273results about How to "Accurate decision" patented technology

The invention relates to the technical field of data analysis, and discloses a business data analysis method, device and equipment based on big data and a storage medium, the method comprises the following steps: collecting and preprocessing original log data to obtain standardized log data, and performing adaptive multi-modal feature extraction to obtain comprehensive feature representation; inputting a heterogeneous graph neural network to carry out anomaly detection and multi-level root cause analysis to obtain a hierarchical anomaly event; executing task scheduling and resource allocation of reinforcement learning to obtain a task allocation scheme; performing deep learning enhanced root cause reasoning and knowledge graph construction on the task allocation scheme to obtain a system state diagram and a causal relationship; and generating a multi-dimensional task data analysis report. According to the method, comprehensive analysis, anomaly detection, root cause reasoning and intelligent scheduling of log data are realized. The reliability and performance of the system can be improved, and more intuitive and accurate decision support can be provided for operation and maintenance personnel.

The invention relates to the technical field of traffic management, and discloses a road obstacle clearance rescue enterprise automatic scheduling method comprising the following steps: S1, through multi-source data fusion and a dynamic convolutional neural network, generating a three-dimensional road model, and predicting accidents and congestion; s2, dynamically adjusting the priority of the rescue task by using a multi-target genetic algorithm in combination with real-time data; s3, path planning and cooperative scheduling are optimized through deep reinforcement learning and game theory algorithms; s4, dynamically correcting the path by using a vehicle-mounted sensor, and optimizing the flow and the energy consumption; s5, in an emergency, an emergency module based on reinforcement learning starts a plan and adjusts resources; and S6, after the task, analyzing an optimization strategy through collaborative learning, and carrying out self-improvement. According to the method, multi-source dynamic data fusion is carried out, a dynamic convolutional neural network is used for processing data in real time, a high-precision three-dimensional road environment model is generated, accidents and congestion are predicted, and it is ensured that a rapid and accurate scheduling decision can be made in a complex and changeable road environment.

The invention provides a new energy station operation intelligent management method and system, and relates to the technical field of new energy station operation, and the method comprises the steps: collecting various data in real time through a sensor and monitoring equipment in a new energy station; transmitting the collected data to a central data center, carrying out iterative optimization processing, and determining final data classification, cleaning and compression parameter combination so as to construct a new energy station big data platform; and according to the constructed big data platform of the new energy station, constructing a digital twinborn model of the new energy station so as to reflect the operation state, the equipment health condition and the environment change of the station in real time. Through real-time data acquisition, digital twinborn model construction and simulation analysis, automatic generation and closed-loop management of an operation optimization scheme are realized, and the operation efficiency, prediction accuracy and fault handling capacity of the new energy station are effectively improved.

The invention relates to the technical field of skin disease health evaluation, in particular to a skin disease health evaluation system integrating multiple clinical evaluation tools. Comprising a patient information management and dynamic risk assessment module, a skin microbiome analysis module, a symptom quantification and assessment module, a physiological index monitoring module, an environmental adaptability monitoring module, an intelligent decision support module and a feedback and optimization module. The method comprises the steps of performing dynamic risk assessment in combination with a machine learning algorithm, performing quantitative assessment on the skin health state of a patient in combination with microbiome analysis and real-time physiological index monitoring, performing quantitative assessment on symptoms through an image analysis technology and a standardized questionnaire, and generating a personalized health assessment report and a treatment scheme by using a deep neural network and an optimization algorithm. Continuously improved closed-loop management is realized through a feedback optimization module; according to the invention, not only is a scientific evaluation basis provided for doctors, but also a patient can obtain customized treatment suggestions.

The invention provides a multi-modal digital twinborn scene building method and device based on image semantic fusion, and relates to the technical field of data processing and digital twinborn scenes. The method comprises the steps of obtaining an image data set and a text data set of a building construction site, and performing preprocessing; inputting the preprocessed image data set and text data set into a natural language processing model to obtain semantic description auxiliary data; inputting the preprocessed image data set into a target detection algorithm to obtain target extraction image data containing a target detection frame; fusing the output results of the two targets to obtain a fusion box of each target; then, a perspective projection algorithm is adopted to calculate a coordinate position corresponding to each target in the BIM, and three-dimensional world coordinates are obtained; and inputting the three-dimensional world coordinates into the BIM by adopting Dynamo to realize the building and real-time updating of the multi-modal digital twinborn scene. According to the method, the accuracy and robustness of scene recognition are remarkably improved, and the defects of a traditional method in the aspect of multi-modal data processing are overcome.

