142results about How to "Effective tracking" patented technology

The invention discloses a road surface dynamic small target tracking detection method and system based on improved YOLOv5 and ByteTrack, and the method comprises the steps: decoding a video stream to obtain a current video frame, and carrying out the marking and desensitization of a target in the current video frame; carrying out clustering and normalization on the data set, and then carrying out enhancement processing and equalization processing on the current video frame; attention is performed on a target feature region of the data set by using the optimized YOLOv5 backbone network structure, and then parameter loss of the optimized YOLOv5 backbone network structure is calculated; and predicting a bounding box by using Kalman filtering through a ByteTrack algorithm, then performing matching between a target and a trajectory by using a Hungary algorithm, outputting a tracking image sequence, and completing dynamic target tracking. According to the invention, the problems of easy loss, error detection and the like of dynamic small target vehicles at a long distance of monitoring equipment are solved, and the traffic safety is improved.

The invention relates to the technical field of fault detection for a wireless communication network, and discloses a fault detection method for the wireless communication network. By collecting data of base station signal strength, network delay, data packet loss rate and user equipment signal quality, the problem of insufficient fault detection caused by a single index is solved. Through multi-dimensional data acquisition, the system can comprehensively evaluate network performance, the fault detection accuracy is improved, and different fault types can be effectively identified especially in a complex fault mode. By storing the data according to the time sequence, the problem of time sequence chaos is avoided, and accurate detection of time-varying faults is ensured. The comprehensive data vector generated by each time of sampling comprises a plurality of network performance indexes, so that the fault detection and positioning capability is improved. Abnormal values are eliminated through data preprocessing, the data quality is ensured, an accurate basis is provided for subsequent analysis, and the reliability of a detection result is ensured.

The invention relates to the technical field of heart data analysis, in particular to an AI (artificial intelligence)-based heart disease prediction system, which comprises a feature relationship modeling module for mapping each type of information into a multi-dimensional feature space based on collected heart data, analyzing the condition correlation degree between feature nodes, connecting the feature nodes through correlation information, and establishing a model; and quantifying a linkage effect between the features to obtain a feature condition dependency relationship analysis result. According to the method, various types of heart health data are noded by constructing a multi-dimensional feature space, and a correlation structure is established according to condition correlation among data features. And a synergistic effect and an interdependence relationship among the features are disclosed. By analyzing the joint state probability of the feature nodes, the linkage effect of the feature combination on the overall risk is quantified, and the analysis depth of the disease risk is enhanced. In addition, by comparing the independent state probability of a single feature with the joint state probability of the feature combination, a feature combination that makes a significant contribution to the risk can be identified.

The invention discloses a security early warning method based on YOLOv8 and DeepSORT algorithms, belongs to the technical field of security early warning, and aims to solve the problems that the performance and accuracy of a target detection tracking algorithm are remarkably reduced, a plurality of targets cannot be captured at the same time, and particularly, repeated capture is difficult under the condition that the targets disappear. The system comprises a data acquisition module, a data processing module, a target detection module, a target tracking module, a behavior analysis module and an early warning mechanism. According to the invention, the YOLOv8 target detection algorithm is used to improve the detection precision, so that the system has high detection speed and precision, and the DeepSORT is used as the target tracking algorithm, so that the system has strong tracking performance, and multi-target tracking and fullness capability are realized.

The invention discloses a pollution discharge license post-license management method and system based on environmental compliance management, and particularly relates to the technical field of environmental management. Primary pollutants and secondary pollutants generated by the primary pollutants are determined according to the types of pollutants discharged by an enterprise, and a simulation model is constructed and corrected to predict pollutant conversion rates in different environments, monitor the concentration of the secondary pollutants, calculate the generation rate and evaluate the tracking stability of equipment to the generation process. Comparing the equipment data with the predicted generation rate, analyzing the tracking capability and response delay of the equipment, determining the real-time level of the equipment in combination with fuzzy logic, and dividing the data into real-time pollution data and non-real-time pollution data; real-time data is continuously collected, a supervision department monitors the emission dynamic state in real time, the pollution discharge compliance is judged, the abnormal degree of non-real-time data is analyzed, the monitoring frequency is adjusted, the timeliness and accuracy of the data are ensured, the problem of combined pollution is effectively solved, and the supervision capacity for pollutant generation and emission is improved.

