Monitoring method and system for flap gate

Abstract

The invention discloses a monitoring method and system for a flap gate, and relates to the technical field of hydraulic engineering, and the method comprises the steps: deploying a monitoring unit at a monitoring point, and monitoring bottom shaft settlement, a negative pressure cavity, bottom shaft rotation and gate leaf vibration of a barrage flap gate at the monitoring point; processing, analyzing and storing the monitored real-time data, and extracting related characteristics influencing the normal operation of the barrage flap gate from the real-time data; and based on the extracted features, constructing an abnormity early warning identification model. According to the monitoring method and system for the flap gate, the multiple monitoring units are arranged, bottom shaft settlement, a negative pressure cavity, bottom shaft rotation and gate leaf vibration of the flap gate can be monitored in real time, based on real-time monitoring data, the system can construct an abnormity early warning recognition model, abnormal conditions are found in time, an alarm is given out, and therefore the safety of the flap gate is improved. Therefore, irreparable damage to the structure is effectively avoided, and monitoring data can be processed, analyzed and stored.

Description

Technical Field

[0001] The invention relates to the technical field of water conservancy engineering, and in particular to a monitoring method and system for a flap gate. Background Art

[0002] The bottom shaft driven flap gate is a special gate type widely used in my country, and has achieved remarkable results in the construction of urban water environment and water ecology. However, the design and construction of this type of gate is restricted by many factors, especially the problem of uneven settlement of the bottom shaft, which poses a potential threat to the safe operation of the gate. In addition, the negative pressure oscillation of the cavity under the gate can easily induce strong vibration of the gate structure, further exacerbating the instability of the gate operation;

[0003] For a long time, due to the lack of in-depth research on key technical issues for the safe use of this type of gate, the real-time monitoring data of metal structures is insufficient, which limits the progress of automated operation and control technology of hydraulic metal structure equipment. In the existing national standards, although key monitoring requirements are proposed for the operation status of the bottom shaft structure of the flap gate, the data collected mainly focus on the operation status of the bottom shaft, and key data that affect the safe operation of the gate structure, such as bottom shaft settlement and negative pressure cavity, are not fully monitored;

[0004] With the development of science and technology, structural monitoring technology has emerged and has been widely used in many industries. Structural health monitoring technology has built a long-term and stable comprehensive platform for equipment structural health monitoring by using modern technologies such as multiple sensor detection methods, damage identification and assessment equipment scientific management and maintenance. However, in the water conservancy industry, although there have been some studies and applications of structural status monitoring technology, they are basically aimed at individual parameters. There is no specific monitoring plan for specific gates such as flap gates, and the monitoring technology is not complete. In this regard, we propose a monitoring method and system for flap gates. Summary of the invention

[0005] In order to solve the above technical problems, a monitoring method and system for flap gates are provided, and this technical solution solves the above problems.

[0006] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0007] A monitoring method for a flap gate, comprising:

[0008] Deploy monitoring units at monitoring points to monitor the bottom shaft settlement, negative pressure cavity, bottom shaft rotation and gate leaf vibration of the flap gate of the dam at the monitoring point;

[0009] Process, analyze and store the monitored real-time data, and extract relevant features that affect the normal operation of the flap gate of the dam;

[0010] Based on the extracted features, an abnormal warning recognition model is constructed;

[0011] Based on the abnormal warning recognition model, fault warning and defect recognition are carried out on real-time monitoring data;

[0012] The gate monitoring results are fed back to the user, and an alarm is immediately issued when an abnormal situation is identified.

[0013] Preferably, monitoring the bottom shaft settlement, negative pressure cavity, bottom shaft rotation and gate leaf vibration of the flap gate of the dam at the monitoring point specifically includes: Several bottom shaft settlement monitoring units are used to monitor the bottom shaft settlement. The bottom shaft settlement calculation formula is: , Where S represents the total bottom shaft settlement, represents the settlement amount monitored by the ith bottom shaft settlement monitoring unit, Indicates the total number of bottom shaft settlement monitoring units, represents the ith bottom shaft settlement monitoring unit; The negative pressure cavity monitoring unit is used to monitor the pressure of the negative pressure cavity. The calculation formula for the influence of the negative pressure cavity on the gate structure force is: ,

