829 results about "Space model" patented technology

The invention discloses a physical information neural network-driven ocean island typhoon scene generation method and system. The method comprises the steps of collecting multi-source heterogeneous meteorological data and performing space-time alignment preprocessing; constructing a coarse-scale space-time probability prediction model, capturing space correlation of meteorological elements by using a graph topology learning network, efficiently processing long-time-sequence dependence of typhoon evolution by integrating a state space model with linear complexity, and generating a probabilistic typhoon scene with coarse resolution through a multivariable joint distribution probability model; further constructing a physical downscaling model, taking a coarse-scale prediction result as condition input, and performing physical consistency downscaling on a coarse-scale scene by embedding an atmospheric fluid mechanics equation in a loss function as a physical hard constraint; and finally, outputting a high-resolution typhoon scene with probability reliability and physical authenticity.

The invention relates to the crossing field of mechanical engineering and automatic control, in particular to a global path planning and anti-swing control method for a ship unloader, and aims to solve the problems of rigid path planning and poor synergism of out-of-control swing of a lifting appliance in traditional ship unloading operation. The method comprises the following steps: constructing a dynamic three-dimensional operation space model with fusion of a laser radar and binocular vision, and updating obstacle information in real time; an improved fast extended random tree algorithm is adopted to generate a high-smoothness initial path; establishing a six-degree-of-freedom lifting appliance swinging dynamic model and identifying parameters on line; path tracking and anti-swing torque are synchronously optimized through a model prediction controller, and the control period is smaller than or equal to 20 milliseconds; and closed-loop feedback correction is implemented by combining an encoder and an inertial measurement unit. According to the scheme, environment dynamic sensing, path-anti-swing depth cooperation and multi-disturbance self-adaptive compensation are achieved, the operation rhythm is improved by 30% or above, the system still operates stably under the 8-level wind condition, and high precision, high robustness and engineering implementability are achieved.

The invention relates to the technical field of industrial robot control, and particularly discloses an intelligent foreign matter sorting robot control system based on AI recognition, which comprises a dynamic spatial feature extraction module, a manipulator motion state coding module, a collaborative conflict detection module, a dynamic trajectory optimization module and an execution control adjustment module, constructing a three-dimensional dynamic space model through multi-sensor fusion, and extracting spatial topological features by utilizing continuous coherence analysis; manipulator motion parameters are converted into topological space representation, and a track feature coding matrix is established; detecting interaction conflicts among the manipulators in real time by adopting a multi-scale coherence analysis method, and generating graded early warning signals; a collision avoidance track is optimized based on topological constraints and a virtual rejection field technology; precise execution is achieved through inverse kinematics of the Lie group theory and self-adaptive control.

The invention discloses an intelligent power prediction method considering dynamic load change, and relates to the technical field of power grid load prediction, and the method comprises the steps: collecting original load data, carrying out the preprocessing, constructing a VMD constraint optimization model, carrying out the four-stage improvement of optimization parameters through employing an improved dung beetle optimization algorithm, so as to generate IMF components, reconstructing the IMF component by calculating a sample entropy to obtain a low-frequency component and a high-frequency component; establishing a Kalman filtering state space model based on the low-frequency component, and decomposing the low-frequency component into a residual component and a pseudo trend component through a Kalman filtering recursive algorithm; external features are obtained, the high-frequency component, the residual component and the pseudo trend component are aligned and spliced with the external features, multi-component collaborative prediction is carried out through a local-global interactive attention mechanism, and a final load prediction result is obtained; and generating a power demand visualization chart based on the final load prediction result. And reliable decision support is provided for power dispatching and energy management.

