86 results about "Matrix form" patented technology

The invention provides a high-speed NTT method and system based on GPU matrix-thread collaborative optimization. The method comprises the steps that an input twiddle factor is converted into a matrix form; transmitting the updated twiddle factor matrix and the input array to a graphics processor; according to the input array and the size of the twiddle factor matrix, the number of blocks in the grid and the number of threads included in each block are set, so that each block is responsible for the operation of multiplying one row by one column, and each thread in the block completes the operation of multiplying one pair of data in the corresponding row-by-column process to obtain a group of intermediate results; and obtaining output data corresponding to the block according to an intermediate result in the block, and sending the output data to a corresponding position of an output matrix to serve as a multiplication result. According to the method, NTT calculation is reconstructed into parallel matrix-vector operation, the maximum utilization of GPU calculation resources is realized through the combination of an efficient thread allocation strategy and an optimized memory access mode, and the calculation efficiency is remarkably improved.

The invention provides a shield soil full-period risk assessment method based on interval spherical fuzzy analytic hierarchy process, and relates to the technical field of shield soil risk assessment. The method comprises the following steps: establishing a shield soil full life cycle risk assessment model which comprises a target layer, a standard layer and an index layer; performing model quantification, including constructing a paired comparison matrix, converting an interval value spherical fuzzy number into a matrix form, determining the consistency of expert judgment through consistency check, calculating a fuzzy weight based on a fuzzy theory, performing defuzzification processing on the weight by using a score function, and performing normalization processing; and weight sorting: carrying out priority sorting on the standard layer according to the local weight, then carrying out further priority sorting on the index layer based on the global weight, and completing weight sorting of the model through comparative analysis with actual data or existing theories. According to the method, the accuracy and consistency of risk assessment are improved by introducing the interval value spherical fuzzy number and consistency check.

The invention discloses a non-uniform sparse sampling single-section near-field test system and method, and belongs to the technical field of test. According to the method, the single-section radiation field is expressed in a matrix form, the unknown transmission coefficient is solved from the system of linear equations through an inverse matrix method, and the defect that a traditional single-section near-field test method based on fast Fourier transform depends on the distribution uniformity of near-field samples is overcome; under the assumption that antenna transmission coefficients have sparsity, a compressed sensing technology is introduced, L1 norm minimization constraint is applied to a transmission coefficient vector q to ensure the sparsity, and under the condition that the number M of single-section near-field samples is smaller than the number J of unknown coefficients, the transmission coefficients can be solved from an underdetermined linear equation set; according to the invention, a non-uniform sampling-based single-section near-field test method is realized, and the flexibility of a sampling mode is improved; the number of sampling points and the scanning time are reduced, and the testing efficiency is improved; and the far-field reconstruction precision is higher.

The invention relates to an aircraft pulsation assembly line balancing method considering complex constraints and a medium. The aircraft pulsation assembly line balancing method comprises the following steps that S1, a process priority relation matrix and a process information matrix are established; s2, generating a feasible sequence and a parallel sequence, expressing the feasible sequence and the parallel sequence in a matrix form, and completing a coding process; s3, in combination with a local splitting strategy, splitting the parallel sequence matrix to each station in a balanced manner; s4, performing line-by-line decoding to obtain a feasible balance scheme corresponding to the sequence, and expressing the feasible balance scheme in a matrix form to complete a decoding process; s5, constructing an optimized feasible balance scheme set through an internal and external double-layer genetic algorithm; and S6, using a fuzzy analytic hierarchy process to calculate the final weight of each index which most accords with the current production demand, and according to the weight, using a sorting method approaching an ideal value to decide an optimal feasible balance scheme in the feasible balance scheme set. Compared with the prior art, the method can further improve the efficiency and result of aircraft assembly pulsation line balance optimization.

The invention discloses a multi-period non-stationary hydrological variable valuation method, and relates to the technical field of hydrological prediction. Comprising the steps of obtaining hydrological variables of a plurality of observation stations in a drainage basin at different time points as hydrological time sequences of the observation stations; constructing an experimental variation function according to the hydrological time sequence of each observation station; selecting a drift model through a trend surface analysis method and model inspection; constructing a multi-period non-stationary variable equation set comprising a Lagrange multiplier and a hydrological variable weight based on the experimental variation function and the drift model; expressing the multi-period non-stationary variable equation set as a matrix form, and calculating a Lagrange multiplier and a hydrological variable weight through a matrix inversion method; and constructing an estimation formula according to the calculation result of the Lagrange multiplier and the hydrological variable weight, and performing multi-period spatial estimation on the hydrological variable of the to-be-measured drainage basin through the estimation formula. Dependence on the number of observation stations can be reduced, and accurate estimation can be achieved only through a small amount of observation station data.

