41 results about "Characteristic matrix" patented technology

A generator fault identification method, device, product and medium relate to the technical field of generator fault category identification. The method comprises the following steps: acquiring a vibration signal and a noise signal; performing multi-mode decomposition processing on the vibration signal and the noise signal to obtain a first sub-mode component and a second sub-mode component; determining a vibration signal sequence based on each first sub-mode component and the vibration signal; determining a noise signal sequence based on each second sub-mode component and the noise signal; determining a vibration characteristic matrix based on the vibration signal sequence; determining a noise feature matrix based on the noise signal sequence; determining a first recognition result and a first confidence coefficient based on the vibration feature matrix; determining a second recognition result and a second confidence coefficient based on the noise feature matrix; when the identification results are inconsistent, determining a fault identification result according to the confidence coefficient; and when the identification results are consistent, determining the first identification result as a fault identification result. The method is advantaged in that generator fault identification accuracy is improved.

The invention discloses a defect identification method and system for a photoelectric detection image of power transmission and transformation equipment, and relates to the technical field of defect identification, and the method comprises the steps: collecting photoelectric detection data of target power transmission and transformation equipment; carrying out coupling analysis in combination with an equipment topology connection relationship, and drawing up a feature matrix; meanwhile, identifying a defect type mode based on a device topology connection relation and a change trend of a gray scale deviation coefficient in a plurality of detection periods; based on the characteristic matrix, combining the defect type mode and the photoelectric detection data to configure a defect confidence factor; and determining a defect severity level by using the defect confidence factor and carrying out graded reminding. The technical problems that in the prior art, electric transmission and transformation equipment photoelectric detection data integration is insufficient, the defect recognition precision is low, and operation and maintenance response lacks pertinence are solved, and the technical effects that integration analysis of multiple types of photoelectric detection data of the electric transmission and transformation equipment is achieved, and the defect recognition accuracy and the operation and maintenance response pertinence are improved are achieved.

The invention discloses a rock permeability prediction method, a medium, equipment and a product, and relates to the technical field of permeability prediction.The method comprises the steps that multi-dimensional logging characteristics are obtained; constructing a Pearson's correlation coefficient matrix thermodynamic diagram to quantify the linear coupling strength of the multi-dimensional logging characteristics and the permeability, obtaining the correlation coefficient of the logging characteristics and the permeability of each dimension, screening the logging characteristics from the multi-dimensional logging characteristics according to the correlation coefficient, and constructing a characteristic matrix; building a parallel permeability model and a vertical permeability model based on a table prior data fitting network, training the parallel permeability model by using the feature matrix, splicing the parallel permeability predicted by the trained parallel permeability model as a new feature to the feature matrix, and training the vertical permeability model by using the spliced feature matrix; and predicting the rock permeability by using the trained parallel permeability model and vertical permeability model. The method can solve the problems that an existing method has high requirements for data quantity and quality and is weak in model generalization ability.

The embodiment of the invention provides a counting method and device for composite actions of an excavator and electronic equipment. The method comprises the steps that a pilot pressure matrix of the composite action of the excavator is obtained; wherein the pilot pressure matrix represents pilot pressure values of actions in composite actions of the excavator; performing feature processing on the pilot pressure matrix so as to perform smoothing processing on the pilot pressure matrix to obtain a pressure feature matrix; wherein the pressure characteristic matrix represents the characteristics of the pilot pressure value of the composite action of the excavator; similarity calculation processing is conducted on the pressure characteristic matrix and a preset composite action matrix, and the number of composite actions of the excavator is obtained; wherein the preset composite action matrix is the characteristic of the standard pilot pressure value of the composite action. The method is used for achieving the effect of accurately and efficiently counting the composite actions of the excavator.

The invention relates to the technical field of electric digital data processing, and discloses a combined bridge interface damage time-varying analysis method and system, and the method comprises the steps: obtaining a slip sequence and temperature gradient data of a joint interface; extracting a high-frequency dynamic response signal through dynamic residual filtering; calculating a displacement compensation amount based on the thermal stress characteristic matrix, and eliminating the compensation amount from the dynamic response signal to determine a net slip characteristic value; mapping the slip space distribution difference into a topological feature vector by using an interface stress distribution mapping operator; analyzing the unsteady oscillation characteristics of the dynamic response signal, determining the cementation failure probability, and outputting a damage analysis index in combination with a topological feature vector, so that accurate decoupling of environment thermally induced displacement interference and a structure damage signal is realized, the influence of non-damage factors is eliminated, and a rigidity degradation critical point is captured by utilizing an interface topological evolution mechanism; the early interface void recognition reliability is improved, and bridge safety evaluation is supported.

