3286 results about "Sequencing data" patented technology

The invention discloses a storage hard disk remote diagnosis system and method based on the Internet of Things, and relates to the technical field of health management of the Internet of Things and storage equipment. The system comprises a data fusion module, a causal analysis module, a risk analysis module and a map construction module. The data fusion module collects SMART parameters, IO operation time sequence data and environment data through Internet of Things equipment, dynamically distributes multi-source data weights by using an attention mechanism, and extracts hard disk health state features. And the causal analysis module is combined with the multi-scale time sequence convolutional network and the Bayesian causal network to identify periodic abnormal fluctuation and generate a fault root cause analysis report. The risk analysis module matches historical cases through federal incremental learning, calculates a hard disk fault risk index and generates an early warning signal. And the atlas construction module optimizes resource isolation, data migration and response paths according to the risk indexes, generates a visual operation and maintenance atlas, provides fault positioning, risk links and repair priorities, and improves the operation and maintenance management efficiency.

The invention discloses a feed production equipment collaborative scheduling and operation optimization method based on deep learning. The method comprises the following steps: S1, constructing an equipment state sequence data set; s2, constructing a disturbance sequence data set; s3, inputting a disturbance resistance residual fusion network to generate a preliminary scheduling scheme; s4, constructing an equipment conflict reasoning graph; s5, embedding the equipment conflict reasoning graph into the scheduling network, and correcting the preliminary scheduling scheme; s6, inputting the corrected preliminary scheduling scheme into an improved NGBoost model, introducing a confidence factor estimation and dynamic distribution calibration mechanism, and outputting a predicted expected value and an uncertainty score of each scheduling behavior; s7, identifying a high-risk behavior according to the uncertainty score, and generating a scheduling correction candidate set; and S8, performing multi-target comprehensive evaluation on the scheduling correction candidate set, and screening the candidate set with the highest score as a final scheduling plan to be issued and executed. According to the method, deep learning and an improved NGBoost model are combined, and intelligent scheduling optimization of feed equipment is realized.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as agent autonomous decision making, financial science and technology and medical health, and discloses a multi-modal sequence data processing method, device and equipment and a medium. Extracting multi-scale feature hierarchies, combining the multi-scale feature hierarchies into a multi-scale feature pyramid set, performing cross-modal feature alignment to generate a multi-scale alignment feature sequence, executing local and global attention processing to generate long-distance dependency features, performing cross-layer information interaction to generate comprehensive multi-scale features, and performing multi-scale feature extraction; and dynamically fusing the multi-modal information and inputting the multi-modal information into a task decision network to obtain a target task result. According to the method, through the multi-scale feature pyramid, cross-modal alignment, attention processing and cross-layer information interaction, the problem of insufficient relevance between different modals and different scales in multi-modal long sequence data is solved, and fine modeling and dynamic fusion of multi-modal and multi-scale features are realized.

The invention discloses a three-dimensional human body posture estimation method based on hybrid architecture space-time modeling, and the method comprises the steps: obtaining historical human body posture data, and generating a three-dimensional human body posture estimation network training set; constructing a three-dimensional human body posture estimation network model, wherein the network model comprises a space-time position embedding module, a space-time Mama block, a space-time self-attention block, a full connection layer and a regression layer; based on the estimation network training set and a loss function training network model, learning a mapping relation from a two-dimensional attitude joint point sequence to a three-dimensional attitude joint point sequence; and inputting two-dimensional attitude joint point sequence data to be estimated into the trained network model, and outputting a three-dimensional attitude joint point sequence. According to the method, the global spatio-temporal features are rapidly extracted by using Mama, then the global spatio-temporal feature information is further supplemented by using Transform, and the spatio-temporal features are fused and complemented more effectively by using a hybrid architecture.

The invention discloses a train dispatcher behavior anomaly detection system based on LSTM-FCN and a dynamic intervention method thereof, and relates to the technical field of intelligent monitoring. Scheduling operation time sequence data, environment state data and physiological monitoring data are collected in real time, a space-time joint feature vector is constructed, and a train dispatcher behavior anomaly detection result is obtained. The LSTM-FCN hybrid network is used to extract time sequence dependence features and spatial pattern features, and an attention weight matrix generated by physiological data and a compensation factor matrix generated by environmental parameters are combined to dynamically calculate a comprehensive abnormal probability value; and when the probability value exceeds a self-adaptive adjustment threshold value, triggering a hierarchical intervention mechanism based on the driving scheduling knowledge graph to realize progressive intervention from interface prompting to manual takeover. By fusing multi-modal data and self-adaptive dynamic compensation, the problem of poor adaptability to complex scenes due to adoption of a static model in traditional train monitoring is solved, and the accuracy and intervention timeliness of abnormal behavior detection of the train dispatcher are remarkably improved.

