74 results about "Integration algorithm" patented technology

The invention relates to an AI server cooling water pump vibration quantity self-adaptive calibration test method and system, an intelligent test platform integrates a six-dimensional force sensor and a temperature compensation vibration exciter, the platform rigidity is automatically calibrated, a water pump-pipeline system transfer function is obtained, and a rotating speed-lift-modal frequency three-dimensional mathematical model is established; a three-axis MEMS accelerometer array is arranged at sensitive parts such as a water pump bearing seat and a motor shell, and vibration, current and pressure signals are synchronously collected; carrying out time-frequency domain signal processing by adopting variational mode decomposition in combination with self-adaptive S transformation, and extracting a 128-dimensional full-frequency domain feature vector containing a modulation side frequency band; effective values of vibration acceleration, speed and displacement are calculated through a frequency domain integration algorithm, and current harmonic interference is corrected through an electromagnetic vibration compensation model; and finally, a vibration health degree evaluation model containing 18 characteristic parameters is established based on a support vector machine, the test system comprises an intelligent test platform unit, a multi-source sensing unit, an edge calculation unit and a data management unit, and microsecond-level synchronous acquisition and GB-level data throughput are realized through a time sensitive network. And online incremental learning and automatic generation of a test report are supported. The method has the effect of improving the water pump vibration quantity test precision.

The invention discloses a credit risk prediction method based on data and dynamic feature optimization and Bagging integration, and the method comprises the following steps: S1, obtaining an initial data set, carrying out the data preprocessing, balancing the data set through an ATGA algorithm, and reducing the data noise; s2, using LightGBM as an agent model, calculating importance scores of all features, gradually screening key features, adaptively adjusting feature screening standards according to data distribution through a dynamic threshold algorithm, gradually screening feature subsets, and inputting the screened features into a subsequent model; and S3, constructing a TabPFN model by using a Bagging integration algorithm, introducing a dynamic weight distribution mechanism, adaptively adjusting the weight of each base model according to sample features, and finally realizing credit risk prediction through the TabPFN model. According to the method, the credit risk prediction process is comprehensively optimized in three aspects of data set optimization, feature screening optimization and model performance optimization, so that indexes such as the accuracy rate and the rate of return are improved.

An online estimation method and system for state of charge (SOC) errors. The method comprises: acquiring an initial SOC error value at a power-on moment of a battery management system, and updating the initial SOC error value by means of an OCV table lookup algorithm when a correction condition is satisfied; calculating a real-time SOC error of a battery by means of an ampere-hour integration algorithm; detecting a real-time calculation process of the ampere-hour integration algorithm, and when a Rint model correction condition is triggered, correcting the real-time SOC error by means of a Rint model algorithm; and when the battery reaches a preset condition, zeroing out the SOC error and then performing updating by means of the ampere-hour integration algorithm until a power-off moment of the battery management system is reached, saving a corresponding real-time SOC error and outputting same. A theoretical basis for SOC error estimation is provided to realize online SOC error estimation; algorithms having higher precision are selected during estimation, so as to reduce SOC errors; and the calculation of a current SOC error can be calibrated, such that the method and system are adapted to scenarios involving different battery packs and different aging conditions.

The invention relates to an icing power transmission line galloping simulation method and simulation system based on a composite time integral. The method comprises the following steps: S1, establishing a nonlinear finite element model of an icing power transmission line; s2, carrying out time domain solution by adopting a composite time integration algorithm; calculating the solution of the moment in two sub-steps according to the solution of the moment, namely from the first sub-step to the second sub-step; the first sub-step adopts a trapezoidal rule for integration, and the second sub-step adopts a three-point backward difference format based on information of three moments; s3, coupling and solving a nonlinear aerodynamic load related to the speed; s4, obtaining the galloping response of the power transmission line; the problems that in a transmission line galloping simulation technology based on a traditional Newmark method, contradiction between calculation efficiency and numerical precision exists in the strong nonlinear coupling problem, accurate tracking of low-frequency galloping and efficient suppression of high-frequency noise are difficult to achieve at the same time under the large time step length, simulation calculation efficiency is low, and convergence is poor are solved.

