534 results about "Cloud modeling" patented technology

The invention relates to the technical field of rail transit, and discloses an intelligent diagnosis method and system for the health degree of urban rail equipment based on a cloud platform and a medium, the intelligent diagnosis method for the health degree of the urban rail equipment based on the cloud platform comprises the following steps: S1, multi-source data collection and edge preprocessing: collecting operation data of the urban rail equipment through a sensor, performing time sequence alignment and abnormal value filtering on the data to generate preprocessed data; s2, triggering type data uploading: based on the preprocessed data, judging whether an uploading condition is met or not through a statistical rule or a model prediction result, and uploading the data meeting the condition to a cloud platform; and S3, cloud model training and health degree prediction: constructing an equipment health state prediction model through a transfer learning algorithm by using the uploaded data. According to the invention, through deep learning modeling, edge calculation deployment and a multi-factor early warning mechanism, efficient evaluation, stable early warning and low-delay response of the equipment state are realized.

The invention discloses an intelligent traffic data analysis and decision generation system based on multi-model dynamic collaboration, and the system comprises a multi-mode collection module which collects voice, text, video and structured data and loads a traffic knowledge graph; the feature processing module is used for extracting semantic, time sequence and spatial features and generating a fusion vector; the semantic modeling module is used for generating an intention vector in combination with the knowledge graph and the interaction record; the dynamic routing module selects an edge or cloud model according to the intention vector to generate a query statement; the Prompt memory module fuses the historical template and the alignment parameters to generate a query draft; the query verification module is used for executing semantic and structure verification and outputting a correction statement; the query execution module generates a response vector; the causal interpretation module generates an interpretation vector; and the response generation module outputs a multi-granularity result according to the user role. According to the method, the cooperative processing capability and semantic analysis precision of complex traffic query are improved.

The invention relates to the technical field of photovoltaic power generation, and discloses a BAPV-based roof photovoltaic intelligent monitoring system and a dynamic tuning method and device. The method comprises the following steps: scanning a preset BAPV assembly through three-dimensional laser, constructing a surface point cloud model, and establishing a three-dimensional coordinate system comprising BAPV assembly installation base points; acquiring a real-time environment parameter set corresponding to the BAPV component; calculating an optimal working angle of the BAPV component according to the real-time environment parameter set based on an improved particle swarm algorithm; simulating an all-weather shadow movement track according to the optimal working angle based on a Monte Carlo method, and generating a component shielding influence weight matrix; establishing a heat balance equation according to the component shielding influence weight matrix so as to calculate a spacing optimization parameter of the BAPV component; a control instruction is generated according to the spacing optimization parameters, and dynamic tuning of the BAPV assembly is completed according to the control instruction; the control instruction comprises a support rotation angle compensation amount, an inverter parameter adjustment amount and an abnormal component isolation instruction. And the pain points of modeling errors, static optimization limitation, thermal runaway risks and the like are solved.

The invention relates to the technical field of model construction, in particular to a method and a system for establishing a parameterized tunnel model based on cloud computing. The method comprises the following steps: acquiring a tunnel section type, and extracting structural semantics to obtain a section feature description set; constructing a parameter dictionary of the tunnel section based on the section feature description set to obtain a dynamic parameter dictionary data set; and modeling a tunnel section geometric structure based on the dynamic parameter dictionary data set to obtain an initial three-dimensional tunnel geometric model. Through dynamic parameter management, a self-correction mechanism and a Latin hypercube sampling technology, efficient automatic modeling and precise simulation optimization of the tunnel section three-dimensional geometric model are achieved, meanwhile, through unified packaging and authority control, safe and efficient management and collaborative sharing of cloud model resources are achieved, and the method is suitable for large-scale popularization and application. And the intelligent level and the engineering efficiency of tunnel model construction and application are comprehensively improved.

