231results about How to "Improve diagnostic efficiency" patented technology

The invention discloses a multi-working-condition-oriented equipment fault diagnosis method based on unbalanced learning, and the method comprises the following steps: collecting the operation data of equipment under different working conditions through a plurality of sensors, carrying out the preprocessing, feature extraction and feature selection of the collected original operation data, and obtaining a feature set; processing the feature set by adopting an unbalanced learning technology and a cost-sensitive learning algorithm to obtain a balanced data set; constructing a multi-working-condition fault diagnosis model based on learning, and optimizing model parameters by adopting a method of combining cross validation and grid search according to a balanced data set in a model training process; deploying a multi-working-condition fault diagnosis model obtained through training, carrying out the online diagnosis of equipment faults, and continuously updating and optimizing the model through a continuous learning mechanism. According to the method, the unbalanced learning technology is applied to multi-working-condition fault diagnosis, the accuracy and robustness of diagnosis can be remarkably improved, and real-time monitoring and predictive maintenance of the equipment health state are achieved.

The invention discloses a multi-mode breast ultrasonic diagnosis system based on a dynamic feature fusion network, belongs to the field of medical image processing, and particularly relates to the multi-mode breast ultrasonic diagnosis system based on the dynamic feature fusion network. The invention aims to solve the problem that the existing method is low in breast ultrasonic recognition accuracy. The system comprises a multi-modal data preprocessing main module used for preprocessing a collected multi-modal mammary gland ultrasound image to obtain a preprocessed multi-modal mammary gland ultrasound image; the multi-modal convolutional neural network main module is used for building a dynamic feature fusion network model; the model training main module is used for training an established dynamic feature fusion network model by using the preprocessed multi-modal breast ultrasound image to obtain a trained dynamic feature fusion network model; and the model test main module is used for loading the trained dynamic feature fusion network model, classifying a to-be-diagnosed multi-modal breast ultrasound image, and identifying benign and malignant breast tumors in the multi-modal ultrasound image.

The invention discloses an on-load tap-changer fault diagnosis method based on lightweight YOLO11, and is used for solving the problem of low mechanical fault diagnosis efficiency of a traditional on-load tap-changer. Comprising the following steps of: converting vibration signals in a motor starting stage and a change-over switch action stage into high-resolution time-frequency images through continuous wavelet transform (CWT) so as to comprehensively reflect time and frequency characteristics of the signals; starNet is adopted to replace a YOLO model backbone network, and the feature extraction capability is enhanced while the network compactness is kept through star operation; and designing an efficient joint convolution detection head EJCD, and realizing model lightweight and multi-scale target detection through grouping normalization and convolution parameter sharing. According to the invention, through combination of the time-frequency image representation capability of continuous wavelet transform and an efficient target detection framework of deep learning, the limitation of a traditional method in non-stationary signal processing is overcome, and the diagnosis efficiency and the model performance are significantly improved.

The invention discloses a multi-source modal data fusion-oriented charging pile fault diagnosis method and system. The method comprises the following steps of: acquiring message stream data, audio data and infrared sensing data associated with a charging equipment fault through a sensor system; and based on the characteristics of each modal data, fault characterization and identification are carried out by adopting a random forest, a convolutional neural network and a YOLO-v8 target detection model. According to the marked fault data, a mapping relation among multi-source data is established, and a Dempster-Shafer multi-source fusion theory is utilized to aggregate a multi-modal fault judgment result, so that the equipment fault is accurately diagnosed. According to the method, the accuracy of fault diagnosis and diversified presentation of fault types are effectively improved, the repair rate is reduced, and the method has a good engineering application prospect.

The invention discloses a train key component online health monitoring method based on multi-sensor signal cooperation, and the method comprises the steps: selecting an indirect signal as an edge end train online abnormal state early warning index based on the correlation analysis of multi-sensor signals, and diagnosing whether the state of a train key component is abnormal or not; the vibration signals serve as signals for further diagnosis at the cloud, state features of the vibration signals are extracted at the edge end, online optimization of sparse coding is carried out, and the vibration signals are transmitted to the cloud to serve as diagnosis signal features for further diagnosis; real-time training precision and sample complexity are integrated into online estimation of a model training process, optimal features are transferred from a source domain-edge end to a target domain-cloud end, and the cloud end diagnoses the type and grade of a fault by means of the complex operation capability of the cloud end; a dynamic aggregation strategy is provided, the features of a prediction model are determined, optimization of nodes is enhanced, and a feature network is made to adapt to and complete an online prediction task.

