1984 results about "Distribution model" patented technology

The invention provides a prediction method and system for a rock stratum fracture disaster in tunnel engineering, and belongs to the field of tunnel engineering. Comprising the steps of multi-modal monitoring data acquisition and synchronization, data preprocessing and feature calculation, multi-modal information entropy evolution analysis, catastrophe point detection and precursor identification, risk assessment and early warning output and the like. Multiple types of sensors are arranged at key positions of a tunnel, an edge computing gateway is accessed through an industrial-grade field bus, and space-time reference unification of multi-source heterogeneous data is achieved; carrying out preprocessing and feature calculation on the collected original data; performing sliding window probability distribution modeling on each modal feature, calculating an information theory index, and analyzing non-linear relevance among the multi-modal features; judging whether a catastrophe precursor event exists or not by adopting multiple methods; and finally, based on a catastrophe point detection result, performing risk assessment on the tunnel region by using a multi-dimensional risk quantification model, and performing early warning output on high-risk and emergency states.

The invention relates to the technical field of traffic accident analysis, and discloses an intelligent analysis method for vehicle and pedestrian collision accident liability, and the method comprises the steps: firstly obtaining multi-source heterogeneous accident data, fusing a cross-platform data source through employing a federal learning framework when the data is insufficient, and reconstructing an accident scene three-dimensional coordinate system; and then, based on a multi-modal data fusion result, constructing a traffic participation entity relation topology model by using a graph neural network, and generating an accident dynamic evolution graph. Then, establishing a collision dynamics digital twin model by utilizing a physical engine, extracting a key collision feature vector, and constructing a responsibility probability distribution model based on a generative adversarial network; and optimizing a responsibility judgment strategy by adopting a double-layer reinforcement learning framework, verifying a physical simulation result through a hierarchical verification mechanism, analyzing a responsibility judgment logic chain, and finally outputting a responsibility analysis report with an interpretable label. The method can accurately and intelligently analyze the accident liability, and has good interpretability.

The present application provides a method and system for surveying and mapping of national land space planning based on dynamic remote sensing technology. The method includes: constructing a multi-source remote sensing data acquisition network based on the LPV model; performing spatiotemporal registration and noise filtering on time series remote sensing data; extracting spatiotemporal features using a deep neural network; identifying key change areas through a dual attention module; constructing a hierarchical dynamic feature library; establishing an uncertainty distribution model; designing probability constraints; generating adaptive safety spacing rules; establishing a multi-objective optimization function; and implementing a spatial planning strategy using the NSGA‑III algorithm. The present application improves the intelligence level and reliability of national land space planning through dynamic remote sensing data analysis and distributed robust opportunity constraint strategies.

The invention relates to the technical field of LED modules, and discloses an LED module breakpoint detection and positioning method and device, and the method comprises the steps: carrying out the voltage and current data collection of series and parallel nodes of an LED module, and obtaining an electrical feature matrix and a node topological graph; performing iterative calculation on the electrical characteristic matrix, and processing a voltage difference value and a current variation between adjacent nodes to obtain a breakpoint characteristic vector; inputting the breakpoint feature vector and the node topological graph into a multi-task neural network for multilayer convolution operation to obtain an electrical fault distribution graph; performing feature segmentation and depth feature extraction on the electrical fault distribution map to obtain fault area feature data; and establishing an electric field distribution model for the fault area feature data, calculating an electric field singular point through a least square method, and obtaining breakpoint space coordinate data, thereby improving the accuracy of LED module breakpoint detection, and realizing accurate positioning of the LED module breakpoint.

