393results about How to "Avoid missing" patented technology

The invention discloses a force sense feedback control method for an intelligent mechanical arm, which comprises the following steps of: 1, acquiring data through a multi-modal sensor and fusing the data to obtain a multi-dimensional perception vector; 3, calculating a force sense tracking error and a change rate and triggering an event-driven control decision mechanism; 4, designing a nonlinear compensation control rule and outputting a control torque instruction, wherein a control system comprises a multi-mode sensing module, a dynamic prediction module, an event-driven control module and a cooperative calculation module; according to the method, multi-mode sensing information is fused with the lifting force sense representation capacity, advanced adjustment is achieved in combination with a dynamic force sense prediction mechanism, event-driven control is used for reducing calculation redundancy, robustness to complex interference is enhanced through a nonlinear compensation strategy, and finally high-precision and low-delay force sense control of the mechanical arm in a dynamic interaction scene is achieved.

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 relates to the technical field of multi-unmanned-aerial-vehicle cooperative operation and three-dimensional modeling, in particular to a multi-unmanned-aerial-vehicle cooperative three-dimensional rapid modeling method for a highway accident scene, and the method comprises the steps: generating a three-dimensional grid map of an accident area through the scanning of a millimeter-wave radar by a main control unmanned aerial vehicle; subareas are divided according to a load balancing strategy and are distributed to slave unmanned aerial vehicles, the slave unmanned aerial vehicles traverse grids along a snake-shaped track, laser radar point clouds and five-view-angle images are synchronously collected, data are bound through double time stamps and space coordinates, the point clouds are preprocessed through edge computing nodes, and the point clouds are stored in a database; the master control unmanned aerial vehicle evaluates quality based on density standard deviation and overlapping matching degree and instructs to reacquire, performs high-precision Poisson reconstruction on an accident core area, performs voxelization processing on a peripheral area, maps image textures, optimizes vehicle deformation details, simplifies a model and retains key element precision, and finally performs data processing. License plate coordinates, a scattered object thermodynamic diagram and an emergency lane occupation state are automatically marked, a visual model is generated, and rapid and accurate restoration of an accident scene is realized.

The invention provides a phytoplankton chromatography sequence identification method and a phytoplankton chromatography sequence model building method, and belongs to the technical field of image enhancement identification. The method comprises the following steps: firstly, acquiring microscopic chromatography sequence data of phytoplankton, performing view field extraction and serialization recombination, and constructing a three-dimensional data set; then, constructing a three-dimensional recognition model containing physical perception and a sequence aggregation mechanism, extracting single-frame semantic features by the model by adopting a parameter-shared twin network, and introducing a physical definition prior module to calculate a space-frequency domain quality score of a slice; secondly, designing a deep perception sequence aggregation module, and adaptively aggregating key features of a high signal-to-noise ratio by taking definition scores as gating signals and combining spatial context information between slices; and finally, training and optimizing the model based on the image-level weak supervision label to obtain an optimal model. According to the method, the problems of information truncation and out-of-focus noise interference caused by extremely shallow depth of field of high-power microscopic imaging are solved, and full-depth-of-field stereoscopic perception can be realized under the condition that frame-by-frame fine labeling is not needed.

The invention provides a river flow online measurement and calculation method based on multi-source data fusion, and belongs to the technical field of river flow measurement. A Bayesian neural network and active learning collaborative hydrological memory reconstruction model is adopted to carry out high-confidence data interpolation on a sensor failure period and quantify uncertainty, fractional calculus is introduced to model a river memory effect, and flow evolution is analyzed through a fractional order water balance equation. And selecting a steady-state or non-constant flow calculation mode according to the flow change rate, inverting an optimal flow field for the non-constant flow by adopting a four-dimensional variational data assimilation method in combination with regularization constraint and time smoothing constraint, and outputting a flow measurement result and an uncertainty quantitative index. The technical problems that flow measurement and calculation data are missing and accurate interpolation is difficult due to sensor failure under the extreme hydrological condition are solved.