The invention relates to the technical field of BIM, in particular to a BIM-based construction progress intelligent management and control method and system, and the method comprises the steps: constructing a BIM digital twin cloud model, fusing a panoramic image and LiDAR point cloud data, dynamically selecting a data source update model through an algorithm, extracting engineering topology, building a construction network diagram, and generating a state evolution trajectory in combination with environment and resource data. Predicting and visualizing milestone time, comparing field data with a BIM model to generate progress deviation information, performing stability analysis, triggering resource allocation when a threshold value is exceeded, synchronizing a material supplier and a field manager, dynamically adjusting personnel, equipment and materials, dynamically adjusting a milestone plan based on multiple data, and presenting and guiding allocation through the BIM model. The construction progress is ensured to be consistent with the plan, and the complex interaction relationship and dynamic characteristics in the construction process are captured through nonlinear dynamic system modeling in combination with LiDAR point cloud and other high-precision data.

The invention discloses an industrial inspection intelligent decision-making method and system based on large and small model collaboration. The industrial inspection intelligent decision-making method comprises the following steps: dividing an inspection task into a plurality of sub-tasks by using a cloud large model, and performing dynamic task planning based on the sub-tasks; calling an edge small model to execute the subtask to obtain multi-modal original data; performing cross-modal feature fusion on the multi-modal original data by using a cloud large model to obtain cross-modal fusion features; and reasoning based on the cross-modal fusion features by using a cloud large model, extracting abnormal factors, and formulating a new inspection task based on analysis of the abnormal factors. Through the comprehensive method of comprehensive multi-mode perception, conditional diffusion model enhancement and cloud-edge collaborative decision, the problems of fault sample scarcity and perception deviation in an extreme environment are effectively solved, high precision and low time delay are considered, and a new breakthrough of intelligent inspection is brought.

The invention relates to the technical field of intelligent control of the welding process, and discloses an online monitoring system and control method for the friction stir welding process, and the system comprises a multi-mode sensor array module which is used for collecting temperature, pressure and displacement data in the welding process; the edge computing node module preprocesses the collected data and extracts spatial-temporal characteristics; the space-time diagram convolutional network module is used for mining a space-time association relationship between the sensors; the hierarchical reinforcement learning controller generates a control instruction according to the associated features; the execution mechanism module receives the control instruction and outputs a welding signal; the digital twinborn verification module performs simulation feedback on the control effect; and the cloud model optimization module fuses the multi-source feedback information to continuously optimize the control strategy. The welding process self-adaptive control technology based on reinforcement learning is adopted, the technical effect of adjusting the welding parameters in real time to optimize the welding quality is achieved, and the problem that in a traditional method, the welding quality is unstable due to working condition changes is solved.

The invention relates to the technical field of construction progress management, in particular to a building construction progress intelligent tracking and pushing method and system based on BIM, and the method comprises the following steps: S1, constructing a 3D-BIM model, building a mapping relation, and forming a progress plan model; s2, collecting dynamic construction progress data of a construction site, and generating a construction real spot cloud model; s3, based on the generated construction real spot cloud model, analyzing the construction progress monitoring data difference between the Ti time period and the Ti + 1 time period, and evaluating the actual construction speed; s4, comparing the construction progress deviation between the actual construction speed and the planned construction speed, and judging whether progress delay exists or not; and S5, when progress delay exists, identifying and classifying delay reasons, generating a subentry progress deviation report, and pushing a deviation correction scheme. According to the invention, dynamic and real-time visual progress tracking and control are realized, the real-time requirement of project design file progress dynamic tracking is met, and the fine management level of construction progress tracking is improved.

The invention discloses a dynamic data closed-loop system based on real-time positioning confidence evaluation and an optimization method. The dynamic data closed-loop system and the optimization method are suitable for high-precision positioning scenes such as automatic driving and unmanned systems. The system collects data of a GPS, an IMU, a laser radar and a camera through a multi-source sensor, and a confidence evaluation module is adopted to fuse sensor data quality, environment interference indexes and historical track consistency to calculate positioning confidence. Based on confidence score, the system triggers different data acquisition strategies and sensor parameter configurations, and dynamically optimizes resource allocation. The system comprises an edge end fusion optimization module and a cloud model iteration module, constructs a closed loop mechanism, uploads data to a cloud for model updating and returns parameters when the confidence coefficient is abnormal, and improves the adaptability and robustness of the system. Experiments show that the method can effectively reduce the positioning error, optimizes the use of computing resources, and has good real-time performance, expansibility and engineering feasibility.

