Intelligent servo barrier gate management system

Abstract

The invention relates to the technical field of intelligent traffic, and discloses an intelligent servo barrier gate management system. According to the system, vehicle identification information, detection information, environmental parameters and a barrier gate equipment state are acquired in real time through the data acquisition unit, comprehensive monitoring of traffic flow is ensured, the processing and analysis unit processes the acquired data by using a deep learning model, a control instruction is quickly generated, and the vehicle passing efficiency is evaluated, so that the traffic flow is ensured. The servo control unit is combined with an adaptive control algorithm to dynamically adjust the switching speed, the torque and the acceleration of the barrier gate, the vehicle passing experience is optimized, the queuing time is reduced, the bidirectional communication capability of the communication unit realizes remote monitoring and adjustment of the state of the barrier gate, and the traffic management decision making efficiency is improved. A manager can timely respond to sudden traffic conditions, the response speed and accuracy of the system are improved, the intelligent system effectively relieves the bottleneck of traditional barrier gate management, and the intelligent level of urban traffic is improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation technology, and in particular to an intelligent servo barrier management system. Background Art

[0002] With the acceleration of urbanization and the continuous increase in the number of cars, urban traffic management is facing increasingly severe challenges. According to statistics, in recent years, the urban population has continued to grow worldwide, the urbanization rate has steadily increased, and the number of motor vehicles in many cities has increased exponentially. This phenomenon has led to more serious urban traffic congestion problems, causing great inconvenience to residents' daily travel. At the same time, traffic accidents, environmental pollution and other problems have also intensified, becoming an important factor restricting the sustainable development of cities. Therefore, it is urgent to take effective measures to optimize traffic management and improve the overall operating efficiency of urban traffic.

[0003] Traditional barrier systems are particularly weak, and many cities still rely on manual management and simple mechanical control methods to manage traffic. This traditional management model is not only inefficient, but also easily affected by human factors, making it difficult to quickly respond to sudden traffic conditions. For example, during peak hours, manual barriers often cause vehicles to queue up, wasting a lot of time. In addition, when traffic flow is heavy, the ability of manual management to divert traffic is limited and cannot effectively alleviate traffic pressure. In addition, traditional barrier systems also have shortcomings in data collection and information feedback, and cannot achieve real-time monitoring and analysis of traffic flow, resulting in a lack of data support for traffic management decisions, slow response speed and low accuracy. Summary of the Invention

[0004] (1) Technical problems solved In response to the shortcomings of the existing technology, the present invention provides an intelligent servo barrier gate management system, which obtains vehicle identification information, detection information, environmental parameters and barrier gate equipment status in real time through the data acquisition unit, ensuring comprehensive monitoring of traffic flow. The processing and analysis unit uses advanced deep learning models to filter, detect anomalies and determine the status of the collected data, quickly generate control instructions, and evaluate vehicle traffic efficiency, so that traffic management decisions are more data-supported. The servo control unit combines with the adaptive control algorithm to dynamically adjust the switching speed, torque and acceleration of the barrier gate, optimizes the vehicle traffic experience, and reduces waiting time in queues. The two-way communication capability of the communication unit realizes remote monitoring and adjustment of the barrier gate status, enabling managers to respond to sudden traffic conditions in a timely manner, improving the response speed and accuracy of the system, so that the intelligent system effectively alleviates the bottleneck of traditional barrier gate management and improves the intelligence level of urban transportation.

