153results about How to "Guaranteed reasonableness" patented technology

The invention discloses a user track privacy protection method and system fusing a generative model and three-stage personalized differential privacy, and relates to the technical field of data security, and the method comprises the steps: obtaining real user track data, and carrying out the preprocessing of the real user track data to form standardized track representation; training a depth generation model to generate synthetic trajectory data; performing density clustering on the synthetic trajectory to obtain a feature region and calculating a region centroid; point-level privacy budgets are distributed according to three stages of the area proportion, the centroid distance and the global density; constructing an adaptive elliptical disturbance domain based on the regional covariance, sampling candidate confusion points in the disturbance domain and applying road feasibility screening; and calculating a score based on the privacy budget and the utility function, and sampling replacement points by adopting an index mechanism to output a privacy protection trajectory. According to the technical scheme, the track structure continuity and the road semantic consistency are remarkably improved under the condition that the differential privacy constraint is met, and the privacy intensity and the data availability are both considered.

The invention discloses an unmanned aerial vehicle mountainous region path planning method, and belongs to the field of unmanned aerial vehicle control, and the planning method comprises the steps: obtaining a three-dimensional terrain model of a mountainous region environment, so as to determine the operation space of an unmanned aerial vehicle; based on the three-dimensional terrain model of the mountain environment, constructing an unmanned aerial vehicle mountain path multi-constraint model; based on the three-dimensional terrain model of the mountain environment, obtaining an unmanned aerial vehicle mountain path planning model; and based on the unmanned aerial vehicle mountain path multi-constraint model, verifying the unmanned aerial vehicle mountain path planning model. The problems that in the prior art, it is difficult to adapt to constraints possibly existing in the mountain area, and the path planning efficiency is low are solved. Therefore, the quality of path planning is fully ensured, and the efficiency of path planning can be improved.

The invention discloses a method for generating a micromechanical property distribution diagram of a solid waste composite cement-based cementing material, and belongs to the technical field of performance prediction of solid waste composite cement-based cementing materials, and the method comprises the following steps: acquiring BSE, EDS and nanoindentation data of a sample; identifying indentation areas in the nanoindentation mark graph, and generating masks corresponding to the areas; performing statistics on the regional masks to obtain element content data of each indentation region; establishing a prediction model by adopting machine learning based on the regional element content and the elasticity data; performing super-pixel segmentation on the BSE image to match the scale of the indentation area; calculating element features of the super-pixel regions, and predicting the elastic modulus of each super-pixel region; and mapping the predicted elastic modulus to a corresponding region to generate an elastic modulus distribution diagram. According to the method, prediction of the two-dimensional space distribution of the internal elastic modulus of the solid waste composite cement-based cementing material can be realized, and data and visual support are provided for material heterogeneity research and multi-scale modeling.

The invention provides a digital quantum simulation method, which comprises the following steps of: performing symmetry analysis and subspace decomposition on a target Hamiltonian to obtain an effective Hamiltonian and a symmetry constraint condition; constructing a target unitary operator according to the effective Hamiltonian, and initializing a quantum circuit population of the target unitary operator; calculating entanglement characteristics of each quantum line in the quantum line population, performing structure variation on the quantum line population based on the entanglement characteristics, and performing line fragment crossing in combination with a symmetry constraint condition to obtain a candidate line set; performing line parameter optimization on the candidate line set based on the fidelity gradient to obtain an optimized line set, and extracting gate distribution structure features of the optimized line set; and non-uniform sampling is carried out on the optimized line set based on gate distribution structure characteristics to obtain a sampling line subset, an optimal quantum line is screened out from the sampling line subset based on the deviation between an experimental observation value and a theoretical expected value, and quantum simulation is carried out based on the optimal quantum line.

The invention discloses an intelligent demand analysis and man-hour evaluation method and system, and belongs to the technical field of computer vision and artificial intelligence. Analyzing the multi-modal demand document containing the text, the picture and the table to generate structured demand data; based on the structured demand data and the system module description, performing module-level splitting on the demand to generate a module task set; retrieving an associated system implementation context for each task in the module task set based on a multi-dimensional knowledge base; fusing the structured demand data, the module task set and the system implementation context to generate a fine-grained technical task; based on a historical case index, calculating reference working hours for the fine-grained technical task; and cross-module cooperative correction and personalized correction based on the ability of the task executor are sequentially performed on the reference working hours to obtain final evaluation working hours, so that automatic deep analysis and accurate and personalized working hour evaluation of software requirements can be realized.

