Intelligent Management Method and Device for Barriers Based on Internet of Things

Abstract

The present invention relates to the technical field of barrier gate management, and specifically to an intelligent management method and device for barrier gates based on the Internet of Things. It includes vehicle information passing verification and barrier gate equipment monitoring and analysis, etc. The present invention realizes the comprehensiveness and accuracy of vehicle passing verification in the target park by performing multi-dimensional verification on vehicle identity information, vehicle owner identity and permissions, and vehicle safety status, ensuring that the vehicle passing verification process is efficient and accurate; when analyzing the abnormal operation of the barrier gate equipment in the target park, the present invention analyzes the abnormal operation of the gate rod, motor, and vehicle identification system, and then judges whether there is a risk of abnormal operation of the barrier gate equipment in the target park. This analysis method can accurately identify potential barrier gate operation risks, timely discover and warn of potential failure and accident risks, and to a certain extent improve the response timely rate of abnormal barrier gate operation and reduce the impact on the normal operation of the barrier gate in the target park.

Description

Technical Field

[0001] The present invention relates to the technical field of barrier gate management, and in particular to an intelligent barrier gate management method and device based on the Internet of Things. Background Art

[0002] With the acceleration of urbanization and the improvement of people's living standards, the number and scale of various parks (such as residential areas, commercial parks, industrial parks, etc.) are constantly expanding, and the traffic in the parks is becoming more and more frequent. The traditional gate management method can no longer meet the current complex and diverse management needs. It has exposed many problems in vehicle traffic management and equipment operation monitoring, as shown below:

[0003] In terms of vehicle traffic management, traditional gate management relies on a single verification method, such as determining whether a vehicle is passable only by license plate recognition. This method has major security loopholes, such as theft of license plates, inconsistency between vehicle appearance and registration information, etc., which may lead to unauthorized vehicles entering the park, posing a threat to the security of the park. At the same time, the traditional method is not accurate and comprehensive enough to verify the identity and authority of the owner, and cannot effectively identify the situation of using fake identities to enter the park, making it difficult to ensure the safety of people and property in the park. In addition, traditional gate management lacks comprehensive consideration of vehicle safety conditions, cannot detect whether the vehicle carries prohibited items, and cannot evaluate the vehicle's historical violations, and cannot prevent potential safety risks in advance. Moreover, during peak vehicle traffic hours, traditional gate management is prone to vehicle congestion due to more manual intervention or low verification efficiency, which reduces the traffic efficiency of the park and brings inconvenience to car owners.

[0004] In terms of gate equipment operation monitoring, traditional methods often lack effective monitoring means. The operating status of key equipment such as gate rods, motors, and vehicle identification systems cannot be accurately grasped in real time, and it is difficult to detect abnormal conditions during equipment operation in a timely manner. For example, when the gate rod is stuck or tilted, it cannot be detected and handled in time, which not only affects the passage of vehicles, but may also cause safety accidents; when the motor fails, if it cannot be discovered and repaired in time, the gate will not work properly, seriously affecting the entrance and exit management of the park; when the vehicle identification system has low recognition accuracy or failure, it will cause vehicles to enter and exit unsmoothly, causing traffic congestion. Moreover, traditional methods do not conduct in-depth analysis of equipment operation data, and cannot predict the potential risks of equipment in advance through data analysis, making it difficult to prevent problems before they occur. The equipment maintenance cost is high and the management efficiency is low. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent management method and device for a barrier gate based on the Internet of Things to solve the problems raised in the above background.

[0006] The object of the present invention can be achieved by the following technical solutions: In the first aspect, the present invention provides an intelligent management method for a barrier gate based on the Internet of Things, including:

[0007] Step 1: Vehicle information capture, capturing the vehicle information of the target park through the vehicle identification system of the barrier gate in the target park.

[0008] Step 2: Vehicle information access verification, verifying the accessibility of the captured vehicle information of the target park. If the verification passes, it is determined that the vehicle is allowed to enter the target park, and the barrier gate rod automatically lifts. Otherwise, it is determined that the vehicle is prohibited from entering the target park, the barrier gate rod remains closed, and corresponding warnings are given.

[0009] Step 3: Barrier gate equipment monitoring and analysis, monitoring the barrier gate equipment in the target park, obtaining the operation data of the barrier gate equipment in the target park, analyzing the abnormal operation conditions of the barrier gate equipment in the target park, and judging whether there are risks in the operation of the barrier gate equipment in the target park. If there are risks, warnings are given and feedback is provided.

[0010] Step 4: Identification of risky barrier gate equipment, identifying specific risky operation equipment when it is determined that there are risks in the operation of the barrier gate equipment in the target park, and giving corresponding warning feedback.

[0011] In the second aspect, the present invention provides an intelligent management device for a barrier gate based on the Internet of Things, including:

[0012] A vehicle information capture module, used to capture the vehicle information of the target park through the vehicle identification system of the barrier gate in the target park.

[0013] A vehicle information access verification module, used to verify the accessibility of the captured vehicle information of the target park. If the verification passes, it is determined that the vehicle is allowed to enter the target park, and the barrier gate rod automatically lifts. Otherwise, it is determined that the vehicle is prohibited from entering the target park, the barrier gate rod remains closed, and corresponding warnings are given.