[0005] (2) Technical solution To achieve the above-mentioned object, the present invention provides the following technical solutions: an intelligent servo barrier management system, comprising a data acquisition unit, a processing and analysis unit, a servo control unit and a communication unit; The data acquisition unit is used to collect vehicle identification information, vehicle detection information, environmental parameters and gate equipment status information; The processing and analysis unit filters and detects data anomalies on the collected vehicle identification information, vehicle detection information, environmental parameters, and gate equipment status information. It uses a trained deep learning model to perform vehicle identification, status determination, and environmental analysis, and quickly generates control instructions. It also calculates vehicle identification probability, vehicle coding value, vehicle environmental safety value, detection anomaly probability, scene complexity, and vehicle dwell time to evaluate vehicle traffic efficiency. The servo control unit is equipped with a linear servo drive motor and an optical rotary encoder, and combined with an adaptive control algorithm, drives the gate according to the control instructions generated by the processing and analysis unit. It also dynamically adjusts the gate opening and closing speed, torque and acceleration through a closed-loop control algorithm, and simultaneously calculates the current gate position, gate drive torque and gate mechanical energy consumption for evaluation of gate opening and closing efficiency. The communication unit is connected via Communication protocol, for signal transmission and interaction between the data acquisition unit, processing and analysis unit and servo control unit, and for two-way communication with the external management platform, supporting remote monitoring of barrier status, barrier status diagnosis and remote adjustment of barrier status.

[0006] Preferably, the calculation formula for the vehicle recognition probability is as follows: ; In the formula, represents the vehicle recognition probability, Indicates the number of vehicles correctly identified in the current detection, Represents the total number of all detected vehicles.

[0007] Preferably, the calculation formula of the vehicle code value is as follows: ; In the formula, Indicates the vehicle code value, Indicates the The predicted probability of the class, Multiple feature vectors representing vehicles, obtained through deep learning models, Represents the fusion function.

[0008] Preferably, the calculation formula of the vehicle environmental safety value is as follows: ; In the formula, Indicates the vehicle environmental safety value, represents the weather safety index, represents the congestion index, represents the probability of abnormal events occurring, 、 、 Represents the weight coefficient of the corresponding indicator, which is obtained through the deep learning model.

[0009] Preferably, the calculation formula for the detection anomaly probability is as follows: ; In the formula, represents the probability of detecting anomalies, Indicates the number of times an anomaly is detected. Represents the total number of all detections.

[0010] Preferably, the calculation formula of the scene complexity is as follows: ; In the formula, Indicates the scene complexity, Indicates the total number of vehicles in the scene, represents the congestion index, represents the scene complexity factor, represents the basic complexity, Represents the adjustment coefficient, which is obtained through the deep learning model.

[0011] Preferably, the calculation formula for the vehicle dwell time is as follows: ; In the formula, Indicates the vehicle dwell time, Indicates the time when the vehicle leaves the detection area. Indicates the time when the vehicle enters the detection area.

[0012] Preferably, the calculation formula for the current gate position is as follows: ; In the formula, Indicates the current gate position. Indicates the gate starting position, Indicates the gate opening and closing speed, Indicates elapsed time.

[0013] Preferably, the calculation formula of the gate driving torque is as follows: ; In the formula, Indicates the gate drive torque, Indicates the dynamic load that the drive system needs to overcome, represents the friction resistance in the drive mechanism, Indicates the inertial force caused by the high-speed change of inertia during movement. Indicates the length of the robotic arm.

[0014] Preferably, the calculation formula of the gate mechanical energy consumption is as follows: ; In the formula, Indicates the mechanical energy consumption of the gate, Indicates the The angle of rotation of the time period, Indicates in The torque value measured at the moment, represents the total efficiency of the mechanical transmission system, represents the total time required for the measurement, Indicates index subscript.

