168 results about "Difference vector" patented technology

The invention relates to the field of biological modeling, and discloses a shoulder joint stress simulation method and system based on a three-dimensional finite element. The method comprises the following steps: acquiring three-dimensional geometric data of a shoulder joint through scanning equipment, and generating an initial grid model; dividing the preliminary calculation grid to determine an initial stress value, simulating stress transfer distribution, and generating a preliminary stress distribution field; calculating the stress gradient change rate of each grid unit and performing local subdivision to generate a stress gradient change rate distribution diagram; performing density subdivision on the grid units exceeding the change threshold to generate a refined grid model; the calculation grid is divided again, the stress value of each grid unit is calculated, and an updated stress field is generated; calculating a difference vector, judging whether the difference vector meets a convergence condition or not, and obtaining a convergence judgment result; and if not, iteratively subdividing the local grid to generate a final stress distribution field. Through adaptive grid subdivision and iterative convergence optimization, the problem of insufficient simulation precision is solved, and the accuracy is improved.

The invention discloses a double-seat cooperative safety control method and system for engineering machinery, relates to the technical field of engineering machinery control, and provides the following scheme that the method comprises the steps that a first operation instruction stream of a main driving seat and a second operation instruction stream of a co-driving seat are collected in real time, and a working condition parameter set of the engineering machinery is synchronously obtained; the operation instruction stream comprises all degree-of-freedom motion parameters of the engineering machinery, inputting the first operation instruction stream into a pre-constructed long-short-term memory model, outputting a prediction operation instruction stream in a future time window, and based on a difference vector of the first operation instruction stream and a second operation instruction stream on each degree-of-freedom motion parameter. According to the method, through the index curve fitting model and Simpson integral calculation, the defect that the co-pilot intervention weight is difficult to quantify is overcome, the dynamic intervention weight based on the physical equidistant interval absolute integral value is realized, and the accuracy of the control signal of the engineering machinery is improved.

The invention discloses a forge piece defect identification data processing method and system based on surface migration prediction, and relates to the technical field of defect identification, and the method comprises the steps: determining an image difference vector of a single forge piece according to image data and a pre-generated surface migration prediction model, determining a temperature vector of the single forge piece according to the temperature time sequence data; constructing an image temperature correlation vector corresponding to the single forge piece according to the image difference vector and the temperature vector, and constructing an image temperature correlation matrix corresponding to the multiple forge pieces according to the image temperature correlation matrix; decomposing the image temperature incidence matrix, and extracting a main feature space; for each forge piece, determining a corresponding identification index according to the corresponding image temperature correlation vector and the main feature space; and carrying out conjoint analysis on the identification indexes of the plurality of forgings to generate a defect identification result. The method has the effect of improving the reliability of forging defect recognition.

The invention discloses a standard courseware generation method for an artificial intelligence learning mode, and relates to the technical field of educational informationization, and the method comprises the steps: firstly collecting a course standard, a world language ready framework and translation memory, and generating a reference semantic vector set with a unique version fingerprint through vectorization; mapping the multilingual courseware draft on the multi-lingual courseware draft, outputting a weighted difference vector and recording a timestamp; the difference vector is decomposed into three factors of emotional tendency, cultural symbols and term consistency, and a deviation registration form is generated according to a dynamic threshold value; generating type rewriting, template replacement or manual prompt is selected according to a rewriting energy function, the high-risk chunks are locally revised, and consistency marks containing version fingerprints are written in; and finally, carrying out global residual error recheck, generating a consistency report after confirming the quality through a global consistency index, packaging the consistency report and the revised courseware into a release package for multi-end synchronization, writing fingerprints on a chain, and constructing a whole-course reliable new cross-language courseware quality closed loop.

The invention relates to the technical field of modular control systems, in particular to a transformation and cooperative operation method and system for a modular control system of a wire barrow, and the method comprises the following steps: obtaining tension data, calculating a change rate, comparing a critical interval related torque, calculating a power correction value, taking a position difference vector, calculating a deviation angle, and comparing a correction steering parameter; according to the method, the tension change rate is obtained to achieve dynamic trend recognition, the torque control response precision is improved, driving and steering parameters are comprehensively judged through the speed, the path deviation and the position difference vector, the advancing stability and the path consistency are improved, and the method has the advantages of being simple in structure, convenient to operate and high in practicability. Guiding deviation and steering correction comparison is combined, guiding precision is enhanced, an adjusting instruction is generated based on the real-time distance and the advancing direction, torque proportion correction is linked, it is guaranteed that the multi-vehicle distance is stable, signal fusion and real-time feedback improve the adaptability to interference and path changes, and the flexibility and safety of collaborative operation are enhanced.

