18370results about How to "Improve efficiency" patented technology

The invention relates to a low-loss corner-lifting and movement tearing cooperative type film tearing process of a screen display glass protective film. The process comprises the following steps: S1, tearing a film on an A surface; s2, transferring the screen display glass; and S3, tearing the film on the surface B. On one hand, in single-face film tearing, based on cooperation of the film tearing clamping jaw, the rolling wheel and the air nozzle, angle lifting is completed while the film tearing angle adhesive force is kept larger than the protective film tearing force, the damage and fracture rate of an adhesive tape and a corner is reduced, the high angle lifting success rate is achieved, meanwhile, through movement cooperation of the screen display glass and the film tearing clamping jaw, the film tearing difficulty is low, control is easy, and the film tearing efficiency is improved. The integrity and continuity of film tearing are high; on the other hand, overturning of the screen display glass is not needed in double-sided film tearing, transferring of the screen display glass is achieved in the protective film tearing process, the accidental friction rate in the glass overturning process is reduced, the structure is optimized, the layout space is greatly reduced, the equipment is miniaturized, and the double-sided film tearing efficiency of the equipment for treating the screen display glass is also improved.

The invention discloses a steel pipe polishing equipment, which comprises a steel pipe polishing equipment body. A main valve of a polishing motor is arranged in the main body of the steel pipe polishing equipment; a polishing handle is arranged on the main valve of the polishing motor; The valve is provided with a rotating roller; the front end of the rotating roller is provided with polishing bristles; one side of the rotating roller is provided with an outer throwing roller; a clamping spring is arranged between the outer throwing roller and the rotating roller; The outer throwing roller is provided with an outer throwing brush; a symmetrical magnet is arranged between the outer throwing brush and the polishing bristles; a polishing wheel is arranged on the outer throwing brush and the polishing bristles; the polishing The grinding wheels are arranged in a figure-eight shape. Its structure is simple, easy to use, and easy to operate. It can realize the polishing of the outer surface and the inner wall. The polishing is fast and uniform, with high efficiency and convenient use.

The invention discloses a full-automatic interval shifting mechanism which comprises a bottom plate. A rodless air cylinder is fixed to the bottom plate. A carrying plate is fixed to a sliding block of the rodless air cylinder. A sliding rail is fixed to one side of the carrying plate. The sliding block is inserted on the sliding rail in a sleeving manner and fixed to the bottom face of a stirring paw installation plate. A pushing air cylinder is fixed to the other side of the carrying plate. The end of a push rod of the pushing air cylinder is fixed to a connecting part of the bottom face of the stirring paw installation plate. A proximity switch is arranged on the rear part of the carrying plate and corresponds to the rear side wall of the carrying plate. A plurality of stirring paws are fixed to the top face of the stirring paw installation plate. A transverse sliding rail is fixed to the bottom plate. A transverse sliding block is fixed to the carrying plate and inserted on the transverse sliding rail in a sleeving manner. An induction piece is fixed to the bottom face of the stirring paw installation plate. Two photoelectric sensors are fixed to the parts, close to the left side and the right side, of the bottom plate and correspond to the induction piece. The full-automatic interval shifting mechanism can automatically move a product machined at one position to a station at another position to be machined, and thus the manual cost and the working intensity are lowered.

The invention relates to an underwater acoustic target recognition system and method based on multi-modal depth feature fusion, and belongs to the field of underwater acoustic target recognition. The method comprises the following steps: firstly, performing preprocessing on an obtained underwater acoustic target original audio and associated metadata, and constructing a multi-modal data set; and then the constructed multi-modal sample is input into an identification model, the model extracts deep representation of each modal through a multi-branch feature coding network, depth alignment and complementary aggregation of different modal features are realized by using a cross-modal cross attention mechanism guided by potential query, and a target identification result is output based on a decision network of mixed experts. According to the method, experimental verification is carried out on two disclosed underwater acoustic data sets, the experimental result verifies the effectiveness of the multi-modal deep fusion and hybrid expert adaptive decision strategy adopted by the method, and through mining the complementary advantages of acoustic features and semantic priori, the multi-modal deep fusion and hybrid expert adaptive decision strategy is obtained. And the robustness and generalization ability of the underwater acoustic target recognition system in the strong-noise and multi-working-condition environment are remarkably improved.