The invention discloses a data management system based on a monitoring video stream, belongs to the field of data analysis, and aims to solve the problems of camera connection acquisition, image quality optimization, pedestrian related information analysis and data storage retrieval and backup in video monitoring. The system comprises a data acquisition module, a target detection module and a data storage module. The data acquisition module is used for identifying and connecting a video monitoring camera, performing video stream data acquisition, adjusting an acquisition frame rate by estimating a data size, and evaluating and optimizing image quality to obtain a detection image data set; the target detection module is used for carrying out feature extraction, classification and marking on pedestrians in the detection image data set, recording pedestrian trajectory information, carrying out real-time tracking and updating, identifying a behavior mode according to the trajectory information, dividing a gathering area and obtaining the pedestrian flow; and the data storage module is used for classifying and labeling the data, constructing an index structure to guarantee efficient retrieval, dividing data grades according to multi-dimensional features, and dynamically adjusting a backup time interval.

The invention provides an MP migration path and settling velocity monitoring system and method based on numerical modeling. The system comprises a sensor module, an offshore sampling device, a preprocessing module, a numerical model module, a discretization solving module, a data analysis module and a visualization module. According to the micro-plastic migration path and settling velocity monitoring system provided by the invention, the prediction process of micro-plastic migration and settling velocity is optimized by utilizing a numerical modeling technology, and the simulation accuracy and efficiency of micro-plastic behaviors in a complex marine environment are remarkably improved. The method is especially suitable for effective tracking and quantitative analysis of the microplastics under dynamically changing ocean conditions, and a more efficient data processing platform is created to monitor behaviors of the microplastics, so that environmental protection measures are optimized, and ocean management efficiency is improved.

The invention discloses a target tracking method and system based on visual identification and an underwater robot, and relates to the technical field of underwater robots. According to the invention, automatic tracking and monitoring of an underwater target are realized by using a visual identification technology, operations such as an image identification technology of YOLOv5, a motion tracking technology of an optical flow and a Kalman filter, a target re-identification algorithm of deep learning, image enhancement and the like are specifically combined, and the intelligent level and the working efficiency of underwater operation are comprehensively improved. According to the method, the diver can be efficiently tracked in a complex underwater environment, and high recognition and tracking precision is ensured under the condition that the diver disappears or the motion trail is complex.

The invention relates to the technical field of permanent magnet synchronous motor control, in particular to a diagnosis-free adaptive fault-tolerant control method and system for a six-phase permanent magnet synchronous motor under an open-circuit fault, and discloses the following steps: S1, converting the voltage and current of the six-phase permanent magnet synchronous motor from a three-phase static coordinate system to a two-phase static coordinate system through Clark conversion, voltage and current in the two-phase static coordinate system are converted into a two-phase synchronous rotating coordinate system through Park conversion; s2, acquiring a current relation between q-axis current of the six-phase permanent magnet synchronous motor and the two-phase static coordinate system according to the two-phase synchronous rotating coordinate system, and further acquiring two-phase static coordinate system reference current for realizing fault tolerance under an open-circuit fault; and S3, optimizing the control signal of the reference current through a phase-locked loop of a second-order generalized integrator. According to the method, diagnosis-free adaptive fault-tolerant control under the open-circuit fault of the six-phase permanent magnet synchronous motor can be realized, and the problems of misdiagnosis and time delay are reduced.

The invention provides a Kalman filtering parameter setting method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a variance between an angular velocity measurement value measured by the MEMS gyroscope and a real angular velocity value when the MEMS gyroscope is at different angular velocities, and establishing a first mathematical relationship for representing a mapping relationship between the real angular velocity value and the variance; and when the angular velocity data acquired by the MEMS gyroscope is filtered in real time by using the Kalman filtering algorithm, determining the measured noise variance at the current moment based on the angular velocity estimated value at the previous moment output by the Kalman filtering algorithm and the first mathematical relationship, so as to determine the angular velocity estimated value at the current moment based on the measured noise variance at the current moment. According to the method, the calculated amount in the Kalman filtering parameter setting process can be reduced, and the dynamic performance of the MEMS gyroscope can be effectively tracked.