The invention discloses a moon leap trajectory planning method, system and device and a medium. The method comprises the steps of obtaining target landing point data; based on the acquired data, establishing a moon surface leap device kinetic model by defining a specific coordinate system and a state variable; based on the kinetic model, discretizing the kinetic equation through a numerical integration algorithm, constructing and solving a leap trajectory convex optimization problem with optimal burnup, and obtaining an obstacle avoidance-free optimal trajectory; based on the obstacle avoidance-free optimal trajectory, performing obstacle avoidance iteration through an iteration obstacle increase strategy to obtain a static obstacle avoidance trajectory; and on the basis of the static obstacle avoidance trajectory, the flight trajectory is adjusted again by calculating the trajectory error band width and the maximum extension distance, and a dynamic safety enhanced trajectory is obtained. According to the method, safe, robust and efficient moon surface leap trajectory planning in a complex and uncertain environment is realized.

The invention relates to the technical field of data processing, and discloses an engineering construction whole process digital management method and system. The method comprises the following steps: collecting and standardizing engineering construction full-period multi-source data; constructing a stacked integrated prediction system by using the standardized data to obtain a three-dimensional index prediction value; allocating resources based on the predicted value in combination with the ergonomics risk index to obtain a configuration scheme; uncertainty factors are quantified for the configuration scheme, an optimal scheme set is calculated through a multi-target symbiotic algorithm, and a management strategy is formed. According to the method, high-precision prediction of the engineering three-dimensional indexes is realized through the stacking integration algorithm, ergonomics risk assessment and resource allocation optimization are combined, and the technical defect that human factor engineering is neglected in a traditional method is overcome.

The invention relates to the technical field of indoor layout design, and discloses an indoor layout design system based on user behavior data analysis. The system comprises a user behavior data acquisition module which captures and transmits a user movement track coordinate sequence, residence time distribution data and equipment interaction event records in real time through a multi-sensor network; the layout analysis module constructs a dynamic layout model by using a decision tree integration algorithm, and outputs theoretical layout parameters including a space occupancy rate and functional region division; the layout difference detection module performs multi-dimensional comparison of space overlapping degree, path conflict index and regional utilization efficiency difference on theoretical and actual layout parameters to generate a difference index vector; the problem region positioning module identifies a layout abnormal region through a spectral clustering algorithm based on the difference index vector and predefined spatial topology information; and the adaptive design strategy generation module automatically generates a layout adjustment strategy instruction according to the abnormal region position and the attribute data.

The invention discloses a three-dimensional design model conformity analysis method and system based on a large language model, and the method comprises the steps: obtaining the related data and design specification constraint conditions of a three-dimensional design model, and building a data interaction channel through a collaborative review platform; calling a semantic geometry collaborative verification algorithm to process the data and extracting feature parameters; generating a rule adaptation weight matrix through a space rule learning adaptation model; constructing a mapping relation between the feature vector and the design specification through a CAD point cloud feature coding mapping model; carrying out multi-dimensional conformity verification on the basis of the result; and outputting the comprehensive analysis data set. The system realizes deep cooperative processing of geometric and semantic data of a three-dimensional design model through multi-unit cooperative linkage and integration of an algorithm and a platform function, improves the accuracy of rule adaptation and feature mapping, comprehensively covers a conformity verification dimension, and meets a high-precision analysis requirement in a complex scene.

The invention discloses a bucket dividing regulation and control method and device for a financial data warehouse and electronic equipment, and relates to the technical field of artificial intelligence or other related fields, and the method comprises the steps: collecting a time sequence performance set of the financial data warehouse through a distributed collection system; multi-level feature extraction is carried out on the time sequence performance set by adopting a sliding window mechanism to obtain a time sequence feature set, and the categories of time sequence features comprise instantaneous fluctuation features, periodic trend features and steady state baseline features; performing association analysis on the time sequence feature set based on a decision tree integration algorithm by taking an expected query performance requirement as a target to obtain a bucket-dividing adjustment strategy; and bucket index reconstruction is carried out on the financial data warehouse based on the bucket division adjustment strategy, and the financial data warehouse after index updating is obtained. Through the method and the device, the technical problem that the system performance is degraded due to the fact that a static bucket dividing mechanism of a real-time data warehouse cannot dynamically adapt to space-time imbalance of data loads in a financial service scene in the related technology is solved.

The invention discloses a customer service event dynamic scheduling method based on time sequence modeling, and the method comprises the following steps: S1, collecting heterogeneous original data, and constructing a service event sequence; s2, inputting the service event sequence into an improved Transform model, and outputting an event semantic feature vector; s3, constructing a multi-scale time window, and generating a time feature vector array; s4, splicing the event semantic feature vector and the time feature vector to generate a joint feature vector; s5, inputting the joint feature vector into an improved decision tree integration algorithm, generating a scheduling priority score value, and recording a decision path and a corresponding feature node index; s6, executing a service event queue operation, and generating a service event scheduling list; and S7, acquiring service event execution feedback information, and updating parameters. According to the method, time sequence modeling, feature scoring and feedback optimization mechanisms are fused, and intelligent recognition, efficient sorting, interpretable scheduling and dynamic evolution of customer service tasks are achieved.