The invention discloses an automatic auditing system and method for road engineering completion settlement. The system comprises a data acquisition and integration module; a data preprocessing module; a settlement index construction module; an intelligent auditing engine; an abnormity early warning and tracing module; a settlement result management module; and a data analysis and optimization module. The auditing method comprises the following steps: S1, collecting and integrating multi-source settlement data; s2, big data preprocessing; s3, constructing a settlement auditing index system; s4, performing intelligent automatic auditing; s5, performing abnormal early warning and tracing; s6, managing settlement results; and S7, performing big data driving optimization. According to the invention, multi-source data intelligent integration, full-process automatic auditing, abnormal accurate tracing and dynamically optimized big data driven settlement auditing can be realized; the method is applied to whole-process management of road engineering completion settlement, and data-driven intelligent auditing and continuous optimization control are realized in the process.

This invention provides a method for rapid assembly of a modular tubular beam car body. The car body is divided into several independent modules, which are manufactured in parallel at independent workstations or assembly lines. Corresponding connections between different independent modules are fitted with interlocking mortise and tenon structures for coarse positioning and fastening. The manufactured independent modules are then automatically transferred to an assembly station, where a robot grasps and rapidly assembles them. After assembly, bolts and / or structural adhesive are used to fasten the mortise and tenon joints. Compared to traditional assembly line production processes, this method significantly improves efficiency and economy, effectively reduces floor space, and overcomes the limitation of existing "unpacking processes" that are only suitable for single-model production, thus facilitating flexible manufacturing and rapid iteration across multiple models.

The invention discloses a traceability application-oriented block chain database data management system, particularly relates to the technical field of block chain database data management, and is used for solving the problems of easy data tampering and low verification efficiency caused by traceability data logic association and physical storage splitting. The method comprises the following steps: firstly, matching a parent physical sector offset through an association analysis module, determining a storage partition, and assembling a write-in instruction; then, a topology binding module executes a disk write operation, synchronously reads parent node hash and performs combinatorial operation in combination with a physical address to generate a topology binding check value bound with the physical position; the aggregation anchoring module uses a Merkel tree aggregation check value to generate a root fingerprint and perform uplink, and establishes an anchoring voucher; and finally, a path verification module positions a physical position according to the certificate, executes reverse recursive addressing and step-by-step Hash recalculation, and assembles a pedigree verification packet, so that a closed-loop trust system from physical storage to chain certificate storage is constructed, and the tamper-proof capability and verification efficiency of the traceability data are improved.

The invention belongs to the technical field of knowledge graph construction, and particularly relates to a multi-source heterogeneous data fusion and knowledge graph automatic construction system, which comprises a semantic extraction module for extracting minimum semantic fragments from multi-source data and performing cross-source alignment to generate a symbolized semantic framework; the data purification module converts multi-source data into symbolic predicates according to the framework, filters low-evidence data and outputs a purification set; the ternary generation module inputs the initial triad candidate set into a minimum rule grammar, and extracts entities, relationships and attributes to generate an initial triad candidate set; the disambiguation calibration module generates entity two-hop topological fingerprints based on the candidate set, completes disambiguation alignment and corrects conflicts, and outputs unambiguous structured knowledge; the mapping fusion module fuses the mapping information with the minimum connected ontology, and constructs and verifies an initial mapping knowledge domain; and the incremental updating module processes newly added data, performs incremental merging and maintains consistency, and forms a complete knowledge graph. According to the method, through full-link automatic construction, efficient fusion and high-quality atlas are realized.

A flow direction control device of heating stream and cooling stream is prepared as connecting said device separately to the first heat exchange unit, the second heat exchange unit, air conditioner and compressor for controlling heated working fluid from the first heat exchange unit to be sent to air conditioner for being used as the first heat media or to be sent to the second heat exchanger for being condensed there then to send it to air conditioner for being used as the second cooling media in order to make air conditioner output hot and cold alternatively, enabling to set heating course on the first heat exchange unit for providing the third heat media out.

The invention is suitable for the technical field of road construction, and provides a wind power plant road intelligent line selection method based on multi-dimensional constraint dynamic modeling, and the method comprises the steps: obtaining a multi-dimensional constraint condition of wind power plant road line selection, and constructing a constraint model; generating a slope grid map based on a digital elevation model, and fusing the slope grid map with a constraint map layer reflecting geological stability and a land cover type to generate a dynamic cost surface; adopting an improved heuristic search algorithm to perform parallel path search from the road starting point to each target fan position, generating a plurality of initial feasible paths, and calculating a comprehensive cost value; screening a candidate line set according to a comparison result of the comprehensive cost values; receiving real-time updating data in a construction process and environment monitoring, inputting the real-time updating data into the constraint model to trigger model updating, performing iterative optimization on the candidate line set based on the updated constraint model, and outputting a final line selection scheme; and the line selection efficiency, economy and reliability are effectively improved.