The invention discloses a power electronic networking equipment stability analysis method, system, equipment and medium, and the method comprises the following steps: building a full-system state space model, carrying out the cross validation of the full-system state space model through employing a modal analysis method, an impedance analysis method and a complex torque analysis method, and obtaining the stability of the full-system state space model; obtaining a multi-mode coupling stability analysis result; according to the multi-mode coupling stability analysis result, constructing a power grid s domain node admittance matrix; establishing a virtual power angle dynamic equation of the network-forming converter, and optimizing control parameters according to an evaluation result; an inertia center reference system is adopted, a dynamic interaction model of the synchronous machine and the network-forming converter is established, and dynamic coupling characteristics between devices are analyzed through a relative motion equation. According to the method, the comprehensiveness, the precision and the engineering applicability of dynamic stability analysis of the novel power system are improved through fusion of a multi-dimensional collaborative evaluation framework and an innovative technical system.

The invention discloses an end-to-end defogging network fusing Mama and multi-scale context enhancement, and belongs to the technical field of computer vision and image restoration. The existing defogging technology has the problems of complex global modeling calculation, poor local detail recovery and insufficient feature fusion self-adaption. The problem is solved through the design of four core structures: 1, a Mamba state space model is introduced, and global fog long-range dependence modeling is realized with linear complexity; 2, designing a multi-scale cavity convolution module and a local context enhancement unit, and covering multi-scale local features; 3, constructing an enhanced adaptive fusion module to realize dynamic adaptation of global and local features; and 4, gradually recovering details by adopting a multi-scale input-cross-scale fusion-multi-scale output framework. The network can efficiently process high-resolution fog-containing images, and is suitable for scenes needing real-time high-precision defogging, such as automatic driving, security and protection monitoring, remote sensing imaging and the like.

The invention discloses a low-light image enhancement method combining a state space model and wavelet transform, and aims to solve the problems of detail loss, inaccurate illumination estimation and high calculation complexity of the existing low-light enhancement algorithm. According to the method, illumination estimation, frequency domain enhancement, wavelet transform, a high-frequency attention mechanism, dynamic state space modeling (Mamba) and a multi-scale U-Net structure are introduced, so that high-quality and detail-retaining enhancement of a low-illumination image is realized. According to the method, firstly, based on the Retinex theory, an illumination prior image is generated by calculating the global mean value of RGB channels of a low-illumination image, illumination features are extracted in combination with an illumination estimation module, and a preliminary enhanced image is generated; then, a multi-scale dynamic coding and decoding network is adopted, a coding layer compresses spatial dimensions step by step and increases feature channels, a decoding layer recovers resolution step by step to preliminarily enhance image fusion, and an enhancement result is well obtained.

The invention relates to the field of power grid real-time monitoring and coordination control, in particular to a distributed photovoltaic grid-connected regional power grid real-time monitoring and coordination control system which comprises a sensing acquisition module, a multi-time scale prediction module, a self-evolution calibration module and a distributed coordination control module. The sensing acquisition module acquires wide-frequency-domain electrical quantity and multi-dimensional meteorological quantity through a double-layer sensing network, and restoration data is obtained through anomaly detection; the multi-time-scale prediction module integrates ultra-short-term, short-term and medium-and-long-term prediction, and realizes cross-scale collaboration based on a combined state space model and the like; the self-evolution calibration module performs online compensation on the multi-scale prediction residual error based on deep reinforcement learning; the distributed coordination control module is combined with an improved alternating direction multiplier method and a non-dominated sorting genetic algorithm to obtain an optimal power instruction meeting voltage, frequency and harmonic ternary constraints; according to the invention, the stability of power grid operation in a high-permeability photovoltaic grid-connected scene is improved.