The invention provides an obstacle association method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining measurement data from at least one sensor, and enabling the measurement data to comprise a plurality of obstacle measurements and detection information thereof; for each obstacle measurement to be associated, selecting a target reference point type from a plurality of predefined candidate reference point types according to detection information of the obstacle measurement to be associated; determining a measurement reference point from the detection information of the obstacle measurement based on a target reference point type selected for each obstacle measurement; determining a matching measure between a measurement reference point and a track reference point based on the measurement reference point measured by the obstacle and the track reference point determined for the fused track; according to the calculation result of the matching measurement between all the obstacle measurements and all the fused tracks, the association relationship between the obstacle measurements and the fused tracks is determined, and the accuracy and the reliability of obstacle association can be remarkably improved by adopting the method.

The invention discloses an intelligent decision support system for urban water conservancy project multi-source data fusion, and relates to the field of engineering planning. Forming a feature region containing a green belt; analyzing to obtain at least one terrain low point and at least one terrain high point; obtaining an operation labor-consuming index of the distribution block; forming at least one first planning scheme; forming a second planning scheme corresponding to the first planning scheme; and combining the first planning scheme and the corresponding second planning scheme into a third planning scheme, and calculating an operation difficulty value of the third planning scheme. The urban water conservancy project to be repaired is classified to form a feature region containing a green belt, the operation labor-consuming index of a distribution block is obtained, and the operation difficulty value of a third planning scheme is calculated, so that the soil excavation difficulty, the excavation length and the relative position relationship between the water conservancy project and the green belt can be synthesized; and the reasonability of the designed planning scheme is ensured.

The application provides a carbon emission evaluation method and system based on graph theory tracing, equipment and medium. The application obtains current carbon flow operation data of each node and branch in a network topology structure constructed based on an integrated resource carbon flow system of a power distribution network; based on the current carbon flow operation data, actual carbon emission flow transmitted between a single generator and a load of each branch is obtained by using a graph theory method; a carbon flow proportion of the target load derived from different generators is determined according to the actual carbon emission flow transmitted between the single generator and the load; and the carbon flow proportion is evaluated and adjusted. The application traces the integrated resource carbon flow system by using the graph theory, obtains the carbon flow proportion of the target load derived from different generators, evaluates and redistributes the carbon flow proportion based on the tracing result, and ensures the rationality of carbon cost distribution of each line.

The application provides a food safety inspection robot control method and system based on industrial vision, which comprises the following steps: controlling the robot to start, obtaining environment point cloud information obtained by the robot based on an industrial vision device after omnidirectional scanning of a current food production environment; planning a path based on the environment point cloud information and a preset target inspection area, generating an initial inspection path, and controlling the robot to move along the initial inspection path; optimizing the initial inspection path based on obstacle information of obstacles collected by the industrial vision device during movement of the robot, and obtaining a target inspection path; controlling the robot to move to the target inspection area along the target inspection path, and performing safety monitoring on a food production process based on sensor data collected by a multi-dimensional perception sensor in the robot, and obtaining a food production safety monitoring result. The application improves the inspection efficiency and ensures efficient and stable inspection work.

The application discloses a high-reliability multi-node cooperative in-situ communication system and method. The system comprises multiple transceivers, one of which is a master node and the rest are slave nodes. The master node comprises a channel collision management and control demodulation and signal modulation unit, a digital AGC compensation control unit, a special working condition signal-to-noise ratio switching unit and a process autonomous ending unit. The slave nodes each comprise a signal transmitting unit, a signal receiving unit and a control unit. The application realizes effective screening and control of multiple access nodes in deep space exploration activities such as lunar exploration, solves the problem of advanced planning caused by a large number of nodes in the lunar near space, and maximally supports flexible communication between lunar nodes.