The invention relates to the technical field of information security, in particular to a score query method and system based on biological characteristic matrix code identity verification, and the method comprises the steps: obtaining a score query request initiated by a user through a score query platform, and enabling the score query platform to call a matrix code management platform to generate and display a dynamic matrix code; scanning the dynamic matrix code through a matrix code verification APP, and collecting on-site biological characteristics of a user; comparing the field biological characteristics with a pre-stored biological characteristic matrix code file to generate a verification result; and if the verification result is successful, the matrix code verification APP sends an encryption confirmation message to the score query platform, and the score query platform calls and displays corresponding score data according to the encryption confirmation message. Through double-platform cooperation and a dynamic matrix code mechanism, high-security identity verification in a score query scene is realized, and identity false use, information leakage and unauthorized query are effectively prevented.

The present application belongs to the technical field of foundation pit engineering monitoring, and relates to a kind of foundation pit intelligent monitoring test method and equipment, which constructs the multi-physical quantity monitoring sequence of time sequence alignment by supporting member mechanical response parameter and medium mechanical response parameter, and establishes mechanical transmission path combined with spatial geometric relationship, can bring originally dispersed monitoring data into the same transmission link for analysis;By extracting the load transmission coefficient and constraint stiffness characteristics of each constraint node, a supporting member-medium coupling response characteristic matrix is constructed, providing a unified feature base for the establishment of theoretical transfer function, and based on the theoretical transfer function at each constraint node, the corresponding overall theoretical response envelope is established, which avoids false alarm or false alarm caused by relying on fixed threshold;By the over-limit combination state of supporting member response deviation and medium mechanical response deviation, the abnormal cause is determined as supporting member problem, medium change problem or supporting member and medium coupling failure problem.

The invention belongs to the technical field of steel structure floor design, and particularly relates to a transient response analysis method for converting structural damping into equivalent viscous damping in a full frequency band, which comprises the following steps of: 1, establishing a finite element model of a structure, and outputting characteristic matrixes of the structure, including a stiffness matrix and a mass matrix; 2, performing modal analysis based on the finite element model of the structure to obtain a modal matrix of the structure; 3, calculating a generalized stiffness matrix and a generalized mass matrix of the structure based on the stiffness matrix, the mass matrix and the modal matrix; step 4, calculating viscous damping of each order vibration mode of the structure based on the generalized stiffness matrix; 5, calculating the transient response of each order vibration mode of the structure based on the generalized stiffness matrix, the generalized mass matrix and the viscous damping of each order vibration mode of the structure; and 6, converting the transient response of each order vibration mode of the structure to a physical coordinate to obtain the transient response of the structure at each moment.

The invention discloses a transformer virtual diagnosis method, which comprises the following steps: acquiring monitoring data of a transformer at a plurality of continuous moments before the current moment, distributing the monitoring data to corresponding component nodes in an atlas model to obtain a characteristic matrix at each moment, inputting the characteristic matrix and the atlas model into a multilayer graph attention network to obtain a state embedding matrix at each moment, respectively inputting a GAT-VAE model to obtain an implicit state variable at each moment; deducing virtual monitoring data of the transformer at a plurality of continuous moments after the current moment, and inputting the virtual monitoring data and all implicit state variables into the time sequence prediction model to obtain an implicit state variable at each moment; calculating a virtual health index of each monitoring part at each moment after the current moment; and inputting the future implicit state variables and the virtualization diagnosis indexes corresponding to the future implicit state variables into the fault classification model to obtain the fault type probability of each monitoring component at each moment after the current moment, and determining the fault type of the monitoring component in combination with the virtual health indexes of the monitoring components at the corresponding moments.

The invention discloses a method for predicting coke quality of single coking coal of a test coke oven, which comprises the following steps of: S1, collecting historical test data of single coal, and constructing a characteristic matrix containing coal quality characteristic parameters and a label vector containing a coke quality index measured value; s2, analyzing data distribution characteristics of the measured values of the coke quality indexes, and determining an optimal clustering number and a classification boundary in combination with clustering validity evaluation; s3, according to the optimal clustering number and the classification boundary, category labels are distributed for historical experimental data, and a semi-supervised parallel classification model is trained by taking the feature matrix as input; s4, dividing the historical experimental data into a plurality of same proton sets according to the classification boundary, screening out key features, and constructing a cascade prediction model; and S5, extracting coal quality characteristic parameters of a to-be-predicted single coal sample, processing the coal quality characteristic parameters into feature vectors, inputting the feature vectors into the semi-supervised parallel classification model to determine the category of the feature vectors, calling the cascade prediction model corresponding to the category, and outputting a predicted value of the coke quality index.

