9888results about How to "Improve adaptability" patented technology

The invention discloses a multi-agent cooperative cue word engineering optimization method, system and device and a medium, and relates to the technical field of cue word engineering, the method comprises the following steps: after initially receiving a user input task, starting a cue word optimization process; an initial cue word template is created through a cue word generation agent, historical optimal templates are retrieved from a knowledge base for fusion, and candidate cue word templates are generated. And the analysis agent inputs the candidate template into the second large language model to generate a candidate answer. And the evaluation agent performs multi-dimensional evaluation on the answers and gives scores and defect analysis. And according to an analysis result, updating the cue word to generate a candidate template of the intelligent agent. And arranging the intelligent agent to cyclically execute the steps until the stop condition is met, and outputting the optimal cue word template. And finally, storing the optimal template into a knowledge base and establishing a semantic index to support subsequent retrieval reuse. According to the method, manual intervention is not needed, and a complete automatic process of generating, evaluating and optimizing the cue word is realized.

The invention discloses an agent task scheduling planning method, and relates to the technical field of agent scheduling. The method comprises the steps of analyzing a task instruction to generate atomic tasks capable of being independently executed, constructing a subtask dependency graph, and defining association constraints between the tasks; determining the real-time resource occupancy state of the intelligent agent to obtain a resource state tensor, completing resource-task association anchoring and dependency priority ranking in combination with the sub-task dependency graph, and generating a task priority sequence with resource constraint; performing dynamic capability matching and predictive load balancing calculation on the sequence through a task-agent adaptation model, and determining a target execution agent of each atomic task; and based on the target execution agent and the subtask dependency graph, performing time sequence scheduling arrangement and conflict resolution, and generating a collaborative execution scheme. The method improves the reasonability and efficiency of agent task scheduling, reduces resource conflicts and execution timeout risks, and is suitable for agent cluster collaborative scheduling in a complex scene.

The invention relates to the technical field of electrical digital data processing, and discloses a lightweight-based multi-modal data enhancement method, which comprises the following steps: acquiring a multi-modal feature tensor; extracting a vector orthogonal projection scalar and determining the novelty; updating the global covariance matrix when the novelty is greater than a redundancy threshold, and maintaining the global covariance matrix in a register state when the novelty is not greater than the redundancy threshold; extracting a main diagonal variance component to determine a differential modulation coefficient; calculating a second-order moment manifold projection operator according to the global covariance matrix, and calibrating the operator by using a differential modulation coefficient; the calibrated operator is used for carrying out orthogonal projection on random noise to generate a structured disturbance vector, the structured disturbance vector is superposed to a multi-modal feature tensor to output enhanced features, a novelty judgment mechanism is used for restraining statistical deviation caused by steady-state redundant data, computing resource occupation is reduced, and it is ensured that semantic alignment between modals is maintained in enhanced feature distribution.

The invention relates to an APP intelligent marketing service method based on intelligent routing and multi-agent cooperation. The method comprises the following steps: receiving input information, wherein the input information comprises user active inquiry information or trigger event information generated based on user behavior monitoring; semantic analysis and intention recognition are carried out on the input information, a task planning directed acyclic graph is generated based on a recognition result, and the task planning directed acyclic graph comprises a plurality of subtask nodes and dependency relationships among the nodes; based on the task planning directed acyclic graph, the state of each functional agent and the historical performance index, executing dynamic routing so as to dispatch the plurality of sub-tasks to the corresponding functional agents; and in the execution process of the plurality of subtasks, performing dependency scheduling and state consistency management on an external tool call chain which is initiated by the functional agent and comprises a plurality of steps, and updating the shared memory associated with the user based on an execution result. By adopting the method, self-adaptive planning, robust execution and continuous optimization of marketing tasks can be realized.