[0014] In the formula, F represents the force of the negative pressure cavity on the gate structure, k is the influence coefficient, and P is the pressure value of the negative pressure cavity;

[0015] The bottom shaft rotation angle is monitored by a bottom shaft rotation monitoring unit, and the bottom shaft rotation angle change rate calculation formula is: ,

[0016] In the formula, represents the rate of change of the bottom shaft rotation angle, Indicates the change in the bottom axis rotation angle over a period of time. Indicates the amount of change over time;

[0017] The door leaf vibration monitoring unit is used to monitor the door leaf vibration. The door leaf vibration amplitude calculation formula is: ,

[0018] Where A represents the vibration amplitude of the door leaf, represents the vibration displacement value of the i-th sampling point, is the number of sampling points.

[0019] Preferably, building an abnormal warning recognition model specifically includes:

[0020] Collect real-time monitoring data on the bottom shaft settlement, negative pressure cavity, bottom shaft rotation and gate leaf vibration of the flap gate of the dam obtained by the monitoring unit;

[0021] Perform preliminary data sorting to remove outliers and noise interference;

[0022] Extract relevant features that affect the normal operation of the flap gate of the dam from the sorted real-time data; the features include the change trend of the bottom shaft settlement, the fluctuation range of the negative pressure cavity pressure, the change law of the bottom shaft rotation angle, and the frequency and amplitude of the gate leaf vibration;

[0023] Build an abnormal warning recognition model, use training data to train the model, and adjust model parameters to improve the accuracy and generalization ability of the model;

[0024] Use test data to evaluate the built model;

[0025] Deploy the verified model to the monitoring system to realize fault warning and defect identification of real-time monitoring data.

[0026] Preferably, the expression of the abnormal warning identification model is:

[0027] Assume that the output of the abnormal warning recognition model is Y, which indicates whether there is an abnormality in the gate. Y=1 indicates an abnormality, and Y=0 indicates normality.

[0028] The input feature vector is , which correspond to the bottom shaft settlement characteristics, negative pressure cavity characteristics, bottom shaft rotation characteristics and door leaf vibration characteristics respectively. The abnormal warning recognition model can be expressed as: ,

[0029] Where f represents the functional relationship of the model.

[0030] Preferably, when training the model using training data, a cross-validation method is used to optimize the model parameters, specifically including:

[0031] Divide the training data into multiple subsets, select one subset as the validation set in turn, and use the remaining subsets as the training set for model training and parameter adjustment;

[0032] For the evaluation of the abnormal warning recognition model, the Matthews correlation coefficient is introduced as an evaluation indicator to measure the performance of the model under unbalanced data conditions;

[0033] The calculation formula of Matthews correlation coefficient is: ,

[0034] In the formula, TP represents true positive examples, that is, the number of samples that are actually abnormal and predicted as abnormal by the model; TN represents true negative examples, that is, the number of samples that are actually normal and predicted as normal by the model; FP represents false positive examples, that is, the number of samples that are actually normal but predicted as abnormal by the model; FN represents false negative examples, that is, the number of samples that are actually abnormal but predicted as normal by the model.

[0035] A monitoring system for a flap gate, used to implement the monitoring method for a flap gate, comprising: a data monitoring module, a data analysis and storage module, an abnormal warning recognition module and a monitoring display module;

[0036] The data monitoring module is used to monitor the bottom shaft settlement, negative pressure cavity, bottom shaft rotation and gate leaf vibration of the flap gate of the dam at the monitoring point;

[0037] The data analysis and storage module is electrically connected to the data monitoring module, and the data analysis is used to process, analyze and store the monitored real-time data, and extract relevant features that affect the normal operation of the flap gate of the dam;

[0038] The abnormal warning recognition module is electrically connected to the data analysis and storage module, and the abnormal warning recognition module is used to build an abnormal warning recognition model to perform fault warning and defect recognition on the real-time monitoring data, and immediately issue an alarm when an abnormal situation is recognized;

[0039] The monitoring and display module is electrically connected to the data monitoring module, the data analysis and storage module analysis and the abnormal warning identification module, and the monitoring and display module is used to feed back the real-time monitoring data and the monitoring results of the gate to the user.