The invention relates to the technical field of intracranial pressure monitoring, in particular to a craniocerebral injury patient secondary brain injury early warning method based on multi-modal fusion, which comprises the following steps: acquiring intracranial pressure, cerebral blood flow, electroencephalogram signals and brain tissue oxygen partial pressure signals, carrying out denoising and normalization processing, constructing a multi-modal normalized time sequence data set, and carrying out multi-modal fusion on the basis of the multi-modal normalized time sequence data set; and inputting a linear discriminant analysis model to generate a time sequence difference vector, constructing a difference driving map based on a Bayesian dynamic network model, identifying abrupt change nodes, outputting a risk early warning section set, and carrying out trend stability analysis and reconstruction optimization. According to the method, the abnormal state recognition sensitivity is improved through normalized processing of multi-modal signals, enhancement of signal fusion capability, quantitative capture of direction change, reconstruction of spatial relationship between signals, modeling of dynamic transition probability, recognition of abrupt change nodes, combination of time difference vector change, evaluation of signal linkage trend, positioning of risk sections and extraction of parameter features; and the early warning and intervention precision is improved.

The invention relates to the field of social security management data processing, and discloses a social security information intelligent analysis system based on dynamic closed-loop regulation, and the system comprises an analysis prediction unit which calculates a risk score based on a feature weight; the subthreshold residual cutoff unit captures weak score data lower than a trigger threshold value; the space-time entropy aggregation unit calculates the Shannon entropy of the weak data; the agility bias unit reduces the trigger threshold according to the low-entropy aggregation state; the self-adaptive damping correction unit utilizes a predicted and feedback difference vector to reversely correct a feature weight, the environmental background pressure is sensed by calculating a space-time entropy value of discarded residual data, a trigger threshold value is dynamically biased according to the environmental background pressure, a damping mechanism correction model is introduced into a feedback closed loop, and a damping mechanism correction model is introduced into the feedback closed loop. The problem of weak risk report omission caused by a fixed threshold value is solved, and self-adaptive dynamic balance of system sensitivity and false alarm rate is realized.

The invention provides a project cost intelligent auditing method and system based on artificial intelligence, and the method comprises the steps: carrying out the structural analysis and project item extraction of an original document related to the project cost, and forming a standardized project item structural unit set; based on the project item structure unit set, constructing a track chain of each project item in three key stages, namely a budgeting stage, a contract stage and a settlement stage, recording attribute state sequences of the track chain in different stages, and calculating a difference vector to represent the variation amplitude of a key field; performing abnormal behavior identification based on the trajectory chain, identifying a potential abnormal trajectory through a multi-dimensional abnormal scoring function combining numerical value change intensity and clause unit matching missing risk, and generating a structured interpretation prompt unit; and a unified explanation prompt structure is constructed according to the structured explanation prompt unit, and the explanation prompt structure summarizes the serial numbers of the abnormal items, the change fields and the clause missing rate to serve as a finally output structured audit result.

The invention belongs to the technical field of artificial intelligence, and discloses a large model self-iterative optimization method and system based on feature distribution difference feedback, and the method comprises the steps: collecting a human-generated text data set and a model-generated text data set, and carrying out the preprocessing of the data sets; extracting features of multiple dimensions from the data set, and calculating a difference vector; based on the difference vector, using a Softmax function to distribute an optimization weight for each feature dimension to obtain a weight vector; based on the weight vector, using a joint loss function to perform fine tuning on the large model; and performing iterative fine tuning until a preset convergence condition is met. According to the method, the naturalness of the generated text and the human similarity are remarkably improved, automatic and low-cost continuous optimization is realized, a refined and explainable optimization direction is provided, and the method has good universality and expandability. According to the method, an automatic iteration closed loop integrating analysis and optimization is constructed, the problem of a'model cavity 'is solved, and quantifiable alignment of microcosmic language features is achieved.