The invention relates to the technical field of tea identification, and discloses a Pu'er raw tea year prediction method and device, electronic equipment and a storage medium, and the method comprises the following steps: obtaining spectral data and chemical component data of Pu'er raw tea; performing principal component analysis on the spectral data to obtain a spectral principal component characteristic matrix; performing mutual information maximization feature fusion on the spectrum principal component feature matrix and the chemical component data to obtain a fusion feature matrix; and based on the fusion feature matrix and a preset year prediction model, obtaining a prediction year of the Pu'er raw tea. According to the method, mutual information maximization feature fusion is performed on the spectral data and the chemical component data of the Pu'er raw tea, and the Pu'er raw tea year prediction is performed based on the year prediction model with high precision and strong interpretability, so that the prediction accuracy is remarkably improved.

The invention relates to the technical field of welding structure monitoring, and provides an intelligent ultrasonic monitoring method for creep damage of a dissimilar steel welding joint, and the method comprises the steps: calculating a waveform distortion index based on a time-frequency characteristic matrix; comparing the waveform distortion index with a self-adaptive threshold value in a monitoring time window, and identifying and marking potential damage signals; performing morphological analysis on the potential damage signal to obtain an envelope contour and a modulation mode, determining the existence of creep damage by adopting a pre-constructed damage verification model, and outputting a damage severity estimated value; integrating the spatial position and timestamp information of the potential damage signal, and executing a density clustering algorithm to detect a damage diffusion mode; and correcting the damage severity estimation value according to the diffusion mode attribute and reconfiguring the monitoring time window. Therefore, the problem that the creep accumulation condition and the residual life are difficult to accurately evaluate due to the fact that the early creep damage characteristics cannot be accurately identified due to the fact that a single method is adopted in ultrasonic data analysis in the prior art is solved.

The present application relates to the technical field of computer, and provides a disease risk prediction method, device, equipment, medium and product, the method comprises the following steps: constructing N characteristic matrices based on metagenomic data of a to-be-tested sample; constructing sample correlation graphs based on the N characteristic matrices respectively, and performing graph feature extraction on the N characteristic matrices based on the corresponding sample correlation graphs to obtain N characteristic representations; performing cross-type fusion on the N characteristic representations to obtain a fusion representation of the to-be-tested sample; performing disease risk prediction based on the fusion representation to obtain a prediction result; wherein N is a positive integer not less than 2. The method can perform prediction based on the fusion representation which retains statistical characteristics and topological structure characteristics of biological information at each level, effectively improves the utilization degree of the original metagenomic data, and improves the accuracy and robustness of disease risk prediction.

The invention relates to the field of mechanical equipment state monitoring, in particular to a wind generating set component abnormity identification method, device and equipment and a storage medium, and the method comprises the steps that dynamic environment parameters and sealing response characteristics collected by sensors arranged inside and outside a sealing system are analyzed, and a coupling characteristic matrix reflecting the state evolution of a sealing interface is constructed; processing by using the sealing efficiency evaluation model to obtain a sealing efficiency evaluation result; and finally, based on the historical change trend of the result, the incubation period from sealing performance degradation to bearing corrosion induction is identified. According to the method, deep perception and prospective early warning of the state of the sealing system are realized, and the corrosion latency period is further identified based on the performance trend, so that the maintenance decision can lead the occurrence of substantive damage. Therefore, the timeliness of fault early warning is remarkably improved, a maintenance window is greatly advanced, the problem of serious corrosion caused by sealing failure is effectively avoided, and the operation and maintenance cost and the safety risk are reduced.

The application discloses a micro-service time sequence prediction method based on multi-scale two-way fusion coding condition diffusion. First, a local segment of a micro-service key performance indicator and a time characteristic matrix thereof are acquired; local historical characteristics and global historical characteristics of the micro-service key performance indicator are extracted and fused to obtain historical fusion characteristics of the micro-service key performance indicator; then, a condition diffusion network is constructed, including a Transformer decoder, a gate linear unit and a characteristic linear modulation, and the historical fusion characteristics of the micro-service key performance indicator are taken as condition information of multi-head attention of the Transformer decoder; finally, based on multi-scale autoregressive diffusion, the condition diffusion network is used to gradually and reversely denoise the local segment of the micro-service key performance indicator at a prediction starting position, weighted fusion is performed on micro-service key performance indicator prediction results at various steps, and finally, micro-service key performance indicator prediction results are obtained. Through capturing local and global information and fully fusing, the prediction accuracy is improved.