[0040] Preferably, the data monitoring module includes: a bottom shaft settlement monitoring unit, a negative pressure cavity monitoring unit, a bottom shaft rotation monitoring unit and a door leaf vibration monitoring unit;

[0041] The bottom shaft settlement monitoring unit is used to monitor the settlement of the bottom shaft of the flap gate of the barrage, providing data basis for judging the stability of the gate structure;

[0042] The negative pressure cavity monitoring unit is used to monitor the negative pressure cavity state of the flap gate to reduce the safety impact of the negative pressure cavity on the gate structure force;

[0043] The bottom shaft rotation monitoring unit is used to monitor the rotation of the bottom shaft, grasp the operating status of the bottom shaft, and ensure the normal opening and closing of the gate;

[0044] The door leaf vibration monitoring unit is used to monitor the vibration of the door leaf and promptly detect possible structural problems or abnormal vibrations.

[0045] Preferably, the data analysis and storage module comprises: a data processing unit, a feature extraction unit and a data storage unit;

[0046] The data processing unit is used to clean and convert the monitored real-time data;

[0047] The feature extraction unit is used to extract relevant features that affect the normal operation of the flap gate of the dam from the processed real-time data;

[0048] The data storage unit is used to store the monitored original data, processed data and extracted feature data.

[0049] Preferably, the abnormal warning identification module includes: a model building unit, an abnormality identification unit, and an alarm signal issuing unit;

[0050] The model building unit is used to build an abnormal warning recognition model based on the features extracted by the data analysis and storage module;

[0051] The anomaly recognition unit is used to use the constructed anomaly warning recognition model to perform anomaly warning and recognition on real-time monitoring data;

[0052] The alarm signal issuing unit is used to issue an alarm after an abnormal situation is detected.

[0053] Preferably, the monitoring display module comprises: a data presentation unit, a result feedback unit and an alarm display unit;

[0054] The data presentation unit is used to present the real-time monitoring data to the user in an intuitive form;

[0055] The result feedback unit is used to feed back the monitoring results of the gate to the user, including whether there are any faults or defects;

[0056] The alarm display unit is used to immediately sound an alarm in a striking manner when an abnormal situation is identified, reminding the user to take timely measures.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] A monitoring method and system for a flap gate proposed in the present invention can monitor the bottom shaft settlement, negative pressure cavity, bottom shaft rotation and door leaf vibration of the flap gate in real time by deploying multiple monitoring units. Based on the real-time monitoring data, the system can construct an abnormal warning recognition model, discover abnormal conditions in time and issue an alarm, thereby effectively avoiding irreparable damage to the structure. The monitoring data can be processed, analyzed and stored, and relevant features that affect the normal operation of the gate can be extracted, and the fault warning and defect identification can be automated. By reducing the frequency of safety inspections, damage to the gate's anti-corrosion coating and the like during each safety inspection is avoided, which is beneficial to protecting the environment and achieving sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 is a flow chart of the method of the present invention; DETAILED DESCRIPTION

[0060] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0061] Reference Figure 1 As shown, a monitoring method for flap gates, firstly, deploys monitoring units at monitoring points to monitor bottom shaft settlement, negative pressure cavity, bottom shaft rotation and door leaf vibration, then processes, analyzes and stores real-time data, extracts features that affect the normal operation of the gate, builds an abnormal warning recognition model, uses the model to perform fault warning and defect recognition on real-time monitoring data, and feeds back the monitoring results to users. Abnormal situations will immediately sound an alarm, specifically including setting special monitoring units at the bottom shaft, negative pressure cavity and door leaf to obtain data, processing data and extracting features, building a model, and using the model to analyze data to issue warnings and feedback results.

