26results about How to "Accurate modeling" patented technology

The invention relates to the technical field of sewage treatment, and discloses a sewage biochemical treatment process abnormity intelligent early warning method and system based on image recognition, and the method comprises the steps: periodically collecting a sludge image and water quality sensing data; extracting floc form, bubble distribution and color texture features of the image, and performing space-time alignment fusion with the sensing data to construct a multi-source fusion feature matrix; inputting the matrix into a pre-trained digital twin evaluation model, calculating a dynamic deviation degree between a current state and a normal working condition, and identifying an abnormal mode; and generating a graded early warning signal according to the deviation degree and the abnormal mode and pushing the graded early warning signal. The system comprises an online image acquisition unit and a water quality sensor array which are used for data acquisition, and a feature processing module used for feature processing and fusion. According to the scheme, early-stage, accurate and intelligent early warning of abnormality in the sewage biochemical treatment process is realized, the false report and missing report rate is reduced, and the accuracy and the reliability of the system are improved. And the operation stability and the regulation and control efficiency of the system are improved.

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.

The application discloses a kind of based on deep separable convolution improved remote sensing image road extraction method and system, it is related to remote sensing image processing technical field.A kind of based on deep separable convolution improved remote sensing image road extraction system, including have: data acquisition module, tree crown shadow compensation module, model construction module, model training module, road extraction module, storage module and display module.The application is compensated by constructing tree crown translucent shadow compensation module, and the shadow attenuation priori graph generated based on radiation transmission physical model DART is fused with original image channel cascade, so that the model can understand the formation process of tree crown shadow from the physical mechanism level, effectively restore the occluded road information, solve the technical problems that traditional deep learning method lacks explicit modeling of translucent shadow, and it is difficult to distinguish real road dark area and shadow false dark area.

The application provides a three-level converter overall power loss calculation and optimization method, and belongs to the technical field of power electronic converters.The application is aimed at the problem that the traditional three-level converter ignores the filter inductance current in loss evaluation, and is based on accurate modeling of the amplitude of the filter inductance current ripple component, and models the overall power loss of the three-level power electronic converter as a mathematical function about the switching frequency, which is composed of the conduction loss power and switching loss power of the semiconductor power device, the filter inductance loss power and the bus capacitor loss power.The application can be used not only for converter loss evaluation, but also for guiding the switching frequency optimization, and further improves the operation efficiency of the converter.

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 provides a low-feature corridor autonomous navigation method and system based on multi-sensor fusion, and relates to the technical field of mobile robot path planning and intelligent control. The steps of the method are as follows: S100, acquiring synchronous collection multi-source data of a distributed sensor network, and extracting cross-medium boundary features; S200, constructing a cross-medium propagation coupling model based on a multi-physical field equation, and calculating physical field distribution parameters; S300, inputting historical data and boundary features into a physical information neural network, and predicting a physical field evolution trend; S400, executing a risk assessment algorithm, and generating a comprehensive safety map containing multi-dimensional risk indicators; and S500, generating a safe navigation trajectory considering multiple constraints based on a multi-objective optimization algorithm. The method has the beneficial effects that the problem of failure of a traditional single physical model when a medium mutates is overcome, the accuracy and physical consistency of the prediction of the physical field evolution trend are improved, and comprehensive environmental safety information is provided for path planning.

The application provides a carbon flow determination method and device based on a multi-energy equipment, equipment and medium, which are used in the technical field of carbon emission analysis, and can solve the technical problems of low accuracy and efficiency of existing park carbon flow determination. The method comprises the following steps: collecting energy information corresponding to each carbon flow structure in a time slot; topologically correlating the carbon flow structure according to the carbon flow direction to obtain an initial carbon flow structure, and constructing a carbon flow balance relationship corresponding to each carbon flow structure in the initial carbon flow structure according to the energy data value to obtain a corresponding target carbon flow structure; determining the carbon flow time sequence curve corresponding to each energy equipment based on the target carbon flow structure corresponding to each time slot, and calculating the target carbon flow data corresponding to the target time based on the carbon flow time sequence curve; generating the carbon flow calculation result corresponding to the energy equipment according to the target carbon flow data and outputting; in this way, the efficiency and accuracy of determining the park carbon flow are improved.

