1280 results about "Modal analysis" patented technology

The invention discloses a video content intelligent management method and system based on multi-modal analysis, and belongs to the field of video content management.The method comprises the steps that multi-modal features of video content are extracted; performing fine structure segmentation on the video content based on the multi-modal features, and segmenting the video into fine segments related to semantics; the fragments obtained after fine structure segmentation are subjected to tagging, and metadata is generated for each fragment; constructing a multi-modal semantic index of the video content based on the generated metadata; according to the method, a semantic retrieval function is provided by utilizing multi-modal semantic indexing, fine fragments required by a user are accurately recalled in combination with text label matching, measurement numerical value scoring, cross-modal vector retrieval and filtering based on a shot language and a shooting technology label, and the user queries and positions a specific time point or the fine fragments in a video through a natural language. According to the invention, the accuracy of video content analysis is improved, the user experience is optimized, and the operation complexity is reduced.

The invention discloses a mixed tower structure full-life damage intelligent sensing array early warning system, which relates to the field of mixed tower structure detection and comprises a multi-modal data collection module, an array topology optimization module, a self-adaptive signal processing module, a digital twin life prediction module, a grading early warning module and a visualization system. The multi-modal data collection module comprises a multi-modal sensor array, a self-powered module and a wireless transmission module. According to the invention, a full-scale sensing network is constructed, full-dimension damage perception from distributed monitoring to sudden damage capture and structural modal analysis is realized, wavelet transform and blind source separation are combined to eliminate environmental noise interference, a damage characteristic ultrasonic attenuation coefficient, an acoustic emission energy spectrum peak value, optical fiber strain gradient anomaly and vibration modal frequency deviation are extracted, and the detection accuracy is improved. And classification and positioning of damage types and intelligent diagnosis of severity levels are realized through a convolutional neural network and long and short memory neural network hybrid model, and a closed-loop processing flow from data acquisition to feature analysis is formed.

The invention discloses a multi-modal analysis method, system and device for an industrial inspection scene and a medium. The method comprises the following steps: acquiring multi-modal data of an industrial inspection area in real time, wherein the multi-modal data comprises a video image and voice data; inputting the currently collected multi-modal data into a scene type identification model, and identifying a scene type; matching a corresponding multi-modal fusion mode based on the determined scene type; according to the matched multi-modal fusion mode, performing feature extraction on the video image by using a deep learning algorithm, performing feature extraction on the text converted from the voice data by using a natural language processing technology, and performing multi-modal fusion on the extracted features; performing joint reasoning on the multi-modal fusion features by using a deep learning algorithm, and identifying potential risks or equipment faults in the industrial inspection area; and generating an inspection report and decision based on the identified potential risk or equipment fault. According to the invention, intelligent analysis of the industrial inspection scene can be realized, and the inspection efficiency and accuracy are improved.

The invention discloses a video feature extraction and multi-dimensional matching-based movie advertisement real-time pushing system, and belongs to the technical field of digital advertisements. According to the system, multi-modal analysis is carried out on visual, audio, text and semantic features of a movie through a video feature extraction module, a dynamic interest tag constructed by a user portrait analysis module is combined, and the weighted matching degree of a video scene, user preference and an advertisement tag is calculated by utilizing a multi-dimensional matching engine; and millisecond-level advertisement putting is completed through the real-time pushing decision module. According to the method, context awareness and streaming computing technologies are fused, the relevance between advertisements and content scenes is remarkably improved, cross-platform deployment is supported, the method is suitable for short videos, live broadcast and other real-time scenes, the advertisement click rate is increased by 55%-72% through tests, the response delay is lower than 200 ms, and the method has the technical advantages of being efficient, accurate and low in delay.