The invention relates to the field of data management, in particular to a VOCs pollution working condition data treatment method based on artificial intelligence. The method comprises the following steps: firstly, constructing a three-dimensional model of a factory, a pipeline and treatment equipment based on a BIM tool and a GIS technology, and deploying an Internet of Things sensor to collect VOCs pollution data; collecting original infrared absorption spectrum data of VOCs, and performing component identification by using a non-negative matrix factorization algorithm; a machine learning model is trained in combination with historical data, and short-term emission prediction is carried out; cooperative scheduling is carried out on the governance equipment through a multi-agent game optimization algorithm, and an optimal operation scheme is generated; constructing volume cloud modeling, and dynamically displaying an optimization effect; and if the predicted emission exceeds the standard, performing emission abnormity traceability analysis by using a graph neural network, and outputting a fault diagnosis report. According to the method, the VOCs emission prediction precision and the treatment efficiency are effectively improved.

The invention relates to the technical field of intelligent assessment, and discloses a dam risk assessment method, device and equipment based on multi-source data fusion, and the method comprises the steps: determining a monitoring project type of dam multi-source monitoring data, building a risk assessment index system through selecting a key index of the monitoring project type, and obtaining a risk assessment result; the method comprises the steps of dividing a dam region by combining measuring point spatial distribution to obtain a dam partitioning result, processing single measuring point actual measurement data of the same region in the dam partitioning result through a dam risk assessment model to obtain a region basic probability distribution value, and further obtaining a risk assessment result. According to the method, the limitation of single data dimension is broken through by constructing a risk assessment index system, accurate mastering of risk heterogeneity of different regions is realized through region division, a dam risk assessment model combining a cloud model and an evidence theory is adopted, the problem of dimension unification of multi-source data is solved, the risk assessment comprehensiveness is ensured, and the risk assessment efficiency is improved. And the accuracy of risk assessment is also improved.

The invention discloses a flexible automatic production management and control system for a new energy high-power high-frequency transformer, and relates to the field of production management and control, the flexible automatic production management and control system comprises a production demand receiving module, a production resource management module, a production planning module, a production execution module and a quality control module, an order is received through an API or EDI, and a work order is generated; collecting equipment and material information in real time, and constructing a production resource association network; carrying out production scheduling and process matching by adopting an improved deep learning model; scheduling materials, procedures and equipment based on a multi-level analysis control model; the quality is evaluated by using a two-dimensional cloud model, and data recording is performed through a distributed database, so that the traceability of the quality problem is ensured. According to the system, the production efficiency and the quality management level are effectively improved, the problem that existing rigid automatic equipment lacks flexibility is solved, and efficient and accurate production of the new energy high-power high-frequency transformer is ensured through cooperative work of a plurality of modules so as to meet different production requirements.

The invention relates to the technical field of rail traffic equipment maintenance and artificial intelligence image recognition, in particular to a rail vehicle cloud edge cooperative detection method and system and inspection equipment. The method comprises the following steps: acquiring image data of an area to be detected, synchronously recording attitude information of shooting equipment, carrying out image quality scoring on the image data, if the image quality score reaches a preset threshold value, calling a local target detection model to carry out defect identification on an image, and outputting an identification result containing a target type, a position and confidence; if the confidence coefficient of the recognition result is lower than a set threshold value, the image data and the posture information are uploaded to a cloud server, a cloud high-precision recognition model is called for secondary recognition, and a cloud recognition result is obtained. The real-time requirement is met through quick response of the local model, the cloud model rechecks a low-confidence result, efficiency and precision are both considered, and the contradiction that in the prior art, manual judgment standards are different, and speed and precision are difficult to consider is solved.

