A tunnel ventilation and lighting intelligent control method based on wireless transmission technology

Abstract

The application discloses a tunnel ventilation and lighting intelligent control method based on wireless transmission technology, and belongs to the field of intelligent environment control. The system uniformly deploys wireless sensors, acoustic sensor arrays and industrial cameras to collect tunnel environment data in real time, uploads the data to the cloud through the LoRaWAN protocol for Kalman filter noise reduction and abnormality detection. The ventilation control adopts a fuzzy PID algorithm to dynamically adjust the fan speed according to the gas concentration error, and the response time is shortened. The lighting control fuses personnel position, illumination deviation and regional priority through an adaptive weighting algorithm to generate a pulse width modulation signal to drive the lighting equipment. The acoustic data are used for device fault pre-diagnosis and rock burst risk early warning based on spectrum analysis. The system integrates an edge computing model to predict the gas diffusion trend and supports a three-dimensional visual human-computer interaction interface. The actual measurement shows that the method still maintains high robustness in a complex noise environment and is suitable for intelligent management of tunnel construction and operation scenes.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of automatic control, in particular to a tunnel ventilation and lighting intelligent control method based on wireless transmission technology. BACKGROUND

[0002] Construction ventilation is one of the important processes of tunnel construction and is the key to safe tunnel construction. For the ventilation control of the tunnel system during construction, the prior art designs a variable frequency ventilation system based on a fuzzy controller, which obtains a control voltage through fuzzy controller inference operation according to the pollution degree of the tunnel construction face to control the ventilation volume of the ventilation system, thereby achieving the purpose of energy saving and efficiency improvement.

[0003] As can be seen from the above technology, the data collection and transmission of the sensor of the current tunnel ventilation system usually relies on the field bus communication mode, and the transmission delay of the data packet in the network communication, the instantaneous error and data packet loss of the communication system, and the inconsistency of the sending and arrival order will destroy the determinacy originally possessed by the traditional control system, making the analysis and synthesis of the control system more complex and negatively affecting the performance of the control system. SUMMARY

[0004] In view of the above shortcomings in the prior art, the present application provides a tunnel ventilation and lighting intelligent control method based on wireless transmission technology.

[0005] In order to achieve the above-mentioned purpose of the application, the technical scheme adopted by the present application is as follows:

[0006] A tunnel ventilation and lighting intelligent control method based on wireless transmission technology, comprising the following steps:

[0007] S1, a tunnel environment data acquisition system is constructed, including gas sensors, oxygen sensors, carbon monoxide sensors, methane sensors, light intensity sensors, acoustic sensor arrays and industrial cameras uniformly arranged in each area of the tunnel, which acquire gas concentration data, light intensity data, acoustic data and personnel position information in real time through a wireless transmission mode via an Internet of Things module;

[0008] S2, the data collected in S1 is uploaded to a cloud server for storage and preprocessing via an Internet of Things transmission device, and the data issued by the cloud is received by a PLC logic controller;

[0009] S3, in the ventilation control subsystem, the fan speed adjustment amount is dynamically calculated by a fuzzy PID control algorithm according to the preset gas concentration threshold and the real-time collected gas concentration data, and the control instruction is sent to the fan controller via wireless transmission;

[0010] S4, in the lighting control subsystem, based on the personnel position information and the illumination intensity data acquired by the industrial camera, each regional lighting brightness target value is calculated through an adaptive weighting algorithm, and a control instruction is sent to a lighting controller through wireless transmission;

[0011] S5, the tunnel environment parameters, the acoustic early warning information and the equipment operation state are displayed in real time through a man-machine interaction interface, and remote parameter configuration and intervention are carried out.

[0012] Further, the data preprocessing in S2 comprises:

[0013] S21, the sensor data is subjected to Kalman filtering denoising processing, and the filtering equation is:

[0014] ;

[0015] In the formula, is the state estimation value at the moment, is the external control input signal at the moment, is the observation value at the moment, is the Kalman gain at the moment, is the system matrix.

[0016] S22, the time series analysis method is used to detect data anomalies, if the data of N consecutive periods exceeds the standard deviation range, an alarm is triggered and redundant sensor data is enabled;

[0017] S23, the audio data collected by the acoustic sensor array is preprocessed and feature extracted.