The invention discloses an online reinforcement learning data increasing and expanding method based on a diffusion model. The method comprises the following steps: processing original trajectory data to generate enhanced feature data; extracting global structure representation and local detail representation; carrying out fusion by adopting a hierarchical integration method to obtain unified hierarchical representation data; generating constraint coding data based on pre-stored physical constraint parameters and task constraint parameters, and fusing to generate constraint perception representation data; generating an evaluation result based on the current strategy parameter; performing adaptive adjustment on the constraint perception representation data to obtain adjusted representation data; modal analysis is carried out, diversified trajectory data is generated, and a candidate trajectory set is obtained; and carrying out quality evaluation, carrying out trajectory screening through a multi-objective optimization method, and updating strategy network parameters to obtain updated strategy parameters. According to the invention, high-quality training data can be automatically generated, and the training efficiency and performance of a reinforcement learning system are improved.

The invention relates to the technical field of data processing, and discloses an intelligent product page automatic generation method and system for the massive health industry. The method comprises the following steps: performing multi-modal analysis and augmented reality processing on a large health industry prototype graph and multi-source medical data to obtain augmented design information; performing dynamic knowledge graph mapping and causal reasoning on the enhanced design information to obtain semantic structure and knowledge associated data; performing adaptive micro-service deployment analysis on the semantic structure and knowledge associated data to obtain a containerized deployment strategy, and performing intention-driven API design and homomorphic encryption enhancement processing to obtain API interface specification data; performing component design and code generation processing on the API interface specification data to obtain a multi-modal interaction page code; and performing intelligent rendering and dynamic content filling on the multi-modal interaction page code to obtain a target product page. According to the method and the device, the efficiency and the accuracy of automatic generation of intelligent product pages in the large health industry are improved.

The invention provides a data decision-making method and system based on multi-modal large model analysis, and the method comprises the steps: collecting multi-modal data, carrying out the preprocessing, and carrying out the multi-modal feature extraction and fusion; performing feature splicing and weighted combination according to the multi-modal features to generate initial situation features, and performing feature optimization on the initial situation features to obtain final situation features representing dynamic situation information under the current task and scene; performing specialized optimization on each modal feature by using the final situation feature to obtain optimized modal features, and splicing the optimized modal features into an optimized joint feature vector; and aligning the final modal specialization optimization features to generate a fused global feature. When specific modal analysis enters, global features are optimized according to specific tasks, and a decision result is generated and explained. According to the method, the higher context sensing capability, the more efficient modal fusion method and the more accurate task decision-making capability can be realized in the multi-modal data decision-making.

The invention discloses a multi-agent dynamic arrangement method based on multi-modal analysis and adaptive retrieval, and relates to the technical field of artificial intelligence and information retrieval. Comprising the steps of S1, converting a text, an image, structured data and voice content input by a user into a unified multi-mode semantic representation, S2, converting the unified multi-mode semantic representation into a specific execution process, and S3, automatically scheduling a reasoning agent, a knowledge obtaining agent and an execution agent according to DAG nodes, task elements and available resources, and obtaining the task elements and the execution agent according to the reasoning agent, the knowledge obtaining agent and the execution agent. S4, after task process construction and agent arrangement are completed, dynamic retrieval, evidence convergence and strategy optimization are carried out on information requirements related to a user task, so that a reasoning agent obtains complete knowledge support with consistent context, and S5, knowledge evidence is combined with a task process, so that the task process is completed. The method comprises the following steps: step S6, implementing problem solving, strategy generation and task closed-loop execution through a reasoning agent, step S6, performing actual operation on a target task by an execution agent according to an executable instruction sequence output by the reasoning agent, and outputting a result, and step S7, performing result verification according to an output result returned by the execution agent, and the correctness, integrity and consistency of an output result are examined through rule verification, model evaluation and evidence alignment.

The invention discloses a contract auditing method, apparatus and device, and a computer readable storage medium. The method comprises the steps of obtaining an image file corresponding to a contract file; inputting the image file into a multi-modal analysis model for model identification so as to obtain text information of each text area corresponding to the image file; performing screening operation on the text information based on a preset keyword to obtain target text information; and auditing the target text information by adopting a large language model based on an entity recognition rule to obtain an auditing result. According to the method, the text information is screened, the target text information is audited on the basis of the large language model, intelligent audit of the contract file is achieved, meaningless input of the large model and the data annotation process can be reduced as much as possible through keyword screening and entity recognition rule matching, and the user experience is improved. The efficiency and accuracy of contract auditing are improved.