The invention provides an equipment abnormal voiceprint detection system. The equipment abnormal voiceprint detection system comprises a voiceprint collection module used for collecting original voiceprint signals in real time and preprocessing the original voiceprint signals; the edge end detection module is deployed on edge computing equipment and is used for carrying out real-time anomaly detection on the voiceprint features by utilizing a one-dimensional lightweight neural network model; the abnormity credibility evaluation module is used for converting a real-time abnormity detection result into a probabilistic abnormity credibility score; the incremental data screening module is used for screening high-value samples from the real-time voiceprint data based on a dynamic density trend sensing algorithm and caching the high-value samples in the edge; the cloud model evolution module is used for carrying out evolution training on the detection model by utilizing a continuous learning mechanism; the generation and playback module is used for jointly generating pseudo samples through a variational auto-encoder VAE and a generative adversarial network GAN; and the model updating module is used for compressing the evolved cloud model and then issuing and replacing the original model in the edge end detection module so as to form a cloud-edge collaborative sustainable evolution closed loop.

The invention relates to the technical field of intelligent monitoring of petroleum equipment, and particularly provides an industrial equipment state diagnosis method based on multi-modal fusion deep learning. The method comprises the following steps: synchronously acquiring load, vibration and current data through an explosion-proof RTU (Remote Terminal Unit), and transmitting the data to an edge node through an industrial LoRaWAN (Load RaWide Area Network); aligning a multi-source signal time sequence by adopting Kalman filtering, and fusing to generate data streams with consistent time domains; features are extracted by using wavelet packet energy entropy, and a lightweight ResNet18 model is input to realize high-precision diagnosis; based on the diagnosis result, a dynamic optimization strategy is generated through an LSTM-Transform parallel model; and finally, issuing a control instruction to the equipment for execution, and collecting feedback data to update the cloud model. Through the multi-rate signal time sequence alignment and feature-decision two-stage fusion technology, the problem of diagnosis errors caused by data asynchronization in a traditional method is solved, the fault recognition precision and the equipment energy efficiency are remarkably improved, and meanwhile the requirements for explosion prevention and real-time performance of the industrial environment are met.

The invention relates to the technical field of monitoring, and particularly discloses an anti-esophageal reflux monitoring system for old patients with dysphagia, which is used for solving the problems of oesophageal elastic degeneration, frequent body position change, low swallowing amplitude and the like of the old patients with dysphagia. Therefore, the problem that an existing reflux monitoring system based on single-point sensing, static threshold control and periodic model updating cannot meet the dynamic reflux early warning requirements of individualization, high sensitivity, real-time performance and low power consumption is solved. Comprising a wearable esophagus comprehensive monitoring module, a body position and esophagus deformation recognition module, a personalized pressure threshold self-adaption module, an intelligent prediction and early warning module, an air bag execution module and a cloud model training and updating module. According to the invention, multi-modal sensing and dynamic threshold, self-adaptive sampling and online model fine tuning are fused, low-amplitude swallowing and early-stage reflux signals are effectively captured, the iteration period is shortened by cloud differential updating, the equipment endurance is prolonged, and the esophageal injury risk is reduced.

The invention discloses a nuclear power plant fault diagnosis method and device, and the method comprises the steps: collecting nuclear power plant data, training and constructing an edge end model group and a cloud model group according to the nuclear power plant data, and the nuclear power plant data comprises operation regulations, real-time operation data and historical operation data; deploying the edge end model group to an edge computing node near equipment in a plant, and deploying the cloud end model group to a cloud platform; inputting the real-time operation data of the nuclear power plant into the edge end model group, detecting a fault type, reconstructing sensor parameters if the fault type is sensor abnormality, and uploading an abnormality report to the cloud platform if the fault type is system abnormality, and reasoning and tracing the abnormal report through the cloud model group, and pushing fault information and a decision scheme.