The invention belongs to the field of stability analysis of a grid-connected system of a grid-building converter, and particularly relates to a grid-building converter grid-connected resonance risk analysis method considering virtual impedance, which comprises the following steps: S1, establishing a three-phase time domain differential equation and simplifying the equation into a single-phase equivalent time domain model; converting into a harmonic state space model of the main circuit; s2, constructing a harmonic state space model of a control system comprising an active power control loop and a reactive power control loop; s3, introducing a virtual impedance control link, and correcting the voltage amplitude reference phasor output by the reactive power control loop; deriving an expression of a modulation signal disturbance phasor; s4, obtaining a complete frequency domain admittance model YGSC of the network construction converter considering the influence of the virtual impedance; an open loop gain TGFM of the grid converter grid-connected system is constructed; and S5, drawing a Nyquist curve based on TGFM, and carrying out resonance risk determination. According to the method, the interaction dynamic characteristics of the network building converter and the power grid under the influence of the virtual impedance can be accurately represented, and the resonance risk of the grid-connected system is evaluated.

The invention relates to the technical field of medical image and artificial intelligence crossing, and discloses a cross-modal-based VMama medical image fusion method and system combining grouped ACmix convolution and selective clustering, and the system comprises an input module, a preprocessing module, a feature extraction module, a multi-scale fusion module, and an output reconstruction module. According to the scheme, cross-modal attention is introduced into a visual state space model for the first time, a new multi-modal image fusion framework LMACV is obtained, the framework performs linkage optimization on ACmix and VMamba structures in a cross-modal medical image for the first time, feature reconstruction efficiency is enhanced through a selective clustering mechanism, and MSE and PSNR are remarkably superior to existing methods such as MPCT, FATFusion and MATR. By fusing a convolutional network and state space modeling, the network can effectively capture local texture details, and meanwhile, long-distance semantic association is reserved; besides, linear state updating and cross-modal dynamic alignment of attention guidance are effectively realized, and compared with the latest MPCT algorithm, the fusion speed is improved by 37.5%.

The invention relates to the technical field of network detection, and provides a dynamic event trigger fault detection method under DoS network attack, which comprises the following steps: establishing a linear state space model of a network control system; defining a non-attack interval and an attack interval of system operation, and constraining attack frequency and duration; the observer gain is switched according to the current non-attack interval or attack interval of the system; a dynamic event triggering mechanism is constructed, and the triggering condition depends on the output state and the internal dynamic variable of the full-order switching observer and is used for dynamically adjusting the data transmission frequency; establishing a closed-loop switching system model; and analyzing system index stability according to the closed-loop switching system model, and cooperatively designing observer gain, controller gain and event triggering parameters. According to the invention, through quantification of the DoS attack model, the dynamic event triggering mechanism and collaborative optimization design, the effects of effectively detecting the system fault and improving the utilization rate of the network channel are achieved.

The invention provides experimental equipment and method for simulating filling and sealing of carbon dioxide in a goaf, and the experimental equipment comprises a simulation system, a slurry injection system, a gas injection system, a data acquisition and processing module, a recording, analyzing and displaying module and a data acquisition system. A simulated overlying strata of the simulation system naturally collapses to form a porous medium space model, slurry and carbon dioxide are injected into the porous medium space model through the slurry injection system and the gas injection system, and data acquisition is conducted on the curing process of the carbon dioxide through the data acquisition system; and the data is transmitted to the recording, analyzing and displaying module through the data collecting and processing module. According to the invention, the influence of carbon dioxide on the slurry solidification behavior and the solidification reaction penetration depth can be comprehensively researched, and a scientific basis is provided for optimizing material selection, proportion design and injection process parameters in practical engineering.

The invention provides a building interior design layout optimization method, and relates to the technical field of indoor modeling and layout optimization, and the method comprises the steps: obtaining building structure boundary data and current situation layout information, constructing an indoor three-dimensional space model, and completing the marking of node passing attributes and a spatial topological structure. On the basis, crowd behavior data of a daily scene and an emergency scene are collected, behavior characteristics such as walking speed, obstacle avoidance response and congestion sensitivity are extracted, and a walking characteristic model used for simulating the individual motion trend is established. And performing multi-scene simulation under a daily event and an emergency evacuation event by using the walking feature model, calculating performance indexes such as passing time, local congestion degree, key node congestion probability and total evacuation time, and identifying a congestion sensitive area. And further constructing a multi-target evaluation model including traffic efficiency, congestion degree and evacuation performance, and performing global optimization on layout adjustable parameters by adopting an algorithm to obtain a candidate layout scheme set.