The invention provides a training method for aero-engine modeling driven by mechanism and data fusion, and relates to the technical field of aero-engine modeling simulation, and the method comprises the steps: decomposing a simulation model of an aero-engine into a plurality of assemblies containing to-be-identified unknown parameters, and employing an assembly integration algorithm to associate the plurality of assemblies, an end-to-end differentiable complete simulation model is formed; based on hierarchical division of the simulation model, performing parameter pre-estimation by adopting a top-down layer-by-layer progressive strategy to obtain an initial estimation value of the unknown parameter; the strategy comprises the following steps: firstly, estimating unknown parameters of components in a highest level according to actually measured output data, and generating a training target of a next lower level based on a calculation result of an estimated level so as to complete estimation of unknown parameters of the lower level in sequence; and performing gradient back propagation in the complete simulation model by taking the initial estimation value as a starting point to optimize and update unknown parameters.

The invention provides an online model updating hybrid test method based on a dual adaptive UKF algorithm, and the method comprises the steps: carrying out the initialization of a to-be-recognized constitutive model parameter, determining a parameter initial value seismic oscillation according to a genetic algorithm, inputting the seismic oscillation into a step-by-step integration algorithm, and obtaining the displacement response dk of an overall structure; obtaining the boundary displacement of the test substructure based on the displacement response dk and the constitutive model parameter estimation value of the previous step, carrying out real loading according to the boundary displacement, obtaining the real counterforce and real displacement of the test substructure, and carrying out parameter estimation based on a dual adaptive UKF algorithm; and obtaining a new constitutive model parameter value according to the new constitutive model parameter value and the displacement response dk of the overall structure, obtaining the counter-force Rk of the overall structure on each dynamic degree of freedom, realizing online identification and model updating of the constitutive model parameter, and performing loop iteration to the process of ending the test, thereby improving the precision and stability of parameter identification, and improving the test efficiency. The problem that the loading boundary of the test substructure is inaccurate is effectively solved.

The invention belongs to the field of aerospace electronic measurement, and provides an inductance type proximity sensor resolving method and system based on a time domain integration algorithm, and the method comprises the steps: applying a generated sine excitation voltage to a measurement loop; synchronously acquiring sine excitation voltage and response voltage of an inductive proximity sensor in a measurement loop; based on a time domain integration algorithm, respectively carrying out orthogonal integration on the sine excitation voltage and the response voltage, and calculating respective amplitudes and phases; according to the amplitude, the phase and the resistance value of the reference resistor, an inductance value and a parasitic resistance value of the inductive proximity sensor are calculated through a trigonometric function relationship; and establishing a piecewise linear fitting model through a least square method, and calibrating the inductance value to obtain a calibrated inductance value. The method has the advantages of high reliability, strong anti-noise capability, no need of complex electronic hardware, and reduction of the development cost at a lower development guarantee level.

The invention belongs to the technical field of machine learning, and particularly relates to an algorithm research based on GNN multi-mode lightweight and edge computing fusion integration, which comprises the following steps: step 1, acquiring sensor data from at least two different modes; and 2, constructing the multi-modal data into a uniform heterogeneous graph, wherein the data of different modals are expressed as nodes or edges of different types. According to the method, a unified heterogeneous graph is constructed, and data from different sources are mapped into different types of nodes or edges in the graph. Then, through a cross-modal attention mechanism, dynamically learning association strength and an interaction mode between different modal nodes, and realizing end-to-end multi-modal information deep fusion in a GNN message transmission process, thereby avoiding information loss caused by simple splicing or later fusion in a traditional method; the cross-modal attention mechanism can dynamically calculate and allocate importance weights of different modals according to specific input samples and contexts.

