11results about How to "Accurate modeling" patented technology

This invention discloses a mobile firefighting equipment specifically designed for lithium battery fires, comprising an autonomous firefighting robot, a guide hose, and a trailer. Based on multi-sensor data fusion, it achieves automatic fire source identification and location, automatic mapping, autonomous navigation, and automatic firefighting, making it suitable for scenarios requiring rapid response and safe firefighting. The mobile chassis houses an industrial control computer, a GNSS navigation module, a mobile power supply module, and a heat dissipation module. Sensors and signal receiving equipment are located on top of the chassis. A thermal imager identifies the fire source, and combined with lidar and a depth camera, it achieves environmental modeling and autonomous obstacle avoidance in complex fire scenes. The industrial control computer calculates the target pose in real time based on sensor data and controls the high-pressure water cannon to aim at the fire. The trailer integrates a high-pressure water pump to provide continuous firefighting power. This solves the problem of limited perception in dense smoke and high-temperature environments. While ensuring a high water capacity, it enhances the robot's intelligence level and has extremely high practical application value.

The invention relates to the technical field of ocean platform pipeline design, in particular to an ocean platform pipeline laying method based on reinforcement learning, and the method comprises the following steps: S1, dispersing a pipeline laying region into a three-dimensional grid matrix according to the physical size of an actual cabin of an ocean platform, and marking a pipeline starting point, a pipeline ending point and an impassable region; s2, performing Q-Learning algorithm parameter initialization configuration, defining an action space adaptive to the linear movement characteristics of the ocean platform pipeline, constructing a Q value matrix adaptive to three-dimensional space coordinates and actions, and performing initialization; s3, entering a training round, and continuously updating the value evaluation matrix by dynamically adjusting a greedy criterion, a multi-dimensional reward mechanism and a time sequence difference learning rule; and S4, after the training is completed, starting from the starting point based on the converged Q value matrix, selecting an optimal action through a greedy to generate a final pipeline path, and improving the quality and search efficiency of pipeline laying on the ocean platform.

This invention provides a method for optimizing cooperative detection array positions for underwater vehicles (AUVs). It constructs a target optimization function for AUV cooperative detection array positions by coupling AUV detection errors with their own positional errors, aiming to minimize the global average detection error. This ensures that passively detected AUVs construct array positions with maximum cooperative detection accuracy. An improved HS-DPSO fusion algorithm iteratively optimizes the target optimization function. During local optimization, a dynamic inertial weight adjustment strategy combining normal distribution randomness and an adaptive expectation mechanism is employed. This allows the inertial weights to fluctuate dynamically and regularly in each iteration, enhancing the algorithm's ability to escape local optima. Furthermore, the adaptive random weight expectation value is dynamically adjusted using random numbers, avoiding the limitations of manually pre-setting fixed random weight expectation values ​​and improving the algorithm's adaptability to different detection scenarios. This method enables each passively detected AUV to quickly obtain its optimal cooperative detection position, improving the efficiency and accuracy of AUV formation cooperative detection.

This invention discloses a method and system for optimizing administrative text element extraction based on natural language processing, belonging to the field of natural language processing technology. The method performs multi-granularity semantic structure parsing on administrative texts to construct multi-granularity semantic structure representation data capable of simultaneously representing word semantics, sentence functional attributes, and textual organization relationships; identifies semantic fragments of candidate administrative elements and constructs an administrative element structure constraint graph to depict the logical dependencies between administrative elements; introduces the structure constraint graph as explicit constraint conditions to perform structure-aware joint reasoning and consistency constraint optimization on administrative elements, suppressing element combinations that do not conform to administrative semantic logic, and outputting structurally consistent administrative text element extraction results; this invention can effectively improve the logical consistency, completeness, and business usability of administrative text element extraction results, and is applicable to application scenarios such as structured processing of administrative documents, government information management, and intelligent auxiliary decision-making.

The application discloses a kind of based on space-time feature separation's gas-solid coupling reaction system modeling and system state prediction method, first using model description to the gas-solid coupling reaction system of metallurgical equipment;Then, extract the key parameters in reaction system model and combination construction different working conditions of system;Again, using the multi-working condition system excitation method based on orthogonal test, excite system under each working condition, obtain the system characteristic data corresponding to working condition;Finally, by intrinsic mode decomposition, the system characteristic data is decomposed into spatial basis function and time coefficient, then establish the prediction model of time coefficient, predict the system state of future time through the prediction value of time coefficient and spatial basis function.The present application can realize the accurate modeling of gas-solid coupling reaction system of metallurgical equipment, and the prediction accuracy of model to system state is high.

