1123 results about "Model extraction" patented technology

The invention provides an intelligent equipment fault diagnosis and reasoning method and system based on unsupervised learning. Belongs to the technical field of industrial equipment fault diagnosis and prediction. 2) unsupervised abnormal feature extraction and detection; (3) dynamic causal reasoning based on unsupervised learning; (4) a dynamic self-optimization inference engine; (5) intelligent fault reasoning and exploration; (6) enhancing the dynamic knowledge graph; and (7) fault prediction and decision support. Aiming at the problems that a large amount of annotated data is needed, causal analysis is lacked, and multi-source data fusion is difficult, non-annotated efficient detection and classification, fault mode diagnosis, prediction and decision support of equipment faults are realized through feature extraction of an unsupervised depth model, construction of a dynamic causal relationship, and fusion of a real-time self-optimization inference engine and a dynamic knowledge graph; therefore, the downtime and the maintenance cost are greatly reduced.

The invention discloses a multi-modal semantic and physical law driven remote sensing image generation method, belongs to the technical field of computer vision and remote sensing image generation, and aims to solve the problems of insufficient cross-modal semantic alignment, low reliability of a generation result and insufficient physical mechanism fusion. The four-stage method comprises the following steps: firstly, rejecting low-quality samples from original data and unifying a spatial scale; then, extracting a multi-modal semantic vector by adopting a BLIP model and a CLIP model, and introducing a remote sensing physical rule to carry out vector optimization; then position coding and physical constraint conditions are embedded in the submerged space, and multi-source information joint modeling is achieved through a cross-modal encoder; and finally, by taking text description, physical priori knowledge and diffusion time steps as joint conditions, performing de-noising reasoning based on a Transform architecture, and completing back diffusion reconstruction by means of a trans-attention mechanism. According to the method, physical rationality and semantic consistency are improved, and a more reliable technical normal form is provided for remote sensing image generation in the fields of disaster monitoring, military simulation and the like.

The invention discloses a data anomaly detection method based on multi-scale feature extraction, relates to the technical field of data anomaly detection, and sequentially performs multi-source data acquisition and environment calibration, noise robustness multi-scale feature extraction, context awareness anomaly detection and sensor redundancy check, and adaptive feedback and collaborative update decision. Intelligent threshold learning and scene self-adaption are realized; the method comprises the following steps: firstly, acquiring and marking acceleration, current, electromagnetic and other multi-path signals, and constructing an environment label; then, utilizing a TCN and LSTM fusion model to extract short-term impact and long-term trend; according to the elevator working condition and the noise index, the threshold value is dynamically adjusted, and the fault is subjected to redundancy check; the model, the sensor weight and the threshold value are corrected through false alarm and missing alarm backflow; and finally, a monitoring-detection-feedback-updating-self-adaptive closed loop is constructed by means of reinforcement learning or meta learning cross-seasonal adaptation, high precision, low false alarm and high robustness are achieved, and elevator operation safety and maintenance efficiency are remarkably improved.

The invention discloses a semiconductor packaging device electromagnetic compatibility comprehensive test method and system, and belongs to the technical field of electromagnetic compatibility testing. The method comprises the following steps: collecting structure parameters, packaging topology and predefined function states of a to-be-tested packaging device, and constructing a polymorphic working model; establishing a disturbance injection control model according to each state and configuring disturbance source parameters; implementing dynamic disturbance injection and acquiring response data in a real working state of the device; performing time domain and frequency domain conjoint analysis on the response data, constructing an electromagnetic response dynamic feature sequence, inputting the electromagnetic response dynamic feature sequence into a machine learning model, extracting multi-dimensional coupling features and predicting tolerance; calculating performance indexes such as an interference tolerance score and a coupling strength index based on model output indexes, comparing the performance indexes with a standard, and evaluating a compatible risk level in a full state; the method realizes quantitative evaluation of the EMC performance of the packaging device with high reduction degree and multi-state coverage, and has the advantages of comprehensive test, accurate prediction, explainable attribution and the like.