The invention discloses an intelligent agricultural product sorting method based on physical feature iterative optimization and a related device, and belongs to the technical field of agricultural product sorting. The method comprises the following steps: collecting a surface reflection image and a near-infrared transmission image of an agricultural product to be sorted; using homomorphic filtering to carry out anti-wrinkle preprocessing on the surface reflection image; extracting physical feature vectors of the two images; iterative optimization is carried out based on the physical characteristic matrix, an optimal judgment threshold parameter combination is obtained, feature extraction and judgment are carried out on agricultural product images collected in real time, and an execution mechanism is controlled to complete the sorting action according to encoder signals and a delay compensation mechanism. According to the method, morphological interference is solved through homomorphic filtering, interpretability detection of quality is achieved through white-box physical characteristic quantification, top-speed self-adaption of parameters is achieved through matrix iteration, and therefore precise and intelligent sorting of complex agricultural products is achieved.

The invention discloses a power load inflection point prediction method based on a multi-persistence graph attention network, and the method comprises the steps: carrying out the collection and preprocessing of the historical active load and corresponding meteorological data of a power grid region, and adaptively determining the number of key load points through a perception PIP algorithm in combination with a fitness function; extracting a key load point set representing the morphological change of the load curve; on this basis, a key load point visible graph is constructed, node degrees and node eccentricity rates are used as two-parameter filtering functions, a series of filtering sub-graphs are generated according to multiple sets of threshold values, Euler-Poincare features are calculated, and a multi-parameter Euler-Poincare feature matrix is formed; carrying out normalization, vectorization and row-based first expansion on the feature vector to obtain a one-dimensional multi-persistence topological feature vector of a fixed dimension; and then, introducing a graph attention network formed by stacking multiple layers of graph attention layers to output inflection point category probabilities of prediction days step by step, and realizing refined prediction of the power load inflection points.

The invention discloses an electrocardiogram classification method and system based on an improved convolution attention mechanism and a residual network, and the method comprises the following steps: obtaining an original electrocardiogram signal, carrying out the preprocessing of the original electrocardiogram signal, and obtaining a standardized time-frequency feature matrix; constructing a ResNet classification network embedded with an improved CBAM module, inputting the standardized time-frequency characteristic matrix into the ResNet classification network, and training and optimizing network parameters to obtain a trained electrocardiogram classification model; inputting an electrocardiogram signal to be classified into the trained electrocardiogram classification model, and outputting an electrocardiogram classification result; by introducing the time sequence attention sub-module, the long-range time sequence dependency relationship of the electrocardiogram signals can be accurately captured, the defect of insufficient attention on time sequence information is effectively overcome, the recognition precision of complex electrocardiogram signals is improved, and the electrocardiogram classification effect is improved.

The present application relates to the field of engineering, and discloses a kind of open-pit mine slope displacement trend identification method, open-pit mine slope monitoring data is collected, the monitoring data collected is marked, original monitoring matrix containing time series characteristics is constructed, and the original monitoring matrix is preprocessed;Each original monitoring matrix is executed adaptive fractional order decoupling processing, and decoupling characteristic matrix is obtained, and physical coupling relationship between slope physical quantity is used to construct physical correlation input tensor;Accelerated deformation identification model is constructed;Physical correlation input tensor is input into accelerated deformation identification model, and accelerated deformation prediction probability is output, and early warning result is obtained.The beneficial effects of the present application are that: using adaptive fractional order decoupling method, the fractional order is dynamically adjusted by mutation ratio and trend continuous coefficient, the low-order inhibition is applied to instantaneous disturbance such as blasting pulse, the high-order reservation is applied to continuous deformation trend, and the problem that fixed filtering strategy is difficult to process heterogeneous signals simultaneously is solved.