The invention discloses a medical ultrasonic image intelligent processing method and system, and relates to the technical field of medical image.According to the main scheme, normal feature coordinates of different organs and tissues are calculated by obtaining normal ultrasonic image data, to-be-processed image data and historical medical record information of a patient; extracting coordinates of a to-be-detected image and comparing the coordinates with normal coordinates for preliminary judgment; performing secondary judgment by combining ovality and circularity; setting a lesion gray threshold to judge whether a lesion exists or not; calculating the size of a lesion area when the lesion exists; according to the method and the system, the comprehensive blood flow evaluation index is calculated in combination with color Doppler blood flow imaging data, the disease evaluation value is calculated according to known medical record data and the like, the disease level is judged, the diagnosis report is automatically generated, the accuracy and the efficiency of medical ultrasonic diagnosis are improved, and powerful support is provided for clinical diagnosis.

The invention provides an electric power system main equipment defect diagnosis method and system based on a large language model, and relates to the technical field of electric power system operation and maintenance and intelligent diagnosis, and the method comprises the steps: collecting defect description information of electric power system main equipment; preliminarily understanding the defect description information by using a pre-constructed natural language representation model, obtaining corresponding embedded representation, executing a defect level classification task and a defect basis classification task, and outputting a defect level classification result and a defect basis classification result; and summarizing by utilizing a large language model, fusing the summarized classification information into a prompt text, exciting the reasoning ability of the large language model through a prompt project, and finally obtaining a diagnosis basis and a diagnosis result corresponding to the defect description information. According to the method, excellent results are obtained in defect level classification tasks and question and answer tasks, and high-quality classification evidences and guidance information can be generated.

The invention discloses an automobile fault diagnosis method and system, and belongs to the technical field of fault diagnosis, and the system comprises an automobile fault identification model, an automobile fault knowledge graph model and a reasoning diagnosis integration model. S100, acquiring a natural language description text of the automobile fault, preprocessing the natural language description text of the automobile fault, and extracting a description entity related to the automobile fault; s200, constructing an automobile fault identification model, inputting the description entity related to the automobile fault into the automobile fault identification model, and outputting a fault type; s300, constructing an automobile fault knowledge graph model, inputting a description entity and a fault type related to the automobile fault into the automobile fault knowledge graph model in combination with Cypher to perform semantic similarity matching, and querying a matched answer; and S400, constructing a reasoning and diagnosis integrated model, inputting the description entity and the answer related to the automobile fault into the reasoning and diagnosis integrated model in combination with prompt, and outputting a fault analysis result and a visual correlation analysis result.

The invention discloses a multistage safety response control method of an industrial and commercial liquid cooling energy storage system, and relates to the technical field of electric energy storage systems, a sensor network collects an operation state data time sequence matrix, and abnormal data cleaning is carried out on the time sequence matrix; the method comprises the following steps: preprocessing a vibration signal of a cooling system, extracting characteristic data of abnormal vibration through Fourier transform and energy distribution analysis, and positioning a time-frequency distribution abnormal region; generating a time sequence multivariable data set according to the characteristic data of the abnormal vibration, determining a cause label according to the fused time sequence multivariable data set, obtaining the inherent frequency and the resonant frequency of the cooling system in combination with simulation analysis, and matching and determining the cause of the abnormal vibration; an LSTW neural network is used for predicting the abnormal resonance risk, and corresponding countermeasures are taken for the cooling network according to the prediction result; unnecessary vibration caused by error adjustment is avoided, the relation between cooling efficiency and safety is balanced, and unnecessary power loss is reduced.

The invention provides a liver echinococcosis ultrasonic image intelligent processing method based on deep learning, electronic equipment and a storage medium. The method comprises the following steps: data collection: collecting liver ultrasonic image data; carrying out image preprocessing on the collected liver ultrasonic image data by using an adaptive denoising and enhancement model, carrying out classification labeling on the data after image preprocessing, establishing a classification data set, and labeling the images as cystic echinococcosis, alveolar echinococcosis and other liver lesions; and based on the classification data set, training an optimized VIT classification model DFEV-VIT. The beneficial effects of the invention are that the method carries out the image preprocessing of the collected data through employing the optimized CLAHE algorithm ADEN, and carries out the training of the classification model DFEV-VIT based on the classification data set after the image preprocessing, can greatly improve the diagnosis efficiency and accuracy, and reduces the missed diagnosis and misdiagnosis of the echinococcosis.