The invention relates to the technical field of operational research, in particular to a near-earth space game situation analysis method based on an improved intelligent pig game theory, and is used for solving the problems that in the prior art, evaluation is not visual, and evaluation is inaccurate due to the fact that main body collaboration is not fully considered. The method comprises the following steps: establishing a game subject list; establishing a near-earth space historical event sample set; classifying the strength degrees of the game subjects; classifying cooperation degrees of game subjects; classifying the cooperation degree of the medium dominant; calculating a target subject game risk; and game behavior decision making. The method comprises the following steps: counting a sample set to determine a strong dominant body, a medium dominant body and a vulnerable dominant body; possible team-type confrontation or cooperation can be quickly judged; the confrontation possibilities of different types of subjects are evaluated in a matrix form, the concept of medium subjects is introduced, and the extensive subjects are identified, namely, the extensive subjects tend to be non-confrontation, so that the possibility of communication and cooperation is provided for the target subjects (themselves).

Embodiments of the present disclosure provide a solution for model-based task processing. The method comprises the following steps: acquiring a basic parameter set of a pre-trained basic machine learning model and a first parameter set and a second parameter set of a trained low-rank machine learning model for a first task, wherein the basic parameter set, the first parameter set and the second parameter set are matrix forms with the same dimension; applying a Hadamard operator to the basic parameter set and the first parameter set to obtain an intermediate parameter set; aggregating the second parameter set and the intermediate parameter set to obtain an updated parameter set; fine-tuning the basic parameter set using the updated parameter metric to obtain a fine-tuned parameter set for a target machine learning model corresponding to the first task; and applying the target machine learning model to perform model reasoning for the first task using the fine-tuned set of parameters.

The invention discloses a renewable energy source data clustering method and system based on an optical quantum computer, and the method comprises the steps: building a renewable energy source data clustering optimization problem based on a quadratic unconstrained binary optimization (QUBO) model based on a discrete optimization objective function, carrying out the matrix of the objective function, carrying out the clustering optimization of the objective function through a kernel method, and carrying out the clustering optimization of the objective function. The method comprises the following steps of: establishing a target function item, converting the target function item into a matrix form, constructing a matrix of a new clustering model, finally solving a new QUBO optimization problem by using CIM, and converting the new QUBO optimization problem into an optimal clustering result. According to the method, data similarity measurement is improved by constructing the QUBO model and combining a kernel method, and a renewable energy source data clustering problem is converted into a form suitable for CIM solution, so that the calculation bottleneck of a classical calculation method in high-dimensional data processing is avoided. According to the method, the nonlinear characteristic of the data can be kept, efficient clustering optimization calculation can be realized, and the calculation efficiency and the clustering precision are remarkably improved.

The invention discloses a fixed time inclusion control method and system for a second-order nonlinear multi-agent system, and the method comprises the steps: defining a state tracking error according to a control target, augmenting the state tracking error into a matrix form, and designing an integral sliding mode surface for suppressing disturbance; designing a distributed fixed time inclusion consistency protocol and a dynamic event triggering mechanism based on position information and speed information according to the integral sliding mode surface; and controlling the multi-agent system model to reach an integral sliding mode surface according to a distributed fixed time inclusion consistency protocol and a dynamic event triggering mechanism, then sliding on the integral sliding mode surface, and controlling the state of the multi-agent system model to realize inclusion consistency within fixed time. According to the invention, the energy consumption of the system and the updating frequency of the controller can be reduced, the service life of the system is prolonged, and under a new distributed fixed time inclusion consistency protocol, the system firstly reaches the sliding mode surface and then slides on the sliding mode surface, so that the inclusion consistency of the system state is finally realized within fixed time.