The invention relates to the technical field of computer 3D modeling, and discloses an urban three-dimensional terrain rapid modeling method and system based on AI point cloud semantic segmentation, and the method comprises the steps: carrying out the high-quality modeling of a feature object three-dimensional model in a target urban terrain according to an urban terrain semantic point cloud fusion preprocessing data set in cooperation with a point cloud three-dimensional modeling platform; precise modeling of the urban terrain in a finite point cloud data state is realized based on semantic segmentation and point cloud fusion; urban terrain three-dimensional model image information is accurately collected through a point cloud three-dimensional modeling platform and screenshot software, and urban terrain three-dimensional model modeling defect intelligent identification is carried out in combination with an AI algorithm and urban terrain feature object standard modeling defect image data; and based on the urban terrain three-dimensional model data and in cooperation with a point cloud three-dimensional modeling platform, performing dynamic and efficient repair and reconstruction processing on modeling defects of the urban terrain three-dimensional model, and realizing accurate modeling repair and reconstruction in an urban terrain finite point cloud data state based on model repair.

The application discloses a process modeling and fault detection method and system for guiding machine learning by control theory, and the method comprises the following steps: acquiring historical operation data of an industrial process, extracting operation dynamics in the historical operation data by using a neural network, and approximating an observer gain part of a kernel representation model to realize complete kernel representation model design; constructing an adjoint system of the kernel representation model by using Hamilton system theory, and guiding and optimizing a learning and training process of the kernel representation model and the adjoint system by using lossless constraints to realize design of a regularized kernel representation model; constructing detection statistics by using residuals of the kernel representation model and the regularized kernel representation model, setting a maximum value of the detection statistics when the industrial process normally operates as a fault detection threshold, and thus realizing fault detection on the industrial process to guarantee safety of the industrial process. In addition, by designing an online fine-tuning strategy, the generalization and applicability of the model are improved, and the method has strong practical value.

The invention discloses a video generation method and device, computer equipment and a storage medium, belongs to the technical field of artificial intelligence, and is applied to the financial field or the health medical field. In the tensor algebraic level, high-order tensor decomposition is carried out on an input image by using a transformation tensor product, and the input image is projected to an orthogonal potential subspace. Then, in a feature processing process, weighting the space factor matrix by adopting a local attention window, and combining with a preset mask matrix constraint; a recursive converter with orthogonal constraints is introduced to process a time sequence factor matrix, and it is ensured that the hidden state sequence is stably expressed in long-time dependence modeling. And finally, in a video reconstruction stage, performing up-sampling and reconstruction on the fusion tensor by adopting a tensor completion algorithm based on nuclear norm constraint and a low-rank synthesis strategy. According to the method, the unification of spatial decoupling, time sequence stability and global low-rank performance is realized, so that the generated video reaches a better level in the aspects of spatial resolution, time sequence dynamics and overall visual quality.

The application discloses a numerical control machine tool thermal error online compensation control method and system, and belongs to the technical field of numerical control machine tool precision machining. The method comprises the following steps: collecting temperature data and operation parameters of key positions of a machine tool, and generating a temperature characteristic vector; a temperature field adjacency matrix is constructed to predict future temperature field distribution; a graph neural network is used to establish a nonlinear mapping between the temperature field and thermal deformation, and a thermal displacement prediction value is output; a compensation instruction is superimposed on an original trajectory for execution; and model parameters are updated online according to calibration data. The application adopts graph convolution operation to capture the spatial correlation characteristics of the temperature field, so that the thermal error prediction accuracy is better than 2 mu m, and the compensation rate reaches more than 85%.

This invention discloses a mosquito multimorphic recognition model, training method, recognition method, and system. This application provides a complete method and system from data acquisition and model training to mobile deployment, enabling high-precision multi-attribute joint recognition of mosquitoes to run in real time on mobile terminals, greatly promoting the practical application of the technology in vector monitoring, public health, and other fields. The recognition method involves the following steps: inputting static images or video stream data of the mosquito to be identified into a terminal device deployed with a trained mosquito multimorphic recognition model; the terminal device performing feature extraction, multi-scale feature fusion, and target detection on the input image / video frame data using the mosquito multimorphic recognition model to complete the joint recognition of the mosquito target's species, sex, and developmental stage; and the terminal device outputting the mosquito target's location information and the corresponding species, sex, and developmental stage recognition results in real time.

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.