The invention discloses an AI-driven complete-cycle scoliosis management system, which belongs to the field of scoliosis management systems and comprises a data fusion module, a feature extraction module, a text processing module, a diagnosis inference module and an output presentation module. Wherein the data fusion module is used for performing deep fusion on sensor data of the wearable equipment and X-ray / CT medical image data to generate comprehensive fusion prediction data; the feature extraction module uses a pre-trained fusion encoder to perform feature extraction and encoding on the fusion prediction data to generate potential feature representation; the text processing module adopts a pre-trained text encoder to encode free text diagnosis information provided by a doctor and a patient and extract semantic features; and the diagnosis inference module is used for fusing the fusion features and the text features. According to the invention, the sensing data of the wearable device and the medical image data can be effectively integrated to give full play to the complementary advantages thereof, so that the diagnosis accuracy is improved.

The invention provides an AI analysis method and system based on a straight ethyl mirror image, and relates to the technical field of AI.The AI analysis method comprises the steps that multi-scale features of the straight ethyl mirror image are extracted through a pre-trained deep convolutional neural network, and feature fusion is carried out through a self-adaptive feature weight distribution module; and performing lesion area detection on the fused feature map by using a double-branch detection network, and classifying the detected lesion areas. And performing three-dimensional reconstruction based on a continuous orthoscope image sequence, mapping a detection result into a three-dimensional model, realizing accurate positioning and three-dimensional visualization of a lesion area, and recording three-dimensional coordinate position information of the lesion area. The method can improve the accuracy and efficiency of the image analysis of the straight ethyl mirror, and assists a doctor in diagnosis.

The invention discloses a medical ultrasonic image quality control system based on multi-modal fusion. The medical ultrasonic image quality control system comprises a data preprocessing module used for collecting medical ultrasonic image data of different modals from different crowds in real time and preprocessing the medical ultrasonic image data; the dynamic self-adaptive fusion module is used for dynamically adjusting a fusion strategy according to the medical ultrasonic image data acquired in real time and introducing a self-adaptive mechanism to dynamically adjust weights of different modal data; the image enhancement module combines medical ultrasonic image data with a three-dimensional anatomical structure of a human body by utilizing an augmented reality technology to generate a visual and three-dimensional diagnostic image; the visualization module adopts a dynamic image display mode to present the change process of the diagnosis image in real time; a complete medical ultrasonic image quality control system is constructed by integrating a data preprocessing module, a dynamic adaptive fusion module, an image enhancement module, a visualization module and the like, the system can automatically process and analyze medical ultrasonic image data, and the quality control efficiency and accuracy are improved.

The invention discloses a wind turbine generator variable pitch system fault diagnosis method for data balance. The method specifically comprises the following steps: acquiring operation data and fault data of a wind turbine generator through a monitoring and data acquisition system equipped on a wind power plant; based on the obtained data, screening sensitive features related to the fault of the variable pitch system, reducing the dimension of a data sample and reducing the input of redundant information; according to the quantitative relation, dynamic balance factors are distributed for various types of samples, the dynamic balance factors are introduced into a multi-stage diagnosis model Dualformer based on double Transformers, and a DBFDformer data balance fault diagnosis model is constructed; and training a DBFDform model by using the screened feature data, and completing dynamic updating of balance factors and optimization of model hyper-parameters through a snow ablation optimization algorithm. The wind turbine generator variable pitch system fault diagnosis method can effectively improve the diagnosis precision and performance of the wind turbine generator variable pitch system fault under the unbalanced data, has excellent generalization ability and robustness, provides a new thought for the fault diagnosis of the wind turbine generator variable pitch system under the unbalanced data sample, and is helpful for improving the utilization rate of wind energy.

The invention discloses an equipment fault diagnosis method and system, a storage medium and an electronic device, and relates to the technical field of industrial automation, and the equipment fault diagnosis method comprises the steps: determining a target service instance which is distributed when equipment registration service is completed for current equipment in a basic service module; a target service module to which the service instance belongs is utilized to analyze device data of the current device, whether the current device has a fault is determined according to an analysis result, the target service module belongs to the upper layer service module, and the target service module belongs to the lower layer service module. The upper-layer service module at least comprises one of an equipment state acquisition service module, an asynchronous message service module, an equipment trend service module, a historical data persistence service module and an equipment state analysis service module, and by adopting the technical scheme, the technical problem of low equipment fault diagnosis efficiency is solved.