[0014] A barrier gate equipment monitoring and analysis module, used to monitor the barrier gate equipment in the target park, obtain the operation data of the barrier gate equipment in the target park, analyze the abnormal operation conditions of the barrier gate equipment in the target park, and judge whether there are risks in the operation of the barrier gate equipment in the target park. If there are risks, warnings are given and feedback is provided.

[0015] A risky barrier gate equipment identification module, used to identify specific risky operation equipment when it is determined that there are abnormal operations of the barrier gate equipment in the target park, and give corresponding warning feedback.

[0016] The cloud database is used to store authorized license plate information, vehicle feature information associated with the authorized license plate, and the allowable range of vehicle traffic dimensions specified in the target park, store feature vectors of associated vehicle owner facial images, store authorized traffic time period information associated with authorized license plates, store historical vehicle traffic records within a set historical period, store standard raising and lowering durations corresponding to normal raising and lowering operations of gate levers, store reference angle difference indicators and gravity component force difference indicators corresponding to normal raising and lowering operations of gate levers, store standard current, standard voltage, and standard speed under normal operating conditions of the motor, and store reasonable clarity thresholds, reasonable contrast ranges, and reasonable exposure ranges of images collected by the license plate recognition system under standard high-quality conditions.

[0017] Beneficial effects of the present invention:

[0018] The present invention realizes efficient and accurate verification of the identity information of vehicles in the target park by successively verifying the license plate number, appearance features and physical features of vehicles in the target park, greatly reducing the risk of misjudgment of identity due to stolen license plates and the like, and ensuring that large vehicles will not cause traffic obstructions or safety hazards in the park due to size issues; through the verification of the owner's identity and the vehicle's authorized passage time period, it effectively prevents others from entering the park by impersonating the owner, ensures the safety of people and property in the park, facilitates the time-divided management of vehicles in the park, improves the efficiency of park resource utilization, and ensures the safety and order of the park within a specific time period; by combining the identification value of the prohibited items detection result and the vehicle behavior safety factor, it realizes the in-depth verification of the vehicle safety status, effectively eliminates potential safety hazards, reduces the possibility of safety accidents, and improves the level of traffic safety in the park.

[0019] The present invention realizes comprehensive and precise verification of vehicle access to the target park by performing multi-dimensional verification of vehicle identity information, owner identity and authority, and vehicle safety status, making up for the deficiency of traditional gate management that relies on only a single verification method. It not only avoids security loopholes caused by insufficient verification, but also directly verifies key information of vehicles and owners through efficient information processing procedures, ensuring that the vehicle access verification process is efficient and accurate.

[0020] When analyzing the abnormal operation of the barrier gate equipment in the target park, the present invention analyzes the abnormal operation of the gate rod, motor, and vehicle identification system, and then determines whether there is an operational risk of the barrier gate equipment in the target park. This analysis method can accurately identify potential barrier gate operation risks, timely discover and warn of potential faults and accident risks, and to a certain extent improve the response timeliness of barrier gate operation abnormalities and reduce the impact on the normal operation of the barrier gate in the target park. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The present invention will be further described below in conjunction with the accompanying drawings.

[0022] Figure 1 It is a schematic diagram of the method flow of the present invention.

[0023] Figure 2 It is a connection diagram of the device modules of the present invention.

[0024] Figure 3 It is a schematic diagram of the logic for verifying the vehicle information passability in the target park of the present invention.

[0025] Figure 4 It is a schematic diagram of the logic for identifying the risk barrier devices in the target park of the present invention. Detailed implementation manners

[0026] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0027] Please refer to Figure 1 As shown, the first aspect of the present invention provides a method for intelligent management of barriers based on the Internet of Things, including:

[0028] Step 1, Vehicle information capture: Capture the vehicle information of the target park through the vehicle identification system of the target park barrier.

[0029] Exemplarily, the vehicle identification system is an intelligent high-definition camera, and the intelligent high-definition camera is responsible for collecting vehicle images, including visual information such as license plates, vehicle models, body colors, and vehicle brand logos.

[0030] Step 2, Vehicle information pass verification: Perform pass verification on the captured vehicle information of the target park. If the verification passes, it is determined that the vehicle is allowed to enter the target park, and the barrier rod automatically lifts. Otherwise, it is determined that the vehicle is prohibited from entering the target park, the barrier rod remains closed, and corresponding warning feedback is given.

[0031] Specifically, please refer to Figure 3 As shown, the performing of the pass verification on the captured vehicle information of the target park includes: respectively performing vehicle identity information verification, vehicle owner identity and permission verification, and vehicle safety status verification on the captured vehicle information of the target park. If the vehicle identity information verification, vehicle owner identity and permission verification, and vehicle safety status verification all pass, it is determined that the vehicle pass verification passes. Otherwise, it is determined that the vehicle pass verification fails.

[0032] Specifically, the performing of the vehicle identity information verification includes:

[0033] Based on image processing technology, the vehicle images in the captured target park vehicle information are processed to obtain the license plate number, vehicle appearance features, and vehicle physical features.

[0034] According to the authorized license plate information stored in the cloud database, the vehicle feature information associated with the authorized license plate, and the allowable range of vehicle passing dimensions specified by the target park, it is respectively determined whether the license plate number, vehicle appearance features, and vehicle physical features pass the verification. If it is determined that the license plate number, vehicle appearance features, and vehicle physical features all pass the verification, then the vehicle identity verification is determined to pass; otherwise, the vehicle identity verification is determined to fail.