The invention discloses a lithology identification method based on a neural network, and the method comprises the steps: obtaining a logging curve, recognizing a missing value in the logging curve, taking the parameter data in the logging curve as sequence data, inputting the sequence data into an established neural network model, carrying out the prediction through the neural network model, obtaining the complementation data corresponding to the missing value, and obtaining the lithology of the logging curve. Filling missing values in the logging curve by using the completion data to obtain a complete logging data set, namely a first data set; based on the first data set, performing equalization processing on lithology classification samples in the first data set to obtain an equalization logging data set, namely a second data set; a whale optimization algorithm is adopted to optimize hyper-parameters of the classification model, target hyper-parameter configuration is obtained, and the classification model is configured; and inputting the second data set into the classification model, and identifying and outputting the lithology category. The method is high in missing value filling precision, excellent in sample equalization effect, remarkable in model performance optimization and high in system practicability.

The invention relates to the technical field of cultural relic identification and visual large model fusion, in particular to a ceramic cultural relic full-dimensional identification system and method based on multi-modal visual large model and microscopic feature fusion. After high-quality applicable data are screened, other data are calibrated with the optimal mode as the reference, cross-mode deviation is eliminated, and a data foundation is built firmly; the core features are converted into topological nodes, association strength is calculated, feature association edges with guidance are established, a traceable and explainable identification guidance link is generated, and the strong ceramic identification logic requirement is met; the optimal link is screened to be combined with the pre-training model, and the identification precision and the careful property are balanced by depending on a confidence hierarchical judgment mechanism, so that the black box pain point of the traditional model is effectively solved, and the credibility of the identification result is improved.

The utility model discloses a power distribution module, which relates to the technical field of electrical engineering, and comprises at least two power distribution units and a first busbar, the at least two power distribution units are stacked and arranged at intervals, each power distribution unit is provided with a confluence terminal, the confluence terminal is bent to form a connecting part, and the connecting part is connected with the first busbar. The first busbar is connected with the connecting part of one confluence terminal of each power distribution unit through a connecting piece, and the connecting piece and the relays on the power distribution units are arranged in a staggered manner; in the technical scheme provided by the utility model, the connecting part of the confluence terminal and the relay in the power distribution unit are arranged in a staggered manner along the stacking direction of the power distribution unit, so that the connecting piece can be ensured not to interfere with the relay even if the connecting piece penetrates out of the connecting part in the installation process, thereby avoiding the puncture of the relay and causing potential safety hazards. By bending the confluence terminal, a part of the confluence terminal is located below the circuit board, so that the rationality of the structure of the device is ensured.

This application relates to the field of artificial intelligence technology, providing a method and system for intelligent energy consumption management using a cloud-based cutting platform. The method includes: acquiring customer steel plate cutting demand information through the cloud-based cutting platform; extracting elements and deploying cutting schemes from the customer steel plate cutting demand information to obtain steel plate cutting application scheme information; acquiring cutting deployment equipment information and cutting application parameter information based on the steel plate cutting application scheme information; uploading the cutting deployment equipment information and cutting application parameter information to an energy consumption prediction and analysis model to obtain energy consumption calculation and prediction information; adjusting the energy consumption calculation and prediction information based on data error factors to obtain closed-loop energy consumption calculation and prediction information; and performing energy utilization management based on the closed-loop energy consumption calculation and prediction information. This method can achieve precise management of energy consumption, improve energy consumption management efficiency, and thus ensure the rational and scientific utilization of energy.

The application relates to the field of balance training, and discloses a balance training method, device and system of a balance board and a computer readable storage medium. The balance training method of the balance board comprises the following steps: outputting an instruction instruction indicating that a trainer treads the balance board according to a plurality of different reference orientations; collecting a maximum inclination angle corresponding to each reference orientation; selecting a plurality of training tread directions and setting an inclination angle coefficient corresponding to each training tread direction; determining an angle threshold corresponding to each training tread direction according to the maximum inclination angle corresponding to each reference orientation, so as to determine a set inclination angle; and outputting a corresponding training prompt according to the training tread direction and the corresponding set inclination angle, so that the trainer performs balance training according to the training prompt. In the application, information about the tread ability of the trainer treading the balance board is obtained, so that the training target of the trainer is reasonably set, the effectiveness and rationality of the balance training are ensured, and the training effect and training experience are improved.