The invention relates to the field of rotating machinery fault diagnosis, and discloses a bearing fault diagnosis method based on a physical perception KAM network, and the method comprises the steps: carrying out the discretization of a bearing vibration signal through a Gabor filter group based on physical prior initialization, and generating modal feature lexical elements with physical frequency band meanings; and inputting the lexical elements into a PC-KAM backbone network, calculating a hidden state vector by using a state space model branch, and dynamically adjusting the position of a primary function node of a Kolmogov-Arnod network branch to realize collaborative dynamic feature extraction. In the training stage, an orthogonal subspace constraint and physical perception low-rank adaptation fine tuning mechanism is introduced. And finally, searching a historical fault case, performing multi-modal fusion on the historical fault case and the deep feature sequence, mapping a fusion representation into a soft prompt, and inputting the soft prompt into a large language model to generate a diagnosis report. According to the method, the problems of poor physical interpretability of characteristics and few-sample diagnosis under variable working conditions are effectively solved, and the generalization and decision-making ability of a diagnosis system are improved.

The invention belongs to the technical field of control detection, and particularly relates to a laser cutting machine cutting control method and system.The laser cutting machine cutting control method comprises the following steps that S1, frequency sweeping excitation is applied to a plurality of preset grid node positions of a laser cutting machine workbench, and vibration response data of a cutting head is collected; a dominant pole and a zero point of a vibration transfer function of each grid node are identified and extracted, spatial distribution is fitted by using a non-uniform rational B-spline surface, and a spatial model of continuous mapping between a cutting head position and a vibration transfer function parameter is established; s2, decomposing a to-be-processed path into a plurality of path infinitesimal segments, and calculating a preliminary feed-forward compensation instruction of each path infinitesimal segment based on a space model and a zero-phase error tracking control algorithm; according to the invention, the contour error caused by structural vibration and non-linear factors is reduced, the processing precision and the cutting surface quality of the laser cutting machine in a full working range are improved, and the forming effect of a complex contour at a high speed is improved.

The invention discloses an automatic mural protection and repair area identification method and system based on image analysis, and the method comprises the steps: generating degradation states of a mural at different time nodes through an image simulation algorithm, converting a two-dimensional damage image into a three-dimensional space model through the combination of a three-dimensional mapping technology, and precisely calibrating a high-risk damage point. The clustering analysis algorithm further integrates and disperses damage points, the property and spatial features of a damage group are extracted, and the curve feature detection algorithm automatically identifies damage boundaries by analyzing texture continuity and line trend. Dynamic sequence analysis tracks inter-frame differential change, a degradation extension track is disclosed, damage information is efficiently compressed and stored by a coding technology, and a matched degradation mode is mined and a potential damage area is updated by historical comparison analysis. According to the method, three-dimensional space modeling is taken as a core, multi-dimensional analysis and efficient storage are fused, accurate positioning, dynamic monitoring and preventive protection of mural damage are realized, and the scientificity and efficiency of cultural relic protection are remarkably improved.

The invention discloses a multi-energy system closed-loop prediction control method, system, equipment and medium, and the method comprises the steps: carrying out the decomposition, dimension reduction and nonlinear modeling of the time sequence characteristics and environmental influence factors of photovoltaic output through a photovoltaic power prediction model, and outputting a future multi-period photovoltaic power prediction sequence; establishing a state equation and an output equation, integrating equipment operation constraints, and constructing a hydrogen-containing energy storage state space model; inputting local load power and a future multi-period photovoltaic power prediction sequence into the hydrogen-containing energy storage state space model, and solving an objective function through a rolling optimization algorithm to obtain a future multi-period optimal scheduling scheme; and applying a first hour control instruction of the optimal scheduling scheme to an actual system, and proportionally superposing the deviation between an actual measurement value and a historical prediction value to a photovoltaic power prediction sequence of a next period through a feedback correction item to realize closed-loop control. According to the method, the photovoltaic local consumption rate can be improved, and the power grid fluctuation rate is reduced.