[0035] It should be noted that the process of determining whether the license plate number, vehicle appearance features, and vehicle physical features pass the verification is as follows:

[0036] The vehicle images in the captured target park are preprocessed, and the license plate images after preprocessing are recognized through a license plate recognition algorithm to obtain the license plate number. If the license plate number exists in the authorized license plate information, it is determined that the license plate recognition passes the verification; otherwise, if the license plate number does not exist in the authorized license plate information, it is determined that the license plate recognition fails the verification.

[0037] It should be noted that preprocessing the vehicle images in the captured target park includes grayscale processing, converting the color image into a grayscale image to reduce the amount of data; using a filtering algorithm to remove image noise and enhance image clarity; using edge detection, contour extraction and other technologies to determine the position of the license plate in the image according to the characteristics of the license plate (such as specific shape, color distribution, etc.), and performing skew correction to improve the accuracy of license plate recognition.

[0038] When the license plate recognition passes the verification, the vehicle appearance feature information is extracted through image recognition technology, including vehicle type classification code, body color, and vehicle brand logo. The extracted vehicle appearance feature information is compared with the vehicle appearance feature information associated with the license plate in the vehicle appearance feature information associated with the authorized license plate stored in the cloud database, and the similarity of vehicle type classification code, body color similarity, and vehicle brand logo similarity are evaluated and analyzed. At the same time, cumulative calculation is performed to obtain the vehicle appearance feature similarity coefficient. The vehicle appearance feature similarity coefficient is compared with the preset vehicle appearance feature similarity coefficient threshold. If the vehicle appearance feature similarity coefficient is greater than or equal to the vehicle appearance feature similarity coefficient, it is determined that the vehicle appearance feature passes the verification; otherwise, if the vehicle appearance feature similarity coefficient is less than the vehicle appearance feature similarity coefficient, it is determined that the vehicle appearance feature fails the verification.

[0039] Specifically, the process of evaluating and analyzing the similarity of vehicle type classification codes, body color similarity, and vehicle brand logo similarity is as follows: Extract the vehicle type classification code. If the vehicle type classification code is exactly the same as the vehicle type classification code associated with the authorized license plate stored in the cloud database in the vehicle appearance feature information, set the vehicle type classification code similarity to 1. Conversely, if the vehicle type classification code is different from the vehicle type classification code associated with the authorized license plate stored in the cloud database in the vehicle appearance feature information, set the vehicle type classification code similarity to 0.

[0040] Extract the RGB values of the body color, denoted as , extract the RGB values of the body color associated with the authorized license plate stored in the cloud database, denoted as , and calculate the color difference value through the Euclidean distance formula , and then obtain the body color similarity according to the body color similarity calculation formula , where represents the preset maximum allowable color difference threshold.

[0041] Extract the vehicle brand logo area, use the feature matching algorithm SIFT in image recognition to extract the feature points of the vehicle brand logo area, and match the extracted vehicle brand logo feature points with the vehicle brand logo feature points associated with the authorized license plate stored in the cloud database based on the BF algorithm. Thus, the number of successfully matched feature points is statistically obtained, denoted as , and calculate the vehicle brand logo similarity by the formula , where represents the total number of vehicle brand logo feature points, represents the total number of brand logo feature points associated with the authorized license plate stored in the cloud database in the vehicle appearance feature information.

[0042] When the vehicle appearance features pass, extract the vehicle physical features through image recognition technology. If the vehicle physical features are within the allowable vehicle passing size range specified by the target park, it is determined that the vehicle physical feature verification passes. Conversely, if the vehicle physical features are outside the allowable vehicle passing size range specified by the target park, it is determined that the vehicle physical feature verification fails.

[0043] It should be noted that the vehicle physical features refer to the physical dimensions such as the length, width, and height of the vehicle.

[0044] Specifically, the verification of the vehicle owner's identity and permissions includes:

[0045] Obtain the facial image of the vehicle owner from the captured target park vehicle image, and identify the facial feature vector of the vehicle owner through the facial recognition algorithm, denoted as , where n is the number of feature dimensions, represents the feature value of each dimension. At the same time, extract the feature vector of the associated vehicle owner's facial image stored in the cloud database , and calculate the similarity between the captured vehicle owner's facial image and the associated vehicle owner's facial image by the formula ;

[0046] Compare the similarity between the captured vehicle owner's facial image and the associated vehicle owner's facial image with the preset similarity threshold. If the similarity between the captured vehicle owner's facial image and the associated vehicle owner's facial image is greater than or equal to the preset similarity threshold, it is determined that the vehicle owner's identity verification is passed; otherwise, if the similarity between the captured vehicle owner's facial image and the associated vehicle owner's facial image is less than the preset similarity threshold, it is determined that the vehicle owner's identity verification fails.

[0047] Extract the capture time point of the current vehicle information, and compare it with the authorized passing time period interval associated with the authorized license plate in the authorized passing time period information stored in the cloud database. If the capture time point of the current vehicle information is within the authorized passing time period interval, it is determined that the vehicle passing permission verification is passed; otherwise, if the current time point is within the authorized passing time period interval, it is determined that the vehicle passing permission verification is passed.

[0048] Specifically, the vehicle safety status verification includes:

[0049] Perform contraband detection on the vehicle through an X-ray scanner. If no contraband is detected, set the contraband detection result flag value to 1; otherwise, set the contraband detection result flag value to 0.