[0062] The monitoring of the bottom shaft settlement, negative pressure cavity, bottom shaft rotation and gate leaf vibration of the flap gate of the dam at the monitoring point specifically includes:

[0063] Several bottom shaft settlement monitoring units are used to monitor the bottom shaft settlement. The bottom shaft settlement calculation formula is: ,

[0064] Where S represents the total bottom shaft settlement, represents the settlement amount monitored by the ith bottom shaft settlement monitoring unit, Indicates the total number of bottom shaft settlement monitoring units, represents the ith bottom shaft settlement monitoring unit;

[0065] The negative pressure cavity monitoring unit is used to monitor the pressure of the negative pressure cavity. The calculation formula for the influence of the negative pressure cavity on the gate structure force is: ,

[0066] In the formula, F represents the force of the negative pressure cavity on the gate structure, k is the influence coefficient, and P is the pressure value of the negative pressure cavity;

[0067] The bottom shaft rotation angle is monitored by a bottom shaft rotation monitoring unit, and the bottom shaft rotation angle change rate calculation formula is: ,

[0068] In the formula, represents the rate of change of the bottom shaft rotation angle, Indicates the change in the bottom axis rotation angle over a period of time. Indicates the amount of change over time;

[0069] The door leaf vibration monitoring unit is used to monitor the door leaf vibration. The door leaf vibration amplitude calculation formula is: ,

[0070] Where A represents the vibration amplitude of the door leaf, represents the vibration displacement value of the i-th sampling point, is the number of sampling points.

[0071] Building an abnormal warning identification model includes collecting real-time monitoring data of bottom shaft settlement, negative pressure cavity, bottom shaft rotation and gate leaf vibration of the dam flap gate, preliminarily sorting the data to remove outliers and noise interference, and extracting features that affect the normal operation of the gate from the sorted data, such as the bottom shaft settlement change trend, negative pressure cavity pressure fluctuation range, bottom shaft rotation angle change law and gate leaf vibration frequency amplitude. Then, the model is built and trained with training data, and parameters are adjusted to improve accuracy and generalization ability. It is then evaluated with test data, and finally the verified model is deployed to the monitoring system to realize fault warning and defect identification of real-time monitoring data.

[0072] The expression of the abnormal warning recognition model is:

[0073] Assume that the output of the abnormal warning recognition model is Y, which indicates whether there is an abnormality in the gate. Y=1 indicates an abnormality, and Y=0 indicates normality.

[0074] The input feature vector is , which correspond to the bottom shaft settlement characteristics, negative pressure cavity characteristics, bottom shaft rotation characteristics and door leaf vibration characteristics respectively. The abnormal warning recognition model can be expressed as: ,

[0075] Where f represents the functional relationship of the model.

[0076] When using training data to train the model, the cross-validation method is used to optimize the model parameters, including:

[0077] Divide the training data into multiple subsets, select one subset as the validation set in turn, and use the remaining subsets as the training set for model training and parameter adjustment;

[0078] For the evaluation of the abnormal warning recognition model, the Matthews correlation coefficient is introduced as an evaluation indicator to measure the performance of the model under unbalanced data conditions;

[0079] The calculation formula of Matthews correlation coefficient is: ,

[0080] In the formula, TP represents true positive examples, that is, the number of samples that are actually abnormal and predicted as abnormal by the model; TN represents true negative examples, that is, the number of samples that are actually normal and predicted as normal by the model; FP represents false positive examples, that is, the number of samples that are actually normal but predicted as abnormal by the model; FN represents false negative examples, that is, the number of samples that are actually abnormal but predicted as normal by the model.

[0081] A monitoring system for a flap gate is used to implement a specific monitoring method, including four modules: data monitoring, data analysis and storage, abnormal warning identification and monitoring display. The data monitoring module monitors the bottom shaft settlement, negative pressure cavity, bottom shaft rotation and door leaf vibration of the flap gate. The data analysis and storage module is connected to the data monitoring module to process and analyze real-time data, extract and store features that affect the normal operation of the gate. The abnormal warning identification module is connected to the data analysis and storage module to build a model for fault warning and defect identification of real-time data, and issue an alarm when an abnormality occurs. The monitoring display module is connected to the other three modules to feed back real-time data and monitoring results to users.

[0082] The use process of the present invention is as follows: a monitoring unit is deployed at a monitoring point to monitor the bottom shaft settlement, negative pressure cavity, bottom shaft rotation and gate leaf vibration of the flap gate of the dam at the monitoring point;

[0083] Process, analyze and store the monitored real-time data, and extract relevant features that affect the normal operation of the flap gate of the dam;

[0084] Based on the extracted features, an abnormal warning recognition model is constructed;

[0085] Based on the abnormal warning recognition model, fault warning and defect recognition are carried out on real-time monitoring data;

[0086] The gate monitoring results are fed back to the user, and an alarm is immediately issued when an abnormal situation is identified.