The invention relates to the technical field of non-electrical variable control, in particular to a digital visual control system and method for a glass bottle production line. The method comprises the following steps: acquiring digital image data of molten glass; molten glass bubble distribution is identified based on the molten glass digital image data, the quality state of the molten glass is evaluated, and micro-defect distribution map data is generated; collecting the internal temperature of the smelting furnace through a temperature sensor array, and constructing a three-dimensional temperature field distribution model of the smelting furnace; and predictively optimizing and adjusting the heating control parameters of the smelting furnace based on the three-dimensional temperature field distribution model of the smelting furnace, and generating a regional heating power distribution strategy. According to the invention, through fusion of a plurality of advanced technologies, accurate sensing and whole-process linkage control of the molten glass state are realized, intelligence, quality uniformity and process visualization level of glass bottle production are significantly improved, and glass manufacturing is promoted to develop towards the intelligent and efficient direction.

The invention provides a soft rock tunnel surrounding rock parameter dynamic identification method and system based on data driving, and relates to the technical field of underground tunnel mechanical parameter dynamic identification, and the method comprises the steps: obtaining multi-element tunnel surrounding rock parameters, and building a joint probability distribution model of the multi-element surrounding rock parameters based on a Copula theory; performing Monte Carlo simulation, and generating a high-dimensional parameter sample library meeting physical constraints in the parameter constraint space based on the joint probability distribution model; establishing a tunnel three-dimensional numerical model and performing automatic numerical simulation to generate multivariate response data; constructing a Kriging agent model of a Gaussian kernel function based on multivariate response data training, establishing a nonlinear mapping relation between parameter input and deformation output, constructing an inversion objective function by taking the minimum root-mean-square error of multi-measurement-point displacement as an objective, and performing inversion solution by using an adaptive particle swarm optimization algorithm to obtain inversion identification parameters, and a dynamic feedback mechanism is constructed to realize adaptive tracking of the time-varying characteristics of the surrounding rock parameters.

The invention discloses a knowledge graph optimization method based on large model and multi-modal data fusion. The method comprises the following steps: S1, constructing a multi-modal data set; s2, forming a preprocessed multi-modal data set; s3, generating a unified semantic vector representation; s4, inputting the unified semantic vector representation into an adaptive Poisson distribution model, performing dynamic modeling on the arrival rate and distribution characteristics of the multi-modal data, and determining adaptive parameters of data sampling and updating; s5, generating a fused semantic representation set; s6, performing entity extraction and relation identification by using the fused data representation, and constructing a preliminary knowledge graph; and S7, carrying out automatic verification, redundant information elimination and structure adaptive adjustment on nodes, edges and attributes of the knowledge graph to form an optimized knowledge graph. According to the method, the data acquisition and atlas updating frequency can be adjusted according to the real-time data semantic change, and it is ensured that the atlas construction process has semantic driving performance and time sensitivity.

The invention discloses a health degree evaluation system and method based on a photovoltaic string, and belongs to the technical field of photovoltaic operation and maintenance intellectualization, and the method comprises the steps: firstly collecting SCADA operation data of a plurality of strings of a photovoltaic power station, including voltage, current, assembly temperature, environment irradiance and power factors; secondly, constructing a whole-station feature reference model, and generating a state vector fusing environment and load characteristics through a nonlinear projection algorithm; aiming at the target group string, extracting a health reference track, calculating a disturbance propagation factor, and identifying a health abnormal state; when the deviation degree exceeds a threshold value, dynamically screening heterogeneous reference group strings, constructing a health score distribution model, and further calculating a confidence score and a prediction residual error; if the score is low and the residual error is unstable, determining that the string is in a sub-health or hidden fault state, and generating an intervention instruction; the method has high accuracy, strong adaptability and good interpretability, and can be widely applied to intelligent diagnosis and refined operation and maintenance management of the photovoltaic power station.

The invention discloses a circuit board AOI detection result analysis method based on an intelligent algorithm, and the core of the method is to carry out the high-precision synchronous collection, denoising normalization processing and time sequence alignment of a detection image and multi-dimensional process parameters, and extract joint features through an attention mechanism. A process parameter-detection result correlation distribution model under each process scene is established, model output abnormal drift detection and tracing reasoning are realized, the detection model and the process parameters are dynamically optimized in combination with interpretability AI, the method realizes efficient tracking, attribution and adaptive optimization of detection misjudgment caused by process parameter variation, and the detection accuracy is improved. And the stability of the production process of the SMT detection system is improved.