The invention discloses a sensitive cue word generation method and system based on parameter sensitivity quantification, relates to the technical field of artificial intelligence security, and aims to solve the problem that sensitive test samples are difficult to generate in integrity verification of a black box large language model. The method comprises the steps of obtaining an original large language model; a comprehensive parameter sensitivity index is constructed, the microscopic sensitivity used for representing tiny parameter modification and the macroscopic sensitivity used for representing large modification such as parameter quantification and pruning are fused, and therefore the sensitivity of cue words is comprehensively quantified; in the continuous embedding space, a gradient optimization algorithm is adopted to maximize the comprehensive index as a target to carry out iterative optimization, and constraints such as semantic rationality are applied to ensure that the generated cue word is natural and smooth; and finally, mapping the optimized embedded vector back to the discrete lexical element sequence to obtain a final sensitive cue word. According to the method, the low-cost and automatic test sample generation is realized, and the accuracy, efficiency and concealment of cloud model integrity verification are remarkably improved.

The invention relates to an end-cloud collaborative video abnormity alarm method and system, and the method comprises the steps: obtaining a video stream; generating end-side detection metadata by a first model of the end-side device; when a preset rule is triggered, executing intelligent event encapsulation to generate event data; the event data is uploaded to a corresponding cloud server through a side device according to the risk level through an elastic pipeline; generating text description information matched with the end-side metadata through a second model of the cloud server; wherein the first model and the second model are generated based on joint adversarial training, and semantic consistency in a severe environment is ensured; generating multi-modal information according to the end side detection metadata and the text description information; and performing key target processing on the multi-modal information to output alarm information. Through the method and the device, the problem of description confusion caused by end cloud model judgment contradiction and cooperative splitting in traditional video monitoring is solved, the anomaly recognition accuracy is improved, and security decision is promoted to be transformed to precision and intelligence.

The invention discloses a water quality monitoring, regulating and controlling system and method for abalone culture, and relates to the technical field of water quality monitoring, regulating and controlling. The invention aims to solve the problems of poor real-time performance, low intelligent level, insufficient system adaptability and the like of the existing abalone culture water quality monitoring and regulating system. The problems are effectively solved by fusing a multi-parameter water quality sensor, cloud intelligent analysis modeling, an automatic control response mechanism and a green energy power supply system. The system has high-frequency and multi-dimensional data acquisition capability, trend prediction and grade response branch control are realized in combination with a cloud modeling algorithm, linkage equipment can be actively triggered for intervention when water quality data are abnormal, and the intelligence and the strain capability of the system are improved; meanwhile, a solar energy and storage battery power supply mode is adopted, and stable operation of the system in a bay and other commercial power-free areas is ensured; and a graphical management interface and an early warning mechanism are matched, so that the scientificity and efficiency of breeding management are remarkably improved.

The invention discloses an intelligent risk early warning method and system based on multi-source data fusion, and belongs to the technical field of construction risk assessment and early warning, and the method comprises the steps: obtaining multi-source information, and obtaining an early warning control quantity based on the multi-source information and a preset quantification processing mode; constructing an initial early warning cloud picture based on the early warning control quantity and a preset grading early warning standard; obtaining a three-dimensional early warning cloud picture based on the initial early warning cloud picture, the two-dimensional matrix distribution and a preset two-dimensional normal cloud model; optimizing and correcting the three-dimensional early warning cloud atlas by adopting a posterior probability support vector method to obtain an improved prediction model; and obtaining a risk early warning result based on the improved evidence fusion method and the improved prediction model. According to the intelligent risk early warning method and system based on multi-source data fusion provided by the invention, a corrected three-dimensional early warning model is constructed by combining historical data on the basis of multi-source data fusion and adopting a posterior probability support vector method and an improved evidence fusion method, and the accuracy, effectiveness and applicability of risk early warning are remarkably improved.