The invention relates to the technical field of water pump energy-saving control, and discloses a water pump energy-saving control system based on an intelligent data model, which comprises a data sensing module, a modal analysis module and the like. The data sensing module collects flow, lift and power data and is provided with a dynamic monitoring window; the modal analysis module divides operation modes and generates an energy efficiency characteristic matrix; the rule construction module extracts a core energy efficiency factor and constructs an energy-saving regulation and control strategy; the priority scheduling module analyzes the control priority matrix and evaluates an energy efficiency deviation index; a mapping calibration module fits the optimal energy efficiency reference curve to generate an energy efficiency deviation gradient map; the control execution module generates a regulation and control instruction set according to the map. The system realizes fine management and energy efficiency optimization of water pump operation through multi-mode energy efficiency analysis and dynamic regulation, solves the problems of poor adaptability of traditional control working conditions, low energy-saving efficiency and the like, is suitable for water pump energy-saving control in the fields of industry, municipal administration and the like, and is accurate and efficient.

The invention provides a vibration analysis method of a reactor vibration isolation system, a program product and electronic equipment. The method comprises the following steps: acquiring a vibration signal of a vibration isolation bracket in a working state; extracting vibration characteristics from the vibration signals by using time domain analysis and frequency domain analysis; according to the vibration characteristics, determining association between the structural characteristics of the vibration isolation bracket and the vibration characteristics to obtain first association; according to the finite element analysis and the first relevance, a finite element model of the vibration isolation support is established; according to modal analysis and a finite element model, the inherent frequency of the vibration isolation support is determined, the correlation between the inherent frequency and the external excitation frequency is determined, and second correlation is obtained; according to the first relevance, the second relevance and a finite element model, the influence of different structural features on the vibration propagation path of the vibration isolation support is simulated, and the damping effect is obtained; and according to the damping effect, the vibration isolation support is optimized. The problem that it is difficult to guarantee the damping effect and the good vibration isolation performance of a vibration isolation support at the same time in the prior art is solved.

The invention relates to the technical field of artificial intelligence, and discloses an abnormal behavior real-time early warning method and system based on intelligent security, and the method comprises the steps: determining a monitoring environment feature set corresponding to a security monitoring region, and formulating a behavior early warning threshold value corresponding to security monitoring equipment; collecting dynamic behavior information of personnel in the security and protection monitoring area, analyzing the behavior modal analysis dimension corresponding to the security and protection monitoring equipment, and evaluating the area security situation corresponding to the security and protection monitoring area; analyzing physical feature attributes corresponding to the personnel in the security and protection monitoring area, analyzing action mode features corresponding to the personnel in the security and protection monitoring area, and calculating behavior risk entropies corresponding to the personnel in the security and protection monitoring area; analyzing the monitoring adaptation degree of the security and protection monitoring equipment in the security and protection monitoring area; and formulating an abnormal behavior early warning strategy corresponding to the security and protection monitoring area, and executing abnormal behavior real-time early warning in the security and protection monitoring area to obtain an early warning result. According to the invention, the real-time early warning efficiency of abnormal behaviors under intelligent security is improved.

The embodiment of the invention provides a multi-modal fusion deep learning analysis method and system. The method is applied to the technical field of multi-modal learning, and comprises the following steps: obtaining multi-modal original data, sequentially processing image, text, audio and video data, and extracting visual features of the image, semantic features of the text, frequency spectrum and time sequence features of the audio, image features and time sequence features of a video frame and time domain features of an audio sequence; and then, according to the complementary information of the multi-source features, fusion processing is carried out to form a unified multi-modal feature representation, the unified multi-modal feature representation is input to a preset deep learning analysis model, and finally a multi-modal analysis result of comprehensive expression is obtained. According to the scheme, information complementarity and robustness are enhanced through multi-modal feature fusion, the comprehensive analysis capability of the model on semantic understanding, behavior recognition and state judgment in a complex scene is remarkably improved, and a more accurate, efficient and stable decision basis is provided for a multi-modal intelligent sensing system.