[0015] Compared with the prior art, the present invention provides an intelligent servo gate management system with the following beneficial effects: The present invention obtains vehicle identification information, detection information, environmental parameters and barrier equipment status in real time through the data acquisition unit, ensuring comprehensive monitoring of traffic flow. The processing and analysis unit uses advanced deep learning models to filter, detect anomalies and determine the status of the collected data, quickly generate control instructions, and evaluate vehicle traffic efficiency, thereby making traffic management decisions more data-supported. The servo control unit combines with the adaptive control algorithm to dynamically adjust the switching speed, torque and acceleration of the barrier, optimizes the vehicle traffic experience, and reduces waiting time in queues. The two-way communication capability of the communication unit realizes remote monitoring and adjustment of the barrier status, enabling managers to respond to sudden traffic conditions in a timely manner, improving the response speed and accuracy of the system, so that the intelligent system effectively alleviates the bottleneck of traditional barrier management and improves the intelligence level of urban transportation. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Schematic diagram of the system flow of the present invention; Figure 2 This is a schematic diagram of the data acquisition process of the system of the present invention; Figure 3 Schematic diagram of the data processing and analysis flow of the system of the present invention. DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0018] The traditional gate system still relies on manual management and simple mechanical control to manage traffic. This traditional management mode is not only inefficient, but also easily affected by human factors and difficult to quickly respond to sudden traffic conditions. To this end, an intelligent servo gate management system is proposed. Figure 1 ,The system includes a data acquisition unit, a processing and analysis unit, a servo control unit and a communication unit; The data acquisition unit adopts multi-sensor fusion technology and is equipped with a high-performance high-definition license plate recognition camera and a multi-spectral high-definition visual sensor. It realizes high-accuracy acquisition of vehicle identification information through deep learning image processing algorithms, including features such as license plate characters, vehicle type and vehicle color. At the same time, combined with laser ranging sensors or laser radar equipment that distinguishes distances, it detects the vehicle position, speed and motion status in real time to obtain accurate vehicle detection information. In terms of environmental parameters, the unit is equipped with ambient light sensors, temperature and humidity sensors, atmospheric pressure sensors and meteorological monitoring equipment at the weather station level. It adopts advanced fuzzy filtering and adaptive calibration technology to realize real-time monitoring and data optimization of light changes, temperature and humidity fluctuations. In terms of gate equipment status information collection, it integrates multi-modal industrial sensors, including motor speed sensors, position encoders, vibration monitors and current and voltage sensors. Through the intelligent control interface, it collects multi-dimensional information such as equipment operation status, mechanical vibration, current load, working temperature, etc., to realize real-time monitoring of the gate hardware health status and fault warning. The data of all sensors and monitoring equipment are transmitted through embedded edge computing nodes (such as: ARM Pre-processing is performed on the Cortex-A series or FPGA platform, including data filtering, anomaly detection, and format standardization, to ensure real-time and accurate transmission, providing a solid data foundation for the efficient operation of the entire system. The processing and analysis unit uses advanced signal processing technology to perform multi-level filtering on vehicle identification information, vehicle detection information, environmental parameters, and gate equipment status information from the data acquisition unit. This includes Kalman filtering, adaptive filtering, and statistical feature-based anomaly detection algorithms to effectively remove noise and interference, ensuring data accuracy and stability. Subsequently, it utilizes extensively trained and optimized deep learning models (such as convolutional neural networks, recurrent neural networks, or fusion models) to perform high-precision identification of vehicle type, license plate information, and vehicle motion status. It also incorporates anomaly detection mechanisms to identify potential anomalies in environmental parameters or equipment status. Based on these analysis results, the system can quickly generate control instructions to guide the opening and closing of the gate and implement dynamic adjustments, such as adjusting the opening speed and torque, to meet the needs of different scenarios. Furthermore, the system also calculates and outputs key indicators in real time, including vehicle identification probability, vehicle code value, environmental safety value, detection anomaly probability, scene complexity, and vehicle dwell time in the detection area. These indicators work together to assess the overall efficiency and safety of vehicle traffic, providing a scientific basis for intelligent scheduling and emergency decision-making, and ensuring the continuous improvement of the reliability and intelligence level of the traffic control system. The calculation formula for vehicle recognition probability is as follows: ; By calculating the vehicle identification probability, the system can determine the reliability of the identification results in real time, thereby reducing the risk of misidentification and missed detection. This is especially important for sensitive scenarios (such as toll booths, security gates, etc.), ensuring that only confirmed vehicles are granted access, effectively avoiding vehicle misoperation or unauthorized passage, and improving the overall safety level. In the formula, represents the vehicle recognition probability, Indicates the number of vehicles correctly identified in the current detection, Represents the total number of all detected vehicles. Real-time monitoring of recognition probability enables the system to dynamically adjust its automatic response strategy based on the reliability of recognition. For example, when the confidence level is insufficient, it automatically triggers manual verification or adds subsequent detection steps. This not only reduces misoperation but also enhances the system's autonomous learning and adaptability, continuously improving the accuracy of the recognition model. The calculation formula of the vehicle code value is as follows: ; The vehicle code value, as the only tag to identify the vehicle, provides the basis for subsequent continuous tracking of the vehicle, historical data management, and anomaly detection. This helps to build a complete traffic and vehicle management database, improve the efficiency and reliability of vehicle tracing, and effectively prevent illegal vehicles from repeatedly passing through. In the formula, Indicates the vehicle code value, Indicates the The predicted probability of the class, Multiple feature vectors representing vehicles, obtained through deep learning models, Representing a fusion function, accurate vehicle code values ​​can be combined with data such as vehicle dwell time and pass frequency to assist in analyzing traffic flow and hotspot locations, providing a basis for intelligent scheduling, thereby optimizing queue lengths, reducing waiting times, and improving overall station efficiency and user experience. The calculation formula of vehicle environmental safety value is as follows: ; By dynamically monitoring the environmental safety value, the system can identify potential safety hazards in real time (such as bad weather, environmental anomalies, traffic congestion, etc.), take control measures in advance, avoid the occurrence of safety accidents, and improve the safety of overall operation. In the formula, Indicates the vehicle environmental safety value, represents the weather safety index, represents the congestion index, represents the probability of abnormal events occurring, 、 、 The vehicle environmental safety value represents the weight coefficient of the corresponding indicator, obtained through a deep learning model. It provides a quantitative indicator for scenario assessment, enabling the control system to adjust the gate opening speed, driving parameters, and even restrict the passage of certain vehicles according to the complexity of the scenario, thereby achieving more intelligent and flexible traffic management and adapting to different environmental changes; The calculation formula for the probability of detecting anomalies is as follows: ; With real-time monitoring of abnormal probability, the system can identify potential equipment failures or abnormal behaviors (such as sensor failure, abnormal vehicle status) in advance, issue alarms in time or automatically take corrective measures to prevent failures from turning into safety accidents or system downtime, and ensure the continuous and stable operation of the system. In the formula, represents the probability of detecting anomalies, Indicates the number of times an anomaly is detected. Represents the total number of all detections. Continuous monitoring of abnormal detection indicators enables maintenance personnel to perform preventive maintenance based on data, reducing emergency repair costs caused by sudden failures, while extending equipment life and improving operation and maintenance efficiency. The calculation formula for scene complexity is as follows: ; The scenario complexity index enables the system to intelligently adjust gate control parameters (such as opening and closing speed, torque adjustment) according to the complexity of the current traffic environment, enhance the system's responsiveness and robustness in complex scenarios, and ensure a balance between traffic efficiency and safety. In the formula, Indicates the scene complexity, Indicates the total number of vehicles in the scene, represents the congestion index, represents the scene complexity factor, represents the basic complexity, The adjustment coefficient is obtained through deep learning models. The estimation of scene complexity