The invention discloses a multi-probe spherical near-field sampling system based on compressed sensing and a test method, and belongs to the technical field of measurement. According to the method, the antenna transmission equation is expressed in a matrix form, the unknown transmission coefficient is solved from the linear equation set through the matrix method, and the defect that a traditional spherical near-field test method based on FFT depends on the distribution uniformity of near-field samples is overcome; according to the method, under the assumption that antenna transmission coefficients have sparsity, a compressed sensing technology is introduced, L1 norm minimization constraint is applied to a transmission coefficient vector q to ensure the sparsity of the transmission coefficient vector q, and under the condition that the number M of spherical near-field samples is smaller than the number J of unknown coefficients, the transmission coefficients can be solved from an underdetermined linear equation set; according to the invention, on the basis of sampling coordinates obtained based on prior information of an antenna, in combination with an adaptive positioning algorithm, each probe is controlled to autonomously select an optimal path and move to a specified position along an annular frame to carry out interference-free sampling, so that data acquisition of a pitching section at any angle is realized.

The invention relates to the technical field of artificial intelligence and knowledge engineering, and discloses a knowledge graph completion method, system and device based on entity relation attention and a medium, and the method comprises the steps: distributing an embedded vector with a fixed length for each entity and relation in a knowledge graph, and employing a first initialization method to initialize the embedded vector; respectively mapping and remodeling the embedded vectors of the head entity and the relationship into a matrix form; capturing a deep relationship between the head entity and the relationship through an attention mechanism, and generating a joint feature vector; calculating the joint feature vector and the embedded vector of the tail entity to obtain a scoring result of the triple; and sorting and selecting all the candidate triads according to the scoring result, and adding the first selection result as a completion result into the knowledge graph. According to the method, the deep semantic association between the entity and the relationship can be effectively captured, and key information is dynamically focused through an attention mechanism, so that knowledge graph completion with better performance is realized.

The present application provides a data processing method for accelerating the calculation of large complex sparse matrices with improved CSR, which is applied to the iterative solution of large complex sparse matrix equations in the fields of electromagnetic calculation, etc., and uses the differential equation method to perform numerical simulation / emulation on the physical model to be calculated, determine the complex sparse linear stiffness matrix, and convert the complex sparse linear stiffness matrix into four sub-matrices in the form of real matrices, and store them in the CSR storage format after forming the real matrix, and then convert the CSR storage format into the improved Fill-CSR format, so as to realize SPMV optimization in iterative solution and accelerate solution calculation. This scheme can make full use of the performance of vectorized acceleration hardware, increase the amount of single memory access calculation, improve the calculation memory access ratio, and thus improve the overall running speed. The number of elements in the even array can align the data to adapt to the code structure of 2-fold loop expansion, so as to achieve better acceleration effect.

THE WAY IN WHICH THE ESSENTIAL ASPECT OF THE STRATEGE "FROM NOTHING - SOMETHING" IS IMPLEMENTED IN INFORMATION TECHNOLOGIES. The initial essence "Nothing" (0, Zero) of the method is the abstract essence. The mathematical model of the Projected Object Entity (PEO) is embodied in material, software, and mixed entity objects from various disciplines—the "something" of the method. The methodology of comparative cause-and-effect analysis is applied in the creation of the PEO, and the methodology of essential synthesis is used in its design: subject-specific and essential interdisciplinary properties of similarity, universal mathematical models in the form of orgraphs and their matrices, graphs with algorithms of nodes and their matrices, as well as essential identities, equations, and differential equations that reflect the balance of actions and counteractions.And since the professional and interdisciplinary properties of similarity for individual objects of different disciplines continue to apply, the transfer of the parental properties, qualities and the collective image of "nothing" and its "something" takes place "according to the image and likeness" of the essence.

The invention discloses a carbon price prediction method and system based on a graph G and a model prediction algorithm, and the method comprises the steps: firstly building a carbon price network, representing the network in the form of the graph G, then introducing a structural equation model to capture key variables of the network, and enabling the structural equation model to be expressed in the form of a matrix, adding additional items into the structural equation model to dynamically and accurately capture the carbon price fluctuation in the complex environment, and finally, constructing a carbon price prediction model by taking the minimum weighted prediction error and policy deviation cost as objective functions, and solving the model by adopting an artificial hummingbird algorithm and adding a chaotic mapping Bernoulli initialized population method. And obtaining the predicted carbon price. According to the carbon price prediction method, the carbon emission price with obvious fluctuation characteristics can be accurately predicted, and the effectiveness of the carbon emission trading market can be realized.