The embodiment of the invention provides a robot control method, a robot control device and a robot, which are applied to an application scene in which a robot needs to be in rigid contact with a target, such as fine surgical operations such as orthopedic joint replacement and dental implantation of a surgical robot. The control method comprises the steps of obtaining contact force data of the robot; filtering the contact force data according to the change rate of the contact force at the current moment so as to determine preprocessed contact force data; servo control parameters of the robot are determined according to the preprocessed contact force data, a self-adaptive admittance control model and a speed Weil method numerical integration algorithm; and controlling the robot to work according to the servo control parameters of the robot. In the filtering processing process of the contact force data, the filtering intensity can be dynamically adjusted according to the change rate of the contact force at the current moment, tool bounce or tremor is avoided when the robot makes contact with a rigid target, and the working stability, precision and safety of the robot are improved.

The application discloses a kind of composite heavy metal pollution site three-dimensional identification and earthwork volume estimation method and system, and it is related to computer data processing technical field.Target site survey data is obtained and engineering voxel space resolution is set, and the chemical action parameter between heavy metals is extracted to construct composite spatial variation function.Three-dimensional distribution model is constructed using three-dimensional space interpolation, and preliminary pollution spatial grid set is extracted according to risk control threshold discretization.Boole logic operation is introduced to cross screen out single and composite heavy metal pollution area, and three-dimensional engineering boundary is obtained by boundary topology reconstruction with minimum mechanical construction envelope as constraint, and then the corresponding pollution total volume is estimated using spatial volume integration algorithm.The application can output three-dimensional boundary with engineering excavation continuity and realize accurate estimation of various pollution quantities.

The invention discloses an efficient simulation method for collision between long ellipsoid particles and a wall surface based on discrete elements, and belongs to the technical field of gas-solid two-phase flow numerical simulation. The method comprises the steps that a universal long ellipsoid particle-wall surface collision elastic force model is established, and the model represents a universal function relation between elastic force and particle geometry, material attributes and kinematics state parameters; based on the elastic force model and the momentum theorem, constructing a particle-wall surface collision dynamic model; based on the kinetic equation, a discrete time method and a numerical integration algorithm are adopted, and force, speed and energy parameters, changing along with time, of the long ellipsoid particles and the wall face in the whole collision period are solved. According to the method, the analytic model is embedded into a discrete element simulation framework to replace calculation-intensive finite element solution, so that second-level efficient solution is realized while the calculation precision is ensured, and the calculation efficiency is remarkably improved.

The invention discloses a space-time multi-parameter cooperative computing method and system based on a universe model, and relates to the technical field of universe and celestial body physics computing. According to the method, an integral kernel function is constructed based on an FLRW degree gauge of a standard universe model, redshift and universe densitology parameters input by a user are received, a Friedemann equation is calculated by using a Gauss-Kronrod adaptive numerical integration algorithm, basic quantities such as co-movement distance and time are solved, and the cosmic movement is realized. And deriving derivative parameters such as co-dynamic volume, angular diameter distance, luminosity distance, universe age, light running time and the like according to universe curvature types (flatness, opening and closing). The system comprises a parameter input module, a core integral calculation engine, a geometric model judgment module, a multi-type distance and derivative parameter calculation module and a result output module. According to the method, the problems of low calculation precision and poor model adaptability in the prior art are solved, a high-precision (error is less than or equal to 10 <-7 >) self-adaptive universe calculation solution with comprehensive parameter coverage is provided, and a powerful calculation tool is provided for universe research and astronomical observation data analysis.

The present application belongs to the field of lithium battery SOC calculation, and particularly relates to a lithium battery SOC estimation method based on high-precision ampere-hour integral algorithm. The method comprises establishing an ampere-hour integral mathematical model and solving, and performing Taylor expansion on the solution; the average slope is obtained by means of linear combination of three-point slope by investigating the slope of two points in the interval, so as to ensure that the precision of the third-order Taylor expansion formula and the equivalent formula is the same. When the algorithm is executed, the charge state and the capacity value are initialized first, then the sampling time is obtained according to the sampling frequency, and then the relevant parameters are calculated according to the current sampling value, and finally the current SOC is calculated according to the specific value and the three slopes and is iterated in a loop. This method can improve the ampere-hour integral precision, and weaken the cumulative error and SOC jump.

The present invention discloses a factor graph optimization combined navigation method, which uses the ISAM2 optimization algorithm to incrementally solve the state variables in the factor graph in the flow space, and compensates for the earth's rotation and harmful acceleration based on the current state and the pre-integration increment. Then, as the system operation time increases, the factor graph optimization scale gradually expands, and the ISMA2 algorithm is used to repeatedly re-compensate the historically affected moments. When a certain number of times is reached, the historical nodes tend to be stable, and the ISAM2 algorithm no longer calls the compensation algorithm to re-compensate them. The present invention uses a high-precision IMU pre-integration algorithm in the flow space, and its positioning accuracy is improved compared to the traditional pre-integration algorithm. At the same time, due to the factor graph optimization method, it has strong scalability and can realize plug-and-play of multiple sensors. In addition, the algorithm using the ISAM2 has a small amount of calculation and is highly practical.