The invention discloses a building elevator detection, diagnosis and decision-making method based on a graph retrieval enhanced agent. The method comprises the steps that 1, elevator detection data are prepared and processed; step 2), knowledge extraction; step 3), knowledge fusion; step 4), visualization and optimization of the knowledge graph; 5) performing graph retrieval enhancement generation; step 6), diagnosing a decision-making agent; according to the method, triple information can be extracted from structural data, text data, visual data and other multi-modal data in the elevator detection field by guiding a multi-modal large model through an elevator detection technical specification, and an elevator detection visual target entity and a text named entity are automatically aligned based on a pre-trained vision-language model; the multi-modal knowledge graph in the field of elevator detection is accurately and efficiently generated, and building elevator detection intelligent diagnosis is carried out on the basis of the multi-modal knowledge graph and the fusion graph retrieval enhancement technology.

The invention discloses a charging robot automobile charging port pose vision measurement method comprising the following steps: 1, controlling a mechanical arm to drive a monocular camera to collect a charging port image, and constructing a charging port image data set; 2, extracting a charging hole contour in each image by adopting an improved Mask R-CNN instance segmentation model; step 3, performing robust ellipse fitting based on each charging hole contour to obtain center coordinates of each charging hole in the two-dimensional image; step 4, establishing a world coordinate system based on charging hole space distribution defined by the automobile charging port standard model, and determining three-dimensional coordinates of each charging hole; and 5, the multi-view two-dimensional center coordinates and the corresponding three-dimensional coordinates are input into the PnP graph optimization model, and the pose of the charging port in the mechanical arm base coordinate system is solved by fusing the re-projection error constraint, the structure prior constraint and the kinematics chain constraint. The invention further discloses a charging robot automobile charging port pose vision measurement charging method and a charging system.

The invention relates to the technical field of geological survey and measurement, in particular to a rock mass anomaly detection method based on vibration and visual data multi-modal fusion, which comprises the following steps: firstly, respectively acquiring vibration data and image data, and then performing data feature extraction and fusion through a deep learning model; specifically, a combined model of a long short-term memory network LSTM and a convolutional neural network CNN is used to extract time sequence features and frequency information of vibration data, a Swinin-T model is used to extract spatial features of image data, and then a shared Transform encoder and a cross attention mechanism are used to realize multi-modal data alignment and fusion. And inputting the fused features into an anomaly detection module for training and tuning, and finally deploying the trained model to edge equipment for anomaly detection of the rock mass. According to the invention, through multi-modal data fusion, abnormity can be accurately identified in various complex environments, so that the probability of false alarm and missing alarm is reduced, and meanwhile, by combining with an edge computing architecture, field deployment and real-time monitoring are facilitated, and the method is suitable for various application scenes.

The invention provides a defect identification method and device for substation equipment and electronic equipment, and relates to the field of image identification. According to the method, an infrared image, an electric field leakage map and a visible light image are obtained through a multi-channel imaging system deployed in a substation site, and a multi-channel image tensor is generated and input into a multi-channel recognition model to extract fusion features. And fusing the features, inputting the fused features into a YOLOv8 backbone network, constructing a joint attention domain in combination with an equipment prior structure, generating a high-confidence candidate box, and performing non-maximum suppression to obtain a detection result. And constructing an inter-frame residual tensor for a detection result to perform time sequence modeling, thereby improving the detection effect. And for equipment with complex shielding, complementing a structure contour through an edge prediction path, and finally outputting target boundary and defect positioning information. By implementing the technical scheme provided by the invention, defect identification of the substation equipment is facilitated.