The invention provides a cloud intelligent gas leakage analysis and decision-making method based on lightweight discrimination, and the method comprises the steps: enabling a mobile inspection platform to movably collect the gas leakage monitoring information of each monitoring point in a monitoring region, and determining a lightweight discrimination index; the mobile inspection platform determines a hard threshold discrimination result by adopting a hard threshold decision rule based on the lightweight discrimination index; if it is judged that the risk level is high, local response is immediately carried out, and a preset decision scheme is matched; if the risk level is judged to be low, edge node region decision is started; an edge node calculates a gas concentration accumulated value, a concentration fluctuation intensity degree and a sudden increase leakage amplification early warning factor, the gas concentration accumulated value, the concentration fluctuation intensity degree and the sudden increase leakage amplification early warning factor are input into a LightGBM time sequence classification model together with a lightweight discrimination index, a leakage risk level is determined according to model output and serves as a soft threshold discrimination result, and a preset decision scheme is matched; and the cloud model optimizes and collects conflict samples to carry out incremental training on the model of the edge node. According to the invention, the missed alarm rate can be reduced, and the discrimination robustness is improved.

The invention discloses a pump station unit state comprehensive evaluation method, and belongs to the technical field of pump station unit detection. The method comprises the following steps: acquiring multi-source monitoring data and generating a standardized monitoring data set; time-varying mutual information between indexes is calculated, a dynamic threshold value is determined in combination with current working condition parameters and a water level difference correction term, and a dynamic association network is constructed; performing physical mechanism characteristic decoupling on the data, including stripping a vibration signal working condition drift component based on a reference curve to obtain a vibration residual error, and calculating an equivalent standard working condition temperature based on a heat balance principle; respectively calculating a first weight based on data statistics and a second weight based on network topology, and adaptively generating a comprehensive coupling weight according to a consistency coefficient of the first weight and the second weight; and finally, judging a health state level by using a cloud model. Through deep fusion of a physical mechanism and data driving, the problems that fault features are difficult to extract and the model robustness is poor under variable working conditions are solved, and accurate evaluation of the unit state is achieved.

The invention discloses a ceramic censer design method and system based on perceptual engineering, and belongs to the technical field of neural networks, and the method comprises the steps: obtaining and preprocessing a ceramic censer sample, and building a morphological analysis table which comprises a plurality of morphological feature vectors of the ceramic censer; establishing a plurality of positive adjectives based on the user comment text; performing cloud model parameter matrix operation on the plurality of frontal adjectives to obtain sentiment word weight vectors, and generating a Kansei evaluation matrix table; the morphological analysis table is input into a preset TCN-Transform-GRU model, and an optimal product morphological design combination is obtained; the optimal product form design combination is input into the preset stable diffusion model, and the ceramic censer rendering graph is obtained.Design information is efficiently expressed through the cross-modal generation technology, evaluation and optimization are conducted through a quantification method, and the generated ceramic censer image is more real and more exquisite.

The invention discloses a cloud edge collaborative robot cluster simulation training and optimization system, which comprises a node marking unit, a node classification unit, a double-model construction unit, a reinforcement learning unit and a periodic parameter acquisition unit, and is used for periodically acquiring real-time cloud model parameters and local model parameters; the parameter fusion unit is used for performing format unification and timestamp alignment on the cloud model parameters and the local model parameters and fusing the cloud model parameters and the local model parameters into global parameters; the global model application unit is used for constructing the global parameters into a global strategy model under a reinforcement learning framework so as to be used for simulation optimization training of the target system; according to the method, the definition of computing resource configuration is improved, unified expression of cross-node task targets is realized, an input basis is provided for centralized strategy optimization, and the consistency of cloud edge collaboration is improved.

The invention relates to the technical field of medical health monitoring, in particular to a portable intelligent breathing mask and a remote monitoring method thereof. The method comprises the steps that airflow, humidity, blood oxygen and temperature data in the mask wearing process of a user are obtained, and time sequence alignment and feature fusion are carried out; inputting the fused physiological feature sequence into an embedded neural network model to recognize respiratory behaviors, and generating an abnormal probability value; carrying out compression coding based on the parameters, setting an uploading period according to a self-adaptive formula, and uploading the data to a cloud end; the cloud model further analyzes and generates health assessment parameters and a risk trend sequence; and dynamically adjusting an early warning threshold according to an evaluation result, and updating a local model and a sampling strategy in combination with user feedback. The portable breathing mask has the functions of fusing multi-modal data analysis, edge-cloud collaborative recognition, self-adaptive uploading scheduling, personalized early warning regulation and control and the like, and the intelligent level and practicability of the portable breathing mask in remote health monitoring are improved.