The invention relates to the technical field of government affair entrepreneurship guidance service intelligent dialogue systems, in particular to a dialogue intention recognition and correction method based on a knowledge graph, and the method comprises the steps: obtaining a voice signal and text information inputted by a user, and carrying out the cross-modal fusion processing to generate a semantic vector; carrying out evolution modeling on the dialogue state by adopting a graph neural network, and outputting a dynamic dialogue state graph containing a time sequence relationship between a user entity and a business object; taking the semantic vector and the focus information as query conditions, executing dynamic subgraph sampling from the government affair knowledge graph, and generating an intention correction strategy vector; and finally, utilizing reinforcement learning to generate personalized guide statements and outputting the guide statements to the user. According to the method, through deep coupling of semantic understanding, state tracking, knowledge reasoning and decision optimization, real-time perception and active completion of dynamic updating of hidden nodes and policies of the government affair process are achieved, and the intention recognition accuracy and systematic adaptive capacity are effectively improved.

The invention discloses an intelligent tooling for paving a photovoltaic module and a control method thereof, the method is suitable for a robot end effector, and the method comprises the following steps: collecting three-dimensional space point cloud data of the photovoltaic module to generate pose information of the photovoltaic module, and controlling the end effector to carry out adaptive pose adjustment according to the pose information; the tooling is controlled to press down, and after it is judged that fitting is in place by monitoring the compression stroke of the suction cup, a vacuum adsorption instruction is started; the adsorption state is judged based on vacuum pressure change curve parallel monitoring and a dynamic balance algorithm, and a carrying permission signal is generated after a preset adsorption success condition is met; continuously carrying out anomaly detection in the carrying process, and if an anomaly is found, triggering a fault processing strategy; and releasing the negative pressure to place the photovoltaic module after the target position is reached, and rechecking the placement pose. Intelligent and self-adaptive picking and placing of the photovoltaic module by the tooling are realized, and the reliability, the safety and the automation level in the carrying process are improved.

The invention provides a BIM-fused port infrastructure digital twinborn operation and maintenance management system and method. The method comprises the steps of collecting a port infrastructure BIM full-life-cycle multi-source heterogeneous data set, constructing a port infrastructure real-time three-dimensional digital twinborn body, constructing an equipment health prediction model and generating a fault diagnosis result; simulating tide influence to form a stress distribution prediction result; a three-level linkage optimization model is constructed, a scheduling scheme is generated, a multi-energy collaborative optimization engine optimization scheme is used, a three-dimensional safety matrix is constructed through unmanned aerial vehicle inspection and visual recognition, risks are monitored in real time, and safety early warning is generated. The prediction model is constructed, faults are accurately diagnosed, the tide influence is quantified, and the prediction precision is improved; multi-target collaborative optimization is realized through a linkage optimization model and an engine, and the problem of single-target optimization is solved; and on the basis of a monitoring and early warning system, rapid identification and hierarchical response are realized, a closed-loop link is formed, and the port operation and maintenance adaptability is improved.

The invention relates to the technical field of multi-beam sounding calibration, and discloses a multi-beam sounding online real-time calibration method. The method comprises the following steps: acquiring original water depth data and corresponding sensor attitude data in real time; generating a data quality evaluation result containing a data credibility index and a data abnormal distribution feature based on the original water depth data; triggering a dynamic calibration mechanism containing a calibration period adaptive adjustment strategy and a calibration parameter optimization rule according to the data credibility index; optimizing the target calibration rule parameters in combination with the sensor attitude data to generate an optimized calibration parameter set, and correcting the original water depth data in real time by using the parameter set to generate primary calibration data; and performing topographic feature consistency check on the primary calibration data, if the primary calibration data does not pass the topographic feature consistency check, starting a re-calibration process containing a rule base updating mechanism and a parameter iteration mechanism, and analyzing a deviation trend based on historical calibration records to generate a system error prediction model.