The embodiment of the application provides a resource processing method and device of a multi-tenant-oriented large model training platform, innovatively designs a resource portrait system, effectively expresses demands through stage division and state space, constructs a multi-level isolation mechanism, combines conflict analysis and parameter optimization to establish reliable resource protection, introduces intelligent scheduling, ensures the rationality of allocation through priority evaluation and scheme generation, and effectively solves the deficiencies of traditional technologies in resource portrait, isolation mechanism and scheduling optimization, thereby providing technical support for large model training.

The invention relates to the technical field of robot performance evaluation, in particular to a robot acceleration performance evaluation method and device, and the method comprises the steps: generating an acceleration mapping matrix of a robot according to a pre-constructed robot dynamic model; performing singular value decomposition on the acceleration mapping matrix to obtain at least one singular value representing the geometric features of the dynamic operable ellipsoid of the robot; and calculating at least one composite acceleration performance index of the robot according to the at least one singular value, and generating an acceleration performance evaluation result of the robot according to the at least one composite acceleration performance index. Therefore, the problem that the overall dynamic performance of the robot under the actual complex working condition cannot be accurately represented due to the fact that the evaluation result is easy to have one-sidedness due to the fact that a single acceleration related index is usually adopted as the evaluation basis in the related technology is solved.

This invention relates to the fields of medical data analysis and artificial intelligence, proposing a dual-latent variable decoupled data analysis method based on plasma Netrin-1 and clinical scale features. First, plasma Netrin-1 test data and clinical characteristics such as UPDRS scale scores and disease duration are acquired from the subjects. Batch effect correction is applied to the raw test values, and an input feature vector is constructed. Then, a variational autoencoder model is established. By setting orthogonal constraints, Netrin-1 regression constraints, and disease duration regression constraints, the latent space is structurally constrained, enabling the model to learn latent variables D representing disease progression and C representing adaptive changes during training. Based on this, an efficiency index η composed of latent variables C and D is calculated, and relevant trend indicator parameters are obtained by combining longitudinal follow-up data. This method can achieve decoupled representation of disease-related features and adaptive change features in multi-source clinical data, providing a new data analysis tool for statistical analysis and research of neurodegenerative disease-related data.

The application discloses a multi-agent material calculation consistency calibration method, belonging to the cross field of material science, artificial intelligence and computational physics. The method adopts a master-slave structure multi-agent system, containing one master agent and at least three slave agents. The master agent is responsible for overall coordination, embedding physical mechanism constraints, consistency calibration and output results. The method steps are: constructing a calculation system, introducing physical mechanism constraints and embedding slave agent calculation models, collecting calculation data, performing bias analysis, adjusting parameters or correcting results through iterative calibration, and finally verifying and outputting the final results. The application introduces physical mechanism constraints, combines bias analysis and iterative calibration, and solves the problems of poor consistency of existing multi-agent material calculation results and easy violation of physical laws. Experiments show that the method reduces the deviation of the calculation results and the experimental measured values to below 3%, improves the calibration efficiency by more than 40%, and improves the calculation accuracy by more than 35% compared with the prior art, thereby improving the accuracy and engineering practicability of high-throughput calculation of material performance.

The invention provides a spatio-temporal network multi-physics field prediction method and system based on physical information and double attention, and the method comprises the steps: obtaining thermal process multi-physics field spatio-temporal sequence data generated based on high-fidelity three-dimensional CFD simulation, and carrying out the fusion alignment of the data and actual measurement time sequence data of limited measurement points of an industrial site, forming a standardized multi-channel training sample; constructing a basic physical information depth space-time prediction model, wherein the basic physical information depth space-time prediction model comprises a double attention fusion module, a physical perception hybrid encoder and a physical constraint multi-field decoder; training the basic physical information depth space-time prediction model by adopting the standardized multi-channel training sample to obtain a target physical information depth space-time prediction model; and inputting to-be-predicted data into the target physical information depth space-time prediction model by adopting a sliding window, and recursively predicting future multi-physical field evolution. The problems that traditional numerical simulation is poor in real-time performance, a pure data driving model is insufficient in physical consistency, and whole-field measurement of an industrial site is difficult are effectively solved.