The invention relates to the technical field of data processing, and discloses a multi-source data difference attribution system and method based on tensor decomposition, and the method comprises the steps: obtaining an original engineering quantity list file, and then carrying out decoupling to generate a standardized list item text sequence and an initial value set, constructing unit prices to form a tetrahedral geometric model set, mapping the tetrahedral geometric model set to a holographic feature vector space, and generating a multi-dimensional list feature vector set; constructing a standard cost reference vector field, identifying a difference vector set and generating a visual cost difference topological data model; a natural language query instruction is responded, a target difference attribution tensor is locked, a logic verification chain is constructed, and an intelligent diagnosis report is output; according to the method, a computable logic distance is established in multi-source heterogeneous data by reversely reconstructing a high-dimensional price-rate feature space, so that the problem of attribution blocking caused by dimension collapse in traditional cost auditing is effectively solved, and accurate traceability and compliance automatic diagnosis of cost differences are realized.

The invention provides an electrocardiograph fault self-diagnosis method and system based on lead wire automatic identification, and the method comprises the steps: triggering a frequency sweep generator according to an equipment starting signal, carrying out the pre-conduction detection of lead wires, then applying an excitation signal in a continuous frequency range to each lead wire, recording the signal collection time length of each frequency point in real time, and carrying out the real-time detection of the signal collection time length of each frequency point; obtaining response voltage and current data to determine a real-time impedance spectrum; performing frequency point alignment processing on the real-time impedance spectrum and a corresponding standard fingerprint, selecting frequency points in a key frequency band of the electrocardiosignal, and comparing amplitudes and phase deviation values of the real-time impedance spectrum and the corresponding standard fingerprint on the key frequency points to obtain a spectrum difference vector; and if the amplitude deviation of the spectrum difference vector exceeds a preset disconnection threshold value, judging that the lead wire has a disconnection fault, performing reverse signal back test on an interface end corresponding to the faulted lead wire, and recording a judgment result in a fault log to obtain a disconnection identification mark.

The invention discloses a synthetic lethal prediction method based on non-generality forgetting and low-rank interaction multi-mode adaptive fusion, and relates to the technical field of bioinformatics, computational biology and drug target screening. The method comprises the following steps: firstly, constructing a multi-view gene embedding expression based on a gene graph structure, a hypergraph structure and a knowledge graph; then constructing a difference vector and an interaction vector for any gene pair so as to characterize the difference and potential complementary relationship between the genes; multi-source information fusion is realized by using a deep learning model containing interactive attention, a forgetting gate and a multi-modal fusion mechanism; on this basis, multi-task optimization is carried out through joint classification loss, modal interaction loss and redundancy suppression loss, and optimal discrimination is realized through a dynamic threshold search strategy; and finally, outputting a sorting result of the candidate synthetic lethal gene pairs according to the prediction score. According to the method, the accuracy and generalization ability of synthetic lethal relationship prediction can be effectively improved, and a high-reliability calculation auxiliary tool is provided for anti-cancer drug target screening and gene therapy strategy design.

The invention discloses an atmospheric ocean multi-source data high-precision assimilation method and device, and relates to the technical field of ocean multi-source data assimilation, and the assimilation method comprises the steps: collecting multi-source observation data of a typhoon region of the South China Sea; preprocessing the data; decomposing the observation field into different scales by using wavelet transform; acquiring a background field from the numerical model, interpolating the background field to a grid consistent with observation, projecting the background field to an observation space, and performing multi-scale decomposition to enable the background field to correspond to an observation scale; calculating a difference vector of observation and background fields on each scale, and dynamically calculating an adaptive weight of each point on each scale according to an observation error, a background error and a current deviation; and constructing a cost function, solving through an optimization algorithm to obtain an analysis field, and outputting a final analysis field. According to the method, a multi-scale decomposition and self-adaptive regularization cooperation mechanism is introduced, so that high-precision dynamic fusion of the multi-source observation data and the numerical model can be realized.

The invention provides an engineering robot positioning method and system fusing a visual system and a laser system, and the method comprises the following steps: collecting a preset reflection array label image through the visual system, and rapidly solving the initial six-degree-of-freedom posture of an engineering robot; based on the initial attitude, a laser system is guided to actively detect beacons on a label, attitude angle drift is accurately corrected by calculating spatial difference vectors between the beacons, and the micro-Doppler effect analysis of reflected laser is utilized to determine the micro-vibration state of the tail end of the robot in real time. And calculating respective data confidence of vision and laser according to image quality and laser signal stability, fusing the initial attitude and the drift correction through a nonlinear optimization algorithm, and outputting a final high-precision six-degree-of-freedom attitude. According to the invention, high-precision robot positioning can be realized by fusing global perception of vision and local accurate measurement of laser.