The invention discloses a reinforcement learning modeling method and device fusing multi-source heterogeneous data, and the method comprises the steps: obtaining the multi-source heterogeneous data from different data sources and having different data formats through an intelligent agent, carrying out the feature extraction through employing a corresponding feature extraction technology, obtaining different modal features, and carrying out the feature extraction through employing a corresponding feature extraction technology; effectively fusing different modal features to obtain environment state representation, and constructing an intelligent model of the intelligent agent by using a deep reinforcement learning algorithm based on the obtained environment state representation; different modal features extracted from different data sources are effectively fused, the fused features can comprehensively and accurately represent the current environment state, the intelligent model of the intelligent agent is constructed by using a deep reinforcement learning algorithm based on the fused environment state representation, and the intelligent model can obtain more comprehensive and accurate environment information, so that the intelligent model can be applied to the intelligent system of the intelligent agent. And the strategy is adjusted in real time to adapt to the change of the environment, so that the generalization ability and the accuracy of the intelligent model are effectively improved.

The invention discloses an AI intelligent collaborative monitoring management system for a multi-index water volume system, and relates to the technical field of monitoring management, the collaborative monitoring management system comprises a data acquisition module, a water volume system analysis module, a collaborative monitoring module, an emergency early warning module, an early warning scheme module and a data visualization module, all the modules are connected through electric signals. According to the AI intelligent collaborative monitoring management system for the multi-index water volume system, abnormal changes in water quality, water volume and water ecological indexes are identified through anomaly detection, an early warning mechanism is triggered according to the detected anomaly, early warning information is sent to management personnel through various modes such as a visual interface, a short message and an email, and the management personnel can carry out early warning on the water quality, the water volume and the water ecological indexes. According to the invention, the time interval from the occurrence of the abnormity to the discovery of the abnormity is greatly shortened, and valuable response time is provided for managers, so that further deterioration of the abnormity is effectively restrained, and the safety and sustainable utilization of water resources are guaranteed.

The invention discloses a customer voice analysis system based on a large model, and the system comprises a voice data collection and preprocessing module which is used for collecting and preprocessing customer voice data; the voice recognition module is used for recognizing the preprocessed customer voice data; the feature fusion module is used for extracting semantic features of the text data and fusing customer voice data features to generate voice text representation; the large model fine tuning module is used for performing fine tuning on the pre-trained starry fire cognition large model; the semantic analysis and emotion recognition module is used for processing the voice text representation, extracting semantic information, recognizing an emotion state and generating an analysis result; and the dialogue context management module is used for associating customer historical voice data, establishing dialogue context information and updating a voice analysis result. According to the method, the stellate fire cognitive large model and the improved quantitative low-rank adaptation are utilized, and the method has the advantages of high precision, low calculation cost and high intelligent adaptability.

The invention discloses a probe card simulation method and system based on counter-acting force analysis, which are used in the field of wafer simulation test, and the probe card simulation method comprises the following steps: integrating multiple physical field working conditions to construct a probe card geometric model, and importing the probe card geometric model into finite element simulation analysis software; carrying out grid division on the geometric model of the probe card by utilizing the hybrid voxel tree model, adjusting the grid density according to the distribution requirement of the multi-physical field, and setting a simulation boundary condition by utilizing a preset boundary condition template according to the actual simulation requirement; starting a simulation calculation process based on the adjusted grid density and simulated boundary conditions; and providing a visual interface of physical field distribution and a structural stress state in the simulation calculation process, and performing iterative optimization on the performance of the probe card in the simulation process according to a reactive force distribution result. According to the method, the grid density is automatically adjusted to adapt to changes of a physical field, and it is ensured that higher simulation precision is obtained in a key area.

The invention relates to a vehicle-mounted geological disaster early warning system and method based on environmental perception, and relates to the technical field of vehicle early warning. The system comprises an information acquisition module used for acquiring geological disaster information, vehicle information and external environment information in real time; the judgment and analysis module predicts a vehicle driving path through a driving path prediction unit; inputting the geological disaster information into a pre-trained geological disaster early warning model through a geological disaster prediction unit, and predicting a geological disaster occurrence probability value of each monitoring point or region; evaluating the risk level of the influence of the geological disaster on the vehicle through a risk evaluation unit; and the early warning execution module is used for matching safety suggestions according to the risk levels and carrying out early warning prompt. According to the method, the information is comprehensively acquired, the geological disaster prediction model is used for evaluating the risk in real time, early warning and safety suggestions are provided in time, and the driving safety can be effectively improved.