The invention relates to the technical field of converters, in particular to an LLC perception type physical information nested neural network parameter estimation method suitable for an LLC resonant converter, and the method comprises the following steps: S1, constructing a continuous time state space model of the LLC resonant converter, and carrying out the discretization processing; s2, an LLC perception type physical information nested neural network is constructed, and the LLC perception type physical information nested neural network comprises a data reconstruction network and a physical information nested neural network which are connected and is used for online parameter identification of the LLC resonant converter; the data reconstruction network comprises a resonance state judgment layer, a K2 operation layer, a pseudo label generation layer, a constraint layer and a data reconstruction layer; the physical information nested neural network comprises a middle state mapping layer and a physical layer; defining LLC perception type physical information nested neural network input; s3, constructing a loss function; and S4, deploying and executing the LLC perception type physical information nested neural network model.

This invention relates to the field of hydraulic engineering, and more particularly to a method for safety assessment and optimization design of large-flow aqueduct structures. The method includes: collecting displacement, strain, and temperature monitoring sequences based on a triple probe, completing grouping, coordinate and time registration, and establishing a monitoring baseline; constructing a spatial model including support nonlinearity and fluid-structure boundary conditions, applying loading conditions, and obtaining response predictions; aligning monitoring and prediction in time and location, performing hierarchical Bayesian filtering inversion, and obtaining parameter posteriors and structural health; under the integration of in-service monitoring and accelerated degradation characteristics, using multi-task learning to generate material evolution and lifespan intervals, and automatically generating and simulating optimization schemes under reliability and maintainability constraints. This application forms a closed loop from assessment to design, effectively enhancing data comparability, traceability of uncertain information, and adaptability of the scheme to engineering projects.

The invention provides a distributed coherent communication system synchronization method, and the distributed coherent communication system enables dispersed nodes in a space to cooperate with each other through the construction of a virtual antenna array, so as to form a directional wave beam, thereby achieving the control and enhancement of a signal direction. A distributed Kalman filtering framework is introduced, a frequency and phase joint state space model is constructed, local observation data of nodes and neighbor state information are optimally fused, and dynamic estimation and compensation of frequency offset and phase errors are achieved. An updating strategy is designed in combination with an event triggering mechanism, local Kalman filtering prediction is executed in conventional iteration to suppress random noise and control the communication burden and calculation complexity of a system, meanwhile, the standard deviation of a residual phase error is effectively reduced by one order of magnitude, and the synchronization performance is improved.

The invention provides an undercarriage system modeling method based on a natural language demand text, and relates to the technical field of model-based computer aided design, and the method comprises the steps: S1, constructing a SysML meta-model knowledge base and an undercarriage system model knowledge base; s2, analyzing the natural language demand text, and generating a structured demand text of the undercarriage system; s3, mapping a structured demand text entity of the undercarriage system into a node type of a semantic graph; and S4, in combination with a historical model template and real-time retrieval, generating an undercarriage system SysML model according to the semantic map obtained in the step S3, and performing undercarriage system engineering analysis through the undercarriage system SysML model, thereby improving the efficiency and quality of aviation equipment development. On the basis of a retrieval enhancement generation technology and an intention routing mechanism, the modeling efficiency and accuracy of the undercarriage system are remarkably improved; a graph neural network and a semantic vector space model are adopted to realize deep fusion and conflict resolution of cross-domain knowledge; and through an incremental learning mechanism, model continuous evolution and design experience closed-loop iteration are supported.