The invention relates to the technical field of battery faults, in particular to a power battery fault analysis method and device, a vehicle and a medium, and the method comprises the steps: obtaining fault data of a power battery; the fault data are input into a fault analysis model, the fault analysis model outputs a fault identifier, and the fault analysis model analyzes the fault identifier corresponding to the fault data based on the fault signal type and a fault trigger condition corresponding to the fault signal type; and determining a fault reason of the power battery according to the fault identifier. Therefore, the problems that in the prior art, specific faults cannot be refined, rapid troubleshooting is not facilitated, and manual modeling is long in time consumption, prone to errors and the like are solved.

The invention provides a new energy equipment system maintenance decision-making method and system based on a fault coupling model, and relates to the technical field of equipment predictive maintenance, and the method comprises the steps: collecting system structure information and system fault data of a complex new energy equipment system, and carrying out the maintenance decision-making of the complex new energy equipment system according to the system structure information and the system fault data; respectively constructing a function model view, a system level fault model view and each flow fault model view; constructing a fault coupling model corresponding to the complex new energy equipment system; probability estimation is carried out on the system fault data according to a kernel density estimation method, the fault probability of each fault node in the complex new energy equipment system is obtained, and key fault nodes in all the fault nodes and key node information corresponding to the key fault nodes are obtained; and mapping the key node information into a risk level corresponding to the key node information, and obtaining an optimal maintenance decision based on a decision tree analysis method and a plurality of maintenance decisions. According to the method, multi-dimensional analysis of a complex system can be realized, and fault modes of different components and subsystems can be identified more clearly, so that the fault detection and diagnosis efficiency is improved.

The invention provides a high-voltage circuit breaker partial discharge monitoring and early warning method and system, and relates to the technical field of partial discharge monitoring, and the method comprises the steps: obtaining the feature information of a target circuit; a circuit analysis model is called for analysis, and a target analysis result is obtained and comprises a plurality of target point locations; dynamically monitoring the first target point location through an ultrahigh frequency sensor to obtain first partial discharge information; calling a signal analysis model for processing and analysis to obtain a first signal feature set; activating a fault diagnosis model to analyze the first signal feature set to obtain a first diagnosis tag; and when the partial discharge fault of the target high-voltage circuit breaker at the first target point location is displayed, a first early warning signal is sent out. The technical problems that in the prior art, partial discharge monitoring mostly depends on artificial experience or fixed rules, intelligent analysis means are lacked, misjudgment or missed judgment is prone to occurring, accurate positioning is difficult, and the safety of the high-voltage circuit breaker is reduced are solved.

The invention provides a train fault diagnosis method and system based on deep learning, and relates to the technical field of deep learning, and the method comprises the steps: searching prior fault feature chain data having an association mode relation with current train fault feature chain data, and respectively generating a low-dimensional prior fault feature chain data sequence and a high-dimensional prior fault feature chain data sequence; and the pertinence and the accuracy of fault diagnosis are obviously improved. Furthermore, the correlation degree between the prior fault feature chain data and the current train fault feature chain data is calculated, the correlation mode fault feature chain data is extracted based on the correlation degree, and reference information is provided for fault diagnosis. Finally, in combination with the current train fault feature chain data and the multiple associated mode fault feature chain data, efficient and accurate diagnosis of the target train fault is realized, and the intelligent level and diagnosis efficiency of train fault diagnosis are greatly improved.

The invention provides a skin state classification method based on progressive transfer learning and modal domain adaptation, relates to the technical field of skin state recognition based on a neural network, and aims to realize a skin health state classification method which is accurate and efficient and is not limited by labeled data volume and not influenced by clinical modals. Comprising the following steps: constructing a plurality of intermediate data sets through unlabeled samples in different types of skin state fields; adjusting color space and feature distribution of the plurality of intermediate data sets based on HSV color space conversion and a CycleGAN model; on the basis of modal domain adaptation, a progressive transfer learning strategy is adopted, parameters of an identification model are finely adjusted step by step through a plurality of intermediate data sets, and data sources of unlabeled and label-free skin images of different sources and different types are adopted as labels of the skin images; and adjusting the identification model according to the marked target data set through a self-adaptive unfreezing method. The method has the advantages of improving the classification accuracy and reducing the dependence on the marked data.