The invention provides a specific single nucleotide polymorphism (SNP) molecular marker for sex identification of cobia and application of the specific single nucleotide polymorphism (SNP) molecular marker. Based on whole genome high-throughput sequencing data of male and female cobia, after deep comparative analysis, the specific molecular marker related to the sex of cobia is screened for the first time, and the primer pair designed for the marker can be used independently or used in a combined mode and is high in flexibility. The method has relatively high judgment accuracy, so that the sex of cobia can be quickly and accurately identified, and the method has a very good application prospect.

The invention relates to the technical field of marine organism community monitoring, in particular to a marine organism community monitoring method and system based on a memory mixed prototype and knowledge distillation. The method comprises the following steps: carrying out data preprocessing on obtained multi-source heterogeneous monitoring data; constructing a multi-modal joint feature by using the preprocessed data; performing cross-modal teacher model training by using the multi-modal joint features; training a student model based on the historical marine ecological sequence data; performing enhanced knowledge distillation on the student model by using the cross-modal teacher model; performing marine ecological community time sequence prediction by using the trained student model; and performing anomaly detection based on the historical data and the prediction data. Based on the breakthrough of the bottleneck of the existing marine organism community monitoring technology, the multi-dimensional technical improvement and application value are realized.

The invention discloses a virtualized container environment monitoring device and method based on eBPF, and aims to solve the problems of poor isolation, insufficient security and platform adaptation deficiency in a virtualized container environment. According to the method, time sequence data of monitored events in a virtualized container environment are collected in real time through an eBPF technology, the time sequence data are transmitted to a data analysis module through eBPF mapping and message queues, data are analyzed through a long-short-term memory network, the deviation degree of the current time sequence data and historical data is calculated, abnormal behaviors are judged, and an alarm is given. According to the method, the user-defined program is operated under the condition that kernel codes do not need to be modified, intelligent analysis and abnormal behavior recognition are conducted on the collected data, comprehensive monitoring and dynamic detection of complex threats are achieved, the safety, stability and compatibility of the system are improved, and the method is suitable for virtual environments under cloud computing and big data scenes.

The invention discloses an SPR response region identification method based on image semantic segmentation and time sequence alignment, and the method comprises the following steps: collecting SPR image frame sequence data, and constructing an original image sequence; performing image preprocessing operation on the original image sequence, and outputting a standardized image sequence; constructing a time sequence window image set composed of multiple continuous frames; inputting the time sequence window image set into an improved SegFormer model, and generating a response region segmentation mask image corresponding to each frame; executing cross-frame time sequence alignment operation of the response area, and outputting time sequence consistency identification mapping of the response area; performing area statistics, intensity analysis and time positioning operation; and generating a structured response region recognition result. The spatial-temporal evolution process of the response area in the SPR image sequence can be effectively recognized, the accuracy and stability of response area recognition are improved, and the method is suitable for high-precision biological detection and real-time molecular analysis scenes.

The invention provides a deep learning inference platform and an operation method and system of a deep learning inference engine. The deep learning inference engine is carried in a processor, and the operation method comprises the following steps: loading a trained inference model, establishing a tensor cache manager, and pre-distributing an output tensor; receiving input data, obtaining an initial tensor corresponding to the input data, performing tensor remodeling on the initial tensor to obtain a target tensor of a target dimension, and storing the target tensor into a first cache variable of a tensor cache manager; calling a target tensor in the first cache variable and performing mixed attention calculation on the target tensor to obtain a mixed attention calculation result; and writing the mixed attention calculation result into an output tensor for outputting. The method can dynamically adapt to multi-step decoding or high concurrency situations, can adapt to diversified texts, audios or other sequence data and other scenes, enables a deep learning inference engine to have higher universality and expandability, and improves the system performance.