The invention relates to the technical field of image processing, in particular to an integrated image restoration method and system based on image mask modeling, and the method comprises the following steps: collecting the boundary data of the whole image, separating the boundary of a mask region from the boundary of a non-mask region, and analyzing the gradient change characteristics of the boundary of the mask region. And extracting local depth information of the mask region from the gradient change features, and constructing a mask region boundary residual distribution model. According to the method, the mask region and the non-mask region are separated, the boundary gradient features are extracted, the residual distribution model is established on the basis of gradient change, the subtle change of the boundary of the mask region is accurately captured, and the accuracy of the boundary features is improved. By constructing a depth information data set and combining local depth information with gradient features, adjustment of layered dynamic weight distribution of a mask area basic structure is realized, effective extension of area features to the inside is ensured, and the possibility of boundary information loss is reduced.

The invention relates to the technical field of electromagnetic spectrum monitoring, and particularly discloses a distance-based electromagnetic spectrum monitoring abnormal data detection method, which comprises the following steps of: performing short-time Fourier transform and normalization processing on an acquired original signal to generate an energy density distribution characteristic graph; constructing a multivariate Gaussian distribution model based on non-abnormal historical data; during real-time monitoring, the mahalanobis distance between the collected data and the mean vector of the historical model is calculated after the collected data is preprocessed. And comparing the distance metric value with a preset threshold value to preliminarily judge abnormity, and calculating a distance fluctuation variance through a sliding window mechanism to perform secondary verification. And finally, processing and positioning anomalies by using image morphology, and dividing the degree of anomalies according to the relative deviation between the energy density and the mean value of the historical model. According to the method, the mahalanobis distance and the multivariate Gaussian distribution are introduced, secondary verification and abnormal positioning are combined, the limitation of a traditional method is overcome, the detection accuracy and reliability are effectively improved, the misjudgment and missing judgment rate is reduced, and the method does not depend on a large amount of labeled data and is high in practicability.

The invention relates to the field of temperature monitoring, and discloses a high-voltage passive induction temperature monitoring system, which comprises a plurality of temperature sensors, the plurality of temperature sensors are arranged in different cabinets and channels in a machine room; a control unit; the control unit is used for sending a detection instruction to each temperature sensor and receiving a feedback temperature signal; the control unit generates a temperature change curve of each monitoring point according to the received temperature signals; the control unit constructs a three-dimensional temperature distribution model of the whole machine room based on the temperature change curve of each monitoring point, identifies a potential hot spot area based on the three-dimensional temperature distribution model and predicts the temperature change trend of the potential hot spot area; a refrigeration unit; the refrigeration unit is used for adjusting the temperature of the located area; the refrigeration unit is communicated with the control unit; wherein the control unit adjusts the power of each refrigeration unit based on the identified potential hot spot areas.

The invention relates to the technical field of projection display, and discloses a projection curtain light field display gain method and system used under ambient light. According to the method, environment light data, projection curtain data and original output signals of projection equipment are collected in real time, and a dynamic light field distribution model is established after fusion processing. And based on the dynamic light field distribution model, adopting an adaptive genetic algorithm to optimize a compensation weight, generating a light field compensation parameter set, performing reverse compensation processing of brightness gain, contrast and chromaticity offset on an original output signal, and generating an enhanced projection signal. The system comprises a multi-source data acquisition module, a light field feature extraction module, a dynamic compensation calculation module, a signal enhancement processing module and a closed-loop feedback control module, ensures that the projection image keeps an optimal display effect under different ambient light conditions, and remarkably improves the watching experience of audiences.