The invention discloses a chaos detection method and device for an early fault signal of a transmission gear and a medium, and the method comprises the steps: constructing a chaos detection system model, and constructing the chaos detection system model based on a Duffing equation; adjusting model parameters of the chaos detection system model, so that the periodic excitation amplitude of the chaos detection system model is kept at 15-20 times of the noise amplitude of the to-be-detected signal; determining a threshold value of the periodic excitation amplitude; enabling the chaos detection system model to be in a critical chaos state according to the requirement of detection sensitivity; inputting a signal to be detected as perturbation of periodic excitation into the chaotic detection system model; carrying out numerical solution on the chaos detection system model through a preset high-precision numerical integration algorithm; performing state judgment on the chaos detection system model according to the obtained phase trajectory diagram and a preset phase change judgment criterion to obtain a detection conclusion; the effect is that the accuracy and precision of transmission gear early fault weak vibration signal detection are improved.

According to the combustion flame temperature prediction method and system provided by the invention, based on RGB and temperature data of flame, an advanced Bagging integration algorithm is adopted, three base models are effectively fused, and a high-precision nonlinear regression prediction model is constructed. According to the method, ten-fold cross validation and an adaptive integration strategy based on error reverse weighting are introduced, a prediction variance is introduced as a penalty factor, and an automatic hyper-parameter optimization framework based on Bayesian inference and a reinforcement learning controller is adopted, so that efficient collaborative tuning of multi-model hyper-parameters is realized; the generalization ability and prediction performance of the model under different working conditions are ensured, the manual parameter adjustment burden is reduced, and the automation level of the system is improved. The method overcomes the defects of the prior art in a high-dimensional, multi-noise and nonlinear environment, and has high practical value and popularization and application potential.

The invention discloses a CFRP structure ultrasonic guided wave propagation time domain spectral element simulation parallel computing method, relates to the technical field of nondestructive testing, and aims to solve the problem of low ultrasonic guided wave propagation calculation efficiency of a large-area composite material plate-shaped structure at present. A multi-node spectrum unit is adopted to carry out grid division on a large-area composite material plate, a grid numbering algorithm is designed to divide a grid into sub-blocks (domain decomposition), subsets of disjoint nodes are created, and a high-order time domain spectrum element method is combined with a compute unified device architecture (CUDA) to realize parallel computing. A step-by-step time integration algorithm based on a central difference scheme is adopted to realize unit-level parallel calculation on a GPU (Graphics Processing Unit) to solve a wave equation, a high-order time domain spectral element method (SEM) is combined with a CUDA (Compute Unified Device Architecture), the calculation efficiency is improved, the propagation calculation of ultrasonic guided waves in a large-area composite material plate-shaped structure can be accelerated, and the calculation efficiency is improved. The composite material plate-shaped structure health monitoring method has a wide application prospect.

The invention discloses a flow calculation method of a multi-sound-path ultrasonic flowmeter, and aims to solve the problem that a fixed weight function flow integration method of a traditional multi-sound-path time difference method ultrasonic flowmeter cannot meet the flow measurement precision in a disordered flow state caused by complex and changeable operation conditions of a large pump station. According to the method, a full-three-dimensional flow field in a flow measurement section, corresponding flow Qj and average flow velocities vji at a plurality of different heights are obtained through reconstruction by completing an inflow flow state bidirectional flow field PIV experiment under each typical working condition of the large pump station, and an average flow velocity vji-flow Qj database at different heights under the typical working conditions is established; on the basis of correlation analysis of the actually-measured average flow velocity and the average flow velocity at the relative height of the typical working condition, weighted integration is conducted on the flow of the typical working condition, the flow value of the actually-measured working condition is obtained, and therefore the measurement accuracy of the multi-sound-path time-difference method ultrasonic flowmeter flow integration algorithm under all extreme working conditions of a large pump station is improved.

The invention provides a switched reluctance motor speed regulation system fault diagnosis method based on an adaptive sliding window integration algorithm, and relates to the technical field of motor speed regulation system fault diagnosis. The method comprises the following steps: firstly, collecting current of each phase under different working conditions to construct fault data, extracting features through normalization and harmonic analysis, obtaining fault features through feature selection, and training a plurality of integrated learning classifiers and neural network classifiers; collaborative decision making is sequentially carried out on data to be diagnosed in the graded sliding window, rapid and accurate online identification of single-tube short circuit and winding turn-to-turn short circuit faults is achieved, and the stability and reliability of a speed regulation system are improved.