The invention discloses a brain-computer interface system based on audio-visual matching error monitoring, which is characterized in that: according to EEG data obtained by three feedback paradigms of audio-visual matching errors, important ERP components related to the audio-visual matching errors are extracted by applying ERP analysis, so that neural responses in a specific time window are interpreted; then, on the basis of time-frequency analysis, dynamic changes of the electroencephalogram signals in different wave bands are captured, and the reliability and stability of the electroencephalogram activity in different feedback modes are revealed by combining ITPC of the electroencephalogram activity; and finally, through an improved EEGNet model, automatic feature extraction and efficient classification of the signals are realized, and the robustness and generalization ability of the system are improved. The electroencephalogram signals have high nonlinearity and specificity, by combining ERP analysis, time-frequency analysis and a deep learning model, potential neural mechanisms of audio-visual matching errors can be more effectively understood, the performance of a brain-computer interface system is improved, and the method is applied to clinical research of error processing related obstacles.

The invention discloses a wild animal multi-target tracking method based on an improved MOTR. The method comprises the following steps: 1) extracting multi-scale deer features with different resolutions from a collected deer video by using a convolutional neural network; 2) carrying out feature fusion on the multi-scale features by using an improved efficient hybrid encoder, and outputting the fused multi-scale features; 3) inputting the fused multi-scale features into a query initialization module based on priori knowledge, and outputting a detection query with initial position information of the deer; and 4) detecting, querying and inputting the fused multi-scale features into a decoder, and finally outputting a prediction result of the current video frame. According to the invention, the improved encoder is adopted to replace the original encoder of the MOTR, so that the small target tracking capability is improved while the low calculation amount is realized; and a detection query strategy is initialized, so that the influence of a complex background is reduced, the missing detection phenomenon is reduced, and the generalization ability of the model applied to different field scenes is improved.

The invention discloses a target monitoring method and device, a terminal and a medium, and the method comprises the steps: obtaining multi-modal sensing data based on a preset event camera, and enabling the multi-modal sensing data to comprise optical modal data and radar modal data; respectively inputting the optical modal data and the radar modal data into a multi-modal fusion module for multi-modal fusion to obtain fused enhanced point cloud features; and based on the fused enhanced point cloud features, determining an object bounding box and angular points thereof of the moving target, and based on the angular points of the object bounding box, determining degree-of-freedom information of the moving target so as to realize monitoring of the moving target. According to the method, the small target moving at a high speed can be monitored, and the recognition accuracy and robustness of the moving target are improved by using a multi-modal sensing fusion technology.

The invention provides a geothermal resource surface geochemical exploration method and system, and the method comprises the steps: determining a matched net degree according to an exploration area range, collecting gas in soil and soil samples, and determining the gas components and element compositions of the samples through different devices; the influence of the formation reaction is considered, and gas and elements with the migration capacity and anti-interference performance meeting the requirements are selected as geothermal chemical exploration indexes of different formation types; the content of soluble inorganic salt in the stratum and the content of gas in the stratum are measured, and the position where heat storage exists is recognized in combination with the set high-salt and low-gas area; and calculating a cover layer score and a reservoir layer score based on geothermal chemical exploration indexes, further calculating a comprehensive score, and realizing quantitative rating of the thermal reservoir stratum according to the comprehensive score. By adopting the scheme, the problems of insufficient comprehensiveness and insufficient exploration precision in the prior art are solved, the optimization of reliable indexes is realized, a heat storage and cover layer comprehensive evaluation method system is formed based on a heat storage mode of high salt and low gas, and underlying stratum geothermal resources can be effectively tracked.

The invention discloses an anti-shielding single-target long-time tracking method and system combined with trajectory prediction, and the method comprises the steps: firstly, manually appointing the position and size of a target, determining a region needing to be tracked, carrying out the feature extraction of the target region, and mapping a feature vector to a high-dimensional feature space, thereby obtaining an initial target template; according to the feature information of the initial target model, positioning a target in a tracking area, calculating the similarity between a target candidate area and an initial target template, selecting the most probable target position, entering a search state when the target is shielded by combining a shielding judgment algorithm, and performing prediction by using historical trajectory data of the target; and dynamically updating the template information of the target according to the obtained target position in combination with the current state and motion condition of the target. The system comprises a target initialization module, a target positioning module and a target template updating module. According to the invention, the target search range can be updated in real time to realize accurate and stable long-time target tracking.