The invention relates to a method and device for measuring pitch deviation based on a line laser sensor, and the method comprises the following steps: S1, calibrating the position of the line laser sensor, and obtaining the coordinate value of a coordinate system of the line laser sensor in a measurement coordinate system; s2, installing a tested gear; s3, adjusting the relative position of the line laser sensor and the measured gear; selecting a measured position in the tooth surface area of the gear teeth of the measured gear, adjusting the position of the line laser sensor, and recording the sum of the offset of the adjusted line laser sensor relative to the center of the measured gear along the x axis and the y axis in the effective measurement range of the line laser sensor at the measured position; s4, performing zero returning operation on the angle sensor of the rotating shaft of the tested gear; and S5, controlling the detected gear to rotate along the center line of the detected gear. According to the method and the device, the pitch deviation can be measured in a non-contact mode, excessive calibration and leveling are not needed before detection, and the measurement efficiency and the measurement precision are both improved.

The invention discloses a CFD calculation method and system based on a neural network and adaptive parameter optimization, and belongs to the technical field of computational fluid dynamics. Initializing a flow field by reading calculation parameters and a grid file; solving the N-S equation, recording solving parameters and outputting an intermediate flow field; inputting the intermediate flow field into a pre-trained physical constraint neural network, sequentially applying boundary condition hard constraint and soft constraint based on control equation residual error, and outputting a corrected flow field; based on the convergence dynamic characteristics and the solving parameters, outputting a parameter adjustment amount through a strategy network of reinforcement learning training, and dynamically optimizing the solving parameters; and taking the corrected flow field as an initial flow field of the next iteration step, updating solving parameters, and circularly executing until convergence. According to the method, the physical prior is embedded into the neural network, intelligent self-adaptive regulation and control of solving parameters are realized through reinforcement learning, and the prediction precision and convergence efficiency of complex flow simulation are remarkably improved.

The invention discloses a key value storage system indexing method oriented to an NVM-NVMe SSD (Non-Volatile Memory-Non-Volatile Memory Express Solid State Disk) hybrid architecture, which comprises the following steps of: constructing a heterogeneous storage architecture taking cold and hot data perception as a core driving mechanism, coordinating and managing two types of storage media, namely a non-volatile memory NVM and a solid state disk NVMe SSD, and realizing efficient identification, layered writing and dynamic migration of cold and hot data. According to the method, the characteristics of low delay, durability and byte addressing of the NVM are utilized, the frequency and I / O overhead of Flush and Compaction operations are reduced in a data write-in path, meanwhile, the problem of mixed storage of cold and hot data is avoided, and therefore the overall performance and storage efficiency of a system are remarkably improved. In addition, by introducing an asynchronous migration module, the method can dynamically adapt to the change of data popularity along with time evolution, and effectively support high performance and high availability of the key value system in long-term operation.

The invention discloses a quantitative recognition device for the number and size of bubbles at a rotating impeller of a multiphase pump. The quantitative recognition device comprises a two-phase flow experiment system, the multiphase pump, a high-speed camera, a light source and the like. The preprocessing and data analysis system and the deep learning system are arranged in the computer; the two-phase flow experiment system and the image acquisition system are electrically connected with the computer through lines. The invention further discloses a quantitative recognition method for the number and size of the bubbles at the rotating impeller of the multiphase pump. According to the method, the problems of low blistering recognition precision, low efficiency and large error caused by the existing manual measurement method are solved, and a reference method is provided for exploring the influence of gas distribution at the impeller in the multiphase pump on pump operation and optimizing design of the multiphase pump.