The invention discloses a multi-sensor fusion unmanned aerial vehicle environment monitoring and early warning system, and relates to the technical field of environment monitoring and early warning, and the system comprises the following components: an unmanned aerial vehicle platform which is used as a carrying basis of sensors and data processing equipment and has excellent flight stability and strong cruising ability; according to the invention, by introducing a task planning mechanism based on autonomous learning, the unmanned aerial vehicle can continuously learn and optimize the flight strategy when executing the monitoring task, the innovation point significantly improves the efficiency of task execution, and the unmanned aerial vehicle can perform real-time monitoring according to the actual environment feedback and historical task data. The unmanned aerial vehicle can automatically adjust the parameters of the flight path, speed and height to avoid adverse factors and select the optimal path, and meanwhile, the unmanned aerial vehicle is endowed with strong scene adaptability by the autonomous learning ability, so that the unmanned aerial vehicle can quickly respond to a complicated and changeable natural environment or a sudden emergency, and smooth completion of a task is ensured.

The invention discloses an enterprise micro-marketing auxiliary method and system based on a large language model. The efficiency and effect of marketing are improved by using the large language model. According to the method, by integrating a vector database, an embedded model and a context learning technology, the capabilities of real-time understanding of user intentions, personalized reply and quick service change adaptation are realized. The method has the core advantages of accurate user demand analysis, personalized service provision and efficient knowledge retrieval system. By analyzing user behaviors and dialogue records, product recommendation and marketing strategies according with user preferences can be provided. In addition, the method also has a real-time verbal skill updating capability, so that the timeliness and accuracy of information are ensured; the automatic order information extraction technology improves the information processing efficiency, and reduces the time and cost of manual operation. According to the invention, the marketing efficiency is improved, the interactive experience between the user and the customer service is enhanced, and competitive advantages are provided for enterprises in the market with intense competition.

The invention discloses a big data read-write adaptive method based on a decision algorithm and a big data read-write system, and belongs to the technical field of intelligent decision of a data center, high-speed read-write operation of data is processed in real time in a service system, and a data read-write strategy and resource allocation are dynamically adjusted by analyzing a data access mode and a system load condition; data are preloaded through an intelligent prefetching strategy, and the storage and access paths of hotspot data are optimized, so that the waiting time of a user and the consumption of system resources are reduced. According to the method, data management can be simplified, data access efficiency can be improved, data consistency and security can be enhanced, the problem of multi-database management and query complexity faced by enterprises can be effectively solved, and more efficient and more consistent data access and management experience can be provided for the enterprises.

The invention belongs to the technical field of automatic driving, and discloses an autonomous driving decision near-end strategy optimization method based on distributed exploration enhancement. According to the invention, a distributed architecture of the reinforcement learning decision module of the autonomous vehicle is designed, through parallelization and distributed processing, multiple agents can execute strategy training in different environments at the same time, and strategy updating and empirical data are shared in real time. In addition, the distributed architecture allows the method to operate in a larger-scale data set and a complex environment, and the adaptability and generalization ability of autonomous driving decision making are improved. In addition, a random network exploration (RND) module is also combined, the diversity and effectiveness of exploration are further enhanced, and a near-end strategy optimization algorithm used by the reinforcement learning decision module is perfected. By combining the distributed architecture and the RND module, the training process is remarkably accelerated, the exploration efficiency is improved, the decision-making quality of the automatic driving system is comprehensively improved, and the reliability and efficiency of automatic driving decision-making are improved.

The invention provides an energy management system based on a cloud and edge integrated optical storage system. The energy management system comprises a cloud module and an edge module, the EMS controller is used for collecting information of the optical storage equipment module and the load module and uniformly sending the collected data to the cloud module; the cloud module predicts future photovoltaic power generation power / electric quantity through a photovoltaic power generation prediction model by receiving data acquired by the EMS controller; predicting a future load through the load prediction model; analyzing peak and valley time periods of the electricity price through the electricity price management model, and sending prediction data to the edge module; and the edge module carries out energy management through the control logic of energy management according to the battery SOC, the chargeable amount, the photovoltaic predicted power and the generating capacity, and superposition peak-valley arbitrage and safe anti-countercurrent logic. Through the arrangement, the whole energy management system has high intelligence and automation levels, energy distribution and use are optimized, and the energy utilization efficiency is improved.