[0018] Further, S23 specifically comprises the following steps:

[0019] S231, the audio signal is subjected to frame division and windowing processing, and the fast Fourier transform of each frame signal is calculated to obtain the frequency spectrum:

[0020] ;

[0021] In the formula, is a discrete audio signal, is a window function, is a frame length, is a frequency component at the frequency

[0022] S232, the energy station ratio in a specific frequency band is calculated as a feature value, and the specific calculation method is:

[0023] ​​​​ ;

[0024] wherein, is the sampling frequency, when an abnormal sound event warning is triggered when the continuous value exceeds the dynamic threshold.

[0025] Further, the cloud server adopts an edge computing architecture, and deploys a lightweight machine learning model for predicting the diffusion trend of the gas concentration, and the prediction model is:

[0026] ;

[0027] wherein, is the gas concentration at time is the Laplacian operator of the gas concentration at time , which is used to calculate the diffusion trend of the gas from a high concentration area to a low concentration area, is the prediction step, is the diffusion coefficient, is the real-time wind speed, is an air flow rate influence factor;

[0028] The edge computing architecture is also equipped with a voiceprint recognition model for recognizing the sound event in S232, and the model is a convolutional neural network, and the convolutional layer operation is:

[0029] ;

[0030] wherein, is the index of the current convolutional layer, indicating the level in the neural network; is the value of the output feature map of the i-th layer at position , is a nonlinear activation function, is the index of the input channel, are the row and column offset indexes of the convolution kernel in the spatial dimension, respectively, are the height and width of the i-th layer convolution kernel, respectively, is the voiceprint map input to the i-th layer, is the weight value of the i-th layer convolution kernel at position and channel , is the bias term of the i-th layer.

[0031] Further, the fuzzy PID control algorithm in S3 specifically includes:

[0032] ​S31, define gas concentration error and error change rate wherein, is a preset threshold, is real-time concentration at the moment;

[0033] S32, design fuzzy rule base, adjust PID parameters according to membership functions of gas concentration error and error change rate, and the output is:

[0034] ;

[0035] wherein, is the output of the controller, is the proportional gain, is the integral gain, is the time integral, is the differential gain;

[0036] S33, generate fan speed regulation instruction according to the output of the controller wherein, is the reference speed, is the regulation coefficient. Further, the adaptive weighting algorithm in S4 is specifically:

[0037] S41, define personnel position weight

[0038] , light intensity weight and area priority weight , and satisfy ;

[0039] S42, calculate target light, and the specific calculation method is:

[0040] ;

[0041] wherein, is the target light, is the personnel-intensive area brightness compensation value, is the light intensity deviation compensation value, is the preset priority brightness;

[0042] S43, generate pulse width modulation signal according to the target light to control the output power of the lighting device, wherein the pulse width modulation signal is represented as:

[0043] ;

[0044] wherein, is the pulse width modulation signal, is the maximum brightness of the lighting device,​ is a nonlinear compensation coefficient, is a dynamic response factor, is a dynamic brightness deviation.

[0045] Further, the personnel position weight , the illumination intensity weight and the area priority weight are dynamically adjusted, and the specific rules are as follows:

[0046] When the personnel density is higher than the set threshold, the personnel position weight is increased, and the illumination intensity weight is reduced;

[0047] When the illumination intensity deviation is higher than the set threshold, the illumination intensity weight is increased, and the area priority weight is reduced;

[0048] When the tunnel area is a construction core area, the area priority weight is fixed.

[0049] Further, the Internet of Things module adopts the LoRaWAN protocol for data transmission, and the dynamic transmission power satisfies:

[0050] ;

[0051] In the formula, is a dynamic output power, is a reference power, is a path loss index, is a transmission distance, is an environmental noise correction term.

[0052] Further, the man-machine interface in S5 supports multi-dimensional data visualization, including:

[0053] S51, a three-dimensional tunnel map is displayed in real time, and the sensor position, environmental parameters and equipment state are marked;

[0054] S52, a historical data trend curve and an alarm log are generated, and filtering is supported according to time, area and parameter type;

[0055] S53, remote control instructions are received through an encrypted communication protocol, and operation permissions are verified before execution.