The invention discloses a civil aircraft PHM modeling method based on cross-modal coupling, a medium and equipment, and belongs to the technical field of aircrafts. The method comprises the steps of S1, multi-modal data preprocessing: performing feature extraction and time-space / event alignment for three modalities of flight time sequence data, text data and image data, and laying a foundation for subsequent fusion modeling; s2, cross-modal feature fusion and holographic data sample construction: constructing a holographic data sample library covering the whole life cycle through cross-domain fusion and dynamic optimization; and S3, PHM physical-knowledge fusion comprehensive modeling: constructing a double-layer collaborative framework combining physical simulation and knowledge reasoning, and realizing controllable fusion of physical simulation and multi-modal knowledge. The problems that in the prior art, multi-source heterogeneous data alignment is difficult, physical mechanism and data driving fusion is insufficient, and model interpretability is poor can be solved.

The invention discloses a posture feature recognition method based on a video stream, and the method comprises the steps: carrying out the preprocessing of a continuous video stream, obtaining video frame training data, extracting a key frame and an adjacent frame in each frame of image, constructing a feature extraction module for a human body region, and obtaining a global frame, performing local extraction on the human body area by using adjacent frames on the left side and the right side to obtain local frames, and constructing semantic association information for the global frame through time sequence continuity between the local adjacent frames and the current key frame; acquiring enhanced feature representation by adopting a conditional feature aggregation algorithm; obtaining attitude sequence data through the attitude detail features; the method comprises the following steps: establishing three-dimensional coordinates, adaptively extracting posture change data by adopting a human body motion decoupling model, predicting human body posture characteristics through a smooth optimization strategy, and introducing a cross attention mechanism to realize deep fusion of spatio-temporal characteristics, so that the understanding ability of the model to a complex action mode is enhanced; and the attitude expression capability of the model in a sheltered or fuzzy region is obviously improved.

The invention provides a personalized learning content recommendation method and system based on neighborhood and hypergraph collaboration, and relates to the technical field of content recommendation. The method comprises the following steps: constructing a hypergraph view and a neighbor graph view by obtaining user session sequence data; extracting global high-order relation features based on the hypergraph view; extracting local co-occurrence relation characteristics based on the neighbor graph view, and generating enhanced co-occurrence relation characteristics; embedding and splicing a sequence position code of a user learning path and learning content semantics, fusing context information through a gating dynamic attention mechanism, and generating a dynamic learning interest embedding expression; based on a global-local contrast learning strategy, performing nonlinear projection on dynamic learning interest embedding and global semantic embedding, and optimizing semantic consistency of the two types of embedding by comparing loss; and combining the cross entropy loss and the contrast learning loss of the recommendation task, constructing a combined optimization objective function, generating a personalized recommendation result, and realizing the technical effect of capturing dominant and implicit relationships between learning contents.

The invention relates to the technical field of typhoon prediction, and discloses a typhoon rapid enhancement prediction method based on time-space sequence and multi-modal feature fusion, and the method comprises the steps: constructing a multi-modal time-space sequence data set and an auxiliary data set based on typhoon optimal path data and multi-source satellite observation data; a unified manifold approximation and projection method is adopted to carry out dimension reduction preprocessing on the high-dimensional multi-modal space-time sequence data, and one-dimensional time sequence embedding representation of the typhoon observation sequence is generated; taking the one-dimensional time sequence embedded representation and the auxiliary data as independent input channels, and inputting a trained typhoon observation network model to predict a typhoon rapid enhancement probability; wherein the typhoon observation network model is a multi-mode time-space fusion deep learning architecture, the core of the typhoon observation network model is composed of a variational attention recurrent neural network, and hyper-parameter optimization is carried out through an improved Harris eagle optimization algorithm. According to the invention, accurate and robust identification of the typhoon rapid enhancement process is realized.

The invention discloses a space-time decoupling human behavior recognition method, device and equipment based on a dynamic semantic guide mask. The method comprises the following steps: extracting human skeleton sequence data from an original video and performing data enhancement; based on a dynamic semantic guide mask mechanism, performing mask operation on the skeleton sequence after data enhancement in two dimensions of space and time; respectively sending the skeleton sequence data after mask operation into a query encoder and a momentum encoder, respectively obtaining spatial representation and time representation, constructing cross-domain contrast loss, and carrying out contrast learning training to obtain a human behavior recognition model; and inputting the to-be-recognized video into the human body behavior recognition model to obtain a prediction result of the human body behavior in the to-be-recognized video. According to the space-time decoupling human body behavior recognition method, device and equipment based on the dynamic semantic guide mask, the human body behavior recognition precision and the environmental adaptability are remarkably improved under the condition that manual labeling is not needed.