The invention discloses a robot virtual-real cooperative training decision optimization system and method based on digital twinning, and the method comprises the following steps: collecting state data and disturbance data of an entity robot in a real environment, and carrying out the preprocessing of the data to generate standardized input; joint coding and state perturbation mapping are carried out on the standardized data, and a feature vector sequence embedded in a hyperspherical manifold space is generated; inputting to a virtual twin control body based on a hypersurface neural element structure, executing disturbance direction sensitive activation, and outputting an activated state vector sequence; virtual and entity control action prediction sequences are generated respectively, an embedded space difference vector is calculated, and control body parameters are updated based on a consistency optimization criterion; after convergence, the control body executes reasoning to generate a target control action sequence, the entity robot is driven to complete action execution, and control strategy optimization is achieved. According to the method, high-precision migration and rapid convergence of a robot control strategy are realized, and the execution stability in a complex disturbance environment is improved.

The invention discloses a sight tracking method and system based on cascade deep learning and distance correction, and a storage medium. The method comprises the following steps: processing an eye image through a cascade deep learning model, and obtaining pupil center coordinates and cornea reflection light spot coordinates of two eyes; calculating a binocular pixel distance as a distance correction factor based on the pupil center coordinates, and calculating a relative position difference vector based on the pupil and light spot coordinates; establishing a three-dimensional nonlinear polynomial regression model taking the relative position difference vector and the distance correction factor as input; obtaining calibration data through a multi-distance sampling strategy and solving a model coefficient; and in the real-time tracking stage, calculating the coordinates of the fixation point of the screen by using the solved model. According to the method, the distance correction factor and the three-dimensional mapping model are introduced, so that geometric errors caused by forward and backward movement of the head of the user are effectively eliminated, meanwhile, the feature point positioning precision is ensured through cascaded deep learning detection, and the precision and robustness of a sight line tracking system are remarkably improved.

The invention discloses a method and a device for decoupling a slow-thinking area and a quick-response area in a large language model. The method comprises the following steps: constructing a slow thinking prompt and a fast response prompt, respectively inputting the slow thinking prompt and the fast response prompt into a large language model, and recording a neuron activation value of each linear layer of the large language model; calculating an activation value difference vector based on the neuron activation values corresponding to the slow thinking prompt and the fast response prompt, and obtaining a prompt difference vector based on the activation value difference vector; iteratively processing a plurality of reasoning problems, obtaining positive value position intersection of prompt difference vectors in all reasoning problems, and identifying a key neuron set related to slow thinking; and constructing a gradient mask matrix based on the position information of the key neurons in the key neuron set, and performing fine tuning on the key neurons by adopting a mask mode based on the gradient mask matrix to obtain an optimized large language model. The technical problem that an existing model is poor in reasoning ability is solved.

The invention relates to the technical field of converter transformer state monitoring, and discloses a converter transformer internal defect detection method based on multi-physics field coupling monitoring, and the method comprises the following steps: collecting and confirming electric field, magnetic field, temperature field, stress field, sound field and flow field data of a converter transformer without internal defects under a normal working condition; taking the data as multi-physics field reference data; collecting real-time multi-physical field data of the converter transformer to be detected; performing feature extraction on the reference data and the real-time data to obtain reference features and real-time features; calculating a feature difference vector between the real-time feature and the reference feature; and comparing the feature difference vector with a preset corresponding relation library of multi-physical field signal features and internal defect types, and determining the internal defect types. Through coupling analysis and difference comparison of multi-physical field data, cross validation of multi-source information is realized, and the accuracy and reliability of defect diagnosis are improved.

The invention belongs to the technical field of antenna testing, and discloses a multi-channel antenna line source automatic testing method and system, and the method comprises the steps: building an inter-channel phase closed-loop matrix through a full-channel mutual excitation sequence before the starting of a multi-channel line source test; compensating phase deviation in real time by using a real-time phase deviation drift prediction algorithm, and outputting phase-locked multichannel amplitude phase data; performing dual-time-scale decomposition on the multi-channel amplitude-phase data, and separating the multi-channel amplitude-phase data into a short-period structure difference component and a long-period environment drift component; drifting rejection is carried out by using an online environment drifting tracking factor, and only a structural difference component is reserved as a multi-dimensional difference vector; mapping the multi-dimensional difference vector into a channel-difference parameter bipartite graph, analyzing a connected domain distribution mode of abnormal parameters, automatically deducing an abnormal source position in combination with a channel physical topology mapping table, and outputting an abnormal channel set with a spatial mode mark; the credible verification of the test result is realized, and the test efficiency is improved.