The invention relates to the technical field of audio and video control, and discloses an audio and video player control method based on a voice instruction. The method comprises the steps that an original voice instruction stream of a user is collected, the instruction stream comprises a time domain audio signal sequence, an environment noise spectrum and user pronunciation characteristic parameters, and voice information can be comprehensively captured; multi-modal instruction analysis processing is carried out on the original voice instruction stream, a structured control instruction set containing acoustic control intention identification, semantic operation object description and context correlation parameters is generated, and the analysis precision is improved; then executing player state adaptation based on the set, generating a dynamic control response sequence containing an equipment state adjustment command, a media content positioning parameter and an interface interaction logic identifier, driving a player to execute a multi-dimensional control operation and generating real-time play control effect feedback data; and finally, multi-modal analysis parameters are optimized according to feedback data, a self-adaptive instruction analysis strategy is generated, and the control experience of a user on the audio and video player is optimized.

The invention discloses a short video intelligent editing method and system based on multi-modal analysis, and relates to the technical field of video editing. The method is used for improving editing efficiency and visual experience and comprises the following steps: extracting lip motion features of a character, visual saliency features of a commodity and a voice emotion intensity value from a target short video stream to form multi-modal time sequence data; afterwards, the voice stream is recorded, a product keyword timestamp is extracted, the alignment degree is calculated through dynamic time warping in combination with a visual saliency peak value, and a preliminary editing point set is generated through weighted evaluation in combination with an emotional intensity value; constructing an editing decision optimization model based on deep reinforcement learning, taking the multi-modal features as state input, adjusting the retention probability of editing points through a joint reward function, and selecting an optimal transition mode; and the lip movement and voice synchronization error before and after the editing point and the emotional and visual continuity of the transition section are analyzed, the discontinuous region is smoothed, and the edited finished product is output, so that precise short video intelligent editing is realized.

The invention relates to the technical field of cognitive disease risk assessment, and discloses a cognitive disease intelligent risk management method based on multi-modal voiceprint data analysis, the method comprises multi-modal feature fusion, dynamic risk assessment and real-time intervention linkage, by setting a multi-modal feature fusion module, when cognitive disease risk assessment is carried out, a multi-modal voiceprint data analysis module is set, the risk of the cognitive disease is evaluated, and the risk of the cognitive disease is evaluated. The recognition specificity of different cognitive impairment types is guaranteed by fusing acoustic characteristics, rhythm rules and multi-dimensional voiceprint characteristics of semantic logic, meanwhile, a comprehensive feature vector is constructed based on depth feature extraction, neural function degeneration signals implied in voiceprint data can be captured in real time, the misjudgment risk caused by traditional single-mode analysis is reduced, and the recognition accuracy of the cognitive impairment types is improved. According to the method, the reliability of early cognitive impairment screening is improved, a dynamic risk assessment mechanism is constructed, a cognitive state score and an adaptive threshold are compared in real time during individualized cognitive tracking, and abnormal fluctuation of a cognitive function is recognized in time.

The invention discloses a multi-element historic building maintenance and restoration method based on three-dimensional laser scanning, and relates to the technical field of computer processing, and the method comprises the following steps: S01, obtaining the three-dimensional point cloud data of a historic building foundation through a ground laser scanner, the basic three-dimensional point cloud data comprises geometric information of a building facade, a ground structure and a near-ground decoration component; s02, an unmanned aerial vehicle carries a multi-line laser scanning device to scan the top and the high-altitude structure of the building, supplementary point cloud data is generated, and the millimeter-level precision three-dimensional coordinates of the pitched roof, the tower eave and the cornice bracket are covered with the supplementary point cloud data; and S03, synchronously acquiring surface texture data of the historic building by adopting multispectral imaging equipment. According to the method, the millimeter-level precision of three-dimensional reconstruction of the historic building is realized through multi-source data fusion and a hierarchical optimization algorithm, and the maintenance decision efficiency is improved by combining automatic disease recognition and a multi-modal analysis technology.