[0050] Extract the vehicle historical passing records within the set historical period stored in the cloud database, and obtain the number of vehicle violation behaviors and the time intervals between adjacent two violation behaviors within the set historical period. Calculate the average value of the time intervals between adjacent two violation behaviors to obtain the value of the average violation behavior interval time. Calculate the vehicle behavior safety coefficient by the formula , where e represents the natural constant, represents the number of vehicle violation behaviors, represents the value of the average violation behavior interval time.

[0051] It should be noted that the violation behaviors include but are not limited to: speeding, illegal parking, and overloading.

[0052] ​​The vehicle safety status coefficient is obtained by multiplying the contraband detection result identification value and the vehicle behavior safety factor. If it is greater than or equal to the preset vehicle safety status coefficient threshold, the vehicle safety status verification is determined to have passed; otherwise, the vehicle safety status verification is determined to have failed.

[0053] In a specific embodiment, the present invention realizes efficient and accurate verification of the identity information of vehicles in the target park by verifying the license plate number, appearance features and physical features of the vehicles in the target park, greatly reducing the risk of misjudgment of identity due to stolen license plates, etc., and ensuring that large vehicles will not cause traffic obstructions or safety hazards in the park due to size issues; through the verification of the owner's identity and the authorized passage time period of the vehicle, it is effectively prevented from others impersonating the owner to enter the park, ensuring the safety of people and property in the park, facilitating the time-division management of vehicles in the park, improving the efficiency of park resource utilization, and ensuring the safety and order of the park within a specific time period; by combining the identification value of the prohibited items detection result and the vehicle behavior safety factor, the in-depth verification of the vehicle safety status is achieved, effectively eliminating potential safety hazards, reducing the possibility of safety accidents, and improving the level of traffic safety in the park.

[0054] In a specific embodiment, the present invention realizes comprehensive and precise verification of vehicle access to the target park by performing multi-dimensional verification of vehicle identity information, owner identity and authority, and vehicle safety status, making up for the deficiency of traditional gate management that relies only on a single verification method. It not only avoids security loopholes caused by insufficient verification, but also directly verifies key information of vehicles and owners through efficient information processing procedures, ensuring that the vehicle access verification process is efficient and accurate.

[0055] Step 3: Monitoring and analysis of barrier equipment: Monitor the barrier equipment in the target park, obtain the operating data of the barrier equipment in the target park, analyze the abnormal operation of the barrier equipment in the target park, and determine whether there is any barrier equipment operation risk in the target park. If so, issue an early warning and provide feedback.

[0056] Specifically, the barrier gate equipment operation data includes barrier gate operation data, motor operation data and vehicle identification system operation data.

[0057] It should be noted that the reasons for choosing the gate bar, motor, and vehicle identification system as the gate equipment monitoring are: 1. The gate bar is the executive component of the gate equipment, and its operating status directly reflects the working condition of the gate equipment. Whether the gate bar rises and falls smoothly and accurately is an important basis for judging whether the gate equipment is operating normally. If the gate bar is stuck, tilted, unable to be fully raised or lowered, etc., it will not only affect the passage of vehicles, but also may pose a safety threat to vehicles and personnel.

[0058] 2. The motor is the power source of the barrier gate equipment, providing power support for the lifting and lowering of the barrier rod. Its operating state directly determines whether the barrier rod can be lifted and lowered normally. If the motor fails, such as short circuit or open circuit of the motor winding, the motor cannot operate, and the barrier rod will stay in the current position, preventing normal vehicle passage and seriously affecting the management of the park entrance and exit.

[0059] 3. The vehicle recognition system is the core component for the barrier gate equipment to achieve automated vehicle management. It identifies the license plate number or other identity identifiers of the vehicle, determines whether the vehicle is authorized to enter the park, and transmits the recognition result to the control system to control the opening and closing of the barrier gate. If the vehicle recognition system fails, such as low recognition accuracy or inability to recognize the license plate, the vehicle cannot enter or exit the park normally, causing traffic congestion and affecting the normal order of the park.

[0060] Exemplarily, the barrier rod operation data includes: lift and fall time deviation, lift and fall smoothness, lift and fall balance.

[0061] It should be noted that the reasons for selecting the lift and fall time deviation, lift and fall smoothness, and lift and fall balance as the barrier rod operation data are as follows: 1. There is a standard setting for the lift and fall time of the barrier gate of the barrier rod. The lift and fall time deviation refers to the difference between the actual lift and fall time and the standard time. In scenarios with large vehicle flows such as morning and evening rush hours in the target park, if the actual lift and fall time of the barrier rod is too long, the vehicle queuing time will increase, seriously reducing the traffic efficiency and causing traffic congestion; if it is too short, it may cause excessive impact on the mechanical components of the equipment, accelerating wear and affecting the service life of the equipment. By monitoring the lift and fall time deviation, problems in the equipment operation efficiency can be detected in a timely manner, and measures can be taken to optimize and ensure rapid vehicle passage. 2. Smooth lift and fall of the barrier rod is a basic condition for vehicles to pass through the barrier gate smoothly. If the barrier rod experiences jamming, shaking or other unsmooth situations during the lift and fall process, it will prolong the vehicle passing time. Especially when multiple vehicles pass continuously, it is easy to cause accidents such as vehicle collisions, bringing a bad passing experience to the vehicle owners. By monitoring the lift and fall smoothness, mechanical problems in the barrier rod operation can be detected and solved in a timely manner, improving the fluency and safety of vehicle passage and enhancing user satisfaction. 3. It is crucial for the barrier rod to maintain good balance during the lift and fall process. If the barrier rod is unbalanced, it may hit vehicles or pedestrians due to the center of gravity deviation during the falling process, causing safety accidents, especially in crowded areas, with more serious consequences. By monitoring the lift and fall balance, the balance problem of the barrier rod can be detected in a timely manner, and adjustment measures such as calibrating the center of gravity and adjusting the counterweight can be taken to eliminate potential safety hazards and ensure the safety of personnel and vehicles.