The invention relates to an Internet of Vehicles computing power scheduling method based on a graph neural network and deep reinforcement learning, and the method comprises the steps: S1, collecting the state information of network entities in the Internet of Vehicles and the association information between the entities in real time, and constructing a dynamic time-space attribute graph; s2, inputting the dynamic space-time attribute graph into a pre-trained graph neural network encoder, and outputting a node embedding feature set containing a high-order topological relation and global graph embedding features through multi-layer message passing and feature aggregation; s3, the node embedding feature set and / or the global graph embedding feature are / is used as the state of a deep reinforcement learning agent and input to a strategy network, the computing power scheduling action at the current moment is output, and the computing power scheduling action comprises the steps of assigning an unloading target node for a to-be-processed computing task and distributing corresponding computing and communication resources; s4, dispatching actions are distributed to the corresponding network entities to be executed, environment feedback is collected and used for model updating and next round of dispatching, and the method has the advantages of improving dispatching efficiency, adaptability and system energy efficiency and the like.

The invention discloses a remote sensing image segmentation method based on multi-scale wavelet transform and Mama. The method comprises the following four steps: firstly, carrying out preprocessing and division on an ISPRS Potsdam data set and a Vaihingen data set; then constructing a segmentation network, wherein the network comprises a local detail extraction branch, a spatial semantic extraction branch, a cross-branch feature fusion part and a decoder; training and optimizing the network by using the training set; and finally, performing segmentation reasoning on a test image by using the trained model. According to the method, the high-frequency detail extraction capability of the image is enhanced through multi-scale wavelet transform, the long-distance dependency relationship is modeled by using the visual state space block, and effective fusion of the features is realized through the double-branch fusion module, so that the feature extraction capability and the semantic segmentation precision are improved, and meanwhile, the training efficiency and the stability are optimized.

The invention discloses an internet information intelligent monitoring method and system, a medium and a product, and relates to the technical field of data processing. The method comprises the following steps: firstly, acquiring internet information data containing text, image and audio contents, extracting features of the three contents respectively, and constructing a semantic map; calculating semantic similarity among different map nodes, and establishing a cross-modal mapping relation to form a mapping set; node attribute characteristics are extracted for the mapping relation to calculate consistency, and mapping lower than a threshold value is marked as a conflict relation; counting the conflict node proportion of each map to obtain a modal conflict degree, calculating the similarity with the reference map to obtain a modal matching degree, and calculating the modal credibility according to the modal matching degree; normalizing the modal credibility to obtain a weight coefficient, and performing weighted fusion on the map structure features to obtain a fusion feature vector; and finally, matching the fusion feature vector with a monitoring rule base to obtain a monitoring result. By implementing the technical scheme provided by the invention, the monitoring accuracy of Internet information can be improved.

The invention discloses an image fusion method based on a double-branch feature decoupling auto-encoder, and belongs to the field of computer vision and image processing. According to the method, for the problems of modal pollution and structural distortion in infrared and visible light image fusion, structural semantic information and high-frequency texture details of a source image are extracted respectively by constructing a content feature coding module and a detail feature coding module, and feature decoupling is achieved. And performing deep fusion on the decoupled features by using an adaptive feature weighting mechanism, and reconstructing a fused image with infrared target saliency and visible light detail definition through a shared decoder. According to the method, an end-to-end two-stage training strategy is adopted for optimization, complex prior or post-processing is not needed, the effects of improving the fused image contrast, edge preservation and target recognition performance are remarkable, and the method is suitable for the fields of weak light monitoring, intelligent perception, unmanned system navigation and the like.

The invention provides a stiffened wallboard life prediction method based on a data enhanced physical information neural network, which comprises the following steps: determining influence parameters of crack propagation fatigue life of a stiffened wallboard and a discourse domain of the influence parameters, obtaining sample points through sampling, calculating a stress intensity factor and fatigue life based on a finite element model, and constructing a training set; a double-layer physical information neural network is established, a first sub-model predicts a stress intensity factor by taking an influence parameter as input, a second sub-model combines the influence parameter and a stress intensity factor prediction value, a physical loss function based on a Paris crack propagation law is introduced, training is performed in combination with a data loss function, and fatigue life prediction is realized; and representing the influence parameters as fuzzy variables, obtaining a multi-level-cut set, and predicting the fuzzy response of the fatigue life by using the trained neural network. According to the method, a physical mechanism and data driving can be effectively fused, and efficient prediction of the crack propagation fatigue life of the stiffened wall plate under the uncertain condition is achieved.