The invention provides a 500kV GIS multi-mode working condition abnormity comprehensive monitoring system and method based on voiceprint recognition. A data acquisition module is used for collecting multi-mode original data from various sensors and devices; the network transmission module is used for transmitting the data to the voiceprint scoring module and the multi-modal analysis module through a network; the voiceprint scoring module is used for extracting sound features from the multi-modal original data, comparing the sound features with a preset voiceprint model, and giving a similarity or anomaly score; the multi-modal analysis module is used for comprehensively analyzing data from different modals, fusing various data and performing data analysis and pattern recognition by applying a deep learning algorithm; the fault judgment module performs fault detection and diagnosis by applying a preset rule or model based on an analysis result of the multi-modal analysis module, and judges whether the system or the equipment has a fault or not; and the result output module is used for displaying the final analysis and judgment result in the form of a chart, a report or an alarm.

The invention relates to the technical field of electric power operation, and discloses a power plant employee violation intelligent identification method and system based on an AI large model, and the system comprises a visual collection module, an edge calculation node, a personnel positioning and tracking module, a three-dimensional scene reconstruction module, a multi-modal analysis module, a grading early warning module, a self-adaptive adjustment module, and an inspection robot. According to the invention, accurate identification of personnel behaviors in a complex industrial environment is realized, the problems of high false alarm rate, response lag and the like of a traditional monitoring system are effectively solved through a specially designed self-adaptive adjustment mechanism and a grading early warning strategy, and the technical scheme has the advantages that through innovative integration of technologies of machine vision, edge calculation, multi-modal analysis and the like, the safety of the system is improved. An intelligent safety management and control system with an autonomous optimization capability is constructed, and breakthrough progress is made in core indexes such as identification precision, response speed, resource utilization efficiency and the like.

The invention relates to the technical field of subtitle translation, in particular to a multi-modal time sequence alignment AI video translation method and system, and the method comprises the steps: 1, carrying out the multi-modal analysis of a to-be-translated video, and obtaining audio separation data, voiceprint feature data and visual time sequence data; 2, performing cross-language translation and context optimization on the basis of the voice of the audio separation data to generate a target language text, and synthesizing target language voice retaining the original voice color in combination with the voiceprint feature data and the target language text; generating a mouth shape animation matched with the target language voice based on the lip key point data and the limb action time sequence data; and step 3, performing four-dimensional alignment on the target language voice, the translated text, the mouth shape animation and the limb action sequence through a cross-modal time sequence encoder, and dynamically adjusting the layout of the bilingual subtitles to adapt to a video picture. According to the method and the device, multi-mode synchronization can be taken into consideration during video translation, so that the body actions such as voice, subtitles and mouth shapes are kept aligned.

The invention discloses a tunnel fire air curtain smoke prevention control method and system, and relates to the technical field of intelligent fire fighting, and the method comprises the steps: building a digital twinborn prediction model, and generating a fire source position coordinate and a smoke diffusion trend prediction map through multi-physics field coupling simulation; analyzing the stability requirement of the air curtain jet flow through a Brapare-Prandtt boundary layer analysis algorithm, and generating a jet flow enhancement requirement parameter table in combination with temperature gradient correction; optimizing an acoustic vortex main frequency band by adopting a Slodhaar number, and generating an acoustic vortex control parameter set in combination with a matched sound pressure level and a configured resonator phase difference; and converting the acoustic vortex control parameter set into an execution instruction, monitoring the jet velocity, the vortex street stability and the smoke retardation effect in real time, and dynamically correcting the parameters of the digital twinborn prediction model. According to the method, physical quantity outline matching of the blocking intensity and the sound pressure level is achieved through the sound intensity-momentum equivalent method, and the optimal phase configuration is determined in combination with POD modal analysis.