The embodiment of the invention provides a radar echo extrapolation prediction method and system based on a test time training strategy. According to the embodiment of the invention, the method comprises the steps: carrying out the time axis calibration and space grid alignment processing of a radar echo historical collection sequence collected by an atmosphere detection system, and generating radar echo sequence data with time-space consistency; constructing an echo evolution relevance representation model based on the data, and generating an echo evolution dependency graph and a multi-step extrapolation initial condition set; then introducing a test time training strategy to carry out adaptive prediction processing, and adjusting edge weight parameters in real time to obtain a radar echo multi-step extrapolation prediction sequence with time dynamic characteristics; and finally, according to the sequence, extracting an echo intensity change characteristic and a space movement track rule, and combining historical climate cycle evolution data to generate a climate evolution trend description set containing an echo intensity trend curve and a path offset vector so as to realize more accurate climate evolution prediction and weather forecast.

The invention belongs to the technical field of wind power equipment fault diagnosis, and relates to a wind turbine generator maintenance method based on multi-modal data fusion and a knowledge graph. Comprising the steps of obtaining text description data, component image data and equipment operation time sequence data of a wind turbine generator; predicting the residual life of the component based on the equipment operation time sequence data, and generating a residual life prediction value; performing feature extraction on the data to obtain corresponding features; performing fusion processing on the features to obtain joint feature representation; analyzing a fault evolution time sequence mode of the wind turbine generator based on the joint feature representation; performing matching retrieval in a historical case library according to a fault evolution time sequence mode; and inputting a matching retrieval result into the dynamic knowledge graph for reasoning, generating a fault traceability result, and generating a maintenance decision scheme including a maintenance priority list in combination with the residual life prediction value. According to the method, accurate fault diagnosis, automatic source tracing of root causes and dynamic optimization of maintenance strategies are realized, the operation and maintenance efficiency is remarkably improved, and the cost is reduced.

The invention provides a genetic disease gene detection data analysis method and system based on big data, and relates to the technical field of gene detection data analys.The genetic disease gene detection data analysis method comprises the steps that quality control and duplicate removal are conducted on gene sequencing original data, sequence comparison and variation detection are conducted, and a standard variation detection report is generated; performing multi-scale feature sampling and optimization by using an improved random field diffusion model, inputting high-dimensional feature distribution into a dual self-activation iterative network for feature extraction and integration to obtain a feature mapping matrix and a feature evolution trajectory, and inputting the feature mapping matrix and the feature evolution trajectory into an integrated predictor to obtain an integrated predictor; and in combination with Gaussian mixture process regression analysis and an improved Bayesian reasoning network, establishing a risk association network and training a deep hierarchical decision tree, and outputting a multi-dimensional risk assessment report.

The invention discloses an intestinal cancer metastasis prediction method and device based on spatial omics, a medium and equipment, and the method comprises the steps: collecting original multi-omics data, and carrying out modal alignment and quality control processing to obtain pre-processed multi-omics data comprising second spatial transcriptome data, second single-cell RNA sequencing data and second pathological image data; performing cross-modal semantic embedding on the second spatial transcriptome data based on the second single-cell RNA sequencing data to generate a spatial enhanced expression profile; performing multi-scale graph construction on the second spatial transcriptome data and the second pathological image data, and extracting spatial heterogeneity features; inputting the spatial enhancement expression spectrum and the spatial heterogeneity features into a pre-trained metastasis risk prediction model, and outputting a liver metastasis probability spatial heat map and a key driving feature list; and finally generating a clinical prediction report containing high-risk area positioning. According to the method, through dynamic optimization of spatial resolution and multi-scale feature collaborative modeling, the sensitivity of early transfer detection is remarkably improved.

The invention relates to a building structure health state assessment method and device, and belongs to the technical field of building renovation, and the method comprises the steps: obtaining original stress time sequence data generated by dynamic strain response of a stress part of a target building under the action of a natural load or artificial excitation, building health state category labeling is carried out on the original stress time sequence data, and a multi-dimensional labeling vector with a timestamp is obtained; obtaining a health state evaluation model based on a hierarchical coding architecture of spatial-temporal feature decoupling, and training the health state evaluation model based on the clean stress time sequence data as labeled sample data in combination with unlabeled sample data to obtain a target health state evaluation model; and based on the target health state assessment model, data analysis is performed on the collected target building stress data, and the building structure health state of the target building is determined, so that the accuracy, continuity and early warning performance of building structure health state assessment can be improved.