The invention discloses an intelligent medical pre-inquiry method and system based on a multi-modal large model and a medium, and the method comprises the steps: obtaining user input data to recognize a target intention, and when a knowledge question and answer intention is recognized, obtaining a knowledge question and answer module; based on the medical knowledge graph, combining with knowledge keywords in the user input data and utilizing a multi-modal large model to obtain medical answers; when the pre-inquiry intention is recognized, inquiry keywords in the user input data are extracted for slot filling to obtain slot information, and a slot filling strategy is dynamically adjusted based on the slot information and user feedback data; and performing visual display on the medical answers and the health management suggestions based on the user interaction interface, and obtaining user interaction data for human-computer interaction. According to the invention, a natural language processing and multi-modal fusion mode is adopted, so that the information input process of a patient is more convenient; and meanwhile, by introducing dynamic medical history information acquisition and interaction, real-time interaction with the patient is realized, and the user experience of pre-inquiry is improved.

The invention provides a strip steel surface defect detection method and system based on a depth map, and the method comprises the steps: S1, carrying out data collection, obtaining steel plate surface point cloud data, and then generating a point cloud data set; s2, after a point cloud data set is obtained, preprocessing is carried out, and defect detection is carried out; s3, judging the category of the defect image; and S4, respectively displaying the defects of the steel plate in combination with detection results. Through the man-machine interaction module provided by the invention, detection results including depth, size, proportion and category conclusions of defects can be visually displayed to a user, so that the user can conveniently know the defect condition of the surface of the steel plate.

The invention provides a calculation unloading optimization strategy based on multi-agent deep reinforcement learning, and relates to the technical field of intelligent algorithms. The core content of the calculation unloading optimization strategy based on multi-agent deep reinforcement learning is a multi-agent deep reinforcement learning algorithm designed for an MEC multi-user calculation unloading scene. According to the method, a task Q network structure for each task is constructed by utilizing a value decomposition technology, and a corresponding task Q value is generated on the basis of considering all task relationships of the intelligent agent. The task Q values can be further aggregated to obtain the Q value of the agent, so that the problem of high time and space complexity faced by the multi-task agent in a complex parameterized action space is effectively relieved. In addition, a self-attention mechanism is utilized in the hybrid network, and corresponding weights are dynamically distributed according to the characteristics of different users. Therefore, more accurate interaction representation among the intelligent agents is realized, and the flexibility and effectiveness of overall decision making are improved.

The invention relates to the technical field of robot teaching-free welding, and particularly discloses a robot teaching-free welding system based on visual localization and a welding method.The robot teaching-free welding system comprises a PLC, a power module, a wireless communication module and a visual localization coordinate conversion module. The path planning optimization module directly generates a welding path according to a visual positioning result, tedious operation is reduced, the problem of poor dependence and adaptability on pre-stored data is solved, the visual acquisition module and the positioning module work cooperatively, workpiece position and posture information is updated in real time, and the positioning precision is improved. The teaching-free intelligent decision-making and optimization module is combined with real-time data to adjust a path and parameters, the image processing and path planning optimization module generates a proper path according to image information for a complex workpiece, the sensor fusion module fuses vision and other sensor data, the adaptability of the system to the complex workpiece is enhanced, and the welding quality is guaranteed.

The invention relates to the technical field of emergency management and big data processing, and discloses an emergency drilling system based on big data, and the system comprises a data collection and recognition module which is used for collecting on-site images and video data in real time through a camera, unmanned aerial vehicle equipment and a mobile terminal, and recognizing the key information of an accident site through an image recognition algorithm; the big data analysis and prediction module is used for processing and analyzing the real-time data acquired from the data acquisition and identification module and predicting the development of the emergency event; and the hierarchical scene analysis and simulation module is used for decomposing the emergency event into a plurality of hierarchies for independent analysis and simulation, and the hierarchies are associated through a preset rule. By means of weapon deduction, multi-level analysis, real-time data processing and intelligent scheduling modules, the problems of experience dependence, decision lag, unreasonable resource scheduling and the like in traditional emergency management are solved, and the ability of coping with complex emergencies is enhanced.