The utility model provides a bank outlet delivery machine control system. The bank outlet delivery machine control system comprises a client, a delivery total device, a dispatching center and an outlet terminal. Data communication connection is established between the client and the delivery total device; and data communication connection is established between the delivery total device and the dispatching center and the network terminal. Wherein the delivery total device comprises a delivery middleware and a delivery transaction cloud, and the delivery middleware adopts an intelligent picking-up cabinet.

This invention discloses an anisotropic sparse deconvolution enhancement method and system for ultrasound tumor image segmentation. The method includes: establishing a local convolution model for ultrasound RF images based on the anisotropic characteristics of the ultrasound xz imaging plane; constructing an axis-aligned elliptical symmetric anisotropic point spread function (PSF), whose lateral and axial spread scales are obtained through physical parameter conversion or data-driven methods; constructing an ultrasound sparse reconstruction objective function incorporating the anisotropic PSF, adding non-negativity constraints, introducing a weighted Hessian continuity term and an L1 sparse prior term in the xz plane direction, and optimizing the solution to obtain the sparse reconstruction result; using this result as input, employing an RL iterative deconvolution method based on the anisotropic PSF, and achieving image boundary sharpening and noise suppression through PSF flip kernel ratio iteration; inputting the enhanced image into a tumor segmentation network to complete training and inference, thereby achieving ultrasound tumor image segmentation.

The application provides an optical element surface simulation method based on a physical information neural network, relates to the technical field of element design and simulation, and comprises the following steps: constructing a feedforward neural network; generating a deformation control equation model library; constructing a three-dimensional simulation database; forming a machine learning parameter library; training a PINN deformation simulation model in combination with the deformation control equation model library, the three-dimensional simulation database and the machine learning parameter library; screening out a target PINN deformation simulation model meeting preset conditions; inputting design parameters, support structure parameters and given external load parameters of different combinations into the target PINN model to obtain key performance prediction results of the optical element; the application can not only quickly evaluate the key performance of the element under various parameter combinations, shorten the design iteration cycle and reduce the trial and error cost, but also provides direct data support for support structure optimization design, and effectively solves the bottleneck problem of the traditional method in restricting the research and development efficiency of a high-precision optical system.

The invention provides a coal-fired boiler ammonia-doped combustion compatible matching system based on radiation characteristic compensation, which relates to the technical field of boiler combustion and comprises an ammonia-doped combustion operation characteristic database construction module, a real-time operation optimization module and a performance evaluation and self-optimization module. According to the method, the ammonia-doped combustion operation characteristic database is constructed, then whether the boiler performance parameters are recorded or not is judged based on the key boiler parameters, and ammonia-doped strategy dynamic adjustment and wall temperature safety regulation and control are carried out based on the ammonia-doped combustion operation characteristic database and the boiler performance parameters. Finally, when the fuel enters the coal-fired boiler, the mixing condition of ammonia and coal is evaluated, whether the fuel mixing strategy is optimized or not is judged, meanwhile, the oxygen content condition of the coal-fired boiler is monitored and adjusted, the position of high-temperature flames in the boiler is adjusted and controlled through flexible grading of the ammonia / coal fuel, matching of heat transfer distribution in the boiler and the coal-fired working condition is achieved, and the coal-fired boiler is obtained. The ammonia-doped combustion compatibility matching effectiveness of the coal-fired boiler is improved, and the problem that the ammonia-doped combustion compatibility matching effectiveness of the coal-fired boiler is low is solved.

This invention discloses an underwater target reflectance inversion method based on an underwater transmission model, comprising: acquiring the original digital values ​​of the underwater target collected by a hyperspectral camera; combining the built-in radiometric calibration parameters of the hyperspectral camera; converting the original digital values ​​into radiance based on the sensor pre-calibration response characteristics; physically modeling the water absorption effect, scattering effect, and path radiation effect during underwater light propagation to determine the linear mapping relationship between radiance and target reflectance; constructing a training radiative transmission model using a non-negative linear regression model; using a calibrated target with known reflectance as a sample to train the radiative transmission model between the original digital values ​​and the target reflectance, and solving for the mapping parameters between the two; and based on the original digital values ​​and mapping parameters of the underwater target to be reconstructed, using the trained radiative transmission model to invert and obtain the true reflectance of the target, thus completing the spectral reconstruction.