[0062] Exemplarily, the motor operation data includes: current deviation rate, voltage deviation rate, temperature equilibrium index, rotational speed deviation rate.

[0063] It should be noted that the reasons for selecting the current deviation rate, voltage deviation rate, temperature balance index, and rotational speed deviation rate as the motor operation data are as follows: 1. The current deviation rate is a key indicator for measuring the motor load. During the operation of the barrier gate, under normal circumstances, the motor current should be maintained within a certain range. When vehicles pass frequently or the barrier rod encounters an obstacle, the motor load increases and the current will increase accordingly. By monitoring the current deviation rate, the load change of the motor can be understood in real time. 2. Stable voltage is a basic condition for the normal operation of the motor. The voltage deviation rate reflects the degree of deviation between the actual working voltage of the motor and the standard voltage. If the voltage is too high, the motor core will overheat and the insulating material is easily damaged; if the voltage is too low, the motor torque will decrease, the rotational speed will drop, and it may even fail to start, which will also cause the current to increase and increase the risk of motor overload. By monitoring the voltage deviation rate, abnormal voltage conditions can be detected in a timely manner, and voltage stabilization measures can be taken to ensure that the motor operates at an appropriate voltage and maintain its stable performance. 3. The motor generates heat during operation, and good heat dissipation is the key to ensuring the normal operation of the motor. The temperature balance index comprehensively considers the difference between the actual operating temperature of the motor and the normal operating temperature range. When the motor has poor heat dissipation, such as a malfunctioning cooling fan or a blocked ventilation opening, the temperature will rise rapidly. By monitoring the temperature balance index, the heat dissipation state of the motor can be intuitively understood. 4. The rotational speed of the motor directly determines the lifting speed of the barrier rod, which in turn affects the passing efficiency of the barrier gate. The rotational speed deviation rate represents the deviation between the actual rotational speed of the motor and the standard rotational speed. In scenarios with a large traffic flow, if the motor rotational speed is too low, the barrier rod will lift and fall slowly, resulting in vehicle queuing and reduced passing efficiency; if the rotational speed is too high, the barrier rod may lift and fall too quickly, increasing mechanical impact and affecting the equipment life. By monitoring the rotational speed deviation rate, it can be ensured that the motor operates at an appropriate rotational speed, ensuring that the barrier rod lifts and falls normally according to the design requirements and improving the overall operation efficiency of the barrier gate.

[0064] Exemplarily, the operation data of the vehicle recognition system includes: clarity, contrast, and exposure.

[0065] It should be noted that the reasons for selecting clarity, contrast, and exposure as the operating data of the vehicle recognition system are as follows: 1. Clarity is directly related to whether the vehicle recognition system can clearly present license plate characters and vehicle appearance details. High-clarity images enable the recognition algorithm to more accurately capture the character information on the license plate, reducing recognition errors caused by blurred images. In complex lighting environments or when the vehicle is moving rapidly, if the image clarity is insufficient, the license plate characters may appear blurred at the edges or have strokes sticking together, making it difficult for the recognition system to accurately judge. By monitoring clarity, the camera parameters can be adjusted in a timely manner or the image can be preprocessed to ensure the accuracy of license plate recognition and guarantee the smooth entry and exit of vehicles from the park. 2. Contrast determines the degree of difference between different regions in the image (such as the license plate and the background, the vehicle contour and the surrounding environment). Appropriate contrast can make the license plate more prominent in the image, facilitating the recognition system to separate the license plate from the complex background. If the contrast is low, the image will appear dull and gray, with the boundary between the license plate and the background blurred, increasing the recognition difficulty. High-contrast images can clearly display the color, texture, and other characteristics of the license plate, helping the recognition system accurately analyze the license plate information, thereby effectively improving the success rate of vehicle recognition and maintaining the normal traffic order at the park entrance and exit. 3. Exposure reflects the overall brightness of the image. Appropriate exposure can ensure that the vehicle recognition system can obtain high-quality images under various lighting conditions. In strong sunlight, if the exposure is too high, the image will be over-bright, resulting in license plate reflection and loss of some character information; in the night or a dimly lit environment, insufficient exposure will make the image too dark and the license plate difficult to see. By monitoring the exposure and making real-time adjustments, the recognition system can automatically adapt to different lighting scenarios, ensuring that clear and distinguishable vehicle images can be captured at all times, providing a reliable data basis for accurate license plate recognition, and enhancing the stability and adaptability of the vehicle recognition system.