The invention discloses an automatic detection system for the running state of a long-distance conveyor, and relates to the technical field of running detection of conveying equipment, multi-dimensional characteristic data such as tension, vibration, temperature and current are collected in real time by deploying multiple types of sensors along the conveyor, and a time-characteristic matrix corresponding to a structural section is constructed; extracting a local variable coefficient, a sudden change gradient and a frequency drift value based on sliding window statistics and frequency domain analysis to form a weak anomaly ternary feature set, and inputting the weak anomaly ternary feature set into the trained neural network model for fault classification and confidence judgment; performing reverse tracking and contribution degree analysis on the high-confidence abnormal section to realize fault type and starting point positioning; further performing fine monitoring by dynamically increasing the sampling frequency, judging an abnormal evolution trend based on a characteristic trend slope, and outputting early warning information; the system has high sensitivity, high real-time performance and high intelligent degree, and is suitable for remote operation state monitoring and early fault early warning of long-distance conveying equipment.

The invention discloses a soil-rock anisotropic interface shear strength prediction model construction method, which comprises the following steps: acquiring lithology, structure and interface bonding parameters of a soil-rock interface, extracting multi-parameter correlation characteristics through a lithology constraint correlation network, learning interface bonding strength characteristics through a heterogeneous interface bonding strength graph neural network, and outputting a characteristic matrix; and then, optimizing the mapping parameters by using a policy gradient algorithm guided by a fabric tensor, inputting the optimized parameters into a multi-modal federated training platform to realize distributed data cooperative training to generate intermediate model parameters, and finally, constructing a shear strength prediction model based on the parameters. According to the method, multiple types of networks and algorithms are integrated step by step, multi-parameter complex association is accurately captured, data privacy and multi-source data utilization are considered through federal training, dependence on a large number of field tests is not needed, and adaptability and prediction accuracy of the model to different engineering scenes are effectively improved.

The invention discloses a mechanical arm grabbing intelligent control method and system based on pose online correction, and relates to the field of mechanical arm intelligent control. The method comprises the steps that a mechanical characteristic matrix is detected through a mechanical sensor at the tail end of a mechanical arm, and drift error prediction is conducted; a binocular vision sensor on the mechanical arm is used for collecting binocular images, registration analysis is carried out, and registration drift errors and registration drift credibility are obtained; performing verification to obtain drift consistency parameters, and performing labeling in combination with registration drift credibility to obtain a drift prediction training data set; and the identification information consistency is analyzed, identification stability parameters are obtained, prediction drift errors and registration drift errors are fused, a processed drift prediction training data set is obtained, and mechanical arm correction control and drift error predictor updating training are carried out. The problems that in the prior art, error monitoring of detection equipment is not accurate, errors are too large in the correction process, the correction effect is poor, and the detection equipment cannot adapt to the complex industrial environment are solved.

The application provides a kind of space-time evolution data processing method and system applied to tunnel deformation analysis, by obtaining the time series monitoring sequence of different monitoring dimensions in tunnel construction process, based on this, the space-time evolution correlation model of tunnel deformation is constructed, by analyzing the dynamic coupling relationship of structure response data and geological environment action data, generate a space-time correlation feature matrix, and based on this, generate the feature evolution sequence of tunnel deformation, nonlinear mutation characteristics extraction is carried out on the feature evolution sequence, and the mutation feature index set is obtained;According to the mutation feature index set and the preset cumulative deformation rate threshold, the dynamic division of tunnel deformation safety level is carried out, and the safety level division result is generated, based on the safety level division result and the spatial distribution data of tunnel construction section, the spatial positioning and boundary contour extraction of potential risk damage area are carried out, and the risk damage area distribution map is obtained.The application can accurately evaluate and effectively control the safety risk of tunnel construction.

The present application relates to lightning protection technical field, especially in non-connection lightning protection equipment's dispelling protection effect evaluation method and system, including: obtaining and preprocessing the electric field intensity data sequence of no equipment state and equipment state, and extracting key characteristic parameters, constructing the characteristic matrix of no equipment state and equipment state. Extracting the characteristic vector of each key characteristic parameter, calculating the observation difference. Determine the difference interval based on the observation difference, determine the step length of equal step length according to the number of preset candidate difference value. Construct the candidate difference value set through hypothesis testing, determine the weight, get the normalized key characteristic parameter. The weighted fusion is carried out to the normalized key characteristic parameter according to the preset priority, the comprehensive protection index is obtained, the dispelling protection effect is judged, and the evaluation is completed. The present application evaluates the protection equipment efficiency through multi-dimensional key characteristic parameters, suppresses the error through equal step length hypothesis testing, and improves the stability and repeatability.