The invention relates to the technical field of power systems and energy management, and discloses an energy-carbon integrated virtual power plant multi-agent collaborative management and control method and system, and the method comprises the steps: S1, constructing a three-layer control architecture comprising a resource sensing layer, a region coordination layer and a global optimization layer; s2, establishing an energy-carbon coupling state space model, and outputting a target optimization result by taking photovoltaic output, energy storage charging and discharging power, a load demand elastic coefficient and a carbon emission right transaction volume as input variables; s3, generating a scheduling instruction by adopting a rolling optimization strategy; and S4, realizing multi-agent collaboration through a distributed consistency algorithm, exchanging power deviation information by each node based on a local constraint condition, and carrying out iterative calculation until the power deviation of all nodes is less than 1%. A three-layer control architecture is constructed, scheduling lag is shortened through decision making and stage processing of the global optimization layer after preprocessing of the regional coordination layer, the limitation of a single node is broken through through cross-regional coordination, and the overall operation efficiency of the virtual power plant is enhanced.

The invention provides a joint optimization method for a stacking sequence and a grabbing posture of a neutron measurement system, and relates to the field of data processing. According to the method, multi-source monitoring data of a neutron measurement system are collected, shadow voxels are constructed in combination with a three-dimensional space model, a neutron resonance shadow domain is formed through clustering superposition, and a dangerous concentration cloud cluster and a safe rarefied area are marked; mapping candidate stacking positions and postures according to sample mass distribution, geometric dimensions, centroid positions and irradiation sensitive characteristics, generating a stacking mode and determining a stacking sequence; performing path risk assessment on the grabbing postures, and selecting the grabbing postures of which the crossing risk meets a threshold value; superposing micro-random jitter on the path to suppress resonance phase locking; and abnormal events are monitored in real time during operation, and a risk domain is updated to execute hierarchical unloading control, so that the instantaneous impact and damage risk is reduced. According to the technical scheme, the probability of instantaneous impact risks caused by stacking overall micro-drift and grabbing track overlapping can be effectively reduced, and therefore the safety of the system is guaranteed.

The invention discloses a photovoltaic court harmonic detection method and system, and the method comprises the steps: employing a Hemma optimization algorithm introducing a random reverse learning strategy, adaptively searching an optimal modal number and a penalty factor of variational modal decomposition, and taking the minimization of a reconstruction error as a target, and determining an optimal parameter combination; based on the optimal parameter combination driving variational mode decomposition algorithm, adaptively decomposing the original noisy harmonic signal into a plurality of intrinsic mode components with limited bandwidth; establishing a state space model for each intrinsic mode component, and performing independent denoising on each intrinsic mode component by adopting adaptive Kalman filtering; and linearly superposing all the denoised intrinsic mode components, and reconstructing a harmonic estimation signal. The method is oriented to a complex power grid environment containing high-proportion power electronic equipment and non-linear loads, and focuses on solving the problem of accurate separation of harmonic waves and inter-harmonic waves under strong noise interference.

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

The invention provides a hydrological flow long sequence prediction method and system of an improved state space model. The method comprises the steps of collecting multi-source data of a drainage basin to be predicted; constructing a time sequence sample pair by the processed multi-source data through a sliding window method, wherein the time sequence sample pair comprises an input sequence and a target sequence; a HydroMama model is constructed according to the time sequence sample pair, a HydroMama prediction model is trained, and an optimal hyper-parameter combination is searched for; based on the trained HydroMama model, traffic prediction and result restoration are realized by adopting an autoregression mechanism; the calculation efficiency is high, and the continuous state equation discretization of the state space model is utilized, so that the long-distance meteorological-hydrological hysteresis effect which is difficult to capture by a traditional cycle model can be captured; according to the method, skewed distribution of hydrological data is processed through logarithmic transformation, and an anti-overfitting objective function is combined, so that the prediction performance of the model on unseen data is remarkably improved, and the practical application value is high.