The invention discloses a respiratory disease oxygen precise conveying system based on a sensor, and relates to the technical field of medical equipment, and the system comprises a sensing module which integrates multiple sensors to collect and process airway and physiological data of a patient; the disease matching module identifies disease types through database comparison; the dynamic adaptation module calculates exclusive oxygen concentration based on a multi-mode fusion initial algorithm; the conveying module executes adjustment; the closed-loop feedback module continuously fine-tunes the oxygen concentration through a closed-loop stepped fine-tuning algorithm to form a closed-loop link, so that accurate dynamic control of oxygen delivery is realized; according to the invention, multi-sensor collaborative acquisition and disease precise matching are combined with a dynamic algorithm and closed-loop feedback, so that individuation and dynamic oxygen delivery are realized, the system avoids misjudgment by relying on a standardized database, and the oxygen concentration, flow and pressure are ensured to be adjusted in real time along with physiological indexes of a patient through multi-parameter collaborative adjustment and stepped fine adjustment; the treatment comfort, the effectiveness and the long-term adaptability are improved.

The invention discloses an injection molding production optimization method based on multi-agent cooperation, and the method comprises the following steps: decomposing a plurality of targets in an injection molding production process, and constructing a layered multi-agent structure; collecting data in the injection molding production process in real time, and constructing a global expert behavior track and a local expert behavior track; based on the global target and each local target, obtaining initial parameters of a global reward function and a local reward function; adopting an inverse reinforcement learning method to obtain an optimal global reward function and an optimal local reward function; obtaining an optimal decision strategy of each agent through a hierarchical collaboration mechanism by each hierarchical agent; when detecting that a conflict exists between the targets, dynamically adjusting the reward weight of the reward function by adopting a conflict coordination mechanism; and continuously collecting new expert behavior data, and updating the optimal decision strategy of each agent. According to the invention, a layered multi-agent inverse reinforcement learning method is adopted, and multi-target dynamic collaborative optimization of injection molding production is realized.

The invention discloses a multi-modal sensor abstract description and packaging method and system based on software definition. Firstly, a unified sensor abstract description model based on software definition is introduced, so that hardware independence is realized, and the differences of a bottom-layer multi-mode sensor in the aspects of interfaces, protocols and data formats can be effectively shielded; secondly, abstract packaging of data is achieved through an autonomous information processing module, and high-bandwidth and high-redundancy original data is converted into low-bandwidth and high-value structured information; in addition, by means of automatic registration, discovery, scheduling and control mechanisms of the sensor management control module on sensor resources, the system realizes a real plug-and-play function. According to the invention, by deploying the autonomous information processing modules at various sensor nodes, the original sensing data is preprocessed, and standardized data packaging with semantic consistency is output, so that the operation load of the central controller is reduced, and the information processing efficiency and the system response capability are also improved.

The invention provides an equipment health monitoring method and device, equipment and a storage medium. The method comprises the following steps: acquiring working condition parameters of target equipment to obtain monitoring data; calculating a reference datum according to the monitoring data through a pre-trained baseline generation model, and comparing the monitoring data with the reference datum to obtain difference data; performing feature extraction and distribution analysis on the difference data to obtain health feature representation; and performing health state judgment according to the health feature representation to obtain a monitoring result. According to the method, the reference benchmark is dynamically calculated through the baseline generation model according to the real-time working condition, a traditional fixed threshold value method is replaced, a depth feature extraction network is adopted to capture equipment degradation features, a probability distribution divergence value is utilized to quantify the health state, and the problem that an existing method is poor in adaptability under the dynamic working condition is solved; and the accuracy and reliability of equipment health monitoring are improved.

The invention discloses a dynamic fusion prediction method and system for photovoltaic power generation power, and a storage medium. The method comprises the steps of collecting historical photovoltaic power generation power and corresponding multi-dimensional meteorological parameters; based on time sequence fragments in the historical data set, similarity measurement and clustering analysis are carried out on hour-level weather-power time sequence fragments in historical data by utilizing a dynamic time warping method, different weather-power mode clusters are divided, and a representative cluster center is determined for each cluster; training a long time sequence baseline prediction model and a plurality of short time sequence correction models in parallel; performing weighted fusion on the two prediction results by using the weight to obtain a final prediction value; and the prediction is updated by using the latest meteorological data by adopting an hour-by-hour rolling mechanism. According to the method, the problem that the long-period trend and the short-time fluctuation are difficult to consider at the same time is effectively solved, and the prediction precision, the stability and the adaptive capacity of the photovoltaic power generation power under the complex and changeable meteorological conditions are remarkably improved.