[0066] Specifically, the steps of analyzing the abnormal operation situation of the gate device in the target park and judging whether there is a risk of abnormal operation of the gate device in the target park include: analyzing the abnormal operation index of the gate rod, analyzing the abnormal operation index of the motor, analyzing the abnormal operation index of the vehicle recognition system, and adding up the abnormal operation index of the gate rod, the abnormal operation index of the motor, and the abnormal operation index of the vehicle recognition system to obtain the abnormal operation coefficient of the gate device;

[0067] Compare the abnormal operation coefficient of the gate device with the preset threshold of the abnormal operation coefficient of the gate device. If the abnormal operation coefficient of the gate device is greater than the preset threshold of the abnormal operation coefficient of the gate device, it is judged that there is a risk of abnormal operation of the gate device; if the abnormal operation coefficient of the gate device is less than the preset threshold of the abnormal operation coefficient of the gate device, it is judged that there is no risk of abnormal operation of the gate device.

[0068] Specifically, the steps of analyzing the abnormal operation index of the gate rod are as follows:

[0069] Extract the deviation degree of the lifting time, the smoothness of lifting and lowering, and the balance of lifting and lowering corresponding to each lifting and lowering operation of the gate rod within the set time interval, and construct a three-dimensional evaluation space for the operation of the gate rod. The three dimensions respectively correspond to the deviation degree of the lifting time, the smoothness of lifting and lowering, and the balance of lifting and lowering. Map the deviation degree of the lifting time, the smoothness of lifting and lowering, and the balance of lifting and lowering corresponding to each lifting and lowering operation of the gate rod within the set time interval to the corresponding dimensions of the evaluation space for the operation of the gate rod according to a preset ratio. According to the preset allowable deviation range of the lifting time, the allowable smoothness range of lifting and lowering, and the allowable balance range of lifting and lowering, delimit a normal operation area in the three-dimensional evaluation space for the operation of the gate rod, and thereby count the number of times that the corresponding points of all lifting and lowering operations of the gate rod within the set time interval fall outside the normal operation area. Calculate the ratio of this number to the total number of lifting and lowering operations of the gate rod within the set time interval to obtain the abnormal operation index of the gate rod within the set time interval.

[0070] It should be noted that the analysis steps for the deviation degree of the lifting time corresponding to each lifting and lowering operation of the gate rod within the set time interval: Set an electronic timing instrument on the turnstile to obtain the start time point of the gate rod rising and the end time point of the gate rod falling corresponding to each lifting and lowering operation of the gate rod within the set time interval in real time, perform a subtraction calculation to obtain the lifting and lowering duration corresponding to each lifting and lowering operation of the gate rod within the set time interval, extract the standard lifting and lowering duration corresponding to the normal lifting and lowering operation of the gate rod stored in the cloud database, calculate the average value of the lifting and lowering durations corresponding to each lifting and lowering operation of the gate rod within the set time interval to obtain the average lifting and lowering duration corresponding to the lifting and lowering operation of the gate rod within the set time interval, and calculate through to obtain the deviation degree of the lifting time corresponding to each lifting and lowering operation of the gate rod within the set time interval.

[0071] It should be noted that the analysis steps for the smoothness of lifting and lowering corresponding to each lifting and lowering operation of the gate rod within the set time interval: Set a speed sensor on the turnstile to obtain the speed of each lifting and lowering operation of the gate rod at each monitoring time point within the set time interval. Use the time monitoring point as the abscissa and the speed as the ordinate to plot the speed curve corresponding to each lifting and lowering operation of the gate rod within the set time interval. At the same time, draw an upper reference line and a lower reference line for the speed of the lifting and lowering operation of the gate rod in the curve. Mark the monitoring time points between the upper reference line and the lower reference line as normal time points, and vice versa as abnormal time points. Thus, count the number of abnormal time points and the duration of each abnormal time point, and accumulate the durations of each abnormal time point to calculate the total abnormal duration. Calculate through to obtain the smoothness of lifting and lowering of each lifting and lowering operation of the gate rod within the set time interval;

[0072] It should be noted that the analysis steps for the balance of lifting and lowering corresponding to each lifting and lowering operation of the gate rod within the set time interval: Set an angle sensor on the turnstile to obtain the angles of the left and right ends and the center position of the gate rod at each monitoring time point of each lifting and lowering operation of the gate rod within the set time interval. Through the angle difference index Calculate the angle difference index of each gate rod lifting operation at each monitoring time point within the set time interval;

[0073] Set a gravity sensor on the turnstile to obtain in real time the component forces of the gravity at the left and right ends of the gate rod along the rotation direction at each monitoring time point for each gate rod lifting operation within the set time interval. Through the gravity component force difference index Calculate the gravity component force difference index of each gate rod lifting operation at each monitoring time point within the set time interval;

[0074] Sum up the angle difference index and the gravity component force difference index of each gate rod lifting operation at each monitoring time point within the set time interval to obtain the total angle difference index and the total gravity component force difference index of each gate rod lifting operation within the set time interval. And extract the reference angle difference index and the gravity component force difference index corresponding to the normal lifting operation of the gate rod from the cloud database. Through the lifting balance degree Calculate the lifting balance degree of each gate rod lifting operation within the set time interval.

[0075] Specifically, the steps for analyzing the abnormal operation index of the motor are as follows:

[0076] Extract the current deviation rate, voltage deviation rate, speed deviation rate and temperature equilibrium index of the motor within the set time interval. Convert them into lengths respectively according to a preset ratio. Construct an ellipse with the lengths of the current deviation rate and the voltage deviation rate as the major axis and the minor axis. Select the center of the ellipse as the starting point and construct a conical solid with the length of the speed deviation rate as the height. Construct a hollow sphere with the length of the temperature equilibrium index as the radius inside the cone. Extract the numerical value of the solid volume of the cone and mark it as the abnormal operation index of the motor.

