical representation on pressure distribution of a joint surface by adopting a polygonal function, and changing geometric parameters to perform multi-group finite element static analysis to obtain a mathematical representation parameter set of the pressure distribution of the joint surface; performing quartic polynomial fitting on the mathematical characterization parameters of the pressure distribution of the joint surface and the geometric parameter set to obtain a mathematical relational expression of the mathematical characterization parameters of the pressure distribution of the joint surface and the geometric parameters; and correcting the joint surface pressure distribution prediction model by considering the changed bolt pre-tightening force. The invention provides the joint surface pressure distribution prediction method for dynamic analysis of the aluminum-steel bolt connecting piece, and important practical values are provided for calculation of the joint surface connection rigidity, prediction of the dynamic performance of the bolt connecting piece and evaluation of the cyclic fatigue life.

The present application relates to a kind of improved radiation transmission model mangrove chlorophyll estimation method, comprising: the measured data of the tree height, crown width and leaf area index of mangrove are collected;Mathematical relationship between leaf area index and tree height, crown width is constructed;Simulated spectral data is generated as training set using improved radiation transmission model;The sensitive feature screening of mangrove functional traits is carried out;Random forest inversion model based on physical constraint is constructed;The present application estimates the chlorophyll content of mangrove using the random forest inversion model based on physical constraint.The beneficial effects of the present application are: the present application can effectively improve the precision of chlorophyll inversion, provide scientific basis for the fine inversion of mangrove chlorophyll, provide data support and decision-making reference for the protection and scientific management of mangrove.

This invention presents a method for establishing large deformation viscoelastic constitutive relations based on Prony technology. It addresses the problems of existing viscoelastic constitutive models lacking universality, and the complexity and programming difficulties associated with their mathematical relationships. The invention includes: decomposing the stress tensor into the sum of the deviatoric stress tensor and the mean stress; decomposing the strain tensor into the sum of the deviatoric strain tensor and the mean strain; and providing a convolutional expression for the deviatoric stress tensor and the mean stress. The increment of the deviatoric stress tensor is expressed as a function of the deviatoric strain increment, and the increment of the mean stress is expressed as a function of the mean strain increment. The tensor form of the incremental constitutive relation is written in matrix form. This method has been programmed, and the numerical results obtained from the program agree well with the experimental results in specimen-level experiments on solid rocket motor propellant grains.

The invention relates to a dual-mode control method and system suitable for a network construction type energy storage converter, and the method comprises the steps: building a power and virtual impedance dynamic mapping model, and achieving the dynamic mapping between the power and virtual impedance through building a mathematical relation model between the power and virtual impedance; a state prediction and compensation mechanism is introduced, a feedforward compensation link based on a system state prediction algorithm is designed, transient behaviors possibly occurring in the switching process are subjected to prospective suppression, a self-adaptive virtual impedance adjustment strategy is designed, and dual-mode seamless switching control is achieved. According to the invention, efficient and stable switching of the energy storage converter in the island and grid-connected operation states is ensured. Key technical support is provided for a smart power grid, and the flexibility and reliability of a power system are remarkably improved.

Systems and methods for generating a representative value of a data set by first compressing a portion of values in the data set to determine a first common value and further compressing a subset of the portion of values to determine a second common value. The representative value is generated by taking the difference between the first common value and the second common value, wherein the representative value corresponds to a mathematical relationship between the first and second common values and each value within the subset of the portion of values. The representative value requires less storage than the first and second common values.

The invention discloses a method for determining a theoretical basis for online monitoring of machining size deviation of a micro-boring small deep hole, and relates to the technical field of machining and monitoring of the micro-boring small deep hole. The method comprises the following steps: extracting specific numerical values of a tool nose flutter amplitude, a boring force average value and a tool nose abrasion loss of a micro-boring tool under the same working condition, then establishing a quantitative relation between the tool nose flutter amplitude and the boring force average value, and establishing a mathematical relation between the tool nose abrasion loss and the boring force average value; and finally, by taking the equivalent boring force as an intermediate value, establishing a mathematical relationship between the tool nose fluttering amplitude and the tool nose abrasion loss, and requiring that the sum of the tool nose fluttering amplitude value and the tool nose abrasion loss value does not exceed the limit value of the machining size deviation value of the deep small hole. Therefore, the upper limit values of the tool nose flutter amplitude and the tool nose abrasion loss are reversely solved according to the limit value of the deviation value of the machining size of the deep small hole, final experimental correction is conducted, and finally the determination method of the basis is formed.

The present invention relates to an intelligent optimization method and system for the irradiation cross-linking process of flexible armor wires, including: S1: Establish a perception network for the wire irradiation cross-linking process through the industrial Internet to collect real-time data of wire irradiation cross-linking; S2: Construct a mathematical relationship model between process parameters and material properties through a cyber-physical neural network; S3: Combine the finite element simulation method to simulate the effects of the thermal field and dose field during the irradiation process and correct unreasonable assumptions in the mathematical relationship model; S4: Based on the corrected mathematical relationship model, under multi-objective process requirements, based on the data collected by the data real-time acquisition system, balance different objectives through an optimization algorithm to determine the optimal process parameters; S5: After applying the optimal process parameters to the production line, obtain the optimization results through the data real-time acquisition system and judge whether they meet the production objectives, thereby completing the intelligent closed loop. The present invention effectively improves production efficiency and reduces the costs and risks of quality management.