The invention discloses a curved surface porous structure modeling simulation method and system for finite element analysis, and belongs to the technical field of mechanical engineering simulation, and the method mainly comprises the steps: carrying out the initial grid division of a target curved surface part, and obtaining a grid node set covering a curved surface; according to a preset hole center coordinate, three closest grid nodes are automatically searched in the grid node set, a local reference plane is established, and the hole axis direction is determined; generating an offset sketch plane in the normal direction of the reference plane, projecting the hole center to the sketch plane, drawing a circular sketch, and executing a stretching and cutting command to form a hole; and circularly executing the operation to realize automatic generation of multiple holes on the curved surface. And finally, the porous structure is subjected to grid division again, so that subsequent operation is facilitated. According to the method, the modeling process is combined with parameterization analysis and an automatic process, the efficiency and stability of porous modeling of the complex curved surface structure are remarkably improved, and a basis is provided for finite element pretreatment of the complex curved surface structure.

The invention discloses a design manuscript code generation and visual error iterative correction method and system based on multi-agent cooperation, and aims to solve the problems of low front-end code generation efficiency, poor visual restoration degree and lack of an automatic correction mechanism in the prior art. The system constructs a four-node state diagram based on LangGraph, automatically initializes a series engineering scaffold, obtains design data and generates a structured protocol, and generates a component code based on the protocol and performs visual verification and iterative correction on four processing nodes. The method comprises the following steps: dynamically generating an engineering scaffold through a large language model agent, converting Figma design data into a structured protocol, and carrying out iterative optimization by utilizing a judger-repairer mode until an error is lower than a threshold value; according to the method, high-fidelity automatic delivery from the design manuscript to the code is realized, and the executable front-end engineering code and the visual reduction degree analysis report are output.

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 drilling parameter optimization control, and provides a petroleum drilling parameter optimization method and system and a storage medium. Comprising the steps that torque fluctuation amplitude and bit pressure response delay data are collected in real time, an original data sequence is constructed, and parameter trend data are obtained; analyzing the parameter trend data to identify a stratum change mode, extracting trend feature information, and when the trend feature information meets a preset condition, determining a corresponding abnormal mode identifier; generating a parameter adjustment direction of drilling control according to the abnormal mode identifier, and optimizing the parameter adjustment direction in combination with parameter weight analysis and a real-time feedback mechanism to obtain final optimization guidance data for guiding drilling operation; and mapping the final optimization guidance data to a control interface, and generating a dynamic adjustment instruction for drilling operation. The problems that in the existing petroleum drilling process, parameter adjustment lags behind, and stratum changes are difficult to adapt are solved, and intelligent optimization and dynamic control over drilling parameters are achieved.

The utility model relates to a green husk removing device for walnuts. The green husk removing device comprises a feeding machine, a green husk removing machine connected with the feeding machine, a vibrating conveyor connected with the green husk removing machine, and a conveyor connected with the vibrating conveyor, the green peel removing machine comprises a rack, a peeling roller arranged on the rack, a plurality of peeling bent knives arranged below the peeling roller, and a guide rail, wherein one end of the guide rail is connected with the peeling bent knives, and the other end of the guide rail is connected with the feeding machine. And the number of the guide rails is the same as that of the peeling curved knives. Walnuts with green husks are conveyed to a green husk removing machine one by one through a feeding machine, the walnuts pass through a gap between a husking roller and a husking bent knife, a first circular fluted disc and a second circular fluted disc to drive the walnuts to rotate and advance slowly, a three-jaw husking disc rotates rapidly, the green husks are dug out layer by layer through digging hooks on husking claws, and green husk removing is completed. Compared with an existing common method, the device can effectively improve the efficiency.

The invention relates to an intelligent agent continuous training and effect evaluation closed-loop method and system fusing work order feedback, and a medium, and relates to the technical field of deep learning. The agent continuous training and effect evaluation closed-loop method comprises the following steps: receiving and analyzing work order feedback information, and recording the number of error information and information storage duration in combination with a preset structure sample label; dynamically judging the number of error information and the information storage duration, triggering a retraining rule of an original agent model, distributing candidate information weights, and screening a target sample set; directionally training and optimizing the original agent model according to a retraining rule in combination with the target sample set, and generating a new agent model; constructing a historical work order problem set, comparing and verifying a new agent model in combination with an original agent model, generating an agent evaluation table, and correcting a candidate information weight; by accurately focusing the weak link, the continuous and efficient enhancement of the model capability is realized, so that the answer accuracy of the intelligent agent is remarkably improved, and the AI illusion is effectively inhibited.