The invention provides an optimization method for a single-quantum-bit 4-element rank 1 positive operator value measurement circuit. The optimization method comprises the following steps: S1, compiling a single-quantum-bit 4-element rank 1 positive operator value measurement scheme # imgabs0 # to be realized into an initial and unoptimized quantum circuit matrix which is expressed as UP; s2, judging the type of the measurement scheme, and dividing the measurement scheme into symmetrical information complete positive operator value measurement or other types of positive operator value measurement; s3, for symmetrical information complete positive operator value measurement, calculating to-be-compiled parameters US and c, and substituting US into the designed fixed line structure to complete compiling; s4, for other types of positive operator value measurement, optimizing the matrix form UP through an iterative algorithm for adjusting redundant parameters until an optimized matrix form # imgabs1 # is obtained, and then executing conventional quantum line compiling on # imgabs2 #; and S5, constructing and executing quantum measurement, obtaining original measurement data, and adjusting an original measurement result according to the compilation parameter c, thereby effectively reducing the noise influence in a measurement circuit.

The present invention discloses a method, a system and a device for extracting vibration signal characteristics of a hydropower unit, relating to the field of feature extraction, including: obtaining the vibration signal of the hydropower unit; hierarchically decomposing the time series of the vibration signal by using moving average and moving difference methods based on conditional bubble entropy to obtain the matrix form of the corresponding operator for each layer; constructing a vector of non-negative integers e and obtaining the matrix form of the corresponding operator for the vector at each layer, and obtaining the hierarchical component at each layer node e by multiplying the matrix form of the corresponding operator for each layer by the time series; calculating the CBE value of each layer, and defining the set of CBE values of all components as the improved hierarchical conditional bubble entropy value HCBE; finding the optimal parameter of HCBE, and when the non-negative integer e is less than the threshold, outputting IHCBE as the feature characterizing the fault. The present invention proposes an improved hierarchical conditional bubble entropy, which makes the obtained information more detailed and improves the accuracy of hierarchical bubble entropy calculation.

The invention relates to a matrix type differential passivity index determination method and device, equipment and a storage medium. The method comprises the following steps: acquiring a linear system model corresponding to target equipment in a power system, wherein the power system is a multiple-input multiple-output system; determining a corresponding differential passivity equivalent system model according to the linear system model; based on the linear matrix inequality, a matrix type passivity index of the differential passivity equivalent system model is solved, the matrix type passivity index is used as a matrix type differential passivity index of the linear system model, the matrix type differential passivity index is in a matrix form, and the matrix type differential passivity index is used as a differential passivity index of the linear system model; matrix elements in the ith row and the jth column in the matrix represent coupling passivity characteristics between an input channel i and an output channel j in the power system. By adopting the method, the passivity characteristics in the MIMO dynamic system can be represented more accurately and comprehensively.

The present invention provides a dense modal response reconstruction method based on a state transfer matrix, belonging to the field of structural response reconstruction, and comprising the following steps: S1, decomposing each order modal response, separating the initial phase from the initial amplitude in the response, and expressing each order modal response in matrix form; S2, constructing the state matrices of the measured point and the point to be reconstructed, respectively, and obtaining the modal mode ratio matrix of the measured point and the point to be reconstructed; S3, constructing the state transfer matrix between the point to be reconstructed and the measured point; S4, reconstructing the response of the point to be reconstructed according to the state transfer matrix. The dense modal response reconstruction method based on a state transfer matrix proposed by the present invention, based on the modal superposition method of linear systems, separates the initial phase from the initial matrix in the modal response, and reconstructs the response according to the state transfer matrix between the point to be reconstructed and the measured point. This method can handle the response reconstruction of structures with dense modal characteristics and ensure the accuracy of the response reconstruction.

The invention discloses a power distribution network state estimation, regulation and control method considering multi-source data fusion. The method comprises the following steps: considering the measurement precision and time scale of multi-source data and the synchronism of state estimation calculation, respectively preprocessing day-ahead and intra-day measurement data, and carrying out classification processing on the measurement data according to different characteristics of a power supply; day-ahead and intra-day data of distributed power supply output and corresponding irradiance, air temperature and wind speed are used as a total data set for training, and then a multi-layer output single-hidden-layer prediction nonlinear model considering system state variables is established; defining an upper limit value and a lower limit value of a state variable, rewriting a multi-layer output single hidden layer prediction nonlinear model into a linear model, and expressing the linear model in a matrix form; constructing an uncertain set to describe a fluctuation interval of a system state, and rewriting the fluctuation interval into a form consistent with an active-reactive coordination optimization model of the power distribution network; and constructing an active-reactive coordinated optimization relaxation model of the power distribution network based on the branch power flow, and optimizing each index of the system.