The invention provides a segmental bridge wind vibration performance evaluation method and system, and relates to the technical field of segmental bridge wind vibration performance. Laser scanning is used for obtaining geometric parameters of a segmental bridge and collecting material characteristics, the geometric parameters and the material characteristics are imported into finite element software, a basic model is established, and boundary conditions are set; modal analysis is executed in finite element software, the natural vibration frequency and the vibration mode are calculated through a Lanczos method, and basic data are provided for wind load analysis; simulating a wind load by using a Davenport wind spectrum, setting a wind speed and a wind direction, and then applying the wind speed and the wind direction to the model; finally, the wind load condition is adjusted, the performance of the bridge under different wind loads is tested, and the displacement, acceleration and stress distribution of the bridge are analyzed and calculated through dynamic response to complete the wind vibration performance evaluation of the segmental bridge.

The invention provides a multi-modal LLM-based building risk prediction management and control method and system, and relates to the technical field of building safety management, and the method comprises the steps: analyzing sensor data, a text report, an image video and a voice instruction of a building construction site through a multi-modal feature extraction module, and generating a structured feature vector set; performing cross-modal semantic fusion and risk coupling analysis by using a multi-modal LLM inference engine to generate a potential risk identification set and a risk level assessment result; dynamically matching a management and control rule of the building safety specification library based on the risk identifier, and outputting a strategy set consisting of an equipment regulation and control instruction, a personnel early warning notification and a regional management and control suggestion; driving a field execution device to implement a control action, and collecting a multi-modal feedback data stream; and calculating a strategy execution efficiency index through a closed-loop optimization module, dynamically updating LLM model parameters and rule weights, and forming a self-adaptive optimization link. The system correspondingly comprises a multi-modal feature extraction and fusion module, an LLM inference engine module, a dynamic strategy generation module, an execution feedback module and a closed-loop optimization module. According to the method, the problems of key feature omission and risk response lag in traditional single-mode analysis are solved, and the risk prediction accuracy and the management and control real-time performance are remarkably improved.

The invention provides an automatic driving decision planning method, electronic equipment and a readable storage medium, and the method comprises the steps: carrying out the path planning of an automatic driving vehicle, and obtaining a target path; projecting the multi-modal prediction trajectory of the dynamic obstacle to the target path in the process that the autonomous vehicle travels along the target path to obtain a multi-scene projection drawing in time and space dimensions; a tree-shaped velocity curve is searched on the multi-scene projection drawing, a tree-shaped velocity curve set is obtained, a target tree-shaped velocity curve is determined in the tree-shaped velocity curve set, and the target tree-shaped velocity curve is composed of multiple tracks behind a bifurcation point and a common track before the bifurcation point; the plurality of trajectories are used for respectively describing speed plans of different scenes which are predicted to occur, and the public trajectory is used for describing speed plans determined according to occurrence probabilities of different scenes; and integrating the target path and the target tree-shaped speed curve to obtain a target tree-shaped trajectory, thereby solving the problem of unreasonable automatic driving decision and planning in related technologies.

The invention discloses a multi-knowledge-base scheduling routing method based on intention recognition and feedback optimization, and the method comprises the steps: S1, receiving multi-modal input from a user, carrying out the deep semantic analysis of the input content through natural language understanding and multi-modal analysis, and outputting a standardized intention label; s2, constructing a capability portrait for each knowledge base, and then generating a route matching score matrix according to a similarity relationship between the user intention tag and the capability portrait of each knowledge base; s3, screening out one or more most relevant candidate knowledge bases according to the scoring matrix and a set matching threshold value, and selecting a retrieval combination strategy to realize information recall; s4, information retrieval is executed based on the selected knowledge base, recall results and original user input are submitted to a large language model for content generation, and answers or suggestions are formed and output; and S5, user feedback collection and strategy optimization are carried out. According to the method, a more accurate, efficient and self-evolutionary knowledge base scheduling mechanism can be realized.