The invention provides a lightweight large model operation method based on end-side deployment. The lightweight large model operation method comprises the following steps: acquiring equipment operation data and storing a model file in a mixed precision format according to quantization precision supported by terminal equipment; determining a mixing precision quantification strategy and carrying out strategy analysis on the equipment operation data; a Key-Value cache file obtained through model reasoning in the historical dialogue is reserved; calculating a semantic embedding vector of the second user request, calculating a cosine similarity between the semantic embedding vector and the first V vector, and judging whether the second user request has a similar intention or not according to a calculation result; and if yes, performing incremental reasoning on the second user request by multiplexing the Key-Value cache file. By monitoring the state of the terminal equipment in real time, dynamically adjusting the quantization level, multiplexing the cache data of the semantic related requests and merging batch request reasoning, the effects of balancing the energy consumption and performance of the terminal equipment, reducing repeated calculation and improving the service time of the equipment and the user experience are achieved.

The invention provides an interactive multi-dimensional data visualization display sand table and method for business data analysis, and the method comprises the steps: carrying out the semantic mapping of state parameters in an interaction control operation when the interaction control operation is received, obtaining a plurality of pieces of visualization intention metadata, and generating a structured query instruction; based on the structured query instruction, extracting a target data subset rendered visually by the heterogeneous commercial data source from a multi-dimensional data cube of the commercial data source, performing visual optimization on the visually rendered chart type, the visual coding channel and the initial visual parameter through the cardinal number of the dimension field in the target data subset and the data distribution type and the numerical range of the measurement field to obtain a visual configuration set of the target data subset; and using the visualization configuration set to render and generate a multi-dimensional data visualization view, and outputting and displaying the data visualization view in a graphical user interface. Based on the scheme, automatic closed loop generation from heterogeneous data to interactive views can be realized.

The invention discloses a credit text analysis method and a credit object auditing method and device, and relates to the technical field of knowledge maps. A specific embodiment of the method comprises the steps of obtaining a target credit card text, wherein the target credit card text comprises at least one target element; a target semantic template corresponding to the target credit document text is obtained, the target semantic template comprises a statement template and an upper sharing hierarchy of the statement template, the statement template comprises at least one target slot position, and the target slot position corresponds to the target element; obtaining a full-link slot position of the target credit card text according to the statement template and the upper sharing hierarchy; and matching the target semantic template with the target credit text to obtain a target slot value corresponding to the full-link slot position. According to the implementation mode, the slot position and the slot value corresponding to the element in the credit card can be automatically obtained, and the related content in the credit card text can be analyzed.

The invention relates to the technical field of industrial vision and artificial intelligence, and discloses an aluminum coating formula prediction method and system based on industrial vision and double-model fusion. The method comprises the following steps: acquiring visible light and near-infrared band images of the surface of an aluminum material coating through multispectral image acquisition, identifying a defect area to generate a binary mask, extracting three types of features of color, texture and spectral reflection, and respectively inputting feature vectors into a random forest regression model and a convolutional neural network model to obtain a target image; and dynamically calculating a fusion weight according to the verification set error, and carrying out weighted fusion on the prediction results of the two models to generate a formula component content prediction value. According to the method, the technical problems of low precision, low efficiency and insufficient generalization ability of a traditional aluminum coating formula determination method are solved, and rapid and accurate prediction of the formula is realized.

The invention discloses a property asset digital operation and intelligent security linkage cooperative processing system and an implementation method, and relates to the technical field of Internet of Things, and the system comprises a protocol adaptive gateway layer which solves the problem of compatibility of multi-manufacturer equipment, and outputs a standardized data stream based on a dynamic protocol sniffing and fingerprint feature library, SDK-free access control, fire protection, monitoring and IoT equipment; the method comprises the following steps: constructing a millimeter-level precision three-dimensional space model by a digital twinborn middle table layer, fusing a security event and operation data, and ensuring that the operation is compliant and audible through blood relationship tracking; the intelligent decision execution layer combines a rule engine and machine learning to generate a linkage instruction, drives a work order to be automatically distributed and positions an equipment fault responsible party; and the edge cooperative computing node locally realizes video millisecond-level analysis, and starts localized linkage according to an emergency plan when the network is disconnected, so as to ensure that the core security function is not interrupted.