The invention provides a multi-scene fusion hyperactivity quantitative evaluation system, which is characterized in that motion data of a subject in different diagnosis scenes are acquired through a diagnosis scene data acquisition module, and motion behavior information of the subject is recorded; then, a single diagnosis scene feature extraction module is used for automatically extracting feature vectors corresponding to scene data of motion sensor time sequence data collected in the single task testing process by using a single scene representation network; using a multi-diagnosis scene efficient fusion module to take the feature vector of each scene data as the feature of a corresponding node on a graph, transmitting information among the feature representations of different scene data by adopting a graph neural network, integrating the scores of different options of a prediction scale, and optimizing a diagnosis process to reduce the task burden of a subject and improve the diagnosis accuracy of the subject. The objectivity and efficiency of symptom evaluation are remarkably improved, the accuracy and operability of ADHD clinical auxiliary diagnosis are effectively improved, and the method has wide clinical application prospects.

The invention discloses a parallel double-path bearing fault diagnosis method and system based on deep learning, and the method comprises the steps: collecting a fault vibration signal of a rolling bearing, and carrying out the feature engineering processing of the fault vibration signal; the feature engineering processing comprises variational mode decomposition and continuous wavelet transform, and intrinsic mode functions of different fault types and corresponding wavelet time-frequency diagrams are generated. Feature data obtained through processing serve as input of a CNN branch in the two-way model, and the other branch adopts an original signal to be input to an MLP branch. And finally, forming a fault diagnosis result by combining the outputs of the two branches through the full connection layer. The method has the characteristics of low sample demand, high diagnosis accuracy and strong robustness, is suitable for bearing fault detection under the condition of limited data samples, and remarkably improves the efficiency and accuracy of fault diagnosis.

The invention provides an aero-engine intelligent fault diagnosis method and system based on meta-learning. The aero-engine intelligent fault diagnosis method comprises the following steps that single-working-condition fault data about an aero-engine is acquired, and high-dimensional features of the single-working-condition fault data are obtained; obtaining multi-working-condition fault data about the aero-engine and obtaining high-dimensional features of the multi-working-condition fault data; training a meta-learning model by using the high-dimensional features of the single-working-condition fault data and the multi-working-condition fault data, and obtaining a trained meta-learning model; acquiring real-time operation data of the aero-engine acquired by a sensor, inputting the real-time operation data into the trained meta-learning model, and obtaining a fault diagnosis result of the aero-engine through the trained meta-learning model; the method has the beneficial effects that the diagnosis accuracy can be improved, the accuracy of a fault diagnosis model is enhanced by introducing a meta-learning technology and utilizing common characteristics of a plurality of related tasks, and particularly, efficient and accurate fault diagnosis is realized under the condition of a small amount of annotated data.

The invention provides a PID (Proportion Integration Differentiation) loop performance rating and diagnosing method based on a fluctuation variable coefficient, which is used for solving the problems that the control performance of a PID control loop is reduced and parameters need to be reset in industrial production. According to the method, data of a process variable (PV), a set value (SV), controller output (MV) and a mode point (MODE) are collected through a DCS, and after data preprocessing is carried out, core performance indexes such as a fluctuation variable coefficient (CV), a relative performance index (RPI) and an autocorrelation coefficient are calculated. The fluctuation variable coefficient CV is used as a dimensionless quantity, the data volatility can be effectively quantified, and the limitation of a traditional evaluation method in a multi-dimensional scene is overcome. The system grades the loops according to the indexes, the loops are divided into excellent loops, good loops, general loops and poor loops, and fault diagnosis and optimization suggestions are provided for the general loops or the poor loops. The method further comprises the steps of establishing a loop rating history library and tracking an optimization effect. The method improves the real-time performance evaluation precision and diagnosis efficiency of the PID loop, and is suitable for the field of industrial automation control.

The invention discloses a fault self-checking and error-reporting system for a robot, and relates to the technical field of computer software, and the fault self-checking and error-reporting system comprises a power electrical detection module, a physical mechanical detection module, a software detection module, an ethical detection module and a fault feedback module. The power electrical detection module is used for detecting short circuit and overload fault conditions in a robot power electrical system; the physical mechanical detection module is used for detecting the fault condition of physical hardware, and the physical hardware comprises but is not limited to a motor, a sensor and electronic skin. According to the fault self-checking and error-reporting system for the robot, a plurality of modules are integrated, so that the operation condition of each component of the robot can be comprehensively and quickly detected and reflected, the fault detection and diagnosis efficiency is remarkably improved, maintenance personnel can quickly position and solve the fault problem, and the fault detection and diagnosis efficiency is improved. The maintenance cost and the maintenance difficulty are reduced, and the running state of the robot can be monitored in real time.