also helps managers identify high-risk or congested areas, conduct key monitoring and scheduling, optimize traffic flow organization, reduce delays caused by congestion, and improve the overall traffic environment. The calculation formula for vehicle dwell time is as follows: ; The time a vehicle spends in the detection area is an important indicator of system efficiency. Accurate calculation can help analyze vehicle congestion points and reasons for detention, provide a basis for improving queuing strategies, and thus improve overall traffic efficiency. In the formula, Indicates the vehicle dwell time, Indicates the time when the vehicle leaves the detection area. Indicates the time when a vehicle enters the detection area. By monitoring the vehicle's dwell time, the gate opening and closing strategy can be dynamically adjusted to reduce unnecessary waiting, reasonably allocate traffic priorities, optimize traffic flow distribution, and improve user satisfaction and station management level; The servo control unit is equipped with a high-performance linear servo drive motor and a high-precision optical rotary encoder, forming a high-speed, high-response closed-loop control system. The linear servo drive motor achieves smooth and precise regulation of the gate opening and closing process through precise linear position and speed control. The optical rotary encoder is responsible for real-time monitoring of the drive motor's actual position, speed, and acceleration information, providing closed-loop feedback data to ensure control accuracy and response speed. Combined with the independently developed adaptive control algorithm, the system can dynamically adjust drive parameters such as opening and closing speed, torque, and acceleration based on the processing and analysis unit (the source of control instructions) to adapt to workload changes and environmental differences in different scenarios, thereby optimizing energy consumption during the opening and closing process and ensuring smooth and efficient mechanical operation. At the same time, the system continuously calculates the current gate position and uses real-time torque monitoring parameters to evaluate the mechanical working status and energy consumption, especially the relationship between drive torque and mechanical energy consumption. This data is not only used for feedback adjustment to improve the smoothness and response speed of gate movement, but also for scientific energy consumption analysis and efficiency evaluation, providing an important basis for subsequent mechanical optimization and energy saving and consumption reduction, thereby achieving efficient, reliable and intelligent management of gate control. The calculation formula for the current gate position is as follows: ; By real-time monitoring of the current position of the gate, the system can ensure that the gate maintains a high-precision motion trajectory during the opening and closing process, avoiding mechanical wear or safety hazards caused by too fast or too slow movement. In addition, precise position control can reduce mechanical pressure and extend the service life of the equipment. At the same time, it can ensure the smoothness and coordination of the gate movement, improve traffic efficiency, reduce the occurrence of faults, and ensure that the system is stable and reliable in various working environments. In the formula, Indicates the current gate position. Indicates the gate starting position, Indicates the gate opening and closing speed, The accurate current location data indicates the elapsed time, enabling the system to achieve remote monitoring and automatic scheduling, dynamically adjusting the switching speed and time according to different traffic loads and scene requirements. At the same time, in the event of anomalies or failures, real-time location data can quickly help detect deviations or jams, providing key evidence for fault diagnosis and early warning, reducing maintenance costs, and improving overall management and safety. The gate drive torque is calculated as follows: ; Real-time monitoring of driving torque can help the system determine changes in driving load and identify overload or abnormal energy consumption. According to the torque changes, the system can dynamically adjust the driving parameters to avoid energy waste and mechanical wear caused by excessive torque, thereby achieving the goal of energy saving and consumption reduction. This not only reduces the operating cost of the equipment, but also extends the service life of the machinery and improves the overall energy efficiency level. In the formula, Indicates the gate drive torque, Indicates the dynamic load that the drive system needs to overcome, represents the friction resistance in the drive mechanism, Indicates the inertial force caused by the high-speed change of inertia during movement. Indicates the length of the robotic arm. Real-time monitoring of the driving torque can reflect the force state during gate movement and prevent failures or safety accidents caused by overload or mechanical jamming. When abnormal torque changes are detected, the system can immediately disconnect or reduce the driving force and issue an early warning, so that timely measures can be taken to ensure the safety and reliability of the gate. This is crucial for ensuring operational safety in diverse environments or emergency situations. The calculation formula of the gate mechanical energy consumption is as follows: ; Real-time monitoring of the gate's mechanical energy consumption under different working conditions helps the system analyze the main sources of energy consumption, identify high-energy consumption links, and take targeted measures to optimize motion parameters or equipment configuration. This process promotes the system's intelligent energy-saving design, reduces overall operating costs, and promotes green and environmentally friendly energy conservation and emission reduction goals, providing strong support for sustainable traffic management. In the formula, Indicates the mechanical energy consumption of the gate, Indicates the The angle of rotation of the time period, Indicates in The torque value measured at the moment, represents the total efficiency of the mechanical transmission system, represents the total time required for the measurement, Represents an index subscript. Changes in mechanical energy consumption often indicate potential abnormalities or wear trends in equipment. Continuous monitoring of energy consumption can detect abnormal conditions in equipment or mechanical components early, providing early warning of maintenance needs, avoiding sudden failures, and reducing maintenance costs and downtime. Energy consumption data is also an important indicator for evaluating equipment performance and control strategy effectiveness, providing a scientific basis for subsequent optimization and upgrades, effectively extending the service life of the machinery, and ensuring the overall stability and efficiency of the system. The communication unit adopts advanced ( ) communication protocol, as a lightweight, publish / subscribe message transmission protocol widely used in the field of Internet of Things, has low bandwidth usage, high efficiency and real-time performance, and good scalability. It can achieve efficient and stable data interaction for each key unit in the entire gate system. Specifically, the communication unit The protocol establishes a reliable communication channel between the data acquisition unit, the processing and analysis unit, and the servo control unit to achieve real-time transmission of equipment status information, detection parameters, and control instructions. The data acquisition unit is responsible for sensing sensor data such as environmental information, vehicle identification data, barrier equipment status, and mechanical status, and transmits the collected information to the data acquisition unit. The client publishes to the designated topic, and the processing and analysis unit subscribes to the corresponding topic. After receiving the data in real time, it uses the built-in edge computing and data preprocessing modules to filter, detect anomalies, and fuse data to generate more accurate decision-making basis, and then sends the control instructions through The agreement is based on a reliable quality level ( ) is sent to the servo control unit to achieve precise control of the gate opening and closing action. In addition, the communication unit not only realizes efficient data exchange between internal units, but also maintains two-way communication with the external management platform. The protocol enables remote monitoring, control and management. The management platform can obtain the working status, movement trajectory, fault information and environmental parameters of the gate in real time, conduct remote monitoring and performance analysis, and improve management efficiency. More importantly, the management platform can remotely issue adjustment instructions, such as adjusting the gate opening speed, optimizing the scheduling strategy, and even remotely diagnose potential fault points, which helps to prevent system failures in advance and reduce maintenance pressure. In order to ensure the security of communication, the system also integrates Encryption mechanism, multi-level authentication measures and subject authentication ensure the integrity and confidentiality of data during transmission. The lightweight nature of the protocol allows communication units to operate in limited network environments (such as 4G / 5G networks or ) to achieve stable communication connections under different environments, while supporting disconnection reconnection, message persistence and offline storage, ensuring that data can be seamlessly synchronized even after network instability or disconnection. Not only that, with the continuous development of Internet of Things technology, the communication unit also plans to introduce an architecture that combines edge computing and cloud platforms. By intelligently processing and filtering data at edge nodes, it can reduce the cloud load and improve the response speed, providing strong technical support for more intelligent and automated traffic management in the future. The design of this system fully reflects the in-depth application of modern communication technology in traffic control. It not only realizes remote monitoring, fault diagnosis and adjustment operations of barriers, but also provides a solid technical foundation for the entire intelligent transportation, and promotes the continuous development and improvement of intelligent transportation systems.