[0087] In summary, the advantages of the present invention are: by deploying multiple monitoring units, the bottom shaft settlement, negative pressure cavity, bottom shaft rotation and door leaf vibration are monitored in real time. An abnormal warning recognition model is constructed based on real-time data to detect abnormalities and issue alarms in time to avoid irreparable damage to the structure. The monitoring data can be processed, analyzed and stored, and relevant features can be extracted to realize automatic fault warning and defect recognition. The frequency of safety inspections is reduced, and damage to the anti-corrosion coating of the gate is avoided, which is beneficial to environmental protection and sustainable development.

[0088] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A monitoring method for a flap gate, characterized in that: include: Deploy monitoring units at monitoring points to monitor the bottom shaft settlement, negative pressure cavity, bottom shaft rotation and gate leaf vibration of the flap gate of the dam at the monitoring point; Process, analyze and store the monitored real-time data, and extract relevant features that affect the normal operation of the flap gate of the dam; Based on the extracted features, an abnormal warning recognition model is constructed; Based on the abnormal warning recognition model, fault warning and defect recognition are carried out on real-time monitoring data; The gate monitoring results are fed back to the user, and an alarm is immediately issued when an abnormal situation is identified.

2. A monitoring method for flap gate according to claim 1, characterized in that: The monitoring of the bottom shaft settlement, negative pressure cavity, bottom shaft rotation and gate leaf vibration of the flap gate of the dam at the monitoring point specifically includes: Several bottom shaft settlement monitoring units are used to monitor the bottom shaft settlement. The bottom shaft settlement calculation formula is: ， Where S represents the total bottom shaft settlement, represents the settlement amount monitored by the ith bottom shaft settlement monitoring unit, Indicates the total number of bottom shaft settlement monitoring units, represents the ith bottom shaft settlement monitoring unit; The negative pressure cavity monitoring unit is used to monitor the pressure of the negative pressure cavity. The calculation formula for the influence of the negative pressure cavity on the gate structure force is: ， In the formula, F represents the force of the negative pressure cavity on the gate structure, k is the influence coefficient, and P is the pressure value of the negative pressure cavity; The bottom shaft rotation angle is monitored by a bottom shaft rotation monitoring unit, and the bottom shaft rotation angle change rate calculation formula is: ， In the formula, represents the rate of change of the bottom shaft rotation angle, Indicates the change in the bottom axis rotation angle over a period of time. Indicates the amount of change over time; The door leaf vibration monitoring unit is used to monitor the door leaf vibration. The door leaf vibration amplitude calculation formula is: ， Where A represents the vibration amplitude of the door leaf, represents the vibration displacement value of the i-th sampling point, is the number of sampling points.

3. A monitoring method for flap gate according to claim 1, characterized in that: Building an abnormal warning recognition model specifically includes: Collect real-time monitoring data on the bottom shaft settlement, negative pressure cavity, bottom shaft rotation and gate leaf vibration of the flap gate of the dam obtained by the monitoring unit; Perform preliminary data sorting to remove outliers and noise interference; Extract relevant features that affect the normal operation of the flap gate of the dam from the sorted real-time data; the features include the change trend of the bottom shaft settlement, the fluctuation range of the negative pressure cavity pressure, the change law of the bottom shaft rotation angle, and the frequency and amplitude of the gate leaf vibration; Build an abnormal warning recognition model, use training data to train the model, and adjust model parameters to improve the accuracy and generalization ability of the model; Use test data to evaluate the built model; Deploy the verified model to the monitoring system to realize fault warning and defect identification of real-time monitoring data.

4. A monitoring method for flap gate according to claim 3, characterized in that: The expression of the abnormal warning recognition model is: Assume that the output of the abnormal warning recognition model is Y, which indicates whether there is an abnormality in the gate. Y=1 indicates an abnormality, and Y=0 indicates normality. The input feature vector is , which correspond to the bottom shaft settlement characteristics, negative pressure cavity characteristics, bottom shaft rotation characteristics and door leaf vibration characteristics respectively. The abnormal warning recognition model can be expressed as: ， Where f represents the functional relationship of the model.