The invention discloses an electromechanical equipment fault cross-domain diagnosis method and system based on minimum category confusion, and the method comprises the steps: selecting an external public data set or labeled historical data as a source domain data set, and then collecting the real-time operation data of electromechanical equipment to be diagnosed, taking the real-time operation data of the electromechanical equipment to be diagnosed as a target domain data set; constructing a fault diagnosis model; performing joint adversarial training on the fault diagnosis model by using the preprocessed source domain data set and target domain data set, and performing lightweight processing and parameter solidification after training is completed to obtain a final fault diagnosis model; and deploying the final fault diagnosis model to an edge computing equipment end, and diagnosing the preprocessed real-time fault data of the electromechanical equipment by using the final fault diagnosis model to obtain a diagnosis result. According to the method, the multi-scale feature extractor is adopted to perform multi-scale feature extraction and fusion, so that the feature extraction capability is improved, and missing of key features is avoided.

The invention discloses a discrete manufacturing multi-view BOM dynamic derivation method and system based on a tree-shaped network structure, and belongs to the technical field of intelligent manufacturing and product data management. The method comprises the following steps: constructing a tree body as a single product data source, and storing a hierarchical relationship, attribute definition and engineering semantics of a product in a tree structure; based on the tree body, dynamically matching derivation rules through a mesh mapping relation library; executing rule matching, semantic verification, structure reconstruction and dictionary verification by utilizing a dynamic view derivation engine to generate a target BOM view; semantic constraint and verification are carried out on the derivation process through a BOM data dictionary module; and realizing access control and audit based on role, scene and data sensitivity through the security and authority control module. According to the method, a new generation of xBOM dynamic derivation architecture which takes a tree form as an ontology, takes a net form as mapping, is configurable in rule and is strong in data specification is constructed; the fundamental problem of discrete manufacturing multi-view BOM management is solved, and engineering rationality and service flexibility are both achieved.

This invention relates to a method, apparatus, electronic device, and storage medium for intelligent agent memory management. The method includes: acquiring interaction content between the intelligent agent and a user; performing semantic segmentation and metadata extraction to generate memory fragments; storing the memory fragments in a multi-level directory structure of a local file system, without deleting memory fragments already stored in the multi-level directory structure; constructing a time index, keyword index, semantic index, and association index for the memory fragments; and, in response to a retrieval request from the intelligent agent or user, retrieving memory fragments matching the retrieval request from the time index, keyword index, semantic index, and association index respectively to obtain a candidate memory set, performing fusion and sorting to obtain sorted memory data. This invention improves the efficiency of intelligent agents in accumulating and reusing knowledge and experience, and also enhances privacy protection and long-term memory reliability.

The utility model discloses a laser radar convenient for unmanned aerial vehicle carrying, and relates to the laser radar technology field, the laser radar comprises an unmanned aerial vehicle body, the bottom of the unmanned aerial vehicle body is provided with a mounting rack, the inner wall of the mounting rack is rotatably connected with a first fixing rack and a second fixing rack, the outer side of the first fixing rack is provided with a mounting assembly, and the outer side of the second fixing rack is provided with a second fixing rack. A connecting frame is fixedly connected to the bottom of the mounting frame, and an adjusting assembly is arranged on the outer side of the connecting frame. The unmanned aerial vehicle radar device has the advantages that by arranging the mounting assembly, the mounting frame can be conveniently separated from the unmanned aerial vehicle body, and therefore the radar body can be conveniently detached, and the deployment time of equipment is shortened; and by arranging the adjusting assembly, the angle of the radar body can be conveniently adjusted, so that the situation that the integrity of three-dimensional modeling is affected due to data loss of a part of areas is prevented.

This application relates to a method for identifying hourly-scale extreme high temperatures using temperature series data. The method includes: preprocessing hourly temperature data to obtain an hourly-scale temperature series, and calculating a daily-scale average temperature series based on a meteorological daily window. Based on the hourly-scale temperature series and historical reference periods, determining hourly-scale extreme high temperature thresholds for different hours and identifying hourly-scale extreme high temperatures. Based on the daily-scale average temperature series and historical reference periods, determining daily-scale extreme high temperature thresholds for different dates and identifying daily-scale extreme high temperature days and non-daily-scale extreme high temperature days. Extracting the corresponding times that meet the hourly-scale extreme high temperature criteria from both daily-scale and non-daily-scale extreme high temperature days to obtain the corresponding hourly high temperature identifier sequences. This method enables accurate identification of the specific time periods and distribution characteristics of extreme high temperature events, avoiding the omission of high-temperature events.