[0019] Through the application of the intelligent servo barrier management system, which integrates perception, control, communication and intelligent analysis, it provides a new solution for traffic control. The system uses a high-precision data acquisition unit and multi-sensor technology to monitor vehicle information, environmental parameters and barrier equipment status in real time to ensure the comprehensiveness and accuracy of the data. The processing and analysis unit combines deep learning and big data analysis algorithms to filter, detect anomalies and evaluate scenarios on the collected information, effectively improving recognition accuracy and system robustness, supporting intelligent scheduling and safety decision-making. At the execution level, the servo control unit is equipped with a linear servo drive motor and an optical encoder, combined with an adaptive control algorithm to achieve precise adjustment of gate position, speed and torque, which helps to achieve smooth and efficient mechanical operation, while monitoring energy consumption and mechanical health status to optimize overall energy efficiency and service life. The communication unit adopts The communication protocol ensures efficient, reliable and secure data transmission between various units within the system. It not only realizes real-time interaction of internal information, but also supports two-way communication with external management platforms, thereby realizing remote monitoring, fault diagnosis and remote adjustment. The entire system has a high degree of intelligence and autonomous control capabilities, and can respond to traffic needs in complex scenarios in real time, improve traffic efficiency, alleviate traffic pressure and ensure driving safety. In addition, the system has been deeply designed in terms of security, stability and scalability to support future platform upgrades and function expansions, and promote the gradual improvement of the intelligent transportation system.