The invention discloses a distillation SAE and dynamic integration converter steelmaking carbon temperature soft measurement method, which comprises the following steps of: training a teacher model on a large-scale data set of a conventional production working condition, and compressing the model by adopting a knowledge distillation technology; then, a small amount of data from unconventional production working conditions is used for fine tuning to obtain a plurality of expert-type student stacking auto-encoder models SAE; in the prediction stage, a to-be-tested sample is mapped to respective feature space through a plurality of SAEs, and the posterior probability of the sample is calculated in each feature space based on a Gaussian distribution model; and finally, respectively inputting a to-be-measured sample into a plurality of corresponding regression devices to obtain a predicted value, and carrying out weighted fusion on the output of each regression device according to a posterior probability to realize dynamic integration selection of the soft measurement model. The method can effectively adapt to complex data distribution of multi-working-condition changes in converter steelmaking, the carbon temperature prediction precision under the unconventional working condition is improved, and the method has high robustness and engineering practicability.

The invention provides a dynamic traffic flow distribution method based on multi-agent reinforcement learning, and the method achieves the dynamic optimization and real-time response of a large-scale vehicle path through a cloud-edge collaborative architecture, and comprises the steps: constructing a multi-agent traffic simulation environment, and carrying out the real-time traffic flow distribution according to the real-time traffic flow distribution. Generating an intelligent body vehicle with a random starting point through the Poisson distribution model; constructing a multi-dimensional observation vector comprising road topological coding, current road density, a neighborhood speed mean value, a target distance ratio and a congestion coefficient; adopting a near-end strategy optimization algorithm to carry out parallel strategy optimization on the multi-agent enhanced strategy network under a Ray RLlib framework; designing a multi-target reward mechanism based on path efficiency, congestion punishment and progress reward; a steering decision is generated according to the real-time observation state, and a vehicle driving route is dynamically updated through a Nash equilibrium solution of the game theory; and a cooperative emergency mechanism is triggered when the traffic jam suddenly occurs. According to the method, the problems of path convergence, response delay and secondary congestion of a traditional method are solved.

The invention relates to the technical field of unmanned aerial vehicle autonomous obstacle avoidance, and discloses an unmanned aerial vehicle autonomous obstacle avoidance optimization method based on a laser radar. According to the method, a real-time point cloud data stream in a flight environment of the unmanned aerial vehicle is obtained through a laser radar scanning module, wherein the real-time point cloud data stream comprises three-dimensional position information and a dynamic movement track of an obstacle; and constructing an obstacle space distribution model based on the real-time point cloud data stream to represent the geometrical shape and the motion state of the obstacle. A reinforcement learning algorithm is adopted, a collision risk value is calculated based on the position information and the motion trail in the model, and an obstacle avoidance path optimization instruction is generated according to the risk value and used for adjusting the flight direction and speed of the unmanned aerial vehicle. And when the instruction is executed, flight path feedback data is recorded in real time, and parameters of the obstacle space distribution model are updated based on the feedback data. According to the method, the adaptability and reliability of autonomous obstacle avoidance of the unmanned aerial vehicle in a complex environment are improved, and the flight safety is guaranteed.

The invention discloses a spatial registration method for tunnel multi-source heterogeneous data, and the method comprises the steps: constructing a lightweight digital twinborn model based on a design parameter library, a construction log library and a real-time monitoring library; mapping the multi-source sensor data in the real-time monitoring library to a digital twin model space node through a space-time synchronization algorithm; probability distribution model-based abnormal value elimination is performed on the geological exploration data, the while-drilling parameters and the geological sketch, and a discrete smooth interpolation algorithm is adopted to generate a surrounding rock mechanical parameter three-dimensional distribution field; the surrounding rock mechanical parameter three-dimensional distribution field serves as input, and front surrounding rock classification probability distribution is dynamically predicted through a machine learning model optimized through transfer learning; based on the three-dimensional point cloud data of the tunnel face in the digital twin model, extracting geometric parameters of the structural plane and constructing a fracture network model constrained by a normal vector; according to the method, the surrounding rock risk location is accurately positioned based on double-threshold triggering, and multi-scale model seamless fusion is realized through gradient constraint topology fusion.