The invention relates to the technical field of air pollution prevention and control, in particular to a sewage deodorization system and method based on a biological filter, and the system comprises a data acquisition module, a system preprocessing module, a system deodorization effect evaluation module, a system operation performance evaluation module, a system efficiency comprehensive analysis and processing module and a system database. According to the sewage deodorization system and method based on the biological filter, the system performance and the deodorization efficiency are comprehensively analyzed, and comprehensive system energy efficiency evaluation is provided. By analyzing the two indexes, the strong and weak points of the system can be identified, so that the resource configuration is optimized, and the overall efficiency of the system is improved. The deodorization effect and the operation performance are comprehensively considered, and more comprehensive and more accurate decisions can be made. By improving the deodorization effect, adverse effects on the surrounding environment and residents can be reduced. Meanwhile, the good operation performance can help to reduce the maintenance and operation cost.

The invention relates to the field of power distribution network cable main insulation fault detection and management, and discloses a power distribution network cable main insulation fault detection and management integrated test platform construction method comprising the following steps: a) data acquisition: installing a plurality of sensors including a partial discharge sensor, a current and voltage sensor, a temperature sensor and a vibration sensor; acquiring real-time operation data of the power distribution network cable; b) data transmission: transmitting the acquired cable monitoring data to a data processing system in a wired or wireless communication mode; and c) data preprocessing: denoising, missing value interpolation, standardization or normalization processing is carried out on the transmitted original data so as to improve the quality of the data. The real-time operation state of the power distribution network cable is monitored through various sensors, and various advanced data processing methods are combined, so that the accuracy of fault detection is effectively improved, and identification deviation caused by data noise or sensor errors in a traditional fault detection method is avoided.

The invention, which relates to the technical field of scientific and technological achievement management, discloses a blockchain-based scientific and technological achievement authenticity verification and evaluation platform comprising an uploading module, a preliminary screening module, a verification module, a distribution module and a comprehensive evaluation module. The uploading module is used for providing an interface and a plurality of uploading modes, optimizing an uploading interface to ensure stable and efficient uploading of files of different scales, and comparing a file hash value with a pre-stored standard value by using a hash algorithm to judge the integrity of the file so as to ensure the accuracy and completeness of the uploaded file; according to the system, the authenticity and reliability of data are ensured through multiple technical means, the file integrity is ensured by the uploading module through a Hash algorithm, the material effectiveness is judged in an auxiliary manner by the preliminary screening module through a natural language and an image recognition technology, the data source is traced and cross-validated based on the block chain by the verification module, and the data problem is effectively avoided through the multi-link synergistic effect. A solid foundation is provided for subsequent evaluation, and the data credibility and accuracy are enhanced.

The invention relates to the technical field of material storage management, in particular to a BIM-based construction site material monitoring management method and system. The method comprises the following steps: obtaining a construction project drawing; performing drawing information extraction on the construction project drawing to obtain construction project material information data and construction project structure data; performing construction project three-dimensional modeling on the construction project material information data and the construction project structure data to generate construction project three-dimensional modeling data; obtaining total construction material data; and carrying out material information modeling on the total construction material data by using a BIM technology to generate total construction material modeling data. According to the invention, accurate three-dimensional modeling, intelligent marking, regionalized storage and real-time allocation of the construction site materials are realized through the BIM technology, and the accuracy of construction site material monitoring management and the emergency response real-time performance are improved.

The invention relates to the technical field of equipment control, in particular to a self-adaptive switching method and system of a current transformer. The method comprises the following steps: collecting primary side parameters and secondary side parameters of a current transformer to obtain primary side collection parameters of the current transformer and secondary side collection parameters of the current transformer; performing current transformer state segmentation initialization according to the current transformer primary side acquisition parameters and the current transformer secondary side acquisition parameters so as to generate a current transformer operation state classification result; switching result judgment is conducted on the current transformers according to the current transformer operation state classification result, and when the current transformer operation state classification result is a current transformer critical state section, parallel buffering is conducted on the current transformers, and critical current transformer switching data are generated. According to the invention, through accurate state classification, a dynamic switching mechanism, time sequence optimization and visual display, the switching sensitivity and stability of the current transformer are improved.