[0020] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent servo gate management system, characterized by: It includes a data acquisition unit, a processing and analysis unit, a servo control unit and a communication unit; The data acquisition unit is used to collect vehicle identification information, vehicle detection information, environmental parameters and gate equipment status information; The processing and analysis unit filters and detects data anomalies on the collected vehicle identification information, vehicle detection information, environmental parameters, and gate equipment status information. It uses a trained deep learning model to perform vehicle identification, status determination, and environmental analysis, and quickly generates control instructions. It also calculates vehicle identification probability, vehicle coding value, vehicle environmental safety value, detection anomaly probability, scene complexity, and vehicle dwell time to evaluate vehicle traffic efficiency. The servo control unit is equipped with a linear servo drive motor and an optical rotary encoder, and combined with an adaptive control algorithm, drives the gate according to the control instructions generated by the processing and analysis unit. It also dynamically adjusts the gate opening and closing speed, torque and acceleration through a closed-loop control algorithm, and simultaneously calculates the current gate position, gate drive torque and gate mechanical energy consumption for evaluation of gate opening and closing efficiency. The communication unit is connected via Communication protocol, for signal transmission and interaction between the data acquisition unit, processing and analysis unit and servo control unit, and for two-way communication with the external management platform, supporting remote monitoring of barrier status, barrier status diagnosis and remote adjustment of barrier status.