The invention discloses an injection molding process fault diagnosis model training method and system based on a large language model and a fault diagnosis method. The model training method comprises the following steps: collecting and cleaning process parameters under the fault working condition of the injection molding machine, converting the process parameters into a natural language text, combining the natural language text with a fault label to construct a textualized data set, and dividing the textualized data set into a training set and a verification set according to a proportion; and in combination with the text data dimension and the fault category number, loading the pre-trained large language model and configuring a diagnosis model structure in a quantitative mode. And inputting the training set into a model to extract semantic features, processing the semantic features by a feature conversion module to generate a high-order feature vector, and inputting a classification head to output a fault category probability. And back propagation is carried out by using a loss function, and model parameters are efficiently and finely tuned in combination with low-rank adaptation and a layered freezing strategy. And repeating training until the performance reaches the standard, and outputting a final diagnosis model. The method is efficient in training, and can effectively reduce the maintenance and use cost of the model.

The invention discloses an industrial defect detection method based on self-supervised fine tuning, and solves the problems of scarcity of industrial scene defect samples and weak model generalization ability. The method comprises the steps that a data set is divided and preprocessed, and the data robustness is improved through size scaling, random luminosity transformation, geometric enhancement and the like; extracting a foreground mask by using a saliency model, synthesizing a Perlin Noise and DTD texture fused pseudo-abnormal image, carrying out self-supervised fine tuning on the ImageNet pre-trained WideResNet-50, and enhancing the industrial data feature extraction capability; a model containing a visual trunk, feature aggregation mapping, noise feature adaptation and a discriminator is established, local neighborhood features are fused through Unfold operation, Gaussian noise is superposed to generate pseudo-abnormal features, and an abnormal score is output by the discriminator after multi-scale fusion. And the training adopts binary cross entropy and focus loss optimization parameters. The innovation points of the method are that self-supervised fine tuning adapts to industrial data distribution, feature aggregation improves fine-grained detection, and multi-scale fusion considers different defects.

The invention discloses a time sequence modeling method for lithium ion battery performance evaluation, and aims to solve the technical challenges of a traditional method in the aspects of multi-scale feature extraction and fusion. The method specifically comprises the steps of obtaining battery capacity data and performing preprocessing; constructing an MTH3 model by combining a time convolutional network and a multi-head attention mechanism on the basis of a Mama model, and performing multi-time scale feature extraction by taking the preprocessed data as input; constructing a NetTDF model based on a U-shaped network, fusing the features extracted by the MTH3 model, and modeling a global relationship; and dividing a training set and a test set, and inputting the training set and the test set into the overall model to obtain a prediction result of the lithium ion battery health state. According to the method provided by the invention, the advantages of each model are fully exerted, an efficient prediction model for the capacity of the lithium ion battery, which can accurately capture local features and effectively model a global relationship, is provided, and reliable support is provided for performance evaluation of the lithium ion battery.

The invention provides a legacy system-oriented large-model-driven API document automatic generation system, belongs to the crossing field of artificial intelligence and software development, and provides a multi-modal data fusion and closed-loop verification mechanism aiming at the defects of a traditional API document generation method in the aspects of semantic comprehension, dynamic context capture and multi-technology stack adaptation. A code static feature and a dynamic track during operation are analyzed through a multi-source data acquisition module, and an interface semantic feature is extracted in combination with a field self-adaptive large model of a semantic enhancement analysis module; deducing an implicit service rule by fusing static / dynamic characteristics through a graph neural network, and generating a standardized document conforming to an OpenAPI specification through a parameterized template generative adversarial network (PT-GAN); and finally, performing three-level verification and closed-loop optimization through a sandbox environment. The method supports a heterogeneous system of 16 programming languages such as Java / C + + / Python, interface version changes can be automatically recognized, document patches are generated, the problems of missing and outdated system documents and low maintenance efficiency are solved, and maintainability and integration efficiency of enterprise-level systems are remarkably improved.