An intelligent standard sample examining and issuing device comprises a base station, and a face recognition module, a management system, a taking mechanism and a sample placing basket are arranged on the base station. The sample placing basket is used for placing a sample bottle, a two-dimensional code is pasted on the sample bottle, and the two-dimensional code comprises sample information of the sample. The face recognition module is used for recognizing identity information of the examined person. The taking mechanism is used for taking the sample bottle from the sample placing basket, and a sample identification module for identifying the two-dimensional code is arranged on the taking mechanism. And the management system is electrically connected with the face recognition module, the taking mechanism and the sample recognition module. Compared with the prior art, the utility model has the following beneficial effects: by integrating the face recognition technology, the mechanical arm operation and the advanced sample management information system, accurate distribution, effective tracking and intelligent management of examination samples are realized, and the efficiency and fairness of the sample examination process are obviously improved.

The invention relates to the technical field of workshop workflow management. The workshop workflow management method comprises the steps that production information of a production workshop is obtained, a production material preparation list is confirmed, material distribution information is determined according to the production material preparation list, the material distribution information is matched with operation report information of an operator, verification error prompt information is obtained, and the verification error prompt information is sent to the computer equipment. The method comprises the steps of obtaining operation record information in the production process according to material information, confirming an abnormal reason causing verification matching inconsistency based on the operation record information and in combination with the material information, obtaining updated operation report information, matching the updated operation report information with material distribution information again, generating corrected operation report information, and sending the corrected operation report information to a server. And checking the corrected operation report information, and storing the corrected operation report information in a production flow database. The device has the effect of improving the working efficiency of a production workshop.

The invention discloses an adaptive coagulation nerve tracking control method for a nonlinear space-time system. The method comprises the following steps: establishing a mathematical model of the nonlinear space-time system and a tracking signal; constructing a coagulation neural network, approximating the nonlinear space-time uncertainty of an open-loop error system into a form of multiplying a coagulation weight by a network base vector, and representing bias input by time-varying disturbance and space-time approximation errors; and constructing a Lyapunov function, counteracting residual bias terms based on a sliding-mode-like smooth function, and designing a virtual control law, a parameter adaptive law and a controller on the basis of a backstepping method. The control scheme designed based on the method can realize effective tracking of any micro-traceable signal by a nonlinear space-time system, and ensures an adjustable bounded tracking error. Meanwhile, when adjustable parameters in the sliding mode function meet the square integrable condition, the tracking error is gradually converged to zero.

The invention discloses a waveform design method and system for tracking a sea surface maneuvering target by an unmanned ship-mounted radar, relates to the technical field of radar communication, and aims to solve the technical problem of inaccurate maneuvering target tracking in a sea surface clutter environment. The method is technically characterized by comprising the following steps: predicting a target state by using a marginalized particle filter algorithm, and obtaining a predicted measurement value; generating a hypothesis tree according to the observation data; performing target state estimation on each assumed branch by using a marginalized particle filter algorithm; a multi-hypothesis tracking algorithm is used to calculate an association probability and update a hypothesis weight, hypothesis pruning and fusion are carried out, and final target state estimation is obtained; a plurality of waveform selection criteria are used as a model, and an interactive multi-criterion selection method is utilized to determine an optimal criterion with the highest effective probability and an optimal transmitted waveform parameter. According to the method, false alarms caused by sea clutters can be effectively handled, the performance of the unmanned ship-mounted radar for processing complex tracking scenes can be improved, and a more accurate and robust target tracking result can be obtained.

The invention discloses a low-complexity channel estimation method and device based on an MIMO system, and the method comprises the steps: recovering a complete matrix through an approximate low-rank matrix, carrying out the singular value decomposition of the complete matrix, building a large-scale spatial multiplexing MIMO system model, carrying out the conversion processing of a received signal on a kth antenna, and obtaining a transmission signal estimation value, and performing channel matrix recovery on the transmission signal estimation value by using an approximation algorithm to obtain a channel matrix estimation value, finally performing signal detection on the channel matrix estimation value by using a zero-forcing technology, and performing iterative updating to obtain an optimal transmission signal estimation value, thereby effectively reducing the algorithm complexity and effectively reducing the calculation amount. The estimated channel matrix is effectively equivalent to the complete channel matrix, and the channel matrix is updated through iterative updating, so that the dynamic change of the channel can be effectively tracked, and the final demodulation signal precision is ensured.