The application discloses a method and device for determining cycling training load, a storage medium and an electronic device. The method comprises: dividing a whole cycling record of a target user into M motion segments, M being a positive integer; determining N characteristic matrices according to data of the M motion segments, N being a positive integer less than or equal to M, and each characteristic matrix comprising at least one of the following: user attribute information, weather information, slope information and speed information; inputting the N characteristic matrices into a target model for processing to obtain training loads of the N motion segments, the target model being trained by a plurality of sets of sample data, each set of sample data comprising a sample characteristic matrix and a sample training load; and accumulating the training loads of the N motion segments to obtain a total training load of cycling of the target user. Through the application, the problem that it is difficult to obtain heart rate data of a user who does not use a wearable product and thus it is difficult to calculate a training load of the user during exercise in the related art is solved.

The invention belongs to the technical field of foundation pit engineering monitoring, and relates to an intelligent foundation pit monitoring test method and equipment. Supporting piece mechanical response parameters and medium mechanical response parameters are constructed into a multi-physical-quantity monitoring sequence with aligned time sequences, and a mechanical transmission path is established in combination with a space geometrical relationship; original scattered monitoring data can be included in the same transmission link for analysis. By extracting the load transfer coefficient and constraint stiffness characteristics of each constraint node, a support-medium coupling response characteristic matrix is constructed, a unified characteristic basis is provided for the establishment of theoretical transfer functions, and a corresponding overall theoretical response envelope line is established based on the theoretical transfer functions at each constraint node. False alarm or missing alarm caused by dependence on a fixed threshold value is avoided; and determining that the abnormal reason is a support piece problem, a medium change problem or a support piece and medium coupling failure problem through an over-limit combination state of the support piece response deviation value and the medium mechanical response deviation value.

The invention provides a knife jumping detection and compensation method and system and a storage medium, and the method comprises the steps: collecting the operation state data between a doctor blade and a plate cylinder, and carrying out the time synchronization and noise reduction processing of the operation state data, and obtaining a knife jumping feature matrix; judging whether a blade jumping event occurs or not based on the blade jumping characteristic matrix, and if not, collecting operation state data between the doctor blade and the plate cylinder; if yes, determining a knife jumping type based on the knife jumping characteristic matrix, obtaining working state data of the gravure press, determining compensation information based on the knife jumping type and the working state data, and generating a compensation instruction based on the compensation information; compensation state data obtained after the compensation instruction is responded is obtained, whether a blade jumping event exists or not is determined based on the compensation state data, and if not, operation state data between the doctor blade and the plate cylinder are collected; and if yes, generating early warning information. The operation state of the doctor blade can be monitored in real time, and the blade jumping event and the type thereof can be accurately judged in time, so that the blade jumping influence is eliminated.

The application discloses a shotcrete bonding strength dynamic test method and system with displacement monitoring, and relates to the technical field of building material testing, and comprises the following steps: S100, a force-displacement full-time domain synchronous observation baseline is established, full-band signals of electromagnetic frequency spectrum are collected in real time in the whole loading process, and an interference characteristic matrix with time resolution is constructed, which is used for recording the dynamic characteristics of electromagnetic interference and serving as a basis for subsequent comparison. The application constructs a force-displacement full-time domain observation baseline, combines a multi-stage interference processing mechanism, realizes adaptive identification and response repair of electromagnetic interference in the bonding strength test, improves data accuracy and stability, is suitable for concrete bonding performance detection in a complex environment, and enhances the reliability of structure safety evaluation and construction quality control.

The invention provides a multi-source data fusion-based land subsidence prediction method and system, and relates to the technical field of land subsidence, and the method specifically comprises the steps: dividing an underpass section, and determining a subsidence time interval; construction parameters are collected, a disturbance intensity coefficient is calculated, and a double-line cross influence section is screened out; obtaining an accumulative settlement simulation value of each day, and comparing the accumulative settlement simulation value with a measured value to obtain an accumulative time sequence deviation sequence; time nodes with the accumulated settlement deviation values smaller than a preset threshold value are screened, a time sequence characteristic matrix is constructed, parameters of the time nodes not smaller than the preset threshold value are zero-set and supplemented to the time sequence characteristic matrix, and the accumulated settlement prediction value of the cross influence section of the next day is obtained. According to the method, the double-line cross disturbance effect is fully considered, the settlement difference between the cross influence section and the single-line underpass section is distinguished, the blindness of global fitting in the prior art is avoided, the settlement rule of the cross influence section is accurately captured, and the settlement prediction precision of the type of areas is greatly improved.