The invention provides a multi-modal knowledge graph completion model training method, a completion method and equipment, and relates to the field of computer systems based on specific calculation models. The method comprises the following steps of: extracting an image block feature vector, a word feature vector, a structure feature vector, a corresponding visual mark, a text mark and a structure mark by adopting a multi-modal knowledge graph completion model; based on each visual mark, the text mark and the structure mark, acquiring multi-modal fusion feature data of each entity by a graph attention mechanism; and complementing the original multi-modal knowledge graph according to each piece of multi-modal fusion feature data, and calculating the target loss of the complemented multi-modal knowledge graph to optimize the multi-modal knowledge graph complementing model. According to the method, fine-grained multi-modal feature extraction and application can be realized in the completion model training process, the reliability and effectiveness of the completion model training process can be effectively improved, and then the accuracy and reliability of multi-modal knowledge graph completion can be improved.

The invention provides a digital modeling method for enhancing connectivity of a low-porosity rock core, a medium and equipment, and relates to the field of digital modeling of rock cores, and the method comprises the following steps: reconstructing a Gaussian random field based on SAXS data to obtain an initial model of a three-dimensional digital rock core pore structure; extracting pore center points of the initial model, and constructing an initial network connecting all the pore center points; based on a minimum spanning tree algorithm, identifying mutually isolated pore clusters in the initial network, and selecting a most efficient seepage path skeleton connected with the isolated clusters; based on the seepage path skeleton, constructing a throat with fractal characteristics; and according to the total volume of the throat, performing morphological corrosion operation on an original pore area in the initial model, embedding the constructed throat into the corroded model, and then performing controllable morphological expansion operation until the volume variation of the final model is smaller than a preset error threshold. According to the method, the reconstruction precision of the digital rock core in microstructure and macroscopic connectivity is effectively improved.

The invention discloses a large-model-based knowledge graph construction method for the field of medical creativity, and the method comprises the steps: S1, carrying out the standardization of obtained multi-modal original medical creativity data, and obtaining a standardized multi-modal data set; s2, performing feature extraction and cross-modal dynamic feature fusion on the standardized multi-modal data set to obtain multi-modal feature representation; s3, inputting the multi-modal feature representation into a fine-tuned large model, and extracting based on the deep thinking ability of the large model to obtain an entity-relation pair; and S4, constructing or dynamically updating a corresponding knowledge graph in the field of department and wound based on the entity-relationship pair extracted by the large model. According to the method, the deep thinking ability of the large model is utilized to extract the entity-relation pair triad from the multi-mode medical and invasive data to construct the medical and invasive field knowledge graph, so that the accuracy, efficiency and quality of construction of the medical and invasive field knowledge graph are improved.

The invention provides a digital twinning application-oriented electrical equipment electromagnetic heat flow coupling rapid calculation method, and belongs to the technical field of electrical digital data processing.The method comprises the steps that firstly, a three-dimensional model of electrical equipment is acquired and preprocessed, and a full-order electromagnetic heat flow coupling calculation model is established and verified through a temperature rise test; generating an experimental point matrix by using a Latin hypercube sampling method, and constructing a current temperature power density relational data set; performing regional division on the power density field by applying a K-means clustering algorithm, and constructing an electromagnetic response surface model through a radial basis function; establishing a heat flow field order reduction model based on an intrinsic orthogonal decomposition technology, and extracting a dominant mode primary function; bidirectional coupling of an electromagnetic field and a heat flow field reduced-order model is achieved, an improved Lagrange multiplier method and a fixed point iteration method are adopted for processing the nonlinear coupling problem, finally, a software development kit supporting an open platform communication unified architecture protocol is packaged, and the electromagnetic heat flow coupling rapid calculation capacity needed by digital twinning application is achieved.