[0056] The present application has the following beneficial effects:

[0057] The tunnel ventilation and lighting intelligent control system based on the wireless transmission technology of the Internet of Things adopts the combination of industrial vision, voiceprint, the Internet of Things and PLC logic control, breaks away from the distance limitation in traditional wired data collection, greatly reduces the number of CPUs used in control, and saves the labor and material costs of laying communication cables in the process of installing sensors. BRIEF DESCRIPTION OF DRAWINGS

[0058] Figure 1 The tunnel ventilation and lighting intelligent control method based on the wireless transmission technology of the present application is shown in the flowchart. DETAILED DESCRIPTION

[0059] The specific embodiments of the present application are described below to facilitate understanding of the present application by those skilled in the art, but it should be clear that the present application is not limited to the scope of the specific embodiments, and for those skilled in the art, it is obvious that various changes are within the spirit and scope of the present application defined and determined by the appended claims, and all applications utilizing the concept of the present application are within the scope of protection.

[0060] A tunnel ventilation and lighting intelligent control method based on wireless transmission technology, as shown in Figure 1 includes the following steps:

[0061] S1, constructing a tunnel environment data collection system, including uniformly arranged gas sensors, oxygen sensors, carbon monoxide sensors, methane sensors, light intensity sensors and industrial cameras in each area of the tunnel, collecting gas concentration data, light intensity data and personnel position information in real time through the Internet of Things module in a wireless transmission manner;

[0062] In this embodiment, the sensors are uniformly arranged in the tunnel, including gas, oxygen, carbon monoxide, methane sensors (range 0-5% vol, accuracy ±0.1%), light intensity sensors (range 0-65535 lux) and acoustic sensor arrays (microphone frequency response range 20Hz-12kHz, sensitivity -36±3dB). Each 50 meters sets a group of nodes. The industrial camera uses an infrared camera (resolution 1080P) to detect personnel position and density. Wireless transmission uploads data to the cloud in real time through the LoRaWAN module (frequency band 868MHz, transmission distance ≥2km).

[0063] S2, uploading the data collected in S1 to the cloud server through the Internet of Things transmission equipment for storage and preprocessing, and receiving the data issued by the cloud through the PLC logic controller.

[0064] In this embodiment, the preprocessing includes the following steps:

[0065] S21, Kalman filter is performed on sensor data for noise reduction, and the filter equation is:

[0066] ;

[0067] In the formula, is the state estimation value at the moment, is the external control input signal at the moment, for example, the fan speed adjustment amount, is the observation value at the moment, is the Kalman gain at the moment, is the system matrix. In the embodiment, the matrix may be a control input matrix, which maps the physical impact of the control signal to the system state . For example, if represents the oxygen concentration in the tunnel is the fan speed increment, then term describes the dilution effect of the fan acceleration on the oxygen concentration. In ventilation control, may be the fan speed adjustment instruction output by the fuzzy PID controller; in lighting control, may represent the PWM duty cycle adjustment signal. If is the gas concentration vector (such as [O2, CH4] T , unit: % vol), the dimension of the matrix needs to match the dimension of . For example, is the scalar fan speed (rpm), then is a 2x1 matrix, representing the combined influence coefficient of the speed on the oxygen and methane concentrations.

[0068] S22, time series analysis method is used to detect data anomalies, if the data of N consecutive periods exceeds the standard deviation σ range, an alarm is triggered and redundant sensor data is enabled.

[0069] In the embodiment, the cloud server adopts an edge computing architecture, and deploys a lightweight machine learning model for predicting the diffusion trend of gas concentration, and the prediction model is:

[0070] ;

[0071] In the formula, is the gas concentration at the moment, is the Laplacian operator of the gas concentration at the moment , which is used to calculate the diffusion trend of the gas from the high concentration area to the low concentration area, For real-time wind speed, For air flow rate impact factor.

[0072] S23, acoustic preprocessing: the edge computing node frames the acoustic data, frame length N=1024, frame shift 512, Hamming window processing, and calculates the FFT spectrum according to the formula. Focus on monitoring the 3kHz-8kHz high frequency band as the common equipment fault characteristic frequency band, , 50Hz-200Hz low frequency band as the rock mass fracture characteristic frequency band, Energy ratio Set the adaptive threshold , its initial value is set to the 95% quantile of the historical energy distribution of the frequency band, when Last for 5 seconds more than the threshold, mark as abnormal sound event, and compress the audio data segment to upload to the cloud soundprint model for accurate classification.