The invention discloses a soft start control method for intelligent temperature control of a glue injection mold, and particularly relates to the technical field of glue injection molds. Initial temperatures of a plurality of heating areas of the mold are collected, and a temperature distribution model is constructed; calculating a thermal inertia score value based on the thermal response characteristic and the thermal capacity parameter of each region; predicting temperature rise rates of different areas by using the thermal inertia score value, the thermal coupling degree abnormal value and the historical control deviation frequency, and setting differentiated soft start amplitude limiting parameters; temperature feedback is collected in real time in the heating process, a temperature rise rate error sequence is constructed, and the future deviation risk is predicted through the dynamic Bayesian network; when the risk value exceeds the limit, the PWM duty ratio or the conduction angle of the heating area is dynamically corrected, and power output self-adaptive adjustment is achieved; the method has the advantages of being high in predictability, accurate in response and intelligent in control, and is suitable for high-precision heating control application of the complex glue injection mold.

The invention discloses a missing mode gamma collaborative retrieval diffusion method for an open environment, and belongs to the field of multi-mode learning and missing mode processing. According to the method, a human brain multi-source context completion mechanism is simulated, and robust multi-modal learning is realized through three innovative modules: a context retrieval enhancement module: a multi-modal memory library is constructed, related instances are retrieved through similarity calculation under a gating mechanism, and context representation of a missing mode is enhanced; the prompt drive diffusion generation module is used for constructing a semantic prompt based on a retrieval result, fusing a de-noising diffusion probability model through an attention mechanism, and realizing context-aware knowledge migration and missing modal generation; and the inverse gamma noise optimization module is used for establishing a mixed normal-inverse gamma distribution model, dynamically sensing noise, realizing uncertainty estimation in multi-modal fusion and ensuring robustness and reliability of a regression result. According to the method, the dependence of the model on the available modal quality is effectively reduced, and the cross-modal knowledge migration effect and the multi-modal learning task performance are improved.

According to the dynamic traffic environment monitoring method and device provided by the embodiment of the invention, the mobile sampling of pollutants such as particulate matters, nitrogen oxides and carbon monoxide is realized by constructing a power-assisted bicycle environment monitoring and collecting network. And the cloud computing platform performs abnormal value identification based on time sequence clustering, constructs a pollutant concentration distribution model through a multivariate convolutional neural network, and generates diffusion trend prediction in combination with a pollutant migration model. And intelligent scheduling of monitoring resources is realized by adopting a deep reinforcement learning algorithm, and an optimized inspection scheme is generated according to the pollution risk level, the vehicle position and the electric quantity. According to the method, the defects of the traditional technology in the aspects of monitoring coverage, data analysis, resource scheduling and the like are effectively overcome, and the accuracy and the practical value of traffic environment monitoring are remarkably improved.

The invention discloses a micro-grid virtual inertia optimal distribution method, and relates to the technical field of micro-grid inertia optimal distribution. Establishing a micro-grid transient model containing photovoltaic, energy storage and controllable load to quantitatively describe virtual inertia support requirements of the maximum frequency deviation and the maximum frequency change rate of the micro-grid, and obtaining the critical inertia of the micro-grid; setting constraint conditions based on the critical inertia of the micro-grid, and establishing a virtual inertia optimal distribution model of the micro-grid by taking the minimum maximum frequency deviation as an optimization target; and solving the micro-grid virtual inertia optimal distribution model to obtain a village-level micro-grid internal virtual inertia optimal distribution scheme. The invention further discloses micro-grid virtual inertia optimization sub-configuration, and a photovoltaic, energy storage and controllable load virtual inertia distribution scheme is obtained on the premise of guaranteeing that the micro-grid inertia is at a safety level and photovoltaic, energy storage and controllable load stable operation.