The invention discloses a combined electric appliance health analysis method and system based on multi-source data fusion, and the method comprises the steps: cleaning text data, carrying out the semantic mapping of the text data to a preset state quantity index, extracting a basic feature value from image data, and generating a standardized evaluation index set; calculating a weight vector of each evaluation index by constructing a judgment matrix, and performing consistency verification; weibull distribution fitting is carried out based on historical state quantity data, and grading threshold values of the positive degradation index and the negative degradation index are calculated; dividing a state interval based on the normal cloud model and calculating a membership degree vector of each state level; the membership degree vector is converted into a basic probability distribution function, evidence fusion is carried out in combination with the weight vector, and a comprehensive state level is output; and identifying the high-conflict evidence according to the Pignistic probability distance, performing refusion after correction, and outputting a final state level. Deep fusion and intelligent evaluation of multi-source heterogeneous data are realized, and the accuracy and robustness of health state judgment of the combined electric appliance are improved.

The invention relates to the field of laser cutting control, in particular to an autonomous controllable high-power laser two-dimensional cutting control method and system. The method comprises the following steps: acquiring a real-time monitoring image of a to-be-cut steel structure workpiece and a preset cutting control log; performing three-dimensional form point cloud modeling on the real-time monitoring image of the to-be-cut steel structure workpiece to construct a three-dimensional workpiece form model; full-stage cutting demand mining is carried out based on a preset cutting control log, and self-adaptive laser power adjustment is carried out, so that self-adaptive laser power parameters are obtained; carrying out multi-time-point cutting form demand analysis and dynamic cutting path planning on the three-dimensional workpiece form model so as to generate a multi-time-point dynamic cutting path; and real-time cutting control is conducted according to the self-adaptive laser power parameters and the multi-time-point dynamic cutting path, cutting track dynamic optical flow tracking is conducted, and a time sequence laser cutting track sequence is constructed. According to the laser two-dimensional cutting control system, efficient and accurate laser two-dimensional cutting control is achieved.

The invention relates to a roof complex curved surface curvature analysis and optimization method and device, equipment and a medium, and the analysis and optimization method comprises the steps: obtaining original point cloud data of a target roof, carrying out the preprocessing of the original point cloud data, and generating a corresponding roof entity point cloud model; establishing a curved surface reconstruction algorithm interface of the roof entity point cloud model, extracting a corresponding space coordinate point set, and generating a corresponding curvature parameter set; inputting the curvature parameter set into a preset elastic mechanical model, and determining a corresponding critical region; performing parameterization adjustment on the critical region to generate optimized curvature data, and converting the optimized curvature data into plate rolling process parameters to generate a corresponding optimization scheme; and pushing the optimization scheme to each application terminal based on the cloud collaboration platform. The precision and efficiency of modeling and analysis of the complex curved surface are improved, closed-loop cooperation of design, analysis and manufacturing is achieved, and the problems of modeling distortion, mechanical analysis lag and data isolation in a traditional method are solved.

The invention discloses an environment monitoring method based on a low-altitude aircraft, belongs to the technical field of low-altitude aircrafts, and realizes high-precision monitoring through cooperation of anti-interference hardware, edge calculation, navigation communication and cloud modeling. According to the system, a hexagonal prism cabin body is integrated with multiple types of sensors, and data are dynamically calibrated through a BP neural network. And aligning the multi-source spatio-temporal data by using extended Kalman filtering, and generating a three-dimensional grid model containing obstacles and a flow field. Navigation adopts Beidou / IMU combination and visual SLAM redundancy switching, and communication is guaranteed through a 5G private network and UWB double links. And the cloud constructs a three-dimensional AQI thermodynamic diagram based on inverse distance weighting and a Kriging algorithm, predicts pollutant diffusion in combination with a random forest, and drives the aircraft to execute a spiral / grid composite route. The problems that traditional monitoring is low in density, complex in model and insufficient in precision are solved, and a full-link anti-interference perception-decision closed loop is achieved.