[0073] The soundprint recognition model uses a lightweight convolutional neural network (CNN) architecture, whose input is a 256-order log-mel spectrogram, and the convolutional layer operation is:

[0074] ;

[0075] Where, is the index of the current convolutional layer, representing the level in the neural network; is the value of the output feature map of the layer at position , is a nonlinear activation function, is the index of the input channel, are the row and column offset indices of the convolution kernel in the spatial dimension, respectively, are the height and width of the layer convolution kernel, respectively, is the soundprint graph input to the layer, is the weight value of the layer convolution kernel at position and channel , is the bias term of the layer.

[0076] The first convolutional layer uses a convolution kernel with a size of for feature extraction, and the activation function ReLU is employed. Training is performed using historical equipment failure audio samples in the cloud, enabling the system to distinguish between common mechanical failure sounds such as wind turbine imbalance, misalignment, and bearing damage, as well as the low-frequency rumbling sound of rock fracturing, achieving an accuracy rate of over 85%. The classification results will be displayed in a three-level alarm system on the human-machine interface: red (high risk - rockburst), orange (medium risk - equipment failure), and yellow (low risk - requires attention), along with suggested maintenance measures.

[0077] S3. In the ventilation control subsystem, based on the preset gas concentration threshold and the real-time collected gas concentration data, the fan speed adjustment is dynamically calculated through the fuzzy PID control algorithm, and the control command is sent to the fan controller wirelessly.

[0078] This embodiment specifically includes the following steps:

[0079] The fuzzy PID control algorithm specifically includes:

[0080] S31. Define gas concentration error and error change rate ,in, For the preset threshold, for Real-time concentration at any given moment.

[0081] S32. Design a fuzzy rule base and adjust the PID parameters based on the membership function of the gas concentration error and its error transformation rate.

[0082] In this embodiment, the fuzzy rule base is based on error. and error change rate Fuzzy set partitioning, dynamically adjusting PID parameters through If-Then logic. and For example, the fuzzy set of numerical variables is defined as follows:

[0083] error : Negative Big (NB), Negative Small (NS), Zero (ZO), Positive Small (PS), and Positive Big (PB);

[0084] Error rate of change: negative fast (NF), negative slow (NS), zero (ZO), positive slow (PS), positive fast (PF).

[0085] Output variables and The adjustment strategy is as follows:

[0086] The proportional effect is enhanced based on the magnitude of the error;

[0087] Adjust the integral action according to the rate of error accumulation;

[0088] The differential action is adjusted according to the error change speed.

[0089] The specific rule base is designed as shown in Table 1:

[0090] Table 1 Fuzzy rule base

[0091]

[0092] The output is:

[0093] ;

[0094] In the formula, is the controller output, is the proportional gain, is the integral gain, is the time integral quantity, is the differential gain.

[0095] S33, generating a fan speed regulation instruction according to the controller output , wherein, is the reference speed, is the regulation coefficient.

[0096] The reference speed of the fan is the basic speed when there is no control signal, which is set according to the tunnel ventilation demand or the equipment specification. For example, the safety speed for maintaining the minimum ventilation volume. The regulation coefficient is a proportional factor for converting the controller output u(t) into the actual speed regulation amount, which is used to adapt to the dynamic characteristics or working condition requirements of different fans. If u(t) is a voltage signal, a represents the speed increment corresponding to each volt. The final calculated fan speed instruction is generated by superimposing the reference speed and the control signal, and needs to be limited within the maximum / minimum speed range allowed by the fan.

[0097] S4, in the lighting control subsystem, based on the personnel position information and the illumination intensity data obtained by the industrial camera, the adaptive weighting algorithm is used to calculate the lighting brightness target value of each area, and the control instruction is sent to the lighting controller through wireless transmission.

[0098] In this embodiment, the adaptive weighting algorithm is specifically:

[0099] S41, defining the personnel position weight , the illumination intensity weight , and the area priority weight , and satisfying ;

[0100] S42, calculating the target light, and the specific calculation method is:

[0101] ;

[0102] wherein, is the target brightness, is the people density area brightness compensation value, is the illumination intensity deviation compensation value, is the preset priority brightness.