The invention discloses a laser power optimization method based on a deep reinforcement learning strategy, belongs to the technical field of simulation and artificial intelligence, and is suitable for a laser powder bed melting (LPBF) process. The method comprises the steps that firstly, an LPBF simulation model is built, a molten pool three-dimensional temperature field distribution model is built through a moving heat source, and the forming process under a laser scanning path is simulated in combination with a part STL file; a state space and a reward function are constructed by extracting state features of a molten pool in real time, and a laser power adjustment strategy is optimized by using a deep reinforcement learning DQN network model. An optimal laser adjustment action is generated by adopting an epsilon-greedy algorithm, and intelligent agent interaction data is stored and updated based on an experience pool, so that network model parameters are optimized, and the stability and uniformity of the depth of a molten pool are ensured. According to the method, internal defects are effectively reduced, the quality and intelligent control level of the LPBF process are improved, and a new thought is provided for efficient and accurate process parameter optimization.

The invention relates to the field of electric power material warehouse management and control, in particular to an electric power material digital intelligent professional warehouse management and control method, which comprises the following steps: acquiring electromagnetic spectrum data, identifying a central frequency point energy distribution diagram and an electromagnetic interference intensity level, constructing a three-dimensional electromagnetic field intensity distribution model of a professional warehouse, and dynamically adjusting the working mode of an RFID reader-writer; the method comprises the following steps: deploying a multi-modal check node in a storage area of a professional warehouse, and generating a composite check result through an infrared contour matching method and a UWB coordinate verification method when the confidence coefficient of electric power material data read by an RFID reader-writer is lower than a preset confidence coefficient threshold value; updating an electric power material inventory database according to a composite verification result, and performing electric power material inventory state analysis in real time through an edge computing node; the problems that inventory data distortion and material positioning deviation are caused by reduction of the accuracy rate of RFID in a strong electromagnetic environment, and inventory accuracy management of an electric power material warehouse is seriously restricted are solved.

The invention discloses a garden landscape design method based on digital twinning, and belongs to the technical field of landscape garden design. According to the method, a target site environment data set is collected, and a site thermodynamic distribution cloud picture is generated through a geographic space grid processing unit; extracting a site space pattern feature matrix on the basis; constructing a landscape effect distribution model, decomposing the feature matrix into topographic relief, seasonal phase color and illumination reflection parameters, driving the model to generate a three-dimensional scene rendering sequence, embedding the model into a three-dimensional digital twin scene of the garden site, and associating the parameters with physical attributes of the twin scene in real time; dynamic optimization of the landscape scheme is executed, an element replacement instruction is generated, and the spatial topological relation network is updated; and outputting a site design scheme map, adjusting facility layout coordinates, and generating a final scheme data packet in combination with the path connectivity, so that the precision and adaptability of garden landscape design can be improved.

The invention discloses a task scheduling method and system for a dense warehousing system, and the method comprises the steps: building an elevator task distribution model, a shuttle vehicle selection model and a warehouse-in and warehouse-out task time mathematical model according to the characteristics of warehousing operation, and preliminarily determining a task distribution scheme of an elevator through employing a heuristic rule on this basis, an improved A * algorithm is used for carrying out path planning on the shuttle vehicle, the time needed by the shuttle vehicle for completing a task is calculated, it is ensured that the elevator and the shuttle vehicle start to run, the scheme is further optimized through an improved PSO-SA algorithm, and the algorithm combines particle swarm optimization and a simulated annealing algorithm, so that the algorithm is optimized. A specific scheduling result of cooperative task execution of the elevator and the shuttle vehicle can be obtained, a scheduling scheme which enables the cooperative work effect of the whole system to be optimal is finally obtained, and maximization of the work efficiency is ensured.