The invention discloses a transformer winding temperature prediction method and system, and the method comprises the steps: input data definition: extracting winding temperature historical data and load historical data, and converting the data into corresponding matrix forms; accumulation sequence and adjacent mean value sequence calculation: on the basis of the original historical data, respectively accumulating the winding temperature data and the load historical data to generate a corresponding accumulation sequence; summing adjacent elements in the winding temperature data accumulation sequence and averaging to generate a corresponding adjacent average sequence; prediction coefficient calculation: constructing a first coefficient matrix, a second coefficient matrix and a third coefficient matrix according to the winding temperature input data, the winding temperature adjacent mean value sequence and the load accumulation sequence, and performing matrix operation according to the three coefficient matrixes to obtain a corresponding first prediction coefficient and a corresponding second prediction coefficient; and performing matrix operation according to the first prediction coefficient, the second prediction coefficient, the winding temperature adjacent mean value sequence and the load accumulation sequence to obtain a corresponding winding temperature prediction value.

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 present invention discloses a method, system, medium and product for error identification of a coordinate measuring machine. The present invention includes obtaining the measurement coordinates during the movement of the space diagonal of a standard sphere in a preset body space and establishing an error equation for each measurement position, which includes the influence caused by the probe error and the error value composed of the polynomial coefficients of the geometric error and the perpendicularity error; after expanding the error equation, neglecting the second-order small quantities to construct a linear equation system in matrix form for multiple measurement positions; subtracting the linear equation systems in matrix form of adjacent measurement positions to obtain a linear equation system in matrix form of the differences between adjacent measurement positions, thereby decoupling the influence caused by the probe error and obtaining the error value composed of the polynomial coefficients of the geometric error and the perpendicularity error, as well as the geometric error and the perpendicularity error. The purpose of the present invention is to simply and reliably achieve the accurate identification of the geometric error of the coordinate measuring machine in order to achieve the precise compensation of the geometric error of the coordinate measuring machine.

The invention discloses a method for calculating the inherent frequency of a porous two-dimensional functionally graded material framework, and belongs to the technical field of structural design, the framework is simplified into a beam model with two clamped ends, a motion control equation of a porous two-dimensional functionally graded material beam in a humid and hot environment is deduced through a Hamilton principle based on a Timoshenko beam theory, and the inherent frequency of the porous two-dimensional functionally graded material beam is calculated. Simplifying the equation by using a variable separation method; the inherent frequency of the beam is solved by adopting a differential quadrature method; a motion control equation is discretized by utilizing a differential quadrature method discretization principle, then the equation is converted into a matrix form, the problem of solving the motion equation is converted into the problem of solving a matrix eigenvalue by adopting a matrix transformation method, and finally solving is carried out by writing a corresponding program. The corresponding solving program is clear in logic and high in uniformity, the calculation efficiency and precision are greatly improved, and a theoretical reference can be provided for the design of the functional gradient skeleton in the later period.

The invention discloses a BP16 matrix multiplication and addition operation circuit based on an RISC-V framework, and relates to the technical field of processor microstructures and artificial intelligence calculation acceleration. The circuit comprises an input module, a matrix multiplication and addition operation module, an accumulation operation module and an output module, wherein the input module is used for inputting a first operand in a BP16 format, a second operand in the BP16 format and a third operand in a preset format; the matrix multiplication and addition operation module periodically performs row and column multiplication operation on a plurality of first floating point elements of a preset row number in the first operand and a second floating point element in the second operand in a matrix form to generate a fourth operand; the accumulation operation module performs accumulation calculation on the fourth operand and the third operand to generate a fifth operand; and the output module outputs the target operand based on the fifth operand. The throughput rate and the energy efficiency ratio of AI acceleration calculation under the RISC-V architecture can be remarkably improved.

The present invention discloses an automatic cooling method for a die-casting system based on deep learning. By introducing a convolutional neural network (CNN) and deep learning, feature extraction is performed on the thermal imaging image of the mold surface after the metal liquid solidifies to form a product and the thermal imaging image of the mold surface after the cooling system is finished working, so that the original input and output of the cooling system are changed from image form to mathematical matrix form, and the loss function can realize the calculation of the input and output images, and then obtain the ideal values ​​of the water flow rate and water flow time. In the present invention, the water flow rate and water flow time in the cooling system are determined by the CNN neural network to achieve an ideal cooling effect. Compared with the empirical method, the method of the present invention is more flexible in the adjustment process in actual production; the purpose of scientifically controlling the cooling system is achieved, and the goal of extending the service life of the mold and reducing the processing error can be achieved.