The invention relates to the technical field of industrial equipment predictive maintenance and asset management, in particular to a machine vision equipment operation and maintenance cost analysis intelligent management method, which comprises the following steps of: acquiring equipment operation state, external environment and historical operation and maintenance work order data through a sensor group and an equipment log interface, and fusing and removing redundancy to form a multi-source data stream; static and dynamic features are extracted by using a pre-trained health state evaluation model and fused by means of an attention mechanism, and a real-time health state index in a 0-1 interval is output; constructing a dynamic cost prediction model, taking health related parameters, spare parts, manpower and depreciation cost as input, and predicting expected operation and maintenance cost of a specific time window in the future; establishing an optimization decision model by taking minimization of the total operation and maintenance cost and maximization of the equipment availability rate as double targets, and generating an optimal maintenance, spare part purchasing and scheduling scheme; and executing the scheme and acquiring actual data, comparing the actual data with a predicted value for feedback, and iteratively optimizing the core model. The operation and maintenance management accuracy and economy are improved, and the method is suitable for intelligent operation and maintenance of the machine vision equipment.

The invention relates to a lithium ion battery life prediction method, and belongs to the field of battery life prediction and intelligent maintenance. The method comprises the steps that S1, a battery capacity degradation sequence is collected, and integrity is checked and normalized; s2, decomposing the sequence by using an improved complete set empirical mode decomposition algorithm, and dividing the sequence into a high-frequency component and a low-frequency component according to a zero-crossing rate; s3, modeling the high-frequency component: fusing multi-scale channel interactive attention, a time sequence convolutional network and a hybrid expert model, and extracting short-term fluctuation and capacity recovery features; s4, modeling a low-frequency component: introducing a two-way gating circulation unit network constrained by a double-index degradation model, and simulating a long-term trend; and S5, constructing a high-frequency migration module through tensor decomposition, improving cross-battery generalization, and fusing high and low frequency results to output a residual life prediction value. According to the method, a dual-channel framework combining signal decomposition, deep learning and physical modeling is combined, the prediction precision and adaptability under complex degradation are improved, and the method is suitable for various battery systems.

The invention discloses a grid-connected photovoltaic power station reverse power protection monitoring method and system, and the method comprises the steps: collecting the time sequence data of each grid-connected node, and carrying out the normalization and time sequence alignment processing; inputting the standardized data into a TimesNet model, extracting time sequence features and predicting a reverse power risk; generating a mathematical expression through symbol regression in combination with historical abnormal data and model output; a protection strategy is generated according to the prediction result and the expression, and the linkage device executes and collects feedback; and the feedback data is used for updating the model and the expression, and a self-adaptive closed-loop control mechanism is constructed. According to the invention, by introducing the time sequence depth model and the symbol regression fusion method, accurate prediction and adaptive protection control of a reverse power event are realized.

The invention relates to a knowledge base construction method, system and equipment based on a large model, and relates to the technical field of artificial intelligence, by adopting the method, system and equipment, a main body and a preset association attribute in original corpus data are extracted through the large model, so that candidate knowledge units associated with the main body and the preset association attribute are obtained through the large model, namely, the candidate knowledge units of the main body and the preset association attribute are obtained. Through flattening redundant storage and knowledge embedding driven by a large model, one-key knowledge unit construction is realized, and dependence of a traditional scheme on a complex association structure is eliminated.

The invention discloses a long-term traffic flow prediction method and system based on a multi-scale spatial-temporal model, and the method comprises the steps: obtaining and preprocessing the traffic flow real-time data of multiple road sections, and obtaining the preprocessing data; constructing a model containing multi-scale convolutional network feature extraction and space-time modeling capability; extracting local features through the model; performing node importance coding and time sequence coding on the local features, and optimizing an attention mechanism based on a node space relationship; linear projection is carried out on the coded local features, high-order features are extracted, and model parameters are optimized based on the high-order features; and realizing feature information interaction through an attention mechanism of the optimized model, so as to obtain a future long-term traffic flow prediction result based on high-order feature mapping. The model is combined with structure coding and a dynamic attention mechanism, so that the traffic flow prediction precision, the training efficiency and the spatial topology perception capability are remarkably improved, and more accurate traffic data prediction support is realized.