The invention belongs to the technical field of data center network and clock synchronization, and discloses a dynamic time synchronization path optimization method and system based on topology awareness. The invention provides a closed-loop framework of state awareness-path optimization-resource assurance-dynamic adjustment and optimization: in the first stage, network topology, link load and PTP message performance data are periodically acquired through an SDN controller, and a global state view is constructed; in the second stage, the optimal path is dynamically calculated for the PTP synchronous flow through a multi-constraint optimization algorithm with the purpose of minimizing transmission delay jitter, and deterministic transmission is achieved in combination with bandwidth reservation and a priority queue; and meanwhile, a seamless switching mechanism of building first and disassembling second is introduced to adapt to the network state change in real time. On the basis of multiplexing existing hardware, the time synchronization error is remarkably reduced, the transmission stability and the dynamic adaptability are improved, microsecond-level time synchronization can be efficiently achieved, and the method is suitable for high-precision service scenes.

The invention relates to a robot standing posture recovery method and device, equipment, a storage medium and a product, and the method comprises the following steps: after the falling state of a robot is detected, collecting joint angles in real time to obtain falling posture data, and calculating contact points with the ground to determine a support part set; in combination with a preset zero moment point reference trajectory, performing inverse dynamics solution based on the set to generate a joint motion sequence, and configuring the joint motion sequence as joint position instructions to form a getting-up planning trajectory; the current output torque of each joint is compensated in real time according to the track, a driving torque instruction is obtained, each joint motor is driven to execute actions, so that the robot is recovered from the current posture to the standing posture, and the problem that the preset rigid action is often not matched with the current actual supporting state when the robot falls to the ground, and consequently the robot cannot stand up when executing the getting-up action is solved. And center-of-gravity unbalance is easy to occur.

The invention discloses a multivariate time sequence prediction method based on dynamic complex spectrum filtering, which comprises the following steps: acquiring multivariate time sequence data of a target scene, determining a predicted target variable and a window length, and constructing a training set, a verification set and a test set based on the data; constructing a DCSFNet prediction model, wherein the model sequentially comprises a reversible instance normalization module, a frequency domain conversion module, a dynamic complex spectrum filtering module, a time domain projection module and an inverse reversible instance normalization module; and realizing multivariate time sequence prediction by using the trained DCSFNet model. According to the method, the frequency domain features are adaptively processed through the dynamic complex spectrum filtering module, and reversible normalization and time domain mapping are combined, so that the problems that an existing method is poor in adaptability to diversified time sequence data, frequency domain information is insufficient in utilization, and prediction precision and calculation efficiency are difficult to consider at the same time are effectively solved; and multivariate time sequence prediction with high precision, high efficiency and cross-scene adaptability is realized.

The invention provides a diffusion-driven channel adaptive point cloud semantic communication method, which belongs to the technical field of wireless communication, and comprises the following steps: obtaining a training point cloud data set, constructing a point cloud feature extraction network based on a hypergraph convolutional neural network as a semantic encoder, and constructing a channel adaptive enhancement module as a channel decoder, a channel adaptive recovery module is constructed at a receiving end as a channel decoder, a diffusion reconstruction network is constructed as a semantic decoder, end-to-end joint training is performed on the point cloud feature extraction network, the channel adaptive enhancement module, the channel adaptive recovery module and the diffusion reconstruction network, the diffusion reconstruction network predicts original point cloud distribution, and the point cloud feature extraction network and the channel adaptive enhancement module are subjected to end-to-end joint training. And a loss function is constructed based on the chamfering distance between the predicted point cloud distribution and the original point cloud. In the communication process, the reconstructed semantic features are input into the diffusion reconstruction network to reconstruct an original point cloud structure. The high-order semantic features of the point cloud can be effectively extracted so as to improve the adaptive capacity of the method to the incomplete point cloud.