The invention relates to the field of artificial intelligence, and discloses an intelligent AI semantic annotation method and system based on multi-modal analysis, images, audios, texts and multi-modal data of a sensor are synchronously collected through AI, deep semantic features of all modals are extracted by using a deep learning model after preprocessing, vectors are generated, and the multi-modal data are subjected to semantic annotation; fusing into a semantic scene vector by means of a cross-modal attention mechanism, constructing a cross-modal association graph, and learning a causal logic path by using a graph neural network; constructing a spatiotemporal causal diagram by combining spatiotemporal information, and integrating an event time sequence and spatial dependence by using a time sequence diagram neural network to form a venation path; according to the method, the two knowledge maps are fused into a comprehensive knowledge map, preliminary annotations are generated through rule base reasoning, a high-precision result is output after optimization, the problems of semantic loss and annotation fragmentation caused by modal splitting and context missing in a traditional method can be effectively solved, meanwhile, the manual verification cost is reduced, and the integrity and efficiency of semantic annotation in a complex scene are improved.

According to the intelligent assessment feedback method and system for artistic creation, the artistic work data uploaded by the user is obtained, the visual perception features and semantic features of the artistic work data are fused and extracted, and the limitation of feature association missing caused by traditional single-mode analysis is broken through; different modal features are dynamically associated by using a cross-modal attention mechanism, and an evaluation result covering multiple dimensions such as color, composition, style and the like is generated, so that the problems of fixed evaluation dimension and poor work type adaptability in the prior art are solved; and comprehensive feedback information containing structured evaluation and personalized recommendation is generated based on a multi-dimensional evaluation result, and a closed loop from data analysis to creation guidance is realized.

The invention discloses a rotor blade dynamic field rapid reconstruction method based on a deep neural network model, and the method comprises the steps: firstly collecting a BTT signal containing blade vibration information, then designing a deep neural network model, and training the deep neural network model through a training data set; processing the actually measured BTT signal by using the trained deep neural network model, and reconstructing a blade vibration displacement signal; performing spectral analysis on the reconstructed blade vibration displacement signal, and identifying vibration frequency, amplitude and phase parameters; then establishing a finite element model of the blade, and determining the inherent frequency and modal shape of the blade through modal analysis; and finally, quickly calculating a displacement field, a strain field and a stress field of the blade in a vibration state based on a modal superposition theory by utilizing the identified vibration parameters and modal analysis results. According to the invention, the vibration displacement and stress condition of the blade can be known in real time, so that real-time decision is supported, and the safety, reliability and durability of a rotating machine are improved.

The invention relates to the technical field of artificial intelligence and psychological health, and discloses a multi-mode psychological counselor skill recommendation method and equipment. The method comprises the following steps: acquiring video, audio and text data in a psychological consultation session, and converting the video, audio and text data into structured data; obtaining the current emotional state of the consultant through multi-modal analysis according to the video, audio and text data after the structure conversion; judging whether psychological intervention skill recommendation is performed or not; if yes, obtaining recommended psychological intervention skills through a trained condition triggering model according to the dialogue between the psychological consultant and the consultant and the current emotional state; and according to the psychological intervention skills and the dialogue between the psychological consultant and the consultant, a dialogue text is generated through a large language model to help the psychological consultant to reply. According to the embodiment of the invention, the recommendation effect of the psychological intervention skills can be improved.

The invention discloses a rock debris lithology analysis method based on a multilevel fusion image algorithm and X-ray diffraction. The method comprises the following steps: S1, acquiring rock debris image data under different resolutions; s2, generating a multi-level rock debris image feature matrix; s3, preliminarily generating a lithology classification result; s4, generating supplementary data of X-ray diffraction analysis; s5, fusing the supplementary data of the lithology analysis and the preliminarily generated lithology classification result, and finally generating a comprehensive lithology classification result; s6, the comprehensive lithology classification result is input into a dynamic adaptive analysis system, the dynamic adaptive analysis system adaptively adjusts an analysis strategy according to the rock debris sample complexity, and parameters of a dual-path convolutional neural network model and a multi-modal analysis algorithm are optimized through a real-time feedback and learning mechanism; and S7, outputting an analysis report containing mineral distribution, crystal structure and comprehensive lithology classification results. The classification accuracy of different types of rock debris samples is improved by about 15%.