The application discloses a kind of based on elastic-plastic body model's masonry beam key block mechanical analytical method, this method will be broken after each other extrusion key block as elastic-plastic body, by model definition and parameter determination, mechanical relationship establishment, deformation geometric relationship establishment, analytical solution and result output step, simultaneously related relationship formula solves key block compression, horizontal thrust and other parameters, further can calculate key block supporting force and working face support resistance.This method considers the real mechanical characteristics such as elastic-plastic deformation, contact surface extrusion effect in the process of key block breakage and extrusion, solves the problem of insufficient analysis accuracy of traditional method, improves the analysis accuracy of strata behavior law, provides reliable theoretical support for hydraulic fracturing parameter design and working face support design, and guarantees coal mine safety production.

The application relates to a power load prediction method based on a CRKformer model, electronic equipment and a storage medium, which comprises the following steps: acquiring time series data, constructing a data set and performing normalization preprocessing; converting the standardized data to a frequency domain, obtaining embedded features through linear dense operation, performing context shaping filter calculation, and obtaining a time domain optimization feature sequence through inverse Fourier transform; obtaining time series dependent features through the combination of self-attention mechanism and cross-attention mechanism of endogenous variable sequence embedding in the strong position coding weight; fitting a nonlinear relationship through a Kolmogorov-Arnold network, and enhancing the time series dependent features by using a learnable activation function; generating a prediction result by linearly projecting the endogenous output embedding, performing inverse normalization, and outputting a load prediction value. Compared with the prior art, the application has the advantages of inhibiting noise interference, accurately capturing time series dependence, and high prediction accuracy.

This invention discloses a method and system for constructing a corridor truss model, comprising: S1. Designing control lines based on the overall layout of the corridor; the control lines include ground control lines, support control lines, and truss control lines; S2. Using the control lines as reference lines for the model, generating a truss model and a support model; S3. Assembling the truss model and the support model based on the truss's span, inclination angle, and positioning information to obtain a three-dimensional model. This invention can directly establish an overall model, improving modeling accuracy and efficiency in model design.

This invention provides an information recommendation method, apparatus, device, and storage medium. The method involves acquiring sequence feature data corresponding to information to be recommended, where the sequence feature data is collected when a user interacts with the information. The method then determines the category corresponding to the sequence feature data; determines the attention information corresponding to the sequence feature data for each category; determines the relevance information between the information to be recommended and the user based on the sequence feature data and the attention information corresponding to the category; and recommends the information to the user based on the relevance information. This invention, while ensuring sufficient cross-category interaction, avoids the risk of information intrusion between sequence feature data from different categories, thereby enabling more accurate modeling of user interests and improving the accuracy of the recommendation algorithm.

A tool management and control method and system of a numerical control machine tool relate to the technical field of numerical control machine tools. The method comprises the following steps: S1, collecting tool signals and preprocessing; S2, constructing a heterogeneous graph, and calculating the feature correlation between nodes by using a graph attention aggregation mechanism; S3, performing dilated convolution and gated residual operation on the embedded tensor on a GPU; S4, constructing a tool wear evaluation network, and performing channel and time sequence weighting on the space-time feature matrix; S5, according to the wear confidence, an improved hierarchical matching algorithm is used to match the embedded tensor and the processing task demand vector; S6, inputting the optimal tool sequence into the numerical control machine tool terminal, and completing parallel scheduling and tool position mapping; S7, after processing is completed, updating the tool whole life cycle database according to the health score, and generating a data report containing replacement suggestions and maintenance plans. The present application realizes accurate identification and intelligent scheduling of tool wear state, and significantly improves tool utilization and numerical control machine tool operation efficiency.

This invention discloses a lemon health analysis method, relating to the fields of intelligent agricultural monitoring and plant health diagnosis. It involves acquiring continuous images of lemon plants using a hyperspectral camera, and then using these images to detect changes in a dynamic differential spectral segmentation model. Sequence modeling is performed on the pixel-level multi-band spectral data. An adaptive motion-aware scheduler automatically suppresses loss when the proportion of motion change mask pixels exceeds a preset dizziness threshold to avoid background misjudgment, generating a pixel-level dynamic change map. This is compressed into a pathological dynamic state vector using a variational autoencoder, and end-to-end training is performed with action-conditional video prediction as the training objective. In the extrapolation phase, a recurrent dynamic model is used to perform multi-step imaginative prediction based on candidate action sequences, outputting the future evolution trajectory of the pathological dynamic state vector and generating a quantitative early warning or decision simulation report on the outbreak risk level. The system also includes change type classification and model closed-loop optimization functions.