The invention discloses an aero-engine missing value filling method based on multi-scale time sequence and attribute feature extraction. Firstly, for data collected by each sensor of an aero-engine, a temporary replacement value of a missing value is generated by adopting linear interpolation; extracting a plurality of significant cycles of the aero-engine sequence data as a time scale, decomposing the sequence into trend terms and season terms according to the significant scale, and splicing all the trend terms and season terms in the time dimension; then, in-scale and inter-scale time sequence dependence is captured in a time dimension by using cross-scale graph convolution so as to carry out missing value filling in time sequence. In the attribute dimension, local neighborhood information is accumulated by adopting a cross-attribute graph volume, global attribute features are calculated by utilizing a relational network, node features are updated, and attribute association is supplemented on the basis of a time sequence filling result. And finally, aggregating the node features of a specific scale through a maximum pooling layer to represent the features, and calculating by adopting two linear layers and an activation function to obtain a final filling result.

The invention provides a non-intrusive load identification method and system based on multi-modal depth feature fusion, and the method comprises the steps: extracting the dynamic features of voltage and current time sequence data through an LSTM network, and capturing the spatial features of an equipment operation image through a pre-training ResNet-18 model; designing a cross-modal multi-head attention mechanism, dynamically aligning semantic information of voltage and current features by taking image features as a query reference, and generating enhanced characterization; a wavelet transform fusion layer is constructed, deep fusion of time-frequency domain features is realized through a multi-stage decomposition-reconstruction mechanism, and the anti-noise capability is improved in combination with a learnable convolution enhancement module; and a cross entropy and contrast learning double-loss joint optimization strategy is adopted, multi-modal feature hidden space distribution is constrained, and model generalization is enhanced. According to the method, 97.1% of average classification accuracy is realized on a plurality of types of household appliance data sets, the accuracy requirement of load identification is met, and an efficient solution is provided for load identification.

The invention belongs to the technical field of gene sequencing, and particularly relates to a method for efficiently identifying an exogenous DNA insertion site in a genome by using a high-throughput sequencing technology. The method comprises the following steps: (a) extracting a sample, building a database and sequencing; (b) data quality control; (c) comparing data; (d) filtering comparison results; (e) preliminary screening; and (f) screening and identifying the positions of the exogenous insertion elements. According to the method, the high-throughput sequencing data and the genome and exogenous fragment sequences are compared and analyzed to obtain the candidate positions of the insertion sites, so that the test efficiency can be remarkably improved, manual judgment and screening work can be effectively replaced, and the result accuracy can be improved.

The invention relates to the technical field of shale gas storage development and yield increase, in particular to a shale gas refracturing potential prediction method and system based on multi-modal data fusion. A data set is established according to the obtained shale gas well data, the data set comprises a multi-dimensional sequence data set and a single-valued data set, the multi-dimensional sequence data set comprises logging curve data and fracturing construction curve data, and the single-valued data set comprises geological data, reservoir physical property data, initial fracturing parameter data, production dynamic data and rock mechanics data; marking the refracturing potential of the well section through the production dynamic data; performing data cleaning and standardization processing, and dividing a data set into a training set, a test set and a verification set; and constructing a shale gas refracturing potential prediction model which meets a preset requirement and takes the multi-dimensional sequence data set and the single-valued data set as input and the refracturing effect type as output. According to the method, efficient prediction of the overall refracturing potential of the shale gas well can be realized.

The invention relates to the technical field of bioinformatics, and provides a visual analysis method and system for a rice multi-tissue single cell expression profile. The method comprises the following steps: comparing sequencing data of an original single cell transcriptome of a rice tissue to obtain a standardized transcriptome data set; performing batch effect correction and integration on the standardized transcriptome data set to obtain a whole plant expression matrix; performing cell type annotation on the whole plant expression matrix to obtain a cell type annotation system; carrying out visual dimension reduction processing on the whole plant expression matrix fused with the cell type annotation system, and carrying out co-expression network construction to obtain a modular tissue correlation analysis model; and establishing an interaction end based on the module organization correlation analysis model, and realizing data visualization analysis through the interaction end. The invention provides a one-stop analysis platform for rice cell heterogeneity research, functional gene mining and molecular breeding.