This invention relates to the field of thermal analysis technology for die-casting molds, specifically to a high-precision virtual prediction method and system for the temperature of large integrated die-casting molds for aluminum and magnesium alloys. The method includes acquiring a three-dimensional geometric model of the mold and corresponding thermal boundary conditions; constructing a three-dimensional unsteady-state heat conduction control equation for the mold based on the three-dimensional geometric model and thermal boundary conditions; spatially discretizing the three-dimensional unsteady-state heat conduction control equation; iteratively solving the discretized three-dimensional unsteady-state heat conduction control equation; and outputting the temperature field distribution of the mold at different time steps during the die-casting process. It considers the influence of latent heat of phase change on the heat conduction process and processes it using the equivalent specific heat method and / or the temperature recovery method, thereby achieving rapid and accurate prediction of the temperature distribution of complex molds. This method has advantages such as fast calculation speed, strong adaptability to complex geometries, and high accuracy in temperature distribution prediction, and can be widely applied to temperature control simulation analysis and optimization design of die-casting molds.

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.

The invention discloses an inner and outer contour measuring device, which relates to the technical field of measuring devices and comprises a cradle assembly, a Y-axis sliding table, an outer contour measuring assembly, a first Z-axis sliding table, an X-axis sliding table, an inner contour measuring assembly and a second Z-axis sliding table. The cradle assembly comprises a product clamping assembly, a cradle body, a cradle support and a cradle driving device. The Y-axis sliding table is used for driving the cradle base to slide in the Y-axis direction. The outer contour measuring assembly is used for measuring the outer contour of the product. The first Z-axis sliding table is used for driving the outer contour measuring assembly to slide in the Z-axis direction, and the X-axis sliding table is used for driving the first Z-axis sliding table to slide in the X-axis direction. The inner contour measuring assembly comprises a climbing needle sensor which is used for measuring the inner contour of the product. The second Z-axis sliding table is connected with the inner contour measuring assembly and used for driving the inner contour measuring assembly to slide in the Z-axis direction. According to the device, measurement of the inner contour and the outer contour is completed through one-time clamping, equipment replacement is avoided, and a structural basis is provided for subsequent data fusion.

The invention discloses a road integrated disease geometric modeling and correction method, device and equipment and a medium. The method comprises the following steps: determining a transition region between a road surface disease and an internal disease, constructing a disease generation path constraint and a disease generation probability field in the transition region, constructing an unknown disease geometric model of the transition region according to the constraint, and fusing the unknown disease geometric model with a geometric model of the road surface disease and the internal disease to obtain an unknown disease geometric model; a complete road integrated disease three-dimensional geometric model is obtained, so that a coupling evolution relation between road surface diseases and internal diseases is effectively excavated, a road surface and internal disease conduction rule is accurately analyzed, morphological characteristics and development trends of diseases in a transition area are accurately constructed, and the road surface and internal disease conduction rule is accurately analyzed. And a data basis is provided for integrated intelligent detection and evaluation of road damage.

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.

The invention discloses a large signal modeling and control method for a high-voltage power supply of an M-module LCC resonant converter. The MCU is arranged on a circuit board, the DC-DC or LDO is used for inputting power to the MCU, the DC-DC booster circuit is connected with 48V input power and is used for boosting the 48V power to a set bus voltage, the inverter driver and the IPOS module are connected with the DC-DC booster circuit, the resonant cavity is connected with the inverter driver and the IPOS module, and the auxiliary power supply is connected with the IPOS module. The MCU is controlled by TMS320F28377D, more complex control logic can be realized through programming, and a complex peripheral circuit required by analog chip control is not needed; according to the LCC resonant converter topology, the system works in an inductive area by controlling the switching frequency and resonance parameters, natural soft switching of a switching device is achieved, additional algorithm control is not needed, and the system efficiency is improved; the natural soft switch enables the switching frequency of the system to be higher, and the high frequency of the system enables the sizes of magnetic elements, capacitors and the like to be small, thereby facilitating the reduction of the size and weight of the system.

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.

The application provides a silicon photon gyroscope stable control method and silicon photon gyroscope under a rapid temperature changing condition, and the stable control method comprises the following steps: setting a light source frequency control digital interval; controlling the light source temperature to make the light source center frequency enter a resonance valley, and recording the valley entering temperature; collecting the CW and CCW resonance signals output after the light source enters the resonance cavity, and calculating the sum of the clockwise and counterclockwise resonance frequency and the light source center frequency deviation; and calculating the feedback control light source current and temperature according to the light source frequency control digital interval where the sum of the frequency deviation is located. Through the adaptive control of the light source current and temperature, the light source center frequency can quickly track the resonance frequency of the resonance cavity under the rapid temperature changing condition, the stable control of the gyroscope is realized, and the gyroscope output accuracy is improved.

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.