The invention discloses a warehousing system scheduling control method, device and equipment and a storage medium, and the method comprises the steps: receiving a task scheduling request which is used for requesting to schedule target equipment in a warehousing system to execute a corresponding equipment instruction; in response to the task scheduling request, searching whether a matched task template matched with the template identifier exists in a task template database or not; if not, acquiring a substitute task template; on the basis of the equipment instruction, a task execution step in the substitute task template and a task instruction corresponding to the task execution step, updating the substitute task template to obtain a target task template; and sending a task scheduling instruction to the warehousing system to schedule the target device to execute a corresponding device instruction based on the target execution information in the target task template. According to the method and the device, when the task execution logic is changed, only the corresponding template needs to be called, or the substitute task template is updated based on the instruction, and program codes do not need to be rewritten, so that human resources are saved, and the calling efficiency is improved.

The invention relates to the technical field of data security, in particular to an interface data security monitoring method and device, a storage medium and electronic equipment, and the method comprises the steps: scanning a dynamic interface and a static interface in a data platform, and recognizing to obtain a target interaction interface; inputting the data transmitted in the target interaction interface into a sensitive data identification model to obtain sensitive data in the sensitive data identification model; and performing real-time anomaly analysis on the interface access behavior corresponding to the sensitive data, identifying an abnormal access behavior, and giving an alarm. According to the invention, through combination of an automatic tool and the hybrid neural network model, comprehensive monitoring of interface data and accurate identification of sensitive data are realized, the security of the data center is enhanced, and potential security threats are warned in time through real-time monitoring and anomaly detection.

The invention provides a time-varying parameter model identification method based on beta wavelet basis function expansion. As shown in the flow in figure 1, the method comprises the following steps: firstly, establishing a nonlinear autoregression time-varying parameter model by using a non-stationary time-varying signal, and determining the order of the model by using an FPE order determination criterion; performing linear weighted expansion on the model time-varying parameters by utilizing a first-order beta wavelet basis function, and converting a model time-varying parameter identification problem into a time-invariant parameter estimation and model effective item selection problem, namely obtaining a beta wavelet expansion time-invariant parameter model; selecting effective items of the model by adopting a classical orthogonal forward regression algorithm, determining the number of the effective items of the model in combination with an APRESS cross validation standard, eliminating redundant items in the expansion model, estimating corresponding time-invariant parameters of the model, and establishing a sparse model structure; and finally, in combination with the obtained effective items and corresponding parameters, obtaining a sparse nonlinear autoregression model and time-varying parameters thereof through reverse solution. Compared with an existing time-varying parameter identification method based on main B-spline multi-wavelet basis function expansion, the time complexity of the identification process can be effectively reduced, the identification precision of a sparse model is improved, and a new thought and a new calculation framework are provided for identification analysis of nonlinear time-varying parameter signals.

The invention relates to a sea area management and control early warning evidence obtaining method based on video intelligent analysis, and the method comprises the steps: collecting equipment parameters: collecting internal and external parameters of equipment and environment parameters; equipment screening control is carried out, controllable and controllable equipment with a good detection effect is selected through equipment state analysis and target detection effectiveness analysis, and follow-up monitoring of a target area is realized through pan-tilt control; performing video intelligent analysis, determining a follow-up concerned target through mapping matching of a video intelligent analysis model and different coordinate system positioning information, and performing accurate linkage and video snapshot by fine tuning equipment; according to early warning and evidence obtaining association, structured attribute analysis is performed on a target and an area detected in a video capturing process, and an evidence obtaining result is added to early warning information with an association relationship, so that repeated evidence obtaining is avoided. According to the invention, the accuracy and efficiency of early warning evidence obtaining can be effectively improved, the utilization efficiency of data and resources is improved, and powerful technical support is provided for sea area management and control.

The invention discloses a psychological counseling digital management platform based on low-code development. The psychological counseling digital management platform comprises a reservation module, a screening module, an initial interview module, a counseling module, a supervision module and a transfer and settlement module. The reservation module facilitates reservation operation of visitors, the screening module allows institutions to incorporate visitors meeting self-setting requirements, the primary interview module accurately records psychological assessment information of primary interviews and determines consultation schemes, the consultation module assists consultants in recording conventional consultation work, and the supervision module allows the supervisors to perform feedback and approval. The transfer and settlement module standardizes related processes; the platform realizes three-party cooperation of a visitor, a consultant and a supervisor, and an organization manager account can manage consultation appointment, transfer and settlement conditions under the principle of not violating confidentiality; the automation function of the platform improves normalization and compliance, provides a multi-user and multi-device unified platform with a low-code solution and a high self-definition degree for multi-professional links, and is suitable for digital management of psychological consultation of different institutions.