The application discloses a charging pile multi-fault intelligent detection system based on improved YoloV11, relates to the technical field of intelligent operation and maintenance of charging piles, and collects four-mode original data in an input layer; a preprocessing module processes the four-mode original data respectively to output uniform size feature maps; a backbone network performs feature extraction and enhancement through a light cross-channel convolution unit and a dynamic local feature module, and a RepResNet-18 module is additionally arranged for visible light mode data to perform feature enhancement; a cross-mode dynamic gate is used to enhance a fault correlation area; a neck network realizes cross-mode multi-level fusion and multi-scale hierarchical layering; a detection head multi-scale feature is used for boundary box prediction, fault category prediction and confidence prediction, and finally, a prediction result is output through an output layer. The application realizes breakthroughs in detection integrity, robustness and efficiency, can be directly applied to an edge intelligent monitoring scene of charging piles, and significantly reduces operation and maintenance costs.

The utility model belongs to the technical field of sterilization kettles, and particularly relates to a circulating pasteurized yoghourt sterilization kettle which comprises supporting legs and a sealing rubber ring, the sterilization kettle is fixedly installed at the tops of the supporting legs, a sealing cover plate is rotatably installed on one side of the sterilization kettle, a supporting assembly is arranged on the inner wall of the sterilization kettle, and the sealing cover plate is rotatably installed on the supporting assembly. A rotating mechanism is arranged on the inner wall of the sterilization kettle, a circulating mechanism is arranged at the bottom of the sterilization kettle, a sterilization assembly is arranged at the top of the inner wall of the sterilization kettle, a fixing assembly is arranged on the side, close to the sterilization kettle, of the sealing cover plate, and the supporting assembly comprises an arc-shaped supporting groove and a flow guide hole. Through the arrangement of the arc-shaped supporting grooves and the flow guide holes, the arc-shaped supporting grooves can provide stable supporting for the storage frame, it is guaranteed that the storage frame does not shake during rotation, and through the arrangement of the flow guide holes, pasteurized paper can smoothly flow into the water outlet to be recycled.

The application provides a kind of operation and maintenance scene visualization report generation method and system based on natural language question and answer, belongs to intelligent operation and maintenance technical field, method includes: receiving the natural language query input by user;Through the pre-training of large language model and the preset operation and maintenance terminology dictionary, the natural language query is parsed and entity is extracted, and the entity-field association table containing demand type is generated;According to demand type and semantic feature, the query type is judged to be data query or visual query;Based on the judgment result, generate structured query language sentence or visual instruction;Query is executed to the interface business database to obtain raw data;Raw data is processed and analyzed to obtain analysis result data;Call data feature adaptation algorithm to match chart type, generate and output visual report.The application realizes the full-link automation from natural language input to visual report output, reduces the operation and maintenance data interaction threshold, improves the operation and maintenance data processing efficiency and accuracy.

This invention discloses a graphic calibration system and method for CNC mold machining. The system includes: a data acquisition module configured to receive CNC machining tasks and acquire graphic data and measurement data of the mold to be machined based on the machining tasks; a data calibration module configured to perform geometric feature calibration on the graphic data based on preset calibration data, generate calibrated calibrated graphic data, and spatially register the measurement data and calibrated graphic data to obtain deviation data; a data processing module configured to map the deviation data to a machining parameter library in the machining task, calculate the correction values ​​of each machining parameter to obtain calibration parameters; and a data feedback module configured to feed back the calibrated graphic data and / or calibration parameters to the system, and perform the remaining graphic data input and machining parameter input for the machining task. This invention significantly improves mold machining accuracy and production efficiency, providing a high-confidence graphic input benchmark for CNC mold machining.

The application relates to the technical field of detection, and discloses a multimode nondestructive testing equipment for nickel plates and cobalt plates, which comprises a detection chamber, the inside of the detection chamber is filled with ultrasonic detection liquid, bases are further arranged on the two sides of the detection chamber, a light plate is slidably arranged on the base, a CCD visual detection probe is arranged on the light plate, the CCD visual detection probe is used for capturing surface defects of the nickel plate, the light plate is used for light compensation and shadow dead angle reduction, and an ultrasonic detection probe is further arranged in the detection chamber. The CCD vision and the ultrasonic detection are cooperated in the application, the defects on the surface and in the inside are comprehensively covered, and missing is avoided; the traditional detection is easily misjudged by light, personnel experience and the like, and the defects cannot be quantified; the application guarantees clear visual imaging by means of the light plate, adjusts the components to drive the ultrasonic probe to accurately move, combines signal quantitative analysis, gets rid of artificial subjective interference, and improves detection accuracy.