The invention relates to the technical field of flood and drought disaster data processing, and provides a slope collapse risk assessment method and system, and the method comprises the steps: constructing a plurality of grid units based on the surface structure of a research region, and constructing a plurality of data matrixes; obtaining future rainfall probability distribution; generating a future rainfall scene library; constructing a soil hydrological mechanism model; based on the future rainfall scene library and the soil hydrological mechanism model, obtaining a plurality of flood peak element results to calculate uncertainty; obtaining a hydrological mechanism data set; and establishing a fault tree model of the slope collapse event, evaluating the slope collapse risk expectation, and evaluating the accuracy of the slope collapse risk expectation according to the uncertainty. By establishing the data matrix related to the time scale, the model simulates the whole process of hydrological cycle, the prediction and understanding of the hydrological process are deeper, the dynamic slope condition change process can be obtained, and the slope collapse risk expectation and the expectation accuracy can be quantitatively evaluated.

The invention discloses a detection method for B-type botulinum neurotoxin and application. The method disclosed by the invention is short in detection time, high in sensitivity as a qualitative detection method, capable of reaching a pictogram level, and obvious in detection advantage on trace toxin samples. Compared with an animal method using antiserum detection, the monoclonal antibody has high detection specificity, is beneficial to accurate decision-making when being used for scientific research detection and food hygiene inspection, and has small error compared with the animal method. In addition, the produced botulinum neurotoxin double-antibody sandwich reaction characteristic peak can be used for stability detection of botulinum neurotoxin, and plays a role which cannot be replaced by other methods in botulinum toxin product production, quality control, new product research and development, quality control and monitoring of standard substances for verification and market supervision. Therefore, the method can be used for guiding establishment of toxin immunodetection standard substances and promoting a capillary immunodetection technology to become a novel botulinum toxin detection standard method.

The invention discloses an intelligent commodity management method, and relates to the technical field of commodity management. Through the design of the whole method, the efficiency can be improved, the intelligent system can automatically manage a plurality of goods management tasks such as inventory management, purchase decision making and demand prediction, manual intervention is reduced, the management efficiency is improved, inventory optimization is facilitated, the intelligent system can accurately predict demands through data analysis and prediction algorithms, and the management efficiency is improved. The inventory level is optimized according to the prediction result, the inventory overstock or stockout phenomenon is avoided, the inventory turnover rate is improved, the cost is reduced conveniently, the intelligent system can help enterprises to optimize purchasing strategies and reduce the inventory cost, the purchasing cost is reduced through price optimization and other modes, and therefore the overall cost of the enterprises is reduced, and the enterprise experience is improved. The customer satisfaction can be improved conveniently under the overall application, and the customer satisfaction can be improved and the customer loyalty can be enhanced by meeting customer requirements in time, accurately predicting the market trend and providing personalized services and intelligent commodity management.

The invention relates to a construction method of an anti-typhoon sea wave sea-crossing steel trestle and platform adjacent to a residential building. The construction method comprises the steps that firstly, deep fusion application of BIM and VR is conducted; step 2, surveying and setting out; step 3, embedding steel bars and pouring strip-shaped concrete; fourthly, the steel pipe piles are transported and lengthened; fifthly, steel pipe piles are driven; sixthly, parallel connection and diagonal bridging construction is conducted; seventhly, bearing beams are installed; eighthly, longitudinal beams are installed; ninthly, the distribution beams are installed; tenthly, bridge deck slabs and guardrails are installed; and 11, an intelligent monitoring and early warning system.

The invention discloses a visual guide type tracheal intubation mirror system, relates to the technical field of guide type tracheal intubation mirrors, and aims to solve the problem of poor treatment effect of tracheal intubation. The constructed environment model not only comprises the diameter and the bending degree of the trachea, but also considers the surrounding tissue structure, so that a doctor can more comprehensively know the trachea and the surrounding environment of the trachea so as to make a more accurate decision, and the depth and angle information in the video image data is combined, so that the visual effect is improved. The real-time three-dimensional attitude of the intubation lens is estimated, the perception precision of the position and attitude of the intubation lens is improved, misoperation can be avoided, the structured light scanning method is combined with depth information and image coordinates for reconstruction, and compared with a traditional method, a three-dimensional model can be generated more quickly, the data quality of video data can be monitored in real time, and the real-time three-dimensional attitude of the intubation lens can be monitored more accurately. And the accuracy and stability of data are ensured. Therefore, data quality problems can be found and solved in time, and errors in subsequent diagnosis or treatment are avoided.