The invention discloses a method for efficiently caching off-line state data of a water affair edge intelligent gateway based on multi-modal data, which relates to the technical field of water affair edge intelligent gateways, and comprises the following steps of: under the condition of identifying that a sensor is temporarily restarted, constructing a modal acquisition reference matrix by using a multi-modal data structure of a historical sampling period; a modal difference vector is generated in combination with the actual collection content of the current period and is used for determining the modal data gap state under the condition that the sensor is temporarily restarted; and based on the determined modal data gap state, carrying out matching analysis on the image data acquisition record at the current time point and the modal data gap vector at the current time point, and identifying modal inconsistency of the image and the sensor in the same time point cache. According to the invention, the problem that the modal gap of the multi-modal data cannot be identified and marked in an offline state is solved, and modal consistency identification and structural integrity guarantee are realized.

The invention relates to the technical field of internetwork settlement, in particular to an account checking method and device based on a block chain oracle machine and secure multi-party computing, computer equipment, a non-temporary computer readable storage medium and a computer program product. According to the method, a credible, automatic and transparent scene on a chain is provided by adopting a smart contract based on a block chain, the contradiction between account checking data privacy and verifiability among operators is solved through privacy perception by adopting secure multi-party calculation, and meanwhile, reverse operation is performed on an oracle machine; complex calculation in the smart contract is put into an out-of-chain oracle machine operation, and finally a difference vector set of account checking is accurately obtained, and then whether internetwork settlement continues to be carried out is judged. On the premise of guaranteeing the privacy, credibility and safety of the account checking data through cooperation of the three, the defects that the account checking efficiency is low, the account checking process is not supervised, and the account checking result is not subjected to closed-loop feedback are overcome.

The invention belongs to the technical field of large language models, and particularly discloses a large language model illusion detection method and system, a terminal and a medium. Comprising the steps of obtaining a middle hidden state of each token in a process of generating an answer by a large language model, and organizing and forming a hidden state sequence according to a generation sequence; performing time sequence preprocessing on the hidden state sequence to complete the preprocessing of the sequence; inputting the pre-processed hidden state sequence and a dynamic feature formed by a difference vector, a trend drift degree or local fluctuation intensity into a dynamic sequence modeling network, and enabling the network to extract a time dependence feature reflecting an internal state change mode based on an evolution relation of a hidden state along with a generation process; and generating an illusion risk score according to the comprehensive time sequence characteristics output by the dynamic sequence modeling network, and determining that the answer contains the illusion content when the score exceeds a preset threshold. The reliability of the output content of the large language model can be improved.

The present application relates to a digital twin-based water turbine unit operation system and method, comprising a water turbine unit top cover, a plurality of soundprint collection arrays are arranged on the water turbine unit top cover, each soundprint collection array comprises a plurality of soundprint collectors arranged in a vertical direction and having a fixed interval. The soundprint collector is used to collect original soundprint data at different positions on the water turbine unit top cover, and send the collected original soundprint data to a data center. After receiving the original soundprint data, the data center pre-processes the original soundprint data, establishes a digital twin benchmark model based on the mapping relationship between real-time working condition parameters and soundprint signal characteristics through digital twin technology, constructs an expected feature vector based on a soundprint collector position compensation vector and a mapping matrix, and adjusts the difference vector using a correction factor. The system uses multiple orientations and multiple soundprint collectors configured in each orientation, and the soundprint collection accuracy is high under extreme water flow conditions.

This invention discloses an artificial intelligence-based method for managing and controlling power grid water dispatch data, specifically relating to the field of power grid water dispatch data management technology. The method involves collecting multi-source heterogeneous water dispatch data and its metadata to construct a unified format dataset; generating a credibility score based on metadata and data features to form a credibility matrix; constructing a multi-dimensional quality assessment tensor and extracting the dominant factor subspace; inputting this tensor into a clustering perceptual neural network to identify abnormal data, outputting an abnormal dataset and abnormal labels; combining a large model enhanced by RAG retrieval with historical similar data to complete abnormal data repair; constructing a data quality improvement report using the difference vectors before and after repair and label information; and finally pushing the report to a data management platform and generating a traceable rectification work order. This method achieves automatic identification, intelligent repair, and feedback tracking of power grid water dispatch data, improving data credibility, governance efficiency, and security, and is suitable for data management needs in complex scenarios under new power systems.