The invention provides an optimization method and device for a blade casting process, equipment and a storage medium, and the method comprises the steps that process parameters of at least one process of blade casting are obtained; generating input sequence data based on the process parameters; inputting the input sequence data into a pre-trained process quality prediction model to obtain a predicted casting qualification rate and a forging defect occurrence probability output by the process quality prediction model; wherein the process quality prediction model is a transformation neural network model, and the process quality prediction model has learned in advance to obtain process parameters based on a blade casting process, and has the capability of predicting the qualification rate of castings and the occurrence probability of forging defects; and optimizing the process parameters based on the predicted qualified rate of the casting and the occurrence probability of the forging defects. According to the technical scheme, the forging qualification rate of the blade can be increased, and the development iteration frequency and period of the turbine blade casting process are reduced.

The invention discloses a water heavy metal concentration simulation and prediction method, which comprises the following steps: integrating original monitoring data, hydrodynamic data, total suspended solids, image remote sensing data and human activity data, and generating a multi-source cleaning sequence data packet; executing cross-modal adsorption capacity estimation by using image remote sensing data in the data packet, inferring particle chemical composition and adsorption isotherm parameters from image textures, and generating a capacity feature packet containing an adsorption capacity upper bound; time-varying travel time is calculated based on the hydrodynamic data and the human activity data, causal alignment is performed on the capacity feature packet and the upstream signal, and a travel time alignment feature packet is generated; and in combination with metal fingerprint parameters, applying an adsorption capacity upper bound as a physical constraint on a form distribution constraint head, explicitly decoupling and predicting the form, and generating a prediction result packet. According to the method, the hydrodynamic physical mechanism and the particle adsorption chemical mechanism are deeply coupled, and the prediction precision and the physical consistency of the model under the unsteady state condition are improved.

The invention relates to the technical field of biological information, in particular to an immunoreaction evaluation method based on tumor neoantigen activity sorting. The method comprises the following steps: obtaining genome data of tumor and normal tissues through whole exon sequencing, extracting multi-dimensional features of candidate somatic mutation, and screening by combining a Gaussian mixture model and a Transform model to obtain a high-confidence mutation genome set; predicting the HLA genotype of a patient based on sequencing data, translating and mutating into a peptide fragment, predicting the binding affinity of the peptide fragment and an MHC molecule by using an XGBoost model, and calculating a new antigen activity score sequence in combination with various parameters; and finally, synthesizing a new antigen peptide fragment according to a sorting result, carrying out in-vitro co-culture to detect an IFN-gamma secretion result, and dynamically optimizing a characteristic combination coefficient through a PPO algorithm to realize intelligent iterative updating, so that the accuracy of new antigen screening and the immunoreaction prediction capability are remarkably improved.

The invention discloses a distribution transformer state evaluation method, system and device and a storage medium, and the method comprises the steps: obtaining current and voltage long sequence data through high-frequency sampling, and forming a high-quality data set through expert labeling; signal preprocessing is realized by adopting dual-channel adaptive wavelet packet denoising and cross-correlation sinchinger interpolation phase correction; constructing an asymmetric convolution pyramid to extract current high-frequency and voltage low-frequency multi-scale features, and performing cross-modal fusion by using compressed multi-head attention and a channel-space double-gating mechanism; dynamic random depth regularization is introduced, and a time domain loss function in a fault sensitive period is focused, so that high-accuracy and low-delay real-time state evaluation is realized; the capacity of capturing and recognizing early weak fault features of the distribution transformer in a complex operation environment is remarkably enhanced, and therefore more sensitive and more accurate state early warning is achieved.

The invention relates to the technical field of deformation prediction, in particular to a lateral deformation prediction system and method based on a deep foundation pit wall body, and the system comprises a deformation data collection module which collects a ground surface settlement monitoring value and a lateral displacement monitoring value of a corresponding measuring point of the deep foundation pit wall body, carries out the time sequence alignment processing of data points, removes an abnormal monitoring value, and obtains a deformation data collection module; and establishing calibration deformation sequence data. According to the invention, through real-time acquisition and strict execution of data time sequence alignment processing, the consistency of ground surface settlement data and wall lateral displacement data is ensured, abnormal monitoring data is effectively eliminated, and the data accuracy and consistency are improved; in the spatial interpolation process, width measurement of a settlement area is introduced, the width characteristic of a settlement groove is determined, and the earth surface deformation trend is captured according to a deformation index; and extracting the maximum lateral displacement of each time section of the monitoring section, establishing a dynamic corresponding relation between the maximum displacement and the width of the settling tank, and identifying the regular influence of the change of the construction process on the lateral deformation.