The invention relates to the technical field of vehicle suspension system control, and discloses a multi-source data fused shock absorber self-adaptive regulation and control method and system.The method comprises the steps that multi-source signal data are synchronously collected through a sensor, and a preliminary signal set is generated in combination with environment change data; analyzing vibration signal peak amplitude and displacement data integral variation trend, distinguishing high-frequency small-acceleration and low-frequency large-amplitude pavement excitation characteristics, and determining signal contribution degree distribution; dynamically adjusting the signal weight and introducing temperature compensation to correct stiffness drift to generate a weighted vector; carrying out weighting processing, smoothing, noise suppression and nonlinear amplitude estimation to generate fusion data, and optimizing the weight to obtain a refined weighted vector; after secondary fusion, a cross-frequency-band transition smoothing technology and a least square method are adopted to determine damping characteristic parameters; according to the vehicle suspension system self-adaptive regulation and control method and system, the self-adaptive regulation and control accuracy and the working condition adaptability of the vehicle suspension system are improved.

The invention provides a new energy automobile endurance prediction method based on working condition identification and prediction, and belongs to the technical field of new energy automobile endurance mileage estimation. According to the method, sliding window statistical characteristics are constructed through speed, current, voltage and other signals, typical working conditions of cities / suburbs / highways and the like are identified, the working condition of a next time window is predicted, and energy consumption priori which can be updated in real time along with changes of road conditions is formed. Secondly, a rolling capacity estimation model is provided, the available capacity of the battery is continuously evaluated based on historical charging and discharging data, and dynamic changes of the SOH of the battery along with time and use conditions are recorded; according to the method, working condition prior, rolling SOH and time sequence energy consumption characteristics are fused, a TCN-BiLSTM multi-model cooperation framework is constructed, short-term dynamic and long-term attenuation trends are considered, high-precision and generalizable endurance mileage prediction under complex working conditions and battery state fluctuation is realized, reliable support is provided for intelligent energy consumption management and journey planning of vehicles, and the method is suitable for popularization and application. The method can also be applied to an intelligent power distribution system with a high reliability requirement, and provides a core solution for technology promotion service in related fields.

The invention provides a general semiconductor test data full life cycle management system, and the system comprises a test source data access module which is used for uploading a source test file in a test machine to the system; the full test data analysis and standard model conversion module is used for converting the uploaded source test file into a test file in a standard data format and transmitting the test file to the multi-dimensional KPI atomic model operation and persistent storage module; the multi-dimensional KPI atomic model operation and persistence storage module is used for calculating a multi-dimensional KPI atomic result according to the data in the test file in the standard data format and storing the multi-dimensional KPI atomic result to a persistence layer database; and the unified service output module is used for receiving a user query request, performing aggregation calculation on the multi-dimensional KPI atomic result in the persistent layer database according to the user query request, and returning a KPI result meeting the user query request. The system can improve the utilization efficiency of semiconductor test data and reduce the operation cost.

The invention discloses a multi-task electromagnetic model based on a hybrid expert network, and belongs to a wireless communication technology. The model comprises a preprocessing module, a feature extraction module, a task output module and a pre-training-fine tuning learning strategy. The preprocessing module carries out standardization processing on the multi-source electromagnetic signals; the feature extraction module is based on a Transform structure, introduces a hybrid expert network to replace part of a traditional feedforward neural network, and dynamically selects an expert sub-network through a task specific routing mechanism; the task output module configures a special structure according to different task targets; in the pre-training stage, a mask auto-encoder is used for pre-training large-scale label-free data, and a downstream task is subjected to full-amount fine adjustment through small-scale label data. According to the method, multi-task collaborative learning and differential expression are realized, and the recognition performance, robustness and processing efficiency of the model in a complex electromagnetic environment are improved.