The invention discloses a switch cabinet fault diagnosis method and device based on deep learning, and belongs to the technical field of power equipment. The method comprises the following steps: S1, acquiring PD data; s2, pre-processing the PD data; the preprocessing content comprises the following steps: performing global statistical feature extraction on the PD data to obtain global statistical features; carrying out data type conversion on the PD data to obtain a grayscale image; s3, processing the grayscale image by adopting a ResNet model combined with a CBAM attention mechanism, and extracting key potential features as key local discrimination features; s4, performing feature splicing on the global statistical features and the key local discrimination features to obtain multi-scale features; and S5, performing classification processing by using an MLP classifier to obtain a switch cabinet fault diagnosis result. According to the method, signal features can be analyzed from different levels and scales through multi-scale feature fusion, and the fault diagnosis precision and adaptability are improved.

The invention provides a leakage diagnosis system and method for a hydraulic support, and belongs to the technical field of coal mine equipment monitoring and diagnosis, and the system comprises a data collection module which constructs a sensor network, monitors and collects the working data of the hydraulic support in real time, and carries out the initial processing of the working data of the hydraulic support; the leakage diagnosis module is used for analyzing the initially processed working data of the hydraulic support and carrying out leakage diagnosis in combination with a preset method; the leakage positioning module is used for determining a leakage position based on a preset sensor network and the working data of the hydraulic support; the leakage analysis module is used for determining the fault type of the hydraulic support based on the leakage diagnosis result and in combination with the leakage position; and the fault early warning module is used for generating a fault early warning report based on the fault type of the hydraulic support and sending the fault early warning report to the control end, and important technical support is provided for intelligent operation and maintenance of coal mine equipment.

The invention belongs to the technical field of histopathology full-slice image analysis, and particularly relates to a histopathology full-slice image classification method and system based on a position coding converter, and the method comprises the steps: obtaining a pathological full-slice data set with an image and a class label thereof, and carrying out the segmentation preprocessing; using a feature encoder to extract block image features, and establishing a converter multi-instance learning network model based on position coding guidance; training a network model by using the pathological full-slice data set containing the weak label; according to the method, the problem that detailed annotations of pathological images are difficult to obtain can be solved through a multi-instance learning weak supervision method, and the importance of spatial and semantic relationships among different sub-regions in the pathological sections can be effectively considered; and completing a classification task of pathological full slices and carrying out visual interpretive analysis.

The utility model discloses a power station fault diagnosis system based on robot feedback, which belongs to the technical field of power equipment monitoring and diagnosis and comprises an inspection robot and a centralized control platform. The inspection robot comprises a four-wheel eight-wheel-drive chassis, a rotary holder arranged above the four-wheel eight-wheel-drive chassis, an electric folding arm arranged above the rotary holder, and a high-definition camera and an infrared camera which are arranged at one end, far away from the rotary holder, of the electric folding arm, and an ARM architecture server is arranged in the inspection robot. According to the power station fault diagnosis system based on robot feedback, the power station equipment monitoring and fault diagnosis process is automated, the diagnosis efficiency is improved, the labor cost is reduced, and the safety of a power system is enhanced.

The utility model provides an endoscope camera convenient to adjust angle, relates to endoscope technical field, including device body and endoscopic mechanism, the surface of device body is equipped with the display screen, the lower part of display screen is equipped with the control box, one side of device body is equipped with the flexible pipe, the flexible pipe is equipped with the control box. And a mounting plate is mounted on the other side of the interior of the hose, a rotating mechanism is mounted at the top of the mounting plate, and an endoscopic mechanism is mounted at the top of the rotating mechanism. According to the endoscope camera convenient to adjust the angle, through the arrangement of the endoscopic mechanism, a doctor can freely adjust the angle and the direction of the camera according to needs so as to obtain more comprehensive and accurate image information and more accurately observe the conditions of the upper side and the lower side in the body of a patient; therefore, a doctor can better observe details and lesion conditions in the body of a patient, the diagnosis accuracy of the doctor is greatly improved, precious treatment time is saved, and the pain of the patient is relieved.