The utility model provides a monitoring and early warning device for a debris flow gully in a canyon fall section, and relates to the technical field of natural disaster early warning. The device comprises a supporting column, a connecting frame, a sliding rail, a debris flow monitoring module and a rainfall monitoring module. Wherein the supporting columns are embedded and fixed on the two sides of a debris flow gully; the connecting frame is fixedly connected to the upper end of the supporting column. The sliding rail is fixed to the lower end of the middle of the connecting frame and extends in the same direction as the debris flow gully. The debris flow monitoring module is slidably connected to the sliding rail and can follow the debris flow state in the debris flow gully when the debris flow disaster occurs. And the rainfall monitoring module is fixed at the upstream position of the debris flow gully. According to the scheme, the debris flow monitoring module can move along the channel for monitoring through the sliding track, dynamic data collection is achieved in combination with the rainfall monitoring module, the problem that traditional fixed-point monitoring cannot adapt to complex canyon terrains is solved, and the accuracy and timeliness of early warning are improved.

The application discloses a variable working condition cross-component causal coupling modeling and fault diagnosis method and device, and relates to the technical field of rotating machinery fault diagnosis. The method comprises the following steps: collecting and preprocessing multi-component signals under variable rotating speed working conditions of a rotating machinery; based on multivariate Granger causal modeling, prior information and dynamic disturbance information are fused to construct a dynamic causal diagram; a double-view structure of a main view of the dynamic causal diagram and an auxiliary view of a correlation diagram is constructed, multi-scale coupling features are extracted through a multi-order spectrum filter and an attention mechanism, a symmetric InfoNCE contrast learning loss and a supervised classification loss are combined to optimize and train a model, and fault classification is realized. The device is correspondingly provided with signal acquisition, dynamic causal diagram construction, feature fusion, model training and fault prediction modules. The application improves the accuracy of fault feature representation and the robustness of diagnosis under variable working conditions, is suitable for complex industrial scenes with multi-component coupling and frequent rotating speed switching, and has strong engineering practicability.

The application discloses a kind of based on substation target detection clustering cluster waypoint automatic generation method, specifically relates to unmanned aerial vehicle inspection field, including establishing substation three-dimensional diagram, the RGB image of equipment needing to be detected is collected, the geometric center of each equipment on the plane is calculated using Graph-Detect3D, according to equipment type, orientation, the coordinate information of all equipment to be inspected target is obtained according to the geometric center of equipment, all equipment to be inspected target is numbered, the geometric center of equipment is clustered using density clustering algorithm DBSCAN, all equipment center geometric center in the same cluster is used as waypoint, and waypoint is generated according to the geometric center coordinates of each class, so the number of waypoints will not be redundant, will be just covered to each equipment, can collect the picture of all equipment of substation with least waypoint, reduce the redundancy of data collection.

The application discloses a network fault root cause positioning method and operation and maintenance equipment, relates to the technical field of network fault positioning, and is used for improving the accuracy of network fault root cause positioning.In the method, the operation and maintenance equipment constructs an elastic backtracking window associated with a network topology delay, combines a time-space alignment drift degree to quantify a cause-effect relationship, establishes a more reasonable log searching interval for each candidate root cause node, reduces time axis out-of-range time sequence misplacement of a preset window range, avoids a situation that related technology is difficult to capture the correlation between the two, and improves the accuracy of network fault root cause positioning.

This invention discloses a ferroptosis target identification system and method based on an attention mechanism, belonging to the field of artificial intelligence technology. The system includes modules for extracting multi-source features of ferroptosis regulation, labeling weak signal targets, calculating attention potential energy mapping, and ranking ferroptosis targets. It extracts multimodal features from transcriptome expression, molecular interaction networks, and ferroptosis pathway annotations. Targets with expression intensities below a preset threshold that participate in the ferroptosis pathway are labeled with weak signals. Weak signal characteristics are injected, and a potential energy bias is constructed to complete attention potential energy mapping. Then, through pathway mapping and cross-pathway linkage identification and fusion weights, the target linkage degree is calculated and ranked. This system solves the problem of traditional methods easily neglecting weak signal targets, and can identify targets with insignificant expression changes in the ferroptosis regulatory network. Through attention and pathway-level linkage modeling, the comprehensiveness of target identification is improved, and the obtained ranking results have stability and discriminative power, making it suitable for the identification and research of ferroptosis-related targets.