The invention provides a touch force feedback simulation method and system based on a pressure deformation signal, and relates to the technical field of touch interaction and tactile feedback, and the method comprises a data collection process: configuring a sensor to collect the physical state of a touch interface, and obtaining an instantaneous pressure and deformation signal; the impedance compensation process comprises the following steps: step 1, acquiring an instantaneous signal; 2, constructing a dynamic hysteresis loop model; step 3, calculating a viscosity energy loss value; 4, generating an energy compensation waveform; 5, an actuator is driven to generate reverse damping vibration; according to the method, the material viscosity loss is quantified through the physical phase space model, reverse energy is accurately injected, and the problems of touch hysteresis and soft collapse caused by viscoelasticity of a touch interface are effectively solved.

The invention discloses an application method, system, equipment and medium of plant inspection navigation in a two-ticket intelligent management and control system based on AOA positioning, and belongs to the technical field of positioning navigation, and the application method comprises the following steps: setting a Bluetooth AOA transmitting base station and a Bluetooth AOA module positioning tag in an inspection area, carrying out signal interaction, and constructing an inspection space model; obtaining an interaction signal between the mobile Bluetooth transmitter and the Bluetooth AOA transmitting base station when an inspector carrying the mobile Bluetooth transmitter enters the inspection area, calculating the distance between the inspector and the Bluetooth AOA transmitting base station according to the signal intensity, and determining the real-time position of the inspector in the inspection space model; an inspection navigation route is generated according to the real-time position of the inspector, a preset inspection route and the work ticket information and the operation ticket information provided by the two-ticket intelligent management and control module; and when the inspector arrives at the inspection point and a preset card punching condition is satisfied, recording inspection card punching information and generating an inspection record. According to the invention, the inspection efficiency and safety are improved.

The invention relates to the technical field of smart home and artificial intelligence, discloses a smart home equipment automatic arrangement method and system fusing 3D modeling and a neural network, and aims to solve the problems of complex manual operation, high technical threshold and high error rate in the traditional scheme design. The method comprises the following steps: acquiring original three-dimensional point cloud data of a target space through laser scanning equipment, and constructing a centimeter-level precision 3D space model; a deep learning semantic segmentation network is adopted to identify building components and functional areas, and a spatial semantic map is generated; performing multi-modal fusion on the spatial semantic features and user requirements to generate equipment type probability distribution and an optimal position thermodynamic diagram; performing iterative correction on the initial arrangement scheme based on a multi-objective optimization algorithm, and synchronously optimizing wireless signal coverage intensity, equipment energy consumption efficiency and project implementation cost; the optimized 3D arrangement scheme is automatically converted into a two-dimensional vector engineering drawing, and a construction drawing is generated; and extracting an equipment list and material consumption according to the final arrangement scheme, and automatically generating a project quotation and an installation process guidance document. The system comprises a spatial semantic analysis module, an equipment arrangement decision module, an engineering drawing generation module and a multi-objective optimization engine. Through adoption of the technical scheme, full-process automation from original spatial data to constructable drawings can be realized, and the efficiency and specialty of smart home scheme design are remarkably improved.

The invention relates to the technical field of logistics data processing and artificial intelligence, and discloses an intelligent warehouse management system based on logistics data processing, and the system comprises a multi-source heterogeneous data fusion preprocessing module which carries out the real-time collection of the data of a global distributed storage center, and outputs a time-space standardized multi-dimensional feature data set; the spatio-temporal correlation demand prediction module is used for constructing a spatio-temporal model and carrying out depth prediction by adopting a long-short-term memory network and an attention mechanism; the multi-constraint global inventory optimization module is used for constructing a multi-objective optimization model and solving an inventory configuration scheme through a decomposition coordination method; the reinforcement learning intelligent scheduling module is used for establishing an environment state space and performing strategy optimization by adopting a deep Q network; the digital twinborn visual decision support module is used for realizing real-time synchronization of physical storage and a digital model and outputting decision suggestions and risk early warning; according to the method, multi-source heterogeneous data can be effectively processed, intelligent prediction of cultural perception is realized, and global collaborative optimization is carried out.