The invention discloses an electric energy metering error evaluation method and system for a direct current charging pile, equipment and a medium, and the method comprises the steps: constructing an electric energy disturbance response function based on transient voltage and transient current, and effectively quantifying the transient metering deviation caused by measurement lag, voltage drop, current peak and other factors in the actual operation of the direct current charging pile. Error reconstruction modeling has higher dynamic adaptive capacity and physical rationality, the limitation that in the prior art, the influence of the high-variable-load working condition on metering cannot be reflected is broken through, and the accuracy and timeliness of direct-current charging pile error monitoring are greatly improved. Besides, a standard error is introduced to carry out fitting calibration on a reconstruction error and carry out energy conservation verification, thereby realizing verifiability on a physical level and controllability in engineering implementation, enhancing credibility and authority of an evaluation result, constructing a closed-loop analysis mechanism from error evaluation to metering correction and then to conservation verification, and improving reliability and reliability of the evaluation result. And the closeness and the adaptive capability of the overall error evaluation are improved.

The invention belongs to the field of industrial control and computer data security, and particularly relates to a millisecond-level power failure data protection system for an industrial control panel. The invention discloses a millisecond power failure data protection system for an industrial control panel. The millisecond power failure data protection system comprises a core processing component, a real-time synchronous shadow memory component, a bus voltage monitoring component, a pre-entropy reduction logic control component, a nonvolatile storage component and a flight control bus arbitration component. The bus voltage monitoring assembly monitors a voltage drop trend and performs early warning, the pre-entropy reduction logic control assembly calculates a minimum data survival set according to energy entropy, the real-time synchronous shadow memory assembly constructs a synchronous shadow control domain and atomizes data, and the flight control bus arbitration assembly compulsively takes over a bus and freezes processor access at the moment of power failure. Atomized writing of data into the nonvolatile storage component is guaranteed. According to the invention, zero-delay data protection and millisecond-level hot start recovery at the moment of power failure are realized, and the data security and reliability of the industrial control equipment in a severe environment are enhanced.

The invention discloses a PCIe-based CPU, NPU and GPU interconnection cluster management method, and belongs to the technical field of interconnection cluster management. According to the method, an interconnection link is established through a PCIe switch, and bandwidth is dynamically allocated by means of the link to adapt to task requirements; multi-priority data transmission is supported, and bandwidth resources are distributed in order according to priorities; a multi-element operation mode is configured in advance, and physical and virtual computing power resources are distributed according to needs; and an equipment state monitoring mechanism is established, idle equipment enters a sleep mode, and meanwhile, all transmission links are combed and an optimal path is selected. According to the method, fine scheduling of cluster resources is realized, the bandwidth and computing power resource utilization rate is improved, the adaptation capability to different tasks is enhanced, the orderliness and stability of data transmission are guaranteed, the cluster operation energy consumption is reduced, and efficient and stable operation of the interconnected cluster is facilitated.

The invention provides an active inertia supporting method, device and equipment for an energy-storage-free photovoltaic grid-connected system, and relates to the technical field of new energy grid-connected power generation and intelligent control. The method comprises the following steps: constructing a direct current-alternating current double-side dynamic model of the energy-storage-free photovoltaic grid-connected system; constructing a total objective function according to the direct current-alternating current double-side dynamic model; the total objective function comprises a plurality of objective functions and penalty functions; taking the power reservation coefficient and the virtual rotational inertia as individuals, and taking a plurality of target functions as a multi-target task cooperation whale optimization algorithm to obtain a Pareto front; respectively inputting individuals in the Pareto frontier into the total objective function to determine an optimal individual; and dynamically introducing the optimal individual into a control module of the grid-forming type virtual synchronous generator through a smooth transition function, so that the energy-storage-free photovoltaic grid-connected system operates in an optimal state. According to the invention, voltage and frequency fluctuation can be effectively suppressed, system stability and photovoltaic utilization rate are both considered, and operation cost is reduced.

The invention relates to the field of mobile data communication services, in particular to a subway rail transit underground space emergency communication system and an arrangement method thereof. The subway rail transit underground space emergency communication system provided by the invention comprises a first communication link used for transmitting service data in a first state; the second communication link is used for transmitting service data in a second state; the communication link monitoring module is in signal connection with the first communication link and is used for monitoring the signal-to-noise ratio of data transmission in the first communication link; and the communication link switching module is in signal connection with the communication link monitoring module, is respectively in signal connection with the first communication link and the second communication link, and is used for switching the data transmission path from the first communication link to the second communication link according to the signal-to-noise ratio of data transmission in the first communication link. According to the invention, through double-link redundancy and rapid switching, automatic switching to a standby link is realized under abnormal conditions such as optical fiber faults or power interruption, and continuous and uninterrupted communication is ensured.