The invention relates to a reinforced concrete frame structure collapse process analysis method and device, and belongs to the technical field of engineering structure simulation analysis, and the method comprises the steps: building a structure constitutive model according to reinforced concrete frame structure data; the mass of the reinforced concrete frame structure in the structural constitutive model is concentrated to all nodes, and a dynamic model is determined; and based on an explicit gradual integration algorithm, solving the dynamic model to obtain a collapse evaluation index, and based on the collapse evaluation index, analyzing the progressive collapse resistance of the structure. According to the method, the mass is concentrated on all nodes, the iterative convergence problem is avoided, the dynamic model is solved in combination with explicit gradual integration, an equation set does not need to be solved through the iterative process, efficient simulation of the whole collapse process of the reinforced concrete frame structure under various column disassembly working conditions is achieved, and the calculation efficiency is effectively improved.

The application provides a photovoltaic array fault diagnosis method based on a composite model soft voting integration, and comprises the following steps: acquiring a plurality of P-V curve data of a photovoltaic array in normal operation and when a fault occurs; preprocessing the plurality of P-V curve data, formulating a fault type label and dividing processing, obtaining a training data set and a test data set; constructing a plurality of composite sub-models, integrating the composite sub-models based on a soft voting integration algorithm, determining each hyperparameter of an integrated diagnosis model based on a global optimization algorithm, and completing the construction of the integrated diagnosis model; inputting the training data set into the integrated diagnosis model for training to obtain a trained integrated diagnosis model; inputting the test data set into the trained integrated diagnosis model to test and evaluate the performance thereof; and diagnosing the photovoltaic array through the integrated diagnosis model to determine the fault type of the photovoltaic array.

The invention relates to a multi-agent collaborative decision-making method and device, and belongs to the technical field of multi-agent collaborative decision-making. The method comprises the following steps: each agent performs joint perception and feature coding on acquired multi-modal input data through a data perceptron; fusing and updating the current multi-modal representation vector and the historical representation set by using a data updater and adopting a time sequence increment consistent integration algorithm; the fused codes are compressed through a data transmitter and sent to an adjacent agent; each agent receives coding information from an adjacent agent through a data receiver, and the coding information is aligned and fused with a fusion code of the agent; and the data updater adopts a multi-level weighted feature pyramid algorithm to update the fusion representation, and new historical representation data is generated to support the next round of collaborative decision. According to the multi-agent collaborative decision-making method, the reliability and robustness of multi-agent collaborative decision-making are improved through the four core components including the data sensor, the data updater, the data transmitter and the data receiver.

The application discloses a three-dimensional code integrated query method for a compatible database and an operating system thereof, and the three-dimensional code integrated query method is characterized in that: by adopting the theory of three-dimensional code positioning, endless massive ordered and disordered information is stored and positioned in a three-dimensional mode and is quickly queried; the principle is that letters and numbers are the basis for processing all information by a computer, for example, Chinese character input is implemented by using numbers or letters as codes to effectively process information, and a permanent corresponding relationship between letter combinations and numbers is established; the application relates to the technical field of data processing. The three-dimensional code integrated query method for the compatible database and the operating system thereof can realize unified integration of data in a plurality of sub-databases through a three-dimensional code integration algorithm in a parent database, and can form a precise unique three-dimensional code for positioning storage, so that the purpose of quickly and accurately obtaining required data information in the database is achieved.

The present application relates to the technical field of data processing, and more particularly to a novel arbitrary polygonal element calculation method for structural simulation analysis, comprising obtaining a geometric model and extracting node coordinate data, calculating polygonal element center point and edge midpoint coordinates to form an extended coordinate data set, generating quadrilateral sub-element topology through geometric connection operation, establishing strain displacement relationship and fusing equivalent strain matrix data through constraint equation calculation, adopting single point integration algorithm and hourglass control algorithm for numerical integration and stability correction, outputting stiffness matrix data controlled by hourglass, integrating into a finite element solving system for numerical solving, and calculating stress and strain distribution based on node displacement data. The present application overcomes the low efficiency and poor accuracy problem caused by the dependence of the prior art on sub-element decomposition, and improves calculation stability and engineering applicability.