The invention discloses an energy system optimization method and device based on offline domain and online domain optimization, and the method comprises the steps: constructing a state sensing module which is used for monitoring the operation state and environment change of a multi-energy system in real time, and providing instant data support for the optimization decision of an RIES system and a CIES system; a security-economy optimization module is constructed, and an energy scheduling and resource allocation scheme optimization model is constructed in combination with a Stackelberg game and a cooperative game strategy; constructing an off-line domain generation module, constructing a matrix form of the energy scheduling and resource allocation scheme optimization model based on a multi-parameter planning theory, analyzing correlation of scenes, iterations and model parameters in the CIES system, and generating a scene domain, an iteration domain and a model domain of an optimization problem based on a critical region; the method comprises the following steps: constructing an online domain optimization module, matching a corresponding region, solving through a linear equation when identifying that the running state of the current CIES system is in a certain critical region, and determining an energy scheduling and resource allocation scheme based on a linear equation solving result of the online domain optimization module.

The application discloses a kind of based on time delay recursive discrete grey model's weld seam tracking error real-time prediction method, the method includes the following steps: step S1: obtaining real-time weld seam tracking error data;Step S2: according to new information priority principle, using new information priority accumulation operation to time series data smoothing weld seam tracking error data;Step S3: using time delay grey differential equation and discrete equation establishes the matrix form of TRDGM;Step S4: using recursive least square method to derive the recursive equation of TRDGM structure parameter;Step S5: construct the nonlinear optimization objective function of TRDGM;Step S6: using heuristic optimization algorithm to optimize the hyperparameter of TRDGM;Step S7: select prediction accuracy index and computational efficiency index, evaluate the prediction performance of TRDGM.The TRDGM proposed in the above can quickly and accurately predict weld seam tracking error under small sample condition, help to predict in advance the risk of weld seam tracking failure.

The application discloses a kind of meta-heuristic test design optimization methods based on matrix transformation, belong to computer-aided test design and statistical modeling technical field, this method uses a two-stage optimization framework: first, in global exploration stage, with minimizing global uniformity index as target to carry out unconstrained optimization, to obtain well space coverage sample set;Subsequently in local development stage, with minimizing adjacent distance variance as target, optimization is carried out under the constraint of maintaining the good global coverage obtained, to fine adjustment sample point local spacing distribution, improve uniformity;The application encodes test design scheme into matrix form individual, is updated iteratively by meta-heuristic algorithm, and adopts global normalization strategy to ensure that sample is always located in design space;The application can systematically consider the global coverage of sample and local uniformity, significantly improve the sample distribution quality in high-dimensional space, generate comprehensive performance excellent test design scheme.

The invention relates to a complex background material tracking method, which comprises the following steps of: adding an id code on the surface of a material, acquiring an original image of the surface of the material by using a camera, processing the original image, establishing an initial model M0 and predicting a new id label; in the final prediction stage, the possible probability of each character is predicted for each position of the id tag, the prediction result is in a matrix form, the complete id tag is obtained through splicing after softmax processing, and finally the id tag is transmitted to the corresponding position of the database for self-increasing; and sending the id tag of each material into a back-end database for unified management. The method can improve the recognition precision when the character recognition is seriously interfered by the external background, and accelerates the recognition algorithm landing speed. On-line incremental learning is carried out by continuously updating the model through on-line data acquisition, so that the cost of manual labeling can be saved, the time of landing application of a deep learning algorithm can be greatly shortened, and the recognition accuracy is further improved.

The embodiment of the application provides a kind of based on agent model's oil reservoir remaining oil prediction method and system, the method includes: setting oil reservoir simulation data, combining oil reservoir simulation module, generate sample data set;Based on the data type of sample data set, corresponding data preprocessing is carried out, and data is converted into two-dimensional matrix form;Residual oil prediction model is constructed, and residual oil prediction model is the encoder-decoder network of fusion residual structure, and coordinate attention mechanism is introduced;Two-dimensional matrix form sample data set is input data, and residual oil prediction model is iteratively optimized, and iteration is until convergence, to obtain the trained residual oil prediction model;The complete input parameter of target oil reservoir is acquired, and the trained residual oil prediction model of input complete parameter is output target oil reservoir remaining oil distribution result.