2. The intelligent servo barrier management system according to claim 1, characterized in that: The calculation formula of the vehicle recognition probability is as follows: ； In the formula, represents the vehicle recognition probability, Indicates the number of vehicles correctly identified in the current detection, Represents the total number of all detected vehicles.

3. The intelligent servo gate management system according to claim 2, characterized in that: The calculation formula of the vehicle coding value is as follows: ； In the formula, Indicates the vehicle code value, Indicates the The predicted probability of the class, Multiple feature vectors representing vehicles, obtained through deep learning models, Represents the fusion function.

4. The intelligent servo barrier management system according to claim 3, characterized in that: The calculation formula of the vehicle environmental safety value is as follows: ； In the formula, Indicates the vehicle environmental safety value, represents the weather safety index, represents the congestion index, represents the probability of abnormal events occurring, 、 、 Represents the weight coefficient of the corresponding indicator, which is obtained through the deep learning model.

5. The intelligent servo barrier management system according to claim 4, characterized in that: The calculation formula for the detection anomaly probability is as follows: ； In the formula, represents the probability of detecting anomalies, Indicates the number of times an anomaly is detected. Represents the total number of all detections.

6. The intelligent servo barrier management system according to claim 5, characterized in that: The calculation formula of the scene complexity is as follows: ； In the formula, Indicates the scene complexity, Indicates the total number of vehicles in the scene, represents the congestion index, represents the scene complexity factor, represents the basic complexity, Represents the adjustment coefficient, which is obtained through the deep learning model.

7. The intelligent servo barrier management system according to claim 6, characterized in that: The calculation formula for the vehicle dwell time is as follows: ； In the formula, Indicates the vehicle dwell time, Indicates the time when the vehicle leaves the detection area. Indicates the time when the vehicle enters the detection area.

8. The intelligent servo barrier management system according to claim 7, characterized in that: The calculation formula for the current gate position is as follows: ； In the formula, Indicates the current gate position. Indicates the gate starting position, Indicates the gate opening and closing speed, Indicates elapsed time.

9. The intelligent servo barrier management system according to claim 8, characterized in that: The calculation formula of the gate driving torque is as follows: ； In the formula, Indicates the gate drive torque, Indicates the dynamic load that the drive system needs to overcome, represents the friction resistance in the drive mechanism, Indicates the inertial force caused by the high-speed change of inertia during movement. Indicates the length of the robotic arm.

10. The intelligent servo barrier management system according to claim 9, characterized in that: The calculation formula of the gate mechanical energy consumption is as follows: ； In the formula, Indicates the mechanical energy consumption of the gate, Indicates the The angle of rotation of the time period, Indicates in The torque value measured at the moment, represents the total efficiency of the mechanical transmission system, represents the total time required for the measurement, Indicates index subscript.

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