The invention provides an inorganic salt collecting, processing and monitoring method and device based on a digital twinning technology, and relates to the technical field of inorganic salt production, the inorganic salt collecting, processing and monitoring method based on the digital twinning technology comprises the following steps: constructing a digital twinning simulation model according to parameter information of inorganic salt collecting and processing equipment; acquiring real-time operation data of the inorganic salt collecting and processing equipment and simulation prediction data of the digital twinborn simulation model; based on the real-time operation data and the simulation prediction data, an extreme value theory method and a graph theory algorithm are used for analyzing the abnormity of the inorganic salt collecting and processing equipment; and generating an anomaly analysis report according to an anomaly analysis result of the inorganic salt collection processing equipment. According to the method, the difference between the actual performance and the expected performance of the equipment can be accurately evaluated, so that potential problems can be found in time, continuous optimization of the equipment is facilitated, and the production efficiency and reliability are improved.

The invention provides a dynamic scene positioning and mapping method and related equipment, and the method comprises the steps: determining an RGBD image, carrying out the target detection and feature point extraction of the RGBD image, and determining a target object and a static feature point; determining a 3D bounding box according to the target object, and determining a dynamic pose according to the 3D bounding box and the RGBD image; calculating a depth pyramid and an image pyramid according to the RGBD image; according to the depth pyramid and the image pyramid, performing camera pose estimation through an optical flow tracking method, and determining a camera pose; and according to the static feature points, the dynamic pose and the camera pose, performing positioning and map construction.

The invention discloses an improved model prediction control method for trajectory tracking of an unmanned vehicle, and belongs to the technical field of trajectory tracking control of unmanned vehicles, and the method comprises the steps: S1, building a three-degree-of-freedom nonlinear dynamic model of a vehicle; s2, making a linear dynamic model of the tire in combination with a magic tire formula; s3, formulating an objective function and constraint conditions of the model prediction control method; and S4, formulating a rule of a fuzzy controller, and enabling the unmanned vehicle to adaptively adjust a time domain coefficient according to the fuzzy rule and complete trajectory tracking. According to the method, the three-degree-of-freedom nonlinear dynamic model of the vehicle and the tire model based on the magic formula are established, the fuzzy control rule is introduced, and the improved MPC algorithm is designed, so that the unmanned vehicle can be adaptively adjusted according to the road, the preset tracking trajectory can be effectively tracked under the low-speed tracking working condition, the tracking error is reduced, and the tracking efficiency is improved. And the trajectory tracking precision is improved.

The invention discloses an autonomously-switched double-view-field camera and a target detection tracking method in the technical field of target detection. The autonomously-switched double-view-field camera comprises a double-view-field camera body and a rotary table arranged at the bottom of the double-view-field camera body to adjust the view direction angle of the double-view-field camera body. A light supplementing lamp panel, a focal panel, a signal board and an interface board which are sequentially connected through copper columns are arranged in the double-view-field camera body, a target detection model is deployed in the signal board, and a target searching module and a target detection tracking module are integrated on the signal board. The target searching module is designed with different searching strategies for a wide field of view and a narrow field of view based on the rotation angle limitation of the rotary table, and can intelligently adjust and switch to the most suitable field of view according to the change of an actual scene and the demand of target detection, thereby achieving the seamless transition from wide-angle monitoring to fine observation. In addition, the target can be efficiently identified and tracked, and high accuracy and robustness can be kept even in a complex and changeable environment.

The invention discloses a clinical test ethical examination system which comprises a submission application end, an examination process management end, a file management end, an examination cooperation end, an examination recording and tracking end, a notification and reminding end, an ethical examination report generation end, a data analysis and statistics end and a privacy and security end which are in data communication through a private network and a private line network. The clinical test ethical examination system aims at simplifying and optimizing management and processes of ethical examination, researchers can more effectively manage and track the ethical examination process through the clinical test ethical examination system, it is ensured that tests meet ethical principles and related regulation requirements, the rights and interests of test participants are protected, and the clinical test ethical examination system has the advantages that the research efficiency is improved, and the research cost is reduced. And the proceeding and development of scientific research are promoted.