The invention discloses a flexible manufacturing-based DPA component matching method, which comprises the following steps of: establishing a DPA component requirement catalog, establishing a management database, and creating catalog entries for each component needing DPA detection; the method comprises the following steps: establishing DPA components, and creating a corresponding DPA component for each basic component needing to execute DPA detection in enterprise product bill of material structure management; the method comprises the following steps: establishing a corresponding relation between a DPA component requirement directory and a component, establishing a mapping relation between a DPA requirement directory and a DPA component, triggering to generate or call a corresponding DPA component demand according to the mapping relation, and realizing automatic conversion from a detection requirement to a specific execution material; and establishing a matched operation system, and integrating the system into a manufacturing execution system or a material demand planning module of an enterprise resource plan. According to the method, automatic identification of DPA detection requirements, intelligent matching of detection materials and accurate accounting of related cost can be realized.

The invention discloses a navigation voice broadcasting method and device, electronic equipment and a storage medium, and relates to the technical field of navigation.The method comprises the steps that a navigation destination request is obtained, and common city information is determined through an address information query module; vehicle running state data including administrative city codes, road grade identification values and subsequent action states are monitored in real time; based on the common city information and the vehicle running state data, intelligent guide judgment is executed, and a detailed broadcast mode recommendation instruction or a simple broadcast mode recommendation instruction is generated; and in response to the recommendation instruction, controlling the touch display panel to render the interaction window, and updating the voice broadcast mode state according to the user touch input state value or the overtime default confirmation logic. Detailed broadcasting is recommended when the vehicle is in a non-common city and the road condition is complex, simple broadcasting is recommended when the vehicle is in a common city and the road condition is simple, a countdown automatic confirmation mechanism reduces the driving interaction load, and warning tone conflicts are avoided by detecting the audio channel occupation state.

The application provides a performance evaluation method and evaluation system of a silicon carbide chip, relates to the field of semiconductor testing, separates a nonlinear disturbance component from dynamic response data; constructs a link transmission compensation function based on the nonlinear disturbance component, determines dynamic characteristic parameters through the link transmission compensation function and the dynamic response data; performs multi-dimensional spectrum analysis on the dynamic characteristic parameters to obtain high-frequency performance parameters, and then derives energy efficiency of the silicon carbide chip according to the high-frequency performance parameters to obtain energy efficiency trend and energy efficiency thermal coupling characteristics; deviates from abnormal values to perform multi-working-condition pulse excitation on the silicon carbide chip to obtain a multi-dimensional compensation matrix; and performs adaptive coupling compensation on the energy efficiency trend and the energy efficiency thermal coupling characteristics based on the multi-dimensional compensation matrix, and then obtains a performance evaluation data set of the silicon carbide chip. The application can realize multi-dimensional adaptive coupling compensation driven by abnormal values to improve the reliability of performance evaluation of the silicon carbide chip.

This invention discloses an intelligent generation method and system for foundation pit construction schemes based on working condition constraint deduction. Through foundation pit engineering data acquisition and analysis, construction of a foundation pit engineering semantic object model, construction of an engineering constraint graph, retrieval and adaptation verification of foundation construction scheme templates, deduction of construction working condition chains, parameter collaborative optimization and generation of candidate foundation pit construction schemes, generation of complete sets of construction schemes, consistency verification, and final scheme output, the invention achieves intelligent and rapid generation of foundation pit construction schemes, improves the accuracy of scheme adaptation to actual foundation pit working conditions, reduces labor costs, and ensures foundation pit construction safety and engineering quality. It solves the problems of low efficiency, insufficient personalization, low matching degree with actual working conditions, and excessive reliance on human experience in existing foundation pit construction scheme generation technologies.

The application discloses a point cloud driven thoracic cavity whole organ dynamic reconstruction and respiratory monitoring method and system, and the method implementation comprises the following steps: based on the thoracic medical image data of a target object, constructing three-dimensional geometric models corresponding to each target structure in the thoracic cavity, and converting the three-dimensional geometric models into static point cloud data; based on the respiratory motion law of the target object, driving the static point cloud data to perform dynamic deformation simulation in a respiratory cycle, and obtaining dynamic point cloud data synchronized with the respiratory phase; synchronously associating the dynamic point cloud data with electrical characteristic parameters in time and space, and constructing a digital twin thoracic cavity model; deploying a virtual sensor assembly that dynamically deforms with the thoracic cavity in the digital twin thoracic cavity model, and simulating and monitoring the dynamic respiratory process of the target object, and generating a virtual physiological signal; and through signal processing on the virtual physiological signal, a dynamic reconstruction image reflecting the respiratory motion is obtained. The continuous simulation of the whole respiratory motion process is realized, and the time and space resolution and diversity of the virtual database are improved.