[0077] It should be noted that the analysis steps for the current deviation rate, voltage deviation rate and speed deviation rate: Extract the working current, working voltage and actual speed of the motor at each monitoring time point within the set time interval, and calculate the differences between them and the standard current, standard voltage and standard speed stored in the cloud database under the normal operation state of the motor respectively to obtain the working current difference, working voltage difference and actual speed difference of the motor at each monitoring time point within the set time interval. Calculate the ratios of the working current difference, working voltage difference and actual speed difference of the motor at each monitoring time point within the set time interval to the standard current, standard voltage and standard speed respectively to obtain the current deviation rate, voltage deviation rate and speed deviation rate at each monitoring time point within the set time interval, and sum them up respectively to obtain the total current deviation rate, total voltage deviation rate and total speed deviation rate within the set time interval, which are used as the current deviation rate, voltage deviation rate and speed deviation rate of the motor within the set time interval.

[0078] It should be noted that the analysis steps of the motor temperature equilibrium index are as follows: Extract the motor temperatures at each monitoring time point within a set time interval, calculate their mean value to obtain the average motor temperature within the set time interval, extract the maximum value and the minimum value respectively from the motor temperatures at each monitoring time point within the set time interval, and calculate the motor temperature equilibrium index within the set time interval through the motor temperature equilibrium index The motor temperature equilibrium index within the set time interval is calculated.

[0079] Specifically, the steps for analyzing the abnormal operation index of the vehicle recognition system are as follows:

[0080] Obtain the clarity, contrast, and exposure of the images collected by each vehicle recognition system within a set time interval, denoted as respectively. At the same time, extract the reasonable clarity threshold, reasonable contrast range, and reasonable exposure range in the standard high-quality state of the images collected by the license plate recognition system from the cloud database. Calculate the mean value of the upper limit and the lower limit of the reasonable contrast range to obtain the reasonable reference contrast, and use the absolute difference between the reasonable reference contrast and the upper limit of the reasonable contrast range as the reasonable deviation contrast threshold. Similarly, obtain the reasonable reference exposure and the reasonable deviation exposure threshold according to the reasonable exposure range;

[0081] Calculate the difference between the clarity of the images collected by each vehicle recognition system within the set time interval and the reasonable clarity threshold, and calculate the ratio of the difference result to the reasonable clarity threshold. Then sum up the ratio results of all the images collected by the vehicle recognition systems to obtain the total value one. Similarly, obtain the total value two and the total value three according to the contrast and the exposure;

[0082] Convert the total value two and the total value three into lengths according to a certain ratio. Construct an ellipse with the length of the total value two and the length of the total value three as the major axis and the minor axis respectively. Convert the total value one into a length according to a certain ratio. Construct a circle with the length of the total value one as the radius, and make the centers of the circle and the ellipse coincide. Then identify the value of the area where the ellipse and the center do not coincide and mark it as the abnormal operation index of the vehicle recognition system within the set time interval.

[0083] It should be noted that the process of obtaining the clarity, contrast, and exposure of the images collected by each vehicle recognition system within a set time interval is as follows: Use the Canny operator to calculate the gradient amplitude of each pixel in the images collected by each vehicle recognition system within the set time interval, and calculate the mean value of all the gradient amplitudes of the pixels to obtain the average gradient amplitude of the images collected by each license plate recognition camera, which is used as the clarity of the images collected by each vehicle recognition system.

[0084] Obtain the gray values of each pixel in the images collected by each vehicle recognition system within a set time interval, extract the maximum gray value and the minimum gray value from them, calculate the ratio of the difference between the maximum gray value and the minimum gray value to the sum of the maximum gray value and the minimum gray value, and at the same time use the result of the ratio calculation as the contrast of the images collected by each vehicle recognition system.

[0085] Calculate the average value of all pixel gray values of the images collected by each vehicle recognition system, and at the same time use the result of the average value calculation as the exposure of the images collected by each vehicle recognition system.

[0086] Step Four: Risk barrier device identification. When it is determined that there is a risk of barrier device operation in the target park, identify the specific operation risk devices and give corresponding early warning feedback.

[0087] Specifically, please refer to Figure 4 as shown, the specific operation risk devices are identified as follows:

[0088] Extract the abnormal operation index of the barrier rod, the abnormal operation index of the motor, and the abnormal operation index of the vehicle recognition system obtained from the analysis, and then compare them with the pre-set threshold values of the abnormal operation index of the barrier rod, the abnormal operation index of the motor, and the abnormal operation index of the vehicle recognition system in the target park respectively;

[0089] If the abnormal operation index of the barrier rod in the target park is greater than the threshold value of the abnormal operation index of the barrier rod, it is determined that the operation risk device is the barrier rod;

[0090] If the abnormal operation index of the motor in the target park is greater than the threshold value of the abnormal operation index of the motor, then it is determined that the operation risk device is the motor;

[0091] If the abnormal operation index of the vehicle recognition system in the target park is greater than the threshold value of the abnormal operation index of the vehicle recognition system, then it is determined that the operation risk device is the vehicle recognition system.

[0092] It should be noted that the specific operation abnormal devices can be one or more of the barrier rod, the motor, and the vehicle recognition system.