The invention provides a terminal operation and maintenance management method and platform for a medical big data platform, and the method comprises the steps: firstly collecting multi-mode historical operation and maintenance data of a platform terminal in a normal working state, and constructing a time sequence feature vector sequence after preprocessing and fusion; a deep sequence learning model training process is then utilized to learn the potential representation to capture the context state of the normal workflow mode and establish a reconstructed baseline model. Acquiring real-time operation and maintenance data of the terminal, constructing a feature vector, extracting potential context representation through the trained model, and calculating a reconstruction error; a current workflow state is identified based on the potential representation, and a context-aware anomaly metric value is calculated in conjunction with state information and reconstruction errors. And analyzing the time evolution characteristic of the abnormal metric value and comparing the time evolution characteristic with a preset abnormal mode criterion to judge whether the terminal has workflow abnormality, and if so, generating an early warning signal containing abnormal evolution characteristic description. The method has the effect of improving the operation and maintenance detection accuracy of the terminal.

The invention relates to the technical field of data processing, and discloses an in-vehicle testing method and system based on a small model. The method comprises the following steps: acquiring an in-vehicle machine screen image, and extracting interface elements through a lightweight recognition model to generate a recognition result; analyzing element function association to determine operation types to form a test operation sequence; an ADB instruction and a mechanical arm action are generated according to the operation sequence to construct a test script; executing the script to drive the vehicle machine to test and collecting multi-dimensional response data; and analyzing test data, calculating a passing rate and response time, and generating a verification report. According to the method, the technical problems of low interface element identification accuracy, poor test script adaptability and insufficient multi-modal data analysis capability in the traditional vehicle-mounted terminal test are solved, and the automation degree of the vehicle-mounted terminal test and the reliability of the test result are remarkably improved.

The invention relates to the technical field of natural language processing, in particular to an archive structured information extraction method and system based on a multi-modal large model and a medium, and the method comprises the following steps: S1, establishing a mapping table of fields to be extracted; s2, data annotation; s3, constructing a layout analysis model, an archive structured extraction model and an archive structured integration model; s4, screening key information pages based on the layout analysis model; s5, extracting single-page structured information based on an archive structured extraction model; and S6, based on the archive structured integration model, integrating single-page structured information results. Through application of the multi-modal large model, accurate layout analysis, strong structured extraction capability and efficient information integration are realized, automatic extraction and integration from archive image data to structured information are realized, manual intervention is reduced, and processing efficiency is improved.

The invention discloses a knowledge graph generation method based on multi-source data integration, and relates to the technical field of knowledge graph generation. The method comprises the following steps: collecting multi-format data, and performing cleaning, standardization and desensitization preprocessing to ensure that the data is integrated; using the fusion model to extract entities and relationships, and adapting to multi-source data types; similarity is calculated in combination with multi-dimensional features, and entity alignment disambiguation is achieved; carrying out weighted fusion on multi-source knowledge to construct a triple, and processing relation conflicts; evaluating the quality of the atlas through multiple indexes; triggering conditions are set, incremental updating and version management are adopted, and dynamic iteration of the atlas is guaranteed. According to the method, the multi-source data preprocessing quality is improved, the entity recognition and alignment precision is enhanced, relation conflicts are solved, and a high-quality time sequence knowledge graph is constructed; incremental updating is efficient and energy-saving, version management is traceable, and multi-field dynamic application requirements are met.

The invention discloses an intelligent marketing document generation device and method based on multi-source data fusion. The method comprises four processes of user instruction analysis, regular cleaning, model parameter extraction, Neo4j knowledge graph verification and structured parameter output. External data acquisition: calling a Baidu large model to acquire data according to parameters, and screening high-quality data through duplicate removal, semantic enhancement and weighting; internal cases are processed, timed slicing cases are stored in a library, filtering, propagation calculation and multi-dimensional scoring are combined, and a CoT inference chain report is generated; and performing multi-modal output, adapting formats, integrating data by means of a BART model and outputting a document with metadata. The device and the method cooperate with each other, through multi-source fusion, knowledge graph association and multi-mode conversion, high-quality marketing documents adaptive to multiple industries are efficiently generated, support is provided for decision making, and enterprises are assisted to improve market response and output efficiency.