The invention discloses an arbitrary thread form hexahedral mesh modeling method based on parameterization control, and relates to the technical field of bolt modeling. Decomposing the tooth profile of the complete pitch cycle corresponding to the target tooth angle into a plurality of feature line segments, converting the curve profile corresponding to each feature line segment into a solvable geometrical relationship, and determining a mathematical relationship of any point coordinate on the profile corresponding to each feature line segment; integrating the mathematical relationships corresponding to the feature lines to obtain an external thread curve formula of the target thread angle; parameterizing an external thread curve formula of the target thread angle through an equation driving curve function in SOLIDWORKS software to obtain an external thread profile curve of the target thread angle bolt; and based on the external thread profile curve of the target thread angle bolt, drawing a two-dimensional plane grid of the bolt, and further obtaining a three-dimensional thread hexahedral grid of the target thread angle bolt. By means of the method, modeling of any thread form can be achieved, and the application range of finite element analysis is greatly expanded.

The invention relates to the technical field of wind tunnel tests, and discloses a flutter wind tunnel test pulse response generation method and system oriented to turbulence excitation, and the method comprises the steps: model construction: constructing a mixed Transform generative model through combining a convolution sampling method and a depth feature extraction method, establishing a mathematical relationship between a turbulent flow response signal and a pulse response signal in the structural dynamics system through a hybrid Transform generative model; model training: preprocessing the data by adopting different normalization modes according to a model training effect; optimizing the model by adopting a loss function mixed by mean square error of a time domain and a frequency domain through a plurality of training optimization methods; and signal generation: taking the measured turbulence response signal as input, and generating a corresponding pulse response signal. According to the invention, turbulence response signals dynamically collected in a wind tunnel can be directly converted into pulse response signals.

The present invention discloses an evaluation method and device for autonomous driving compliance. A specific implementation manner of the method includes: obtaining current driving data generated by a target vehicle during autonomous driving; performing parsing processing on a preset compliance engine system configuration file to obtain a parsing result; based on the parsing result, calling a mathematical function library to perform compliance evaluation on the current driving data to generate a compliance evaluation result. This embodiment can convert legal language into logical descriptions and mathematical equivalence relationships; and generate software code in embedded language according to the logic and mathematical relationships, and then evaluate the current driving data of the target vehicle based on the software code to automatically generate a compliance evaluation result. Thereby, it is possible to automatically evaluate the compliance of autonomous driving based on the current driving data of the target vehicle; which not only meets the requirements of the target vehicle for autonomous driving, but also is applicable to different scenarios, improving the universality of the scheme application.

The invention discloses a finite iteration learning control method based on a neural network, which is used for solving the technical problem of realizing accurate tracking control of a magnetic control shape memory alloy actuator within a finite iteration number. The method comprises the following steps: 1) utilizing a neural network to approach a nonlinear function in a control law, improving the adaptability and learning ability of a controller, and realizing accurate tracking control of a magnetic control shape memory alloy actuator in finite iteration; and 2) based on a compression mapping principle and a Lipschitz condition, deriving a mathematical relationship between a tracking error and an upper bound of the number of iterations, giving a sufficient condition for system convergence, and providing a theoretical basis for controller design. Compared with the prior art, the method has better adaptability and convergence speed, is particularly suitable for precision control in a complex industrial process, and has a wide application prospect.

The present invention discloses a 3D printing control method and system based on multi-nozzle flow data. The method comprises: obtaining historical flow data and historical printing plans for multiple nozzles of the same caliber for a target 3D printing device; the historical flow data includes flow data for multiple different printing materials; fitting and analyzing the historical flow data and historical printing plans based on a dynamic programming algorithm to obtain a mathematical relationship model between the flow rate and time of the target 3D printing device; obtaining real-time flow data and real-time printing material type of the target nozzle; and determining whether the target 3D printing device has an abnormal printing speed based on the mathematical relationship model, the real-time flow data, and the real-time printing material type. This method can efficiently and accurately monitor the printing speed of a 3D printing device and determine abnormalities, thereby improving monitoring accuracy and printing stability.

The present invention provides a method, device and equipment for predicting the performance degradation of an air spring based on structural characteristics, and relates to the technical field of air springs. The method comprises: obtaining the dynamic stiffness and structural parameters of the air spring to be evaluated; inputting the dynamic stiffness and structural parameters into an air spring performance degradation model to obtain fatigue loading parameters output by the air spring performance degradation model; wherein the air spring performance degradation model is a pre-constructed model of the mathematical relationship between the fatigue loading parameters of the air spring and the dynamic stiffness of the air spring; based on the fatigue loading parameters, the fatigue life of the air spring to be evaluated is determined. This method can pre-estimate the fatigue characteristics of the air spring according to the actual application scenarios of the vehicle in the early stages of vehicle design, thereby helping to design stable and reliable air springs, improve product reliability, and shorten the development cycle, so that companies do not need to undergo many experiments to design air springs, saving development costs.