The application provides a consumer rights protection intelligent review method and system based on a large language model, and relates to the technical field of artificial intelligence and natural language processing. The method comprises: preprocessing a text to be reviewed to obtain structured text feature data; using a pre-trained large language model to perform semantic understanding on the structured text feature data to obtain a text semantic vector containing domain semantic information; performing joint retrieval and matching on the text semantic vector in combination with a knowledge graph and a rule base to identify and output a risk element set; and performing compliance determination based on the risk element set to obtain a preliminary review conclusion containing a risk level, evidence sufficiency, rule matching degree and benchmark confidence. The application automatically converts an original text into a high-confidence review conclusion with explainability through intelligent processing of the whole process.

The invention relates to the technical field of semiconductor test equipment, in particular to a full-automatic high-precision probe station, which utilizes multi-physics field coupling sensing and compensation to remarkably improve the positioning precision: by comprehensively collecting multi-physics field data such as a temperature field and a stress field and combining a coupling algorithm to optimize a compensation strategy, multi-factor interference is effectively counteracted, and the accuracy of the probe station is improved. The positioning repetition precision can reach + / -0.05 [mu] m, and the test requirement of the advanced semiconductor technology is met; a dynamic three-dimensional modeling technology and dynamic adaptability enhancement are achieved, a dynamic three-dimensional modeling system with the size affected by multi-physical field data can update the form of a test target in real time, test deviation caused by target deviation or surface fluctuation is avoided, and the adaptability of equipment to a complex test scene is improved. The automation degree is high, the testing efficiency is improved, the whole process of data acquisition, modeling, compensation and testing can be completed without manual intervention, the calibration process is simplified, the testing period is shortened, and the method is suitable for batch detection scenes.

The invention discloses a multi-mode defect automatic classification method, device and equipment and a storage medium. The method comprises the following steps: acquiring a microscope image, an X-ray image and defect feature information data of a to-be-detected product; first classification processing is preferentially carried out based on the microscope image, when the first classification processing result meets a first preset reliability condition, the first classification processing result is taken as a final classification result, and when the first classification processing result does not meet the first preset reliability condition, the final classification result is taken as a final classification result. Performing feature fusion based on the X-ray image and the defect feature information data, and executing second classification processing; when the second classification processing result meets a second preset reliability condition, the second classification processing result serves as a final classification result, and when the second classification processing result does not meet the second preset reliability condition, defect type judgment is conducted based on key parameters in the defect feature information data, and the final classification result is output; the classification accuracy and efficiency can be improved, and the error recognition rate is reduced.

The invention relates to a vibration excitation method and system based on impedance identification, and the method comprises the steps: carrying out the frequency domain conversion of force-speed data obtained by a displacement closed-loop system, and obtaining the mechanical impedance of a drive point; performing flexibility compensation on the mechanical impedance of the driving point to obtain the real impedance of the structure end; extracting an imaginary part and constructing a matching residual error by using the real impedance of the structure end, correcting the excitation frequency of the current vibration exciter through gradient frequency, and iteratively determining the optimal excitation frequency of the vibration exciter meeting a convergence condition; mechanical active power generated by the vibration exciter is calculated under the optimal excitation frequency, dual-target optimization is carried out on the amplitude of the vibration exciter, and the optimal amplitude with the maximum mechanical active power and the minimum equivalent output of the actuator is obtained; based on the real impedance of the structure end, the matching criterion and the optimal amplitude, variable parameters are introduced to construct a source-side impedance model, a source-side impedance matching frequency domain interval is expanded through slow time mark setting parameters, and mechanical active power generated by a vibration exciter is coupled to a tested structure.