The present application relates to the technical field of highway tunnel lighting, in particular to a tunnel portal flexible light guide lighting system and a lighting transition method, comprising: acquiring a response time delay parameter and an illumination cross distribution matrix; determining an illumination gap value of each road node according to the response time delay parameter, the illumination cross distribution matrix, a natural light illumination sequence, a current output duty cycle of each light emitting device and a preset safety illumination threshold; determining a final duty cycle allocation proportion of each light emitting device according to the illumination cross distribution matrix and the illumination gap value of each road node; determining a target control duty cycle of each light emitting device according to the final duty cycle allocation proportion of each light emitting device, a preset safety step duty cycle quota and the current output duty cycle of each light emitting device, and issuing the target control duty cycle of each light emitting device to the corresponding light emitting device. The present application improves the visual transition smoothness of the mixed lighting of the tunnel portal transition section as a whole.

This invention relates to the field of three-dimensional image data processing technology, and particularly to a method and system for wire connection node matching and conductor reconstruction based on multi-source point cloud constraints. The method and system, by comprehensively utilizing conductor point cloud data, tower point cloud data, and tower 3D model data, achieve automatic identification and accurate reconstruction of conductor connection relationships between adjacent towers. It fully utilizes the real spatial morphology reflected by the conductor point cloud to establish a globally optimal matching relationship among candidate wire connection node sets of adjacent towers, and integrates structural priors and engineering constraints to accurately determine the actual conductor connection position and achieve continuous spatial reconstruction of the conductor. This solves the problems of low accuracy, poor robustness, and insufficient engineering applicability in existing technologies for conductor connection node matching.

This invention discloses a smart tunnel construction safety monitoring and early warning system and method, belonging to the field of tunnel engineering monitoring technology. The method includes: collecting construction quality data and environmental data during tunnel construction; preprocessing the data and extracting key features from the preprocessed data to generate a daily data analysis report; generating dynamic thresholds for construction quality data based on the Dynamic Adaptive Threshold Optimization Model (DATOM); adjusting early warning triggering conditions according to real-time construction stage labels; and sending alarm information to terminal devices when construction quality data and environmental data deviate from the thresholds to reach the early warning conditions. This invention can meet the needs of dynamic changes in monitoring parameter thresholds at different construction stages, and has relatively high accuracy in safety monitoring and early warning.

The invention relates to an intelligent replacement method for a short play video advertisement article, which relates to a video fusion technology, and comprises the following steps: carrying out multi-frame dynamic image acquisition and advertisement area three-dimensional space information extraction on a to-be-processed short play video, and establishing a physical attribute inversion and dynamic physical attribute field model; rendering parameter fitting and standardized input are carried out on the advertisement three-dimensional model according to physical parameters such as dynamic illumination, materials and roughness of an original scene, a three-dimensional space self-adaptive physical rendering engine is driven, and synchronous physical reconstruction of advertisement content and continuous frame scene illumination and material changes is achieved. In combination with multi-foreground-layer dynamic shielding and space superposition, in cooperation with the steps of boundary adaptive hybrid rendering, time sequence image quality optimization and the like, the advertisement content can be dynamically and accurately transformed according to the environment and the foreground, and finally high consistency and seamless natural fusion of the advertisement and the movie video content in physical attribute and visual perception are achieved.

The present disclosure belongs to the technical field of computer learning, and particularly relates to a hotel price change prediction method and system based on information entropy and a large language model. The method comprises the following steps: obtaining hotel price change data from different data sources; extracting data features of the hotel price change data; wherein the hotel price change data comprises price change days, daily price change days and the farthest price change day; the data features comprise information entropy of the price change days; constructing a natural language prompt word according to the data features; calling a large language model for reasoning according to the natural language prompt word to generate a query plan containing a query date, a time window and a priority; and performing hotel price change prediction through the query plan. The present disclosure realizes intelligent conversion from data features to strategy generation through a large language model, does not need manual rule making, and can automatically adapt to complex and changeable hotel price change modes.