5. A monitoring method for flap gate according to claim 3, characterized in that: When using training data to train the model, the cross-validation method is used to optimize the model parameters, including: Divide the training data into multiple subsets, select one subset as the validation set in turn, and use the remaining subsets as the training set for model training and parameter adjustment; For the evaluation of the abnormal warning recognition model, the Matthews correlation coefficient is introduced as an evaluation indicator to measure the performance of the model under unbalanced data conditions; The calculation formula of Matthews correlation coefficient is: ， In the formula, TP represents true positive examples, that is, the number of samples that are actually abnormal and predicted as abnormal by the model; TN represents true negative examples, that is, the number of samples that are actually normal and predicted as normal by the model; FP represents false positive examples, that is, the number of samples that are actually normal but predicted as abnormal by the model; FN represents false negative examples, that is, the number of samples that are actually abnormal but predicted as normal by the model.

6. A monitoring system for a flap gate, characterized in that: Used to implement a monitoring method for a flap gate as described in any one of claims 1 to 5, comprising: a data monitoring module, a data analysis and storage module, an abnormal warning identification module and a monitoring display module; The data monitoring module is used to monitor the bottom shaft settlement, negative pressure cavity, bottom shaft rotation and gate leaf vibration of the flap gate of the dam at the monitoring point; The data analysis and storage module is electrically connected to the data monitoring module, and the data analysis is used to process, analyze and store the monitored real-time data, and extract relevant features that affect the normal operation of the flap gate of the dam; The abnormal warning recognition module is electrically connected to the data analysis and storage module, and the abnormal warning recognition module is used to build an abnormal warning recognition model to perform fault warning and defect recognition on the real-time monitoring data, and immediately issue an alarm when an abnormal situation is recognized; The monitoring and display module is electrically connected to the data monitoring module, the data analysis and storage module analysis and the abnormal warning identification module, and the monitoring and display module is used to feed back the real-time monitoring data and the monitoring results of the gate to the user.

7. A monitoring system for flap gates according to claim 6, characterized in that: The data monitoring module includes: bottom shaft settlement monitoring unit, negative pressure cavity monitoring unit, bottom shaft rotation monitoring unit and door leaf vibration monitoring unit; The bottom shaft settlement monitoring unit is used to monitor the settlement of the bottom shaft of the flap gate of the barrage, providing data basis for judging the stability of the gate structure; The negative pressure cavity monitoring unit is used to monitor the negative pressure cavity state of the flap gate to reduce the safety impact of the negative pressure cavity on the gate structure force; The bottom shaft rotation monitoring unit is used to monitor the rotation of the bottom shaft, grasp the operating status of the bottom shaft, and ensure the normal opening and closing of the gate; The door leaf vibration monitoring unit is used to monitor the vibration of the door leaf and promptly detect possible structural problems or abnormal vibrations.

8. A monitoring system for flap gates according to claim 6, characterized in that: The data analysis and storage module includes: a data processing unit, a feature extraction unit and a data storage unit; The data processing unit is used to clean and convert the monitored real-time data; The feature extraction unit is used to extract relevant features that affect the normal operation of the flap gate of the dam from the processed real-time data; The data storage unit is used to store the monitored original data, processed data and extracted feature data.

9. A monitoring system for flap gates according to claim 6, characterized in that: The abnormal warning identification module includes: a model building unit, an abnormal identification unit, and an alarm signal issuing unit; The model building unit is used to build an abnormal warning recognition model based on the features extracted by the data analysis and storage module; The anomaly recognition unit is used to use the constructed anomaly warning recognition model to perform anomaly warning and recognition on real-time monitoring data; The alarm signal issuing unit is used to issue an alarm after an abnormal situation is detected.

10. A monitoring system for flap gates according to claim 6, characterized in that: The monitoring and display module includes: a data presentation unit, a result feedback unit and an alarm display unit; The data presentation unit is used to present the real-time monitoring data to the user in an intuitive form; The result feedback unit is used to feed back the monitoring results of the gate to the user, including whether there are any faults or defects; The alarm display unit is used to immediately sound an alarm in a striking manner when an abnormal situation is identified, reminding the user to take timely measures.

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