The invention discloses a multi-scene adaptive stage lighting control method and system and a medium, and relates to the technical field of novel lighting, and the method comprises the steps: obtaining a type parameter and initial environment data of a target stage scene, and obtaining an initial control parameter of a stage lighting system; in the process that the stage lighting system carries out scene light supplement according to the initial control parameters, performance dynamic data are collected in real time, and the adaptive deviation value of the current lighting effect and the scene type parameters is calculated; performing multi-dimensional parameter fusion processing on the adaptation deviation value to obtain a dynamic light adaptation parameter; according to the dynamic light adaptation parameters, calculating a light effect deformation quantity in the current scene, and according to the light effect deformation quantity, synchronously adjusting a projection angle, a color temperature parameter and a light intensity distribution model of the stage light device; the invention provides a stage lighting intelligent control technology which not only can adapt to various performance scenes, but also can improve the lighting control effect.

The invention relates to the technical field of geophysical exploration, in particular to an exploration surveying and mapping method based on remote sensing and a surveying and mapping instrument thereof. The exploration surveying and mapping method based on remote sensing comprises the following steps: step 1, multi-platform collaborative data dynamic acquisition; step 2, multi-modal data fusion and feature enhancement: vegetation noise filtering is performed on the laser radar point cloud data, and a surface terrain model under vegetation coverage is reconstructed; seismic wave speed data and ground penetrating radar dielectric constant data are combined to construct an underground medium physical property combined distribution model; associating the reflectivity characteristics of the multispectral image with the underground physical property parameters, and identifying a geological abnormal region; and step 3, hidden geologic body dynamic modeling and risk assessment. By means of the method, real-time monitoring and risk assessment of the complex geologic body can be achieved, and the geological disaster prediction and prevention and control capacity is greatly improved.

The invention discloses a marine fishery resource survey station adaptive optimization method based on a space-time coupling model. According to the method, a space-time continuous species distribution dynamic reference field is constructed by coupling a Bayesian hierarchical species distribution model and a regional ocean model system; performing ecological layering based on time, space and environment dimensions; and when the environment variables change significantly, a habitat suitability index is dynamically reconstructed, and the station layout is optimized again, so that the coverage of a key ecological hot spot area is ensured, and the in-gradient representativeness is maintained. The method has the advantages of fast environmental response, strong spatial representativeness, high adaptability and the like, and is suitable for scenes of resource assessment, ecological protection, biodiversity monitoring and the like.

The invention discloses an overhead layer climbing frame wall-attached support steel truss stand column safety monitoring system fused with big data analysis, relates to the technical field of construction monitoring, and is used for solving the problem of recognition misjudgment caused by construction disturbance. According to construction behavior control signals, behavior labels are extracted, a semantic mapping relation between construction behaviors and structure responses is constructed, a behavior-driven data set is generated, a disturbance change trend is extracted through node response tensor and time difference analysis, the behavior labels are fused to construct a structure state vector, and a structure behavior distribution model is established. Analyzing the deviation degree between the current state and the model response, identifying the propagation path of the anomaly in space by combining a structure layout diagram, extracting a high-risk abnormal block mass region by adopting a diagram clustering method, analyzing the structural state change and construction behavior of the abnormal region, triggering different monitoring response strategies, and obtaining a high-risk abnormal block mass region; the structural risk identification and control in the construction process are realized, and the structural safety monitoring efficiency in the floor construction stage is improved.

The invention relates to the technical field of ship control, and particularly provides an energy optimization method and device for a pure electric tug, a storage medium and a control system. According to the method, the operation navigation data, collected in real time, of the pure electric tug can be input into the pre-constructed operation classification model, and the real-time operation state is obtained. If the real-time operation state of the pure electric tug is a heavy load dragging state, determining the current propulsion power demand and the total power demand of a propulsion system in the pure electric tug, and inputting the operation navigation data, the current propulsion power demand and the total power demand into a pre-constructed power distribution model, and performing power distribution according to a power distribution coefficient output by the model. Therefore, the power distribution response can be quickly and accurately made based on the intelligent power distribution model, and the intelligent ship is realized. Moreover, electric energy supply of the propulsion system can be guaranteed preferentially, so that sufficient power supply of the propulsion system can be guaranteed, and the operation efficiency of the pure electric tug can be improved.