The invention provides a power demand prediction method, which comprises the following steps of: 1, collecting a power load data set, and preprocessing the power load data set; 2, inputting a power load data set, and executing a crown porcupine optimization algorithm CPO to optimize dynamic parameters of a variational mode decomposition (VMD) algorithm; step 3, establishing a serial model xLSTM-Informer (x < LSTM >-Informer >); 4, performing data prediction by adopting an Informer model: inputting the features extracted by the extended long-short-term memory network xLSTM model into the Informer model to perform power demand prediction, and outputting a comparison result of a power demand prediction value and a true value; and 5, evaluating the power demand prediction model obtained in the step 4. According to the method, data with different characteristics can be flexibly processed, and relatively good prediction performance can be kept in different power demand scenes.

The invention discloses a semantic analysis model training method, a video data transmission method, electronic equipment and a computer readable storage medium, and belongs to the technical field of artificial intelligence and video transmission. According to the training method, a multi-task fusion network is constructed based on a MobileViT-S architecture, and target, action and scene semantic information in a video is synchronously extracted in combination with a lightweight Transform and a convolution module. Training adopts a two-stage strategy; in a pre-training stage, a general data set is used for optimizing the basic capability; in the fine tuning stage, curriculum learning and time sequence smooth loss are introduced, complex ocean scene sample weights are gradually increased, a multi-task loss function is jointly optimized, and weight balance precision and bandwidth adaptation are dynamically adjusted. Deploying the model at a ship end to extract structured semantic information; a shore base restores a video through a GAN + Diffusion model, and the time sequence consistency is improved in combination with optical flow interpolation and Kalman filtering. Therefore, the problem of video real-time transmission in a low-bandwidth scene is solved, the method can be applied to the fields of ship monitoring, remote cooperation and the like, and the transmission efficiency and reliability are remarkably improved.

The invention relates to the technical field of constructional engineering detection, and discloses a cast-in-place pile cross-hole CT detection method, system and equipment based on multi-wave integration, and the method comprises the steps: laying inspection holes, deploying a time-sharing excitation sensor, collecting multi-wave data, fusing the multi-wave data, correcting and compensating, and constructing a joint objective function to invert material parameters. A three-dimensional model is generated, defect space information is extracted, and hidden defect accurate detection is achieved; the system comprises a probe control module, a data acquisition module, a data processing module, a three-dimensional modeling module and a user interaction module. The device comprises an integrated probe device and a three-layer sensing structure from inside to outside. According to the method, anti-interference detection is achieved through multi-wave-type collaborative excitation and layered shielding probe design, and pile body defects are accurately positioned in combination with cross-wave-type data fusion processing and a self-adaptive inversion algorithm; defect space distribution is visually quantified based on dynamic threshold three-dimensional modeling, an integrated system adapts to a complex engineering environment, and the detection precision and efficiency of the cast-in-place pile are remarkably improved.

The invention provides a self-adaptive spraying mechanical arm based on multi-mode perception and a control method of the self-adaptive spraying mechanical arm. The method comprises the steps that multi-source perception data of a to-be-repaired area is obtained through a dual-mode vision system and a laser scanning device, a two-dimensional repairing area segmentation map is generated, and space registration and local geometric modeling are achieved in combination with three-dimensional point cloud; on the basis of local geometric features extracted by the model, retrieving matched process parameters from the knowledge graph, generating a digital spraying instruction set containing tracks, postures and dynamic process parameters, and performing simulation verification in a digital twin environment; the mechanical arm state and the virtual model are synchronized in real time in the execution process, the paint film thickness is dynamically monitored, and compensation adjustment is triggered; and after spraying is completed, the coating quality is detected, and a result is fed back to the knowledge graph to update the parameter mapping relation. According to the method, closed-loop control from sensing, decision making, execution, evaluation to learning is achieved, and the spraying uniformity, the self-adaptability and the intelligent level under the complex working condition are improved.