[0103] people position weight , illumination intensity weight and area priority weight are dynamically adjusted, and the specific rules are as follows:

[0104] When the people density is higher than the set threshold, the people position weight is increased , and the illumination intensity weight is decreased ;

[0105] When the illumination intensity deviation is higher than the set threshold, the illumination intensity weight is increased , and the area priority weight is decreased ;

[0106] When the tunnel area is the construction core area, the area priority weight is fixed .

[0107] S43, generating a pulse width modulation signal according to the target brightness, and controlling the output power of the lighting device, wherein the pulse width modulation signal is represented as:

[0108] ;

[0109] In the formula, is the pulse width modulation signal, is the maximum brightness of the lighting device, is the non-linear compensation coefficient, is the dynamic response factor, is the dynamic brightness deviation.

[0110] In this embodiment, the core term linearly maps the target brightness to the duty cycle range (0%~100%), ensuring that the brightness requirement directly drives the PWM signal, for example, if , , the basic duty cycle is 60%.

[0111] The actual brightness of the lamp such as LED may be exponentially or logarithmically related to the duty cycle, and needs to be calibrated by η to linearize the model. If the actual measured duty cycle of 60% corresponds to an actual brightness of 280 lux (lower than the theoretical value of 300 lux), set η=1.07 to compensate for the non-linear loss.

[0112] When personnel suddenly enter the area ( ) or light drops ( ), by amplification duty cycle adjustment range, improve response speed. For example, if κ = 0.1, , then the dynamic correction term is 0.1⋅0.2=0.02, the duty cycle increases by 2%.

[0113] The expression form of the PWM signal realizes precise control of the lighting system by combining target brightness calculation, nonlinear compensation and dynamic response correction. The parameter design takes into account both the theoretical model and the actual working condition, ensuring that the system can operate efficiently and stably in both static and dynamic scenarios.

[0114] S5, through the man-machine interface, real-time display of tunnel environment parameters and equipment running state, and remote parameter configuration and intervention.

[0115] In this embodiment, a three-dimensional tunnel map is constructed based on WebGL technology, real-time sensor data is labeled, and historical curves can be viewed by clicking. At the same time, AES-256 encryption protocol is adopted, and remote commands need to pass two-factor authentication. In the test, the interception attempt rate of illegal intrusion is 100%.

[0116] The principles and implementation methods of the present application are described in the specific embodiments in the present application, and the above examples are only used to help understand the method and core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation and application range will be changed, and the above description should not be understood as a limitation of the present application.

[0117] Those skilled in the art will realize that the embodiments described herein are intended to help the reader understand the principles of the present application and should be understood as not limiting the scope of protection of the present application to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations according to the technical inspiration disclosed in the present application without departing from the essence of the present application, and these modifications and combinations are still within the scope of protection of the present application.

Claims

1. A method for intelligent control of tunnel ventilation and lighting based on wireless transmission technology, characterized in that, Comprise the following steps: S1, build a tunnel environment data acquisition system, including uniformly arranged in each area of the tunnel gas sensor, oxygen sensor, carbon monoxide sensor, methane sensor, light intensity sensor, acoustic sensor array and industrial camera, through the Internet of things module to collect gas concentration data, light intensity data, acoustic data and personnel location information in real time by wireless transmission mode; S2, the data collected in S1 is uploaded to the cloud server for storage and preprocessing through the Internet of things transmission equipment, and the data issued by the cloud is received through the PLC logic controller; S3, in the ventilation control subsystem, according to the preset gas concentration threshold and the real-time collected gas concentration data, the fan speed regulation amount is dynamically calculated through the fuzzy PID control algorithm, and the control instruction is sent to the fan controller through wireless transmission, and the fuzzy PID control algorithm specifically comprises: S31, define gas concentration error and error change rate wherein, is a preset threshold value, is real-time concentration at the moment S32, design fuzzy rule base, adjust PID parameters according to the membership function of gas concentration error and its error transformation rate, and its output is: ； wherein Kp is a proportional gain, Ki is an integral gain, Kd is a derivative gain, t is a time integral quantity, Kd is a derivative gain, S33, outputting the controller generating a fan speed regulation instruction wherein, is a reference speed, is a fan speed regulation coefficient; S4, in the lighting control subsystem, based on the personnel location information and light intensity data obtained by the industrial camera, the adaptive weighting algorithm is used to calculate the lighting brightness target value of each area, and the control instruction is sent to the lighting controller through wireless transmission, and the adaptive weighting algorithm is specifically: S41, define the personnel position weight , light intensity weight , and area priority weight , and satisfy ; S42, calculate the target light, the specific calculation method is: ； wherein, is a target brightness, is a people density area brightness compensation value, is an illumination intensity deviation compensation value, is a preset priority brightness; S43, generate pulse width modulation signal according to the target light, control the output power of the lighting device, wherein the pulse width modulation signal is represented as: ； wherein is a pulse width modulation signal, is a maximum luminance of the lighting device, is a non-linear compensation factor, is a dynamic response factor, is a dynamic luminance deviation; S5, real-time display tunnel environment parameters, acoustic warning information and equipment running state through man-machine interface, and remote parameter configuration and intervention.