The invention relates to an industrial quality inspection data cooperative transmission method driven by image recognition, in particular to the field of artificial intelligence, which analyzes video frames in real time through a lightweight neural network, accurately locates and extracts key areas and features in industrial product images, constructs a dynamic semantic scene graph to understand contents and divide priorities, and improves the quality inspection efficiency. The core of the method is that non-uniform intelligent coding is carried out on video streams according to semantic importance of contents, high-fidelity transmission of key information such as defects is ensured, non-key areas are greatly compressed to save bandwidth, scheduling is carried out in the transmission process according to priorities, confidence feedback based on a cloud recognition result is introduced, closed-loop control is formed, and high-fidelity transmission is realized. According to the method, the front-end analysis model, the coding strategy and the network path are adaptively optimized, so that low-delay and high-reliability visual quality inspection data transmission and accurate identification can be continuously and stably realized in a complex industrial network environment, and the overall efficiency and the intelligent level of online quality inspection are remarkably improved.

The invention discloses an unmanned aerial vehicle cluster task allocation method based on a large language model optimization genetic algorithm, and belongs to the field of computers. The method comprises the following steps: setting a specific chromosome coding mode; generating a multi-constraint initial population; calculating fitness to quantify the advantages and disadvantages of individual genes of the population; when the optimal individual meets the requirement or the maximum iteration round is reached, ending; retaining the optimal individual as a filial generation; generating a batch of new filial generation individuals by the large language model, and fusing the new filial generation individuals with the current filial generation population; calling an optimized large language model to analyze individual chromosome semantics, and outputting an evolutionary potential score; obtaining an individual comprehensive selection probability by integrating the fitness and the evolution potential score, and executing a selection operation; and selecting individuals based on the individual comprehensive selection probability to carry out crossover and mutation operation to generate offspring. The large language model is embedded into the core link of the genetic algorithm, and the algorithm efficiency is improved by improving the population diversity of the genetic algorithm in the unmanned aerial vehicle cluster task allocation scene.

The invention discloses an industrial network fault intelligent diagnosis method and system based on multi-source data, and the method comprises the steps: synchronously collecting data from a plurality of data sources of an industrial network, and extracting a time sequence statistical feature, a flow entropy feature and a protocol conformity feature to form a multi-dimensional feature vector; establishing a dynamic baseline model by adopting a sliding window online learning method, and calculating a comprehensive anomaly score for anomaly detection; the fault suspicion degree is calculated based on the equipment incidence matrix and the fault propagation model to realize fault source positioning; carrying out fault type identification and root cause analysis by adopting Bayesian reasoning and a knowledge rule base; and outputting a structured diagnosis report containing the fault source, the type, the root cause and the disposal suggestion. According to the invention, early warning, accurate positioning and intelligent diagnosis of industrial network faults are realized, and the operation and maintenance efficiency, safety and reliability of the industrial control network are significantly improved.

The invention discloses a digital electroencephalograph, and belongs to the technical field of brain-computer interface signal processing. An electroencephalogram signal processor of the digital electroencephalograph acquires digital neural signals of a plurality of channels for a target object, wherein the digital neural signals comprise LFP signals and pulse signals; performing feature extraction on the two signals based on a unified time window and a sliding step length, and integrating a time-aligned feature matrix into a first fusion feature tensor; performing dynamic normalization processing on the first fusion feature tensor, wherein the statistical magnitude is updated along with the change of the input data; and n parallel LSTM networks are used to decode the normalized second fusion feature tensor to obtain n decoding results of different dimensions, the n LSTM networks are mutually independent, and each LSTM network has an independent optimizer and an independent parameter. According to the method, multi-modal features can be aligned in time, signal drift is compensated, decoding robustness is improved, and inter-task optimization decoupling and personalized training are achieved through independent parameters and an optimizer.

The invention discloses an XR-based immersive three-dimensional modeling and straight-through 3D printing system and method, and the method comprises the steps: S1, collecting user input, and converting the user input into an interaction instruction; s2, generating three-dimensional model data; s3, superposing the three-dimensional model into a real physical environment according to a proportion of 1: 1, and outputting a virtual-real fusion display result; s4, performing printability analysis on the model, marking a problem area, guiding a user to modify and providing an automatic repair suggestion; s5, generating a support structure, and providing visual preview and interactive editing; s6, slicing and path planning are executed, and a printing instruction file meeting the control requirement of the 3D printer is generated; s7, communication connection with the 3D printer is established, and printing state information is fed back; and S8, providing storage and calling support of the three-dimensional model file, parameter configuration and environment map data. According to the invention, seamless integration of immersive XR modeling, MR real-time verification and one-key direct-through 3D printing is realized.