This invention discloses a method, device, and medium for correcting human motion posture based on AI vision, relating to the field of AI vision technology. The method includes: acquiring multi-channel image sequences of human posture using multiple cameras; performing temporal alignment and normalization on the multi-channel image sequences; obtaining a human probability map using a pre-trained human semantic segmentation network; performing morphological cleanup to obtain a human silhouette sequence; performing back-projection according to camera intrinsic and extrinsic parameters to obtain a 3D joint sequence from a 3D AI visual volume; extracting the human motion manifold to perform contact-sensing neural motion priors and outputting prior signals; and using posture feasibility scores and joint importance weights to weightedly fuse the geometric difference vector and the center of gravity correction component to obtain a comprehensive joint correction vector. This invention generates a 3D AI visual volume through back-projection and extracts the central skeleton, using constant bone segment lengths for constraint and correction, thus enhancing the reliability of posture stability analysis and alignment with standard action templates.

There is provided a method for augmenting a visual feature, comprising: extracting a visual feature from an input image; embedding into a text space respectively, a class of the input image and an attribute class formed by reflecting attribute information onto the class; calculating a difference vector between an embedded vector of the class and an embedded vector of the attribute class; and augmenting the visual feature corresponding to the input image based on the difference vector, wherein the apparatus includes: an encoder that extracts the visual feature from the input image; and a predictor that generates predicted class of the input image based on the augmented the visual feature in order to compare whether the predicted class is matched with the class of the input image.

The invention relates to a text mining method and system for identifying a material chemical formula. The method comprises the steps of firstly obtaining a text set and preprocessing to obtain a target text set; marking the positions of the chemical elements, correcting errors to obtain a mark vector, and obtaining a difference vector through exponential offset and difference processing; and then calculating the maximum rising subsequence length of the outlier sequence, setting the cluster number, performing k-means clustering on the offset vector, and finally marking the text according to the k-means clustering result. Compared with the prior art, the method has the advantages of high text chemical formula extraction accuracy, high efficiency and the like.

The invention discloses a vector calculation-based water meter water quantity and water rate calculation method, and aims to solve the technical problems of I / O bottleneck, high calculation delay and poor expansibility in existing large-scale water meter charging. The method comprises the following steps: constructing a water meter data column vector, calculating an original difference vector based on a current-period reading vector and a previous-period reading vector, and performing Boolean mask processing to obtain a meter reading water volume vector; actual water consumption accounting, total and score table allocation and allocated water amount calculation are achieved through vector operation; a billing water volume vector is obtained in combination with a preferential strategy, and an actual water fee is finally obtained through mixed water splitting, stepped billing matrix operation and water fee preferential adjustment. According to the method, vectorization and matrix operation are adopted to replace traditional line-by-line calculation, so that the operation efficiency can be improved through parallel calculation, the vector algorithm is designed to adapt to complex water affair billing logic, the processing performance is effectively improved, efficient and large-scale billing is achieved, the intelligent water affair development requirement is met, and practicability and popularization value are achieved.

The application discloses a shield tunnel excavation disturbance data intelligent identification method based on dynamic time sequence prediction, which comprises the following steps: in the case of excavation disturbance, collecting various time sequence data of shield tunnel monitoring in real time, carrying out denoising, interpolation and normalization processing on original data, and inputting time window samples into a DFCM algorithm; calculating a membership matrix based on the distance between samples and cluster centers, and updating the membership in real time, and constructing an LSTM model of the membership vector output by the DFCM clustering algorithm; predicting the membership of the next time step, outputting a predicted difference vector, and determining a difference dynamic threshold value by using an adaptive threshold method, and when the predicted difference exceeds the threshold value, it is judged that the data is disturbed. The method can realize real-time identification and uncertainty quantification of disturbed data in a tunnel structure monitoring environment with strong interference and high noise, improve the accuracy and stability of underground structure health monitoring, and is suitable for urban shield tunnel operation monitoring and maintenance.

This invention discloses an efficient offline state data caching method for water edge smart gateways based on multimodal data, belonging to the field of water edge smart gateway technology. The method includes the following steps: When a sensor temporary restart is detected, a modal acquisition reference matrix is ​​constructed using the multimodal data structure of historical sampling periods. A modal difference vector is generated by combining this matrix with the actual acquisition content of the current period to determine the modal data gap state under the temporary sensor restart condition. Based on the determined modal data gap state, the image data acquisition record at the current time point is matched and analyzed with the modal data gap vector at the current time point to identify modal inconsistencies between the image and the sensor cached at the same time point. This invention solves the problem of the inability to identify and mark modal gaps in multimodal data under offline conditions, achieving modal consistency identification and structural integrity assurance.