2. The method of claim 1, wherein, The data preprocessing in S2 comprises: S21, Kalman filter noise reduction processing is carried out on the sensor data, and the filter equation is: ； wherein is the state estimation value at the time point, is the external control input signal at the time point, is the observation value at the time point, is the Kalman gain at the time point, is a system matrix; S22, adopt time series analysis method to detect data anomaly, if continuous N cycle data exceeds standard deviation range, trigger alarm and enable redundant sensor data; S23, the audio data collected by the acoustic sensor array is preprocessed and feature extracted. 3.The intelligent control method of tunnel ventilation and lighting based on wireless transmission technology according to claim 1, wherein, S23 specifically comprises the following steps: S231, frame and window processing are carried out on the audio signal, and the fast Fourier transform of each frame signal is calculated to obtain the frequency spectrum: ； wherein is a discrete audio signal, is a window function, is a frame length, is a frequency spectral component at S232、Calculate the energy proportion in the specific frequency band as a feature value, and the specific calculation method is: ； In the formula, is the sampling frequency, and when Anomaly acoustic event warning is triggered when the continuous exceeds the dynamic threshold.

4. The method of claim 3, wherein the wireless transport technology-based tunnel ventilation and lighting intelligent control method is characterized by, The cloud server adopts edge computing architecture, deploys lightweight machine learning model, which is used for predicting gas concentration diffusion trend, and the prediction model is: ； wherein, is the gas concentration at the moment, is the gas concentration at the moment is the Laplacian operator for calculating the tendency of gas diffusion from a high concentration area to a low concentration area; is the prediction step, is the diffusion coefficient, is the real-time wind speed, is the air flow rate impact factor; The edge computing architecture is equipped with a voiceprint recognition model at the same time, which is used for identifying the sound event in S232. The model is a convolutional neural network, and the convolutional layer operation is: ； In the formula, This is the index of the current convolutional layer, representing the level in the neural network; For the first Layer output feature map at position The value, It is a non-linear activation function. This is the index of the input channel. These are the row and column offset indices of the convolution kernel in the spatial dimension, respectively. The order is number 1 The height and width of the convolution kernel. For the first The voiceprint image input by the layer. For the first Layer convolution kernel at position and channels The weight value, For the first Layer bias terms.

5. The method of claim 1, wherein, said person position weight , light intensity weight and area priority weight are dynamically adjusted, and the specific rules are: When the density of people is higher than a set threshold, then increase the people position weight , decrease the light intensity weight ; When the light intensity deviation is higher than a set threshold, then increase the light intensity weight , decrease the region priority weight ; When the tunnel area is a construction core area, the fixed area priority weight is preferred .

6. The method of claim 1, wherein, The Internet of things module uses LoRaWAN protocol for data transmission, and its dynamic transmission power meets: ； In the formula, is a dynamic output power, is a reference power, is a path loss exponent, is a transmission distance, is an ambient noise correction term.

7. The method of claim 1, wherein the wireless transport technology based tunnel ventilation and lighting intelligent control method is characterized by, The man-machine interface in S5 supports multi-dimensional data visualization, including: S51, real-time display of tunnel three-dimensional map, and mark sensor position, environment parameters and equipment state; S52, generate historical data trend curve and alarm log, support filtering by time, area, parameter type; S53, receive remote control instruction through encryption communication protocol, and execute after verifying operation permission.

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