[0093] In a specific embodiment, when the present invention analyzes the abnormal operation of the barrier devices in the target park, by analyzing the abnormal operation conditions of the barrier rod, the motor, and the vehicle recognition system, it is further determined whether there is a risk of barrier device operation in the target park. This analysis method can accurately identify potential barrier operation risks, timely discover and warn of potential failure and accident risks, and to a certain extent improve the response timely rate of abnormal barrier operation and reduce the impact on the normal operation of the barrier in the target park.

[0094] Please refer to Figure 2 as shown, the second aspect of the present invention provides an intelligent management device for barrier based on the Internet of Things, including:

[0095] A vehicle information capturing module captures the vehicle information of a target park through the vehicle information capturing device of the barrier gate in the target park.

[0096] A vehicle information access verification module conducts access verification on the captured vehicle information of the target park. If the verification passes, it is determined that the vehicle is allowed to enter the target park, and the barrier gate rod automatically lifts. Otherwise, it is determined that the vehicle is prohibited from entering the target park, the barrier gate rod remains closed, and corresponding warnings are given.

[0097] A barrier gate device monitoring and analysis module monitors the barrier gate device in the target park, obtains the operation data of the barrier gate device in the target park, analyzes the abnormal operation conditions of the barrier gate device in the target park, and determines whether there are risks in the operation of the barrier gate device in the target park. If so, warnings are given and feedback is provided.

[0098] A risk barrier gate device identification module identifies the specific malfunctioning devices when it is determined that there are abnormal operations of the barrier gate devices in the target park, and gives corresponding warning feedback.

[0099] A cloud database stores authorized license plate information, vehicle feature information associated with the authorized license plate, and the allowable range of vehicle passing dimensions specified in the target park, stores the feature vectors of the facial images of the associated vehicle owners, stores the authorized passing time period information associated with the authorized license plate, stores the vehicle historical passing records within a set historical period, stores the standard lifting and lowering duration corresponding to the normal lifting and lowering operations of the barrier gate rod, stores the reference angle difference index and gravity component force difference index corresponding to the normal lifting and lowering operations of the barrier gate rod, stores the standard current, standard voltage, and standard rotational speed under the normal operating state of the motor, and stores the reasonable clarity threshold, reasonable contrast interval, and reasonable exposure interval in the standard high-quality state of the images collected by the license plate recognition system.

[0100] The above content is only an example and explanation of the structure of the present invention. Those skilled in the art of this technology can make various modifications or supplements to the described specific embodiments or use similar methods for substitution. As long as they do not deviate from the structure of the invention or exceed the scope defined by this claim book, they should fall within the protection scope of the present invention.

Claims

1. The intelligent management method of the gate based on the Internet of Things is characterized by: include: Step 1: Capture vehicle information: Capture the vehicle information of the target park through the vehicle identification system of the target park gate; Step 2: Vehicle information access verification: The captured target park vehicle information is verified for accessibility. If the verification is passed, the vehicle is determined to be allowed to enter the target park, and the gate bar is automatically lifted. Otherwise, the vehicle is determined to be prohibited from entering the target park, the gate bar remains closed, and corresponding early warning feedback is given; The passability verification includes: verifying the vehicle identity information, the owner identity and authority, and the vehicle safety status of the captured vehicle information of the target park; if the vehicle identity information verification, the owner identity and authority verification, and the vehicle safety status verification are all passed, the vehicle passability verification is determined to have passed, otherwise it is determined that the vehicle passability verification has failed; Step 3: Monitoring and analysis of barrier equipment: Monitor the barrier equipment in the target park, obtain the operating data of the barrier equipment in the target park, analyze the abnormal operation of the barrier equipment in the target park, and determine whether there is any barrier equipment operation risk in the target park. If so, issue an early warning and provide feedback; The analysis of the abnormal operation of the barrier equipment in the target park and the determination of whether there is a barrier equipment operation risk in the target park include: analyzing the barrier operation abnormality index, analyzing the motor operation abnormality index, analyzing the vehicle identification system operation abnormality index, and accumulating the barrier operation abnormality index, the motor operation abnormality index, and the vehicle identification system operation abnormality index to obtain the barrier equipment operation abnormality coefficient; The barrier device operation abnormality coefficient is compared with the preset barrier device operation abnormality coefficient threshold. If the barrier device operation abnormality coefficient is greater than the preset barrier device operation abnormality coefficient threshold, it is judged that there is a barrier device operation risk. If the barrier device operation abnormality coefficient is less than the preset barrier device operation abnormality coefficient threshold, it is judged that there is no barrier device operation risk. The steps of analyzing the motor operation abnormality index are as follows: The current deviation rate, voltage deviation rate, speed deviation rate and temperature balance index of the motor within the set time interval are extracted, and they are converted into lengths according to preset ratios respectively. An ellipse is constructed with the lengths of the current deviation rate and the voltage deviation rate as the major and minor axes. The center of the ellipse is selected as the starting point, and the length of the speed deviation rate is used as the height to construct a cone entity. A hollow sphere is constructed inside the cone with the length of the temperature balance index as the radius. The entity volume value of the cone is extracted and marked as the motor operation abnormality index. Step 4: Identification of risky barrier equipment: When it is determined that there is abnormal operation of barrier equipment in the target park, identify the specific risky equipment and provide corresponding early warning feedback.

2. The intelligent gate management method based on the Internet of Things according to claim 1 is characterized in that: The vehicle identity information verification includes: Based on image processing technology, the vehicle images in the captured target park vehicle information are processed to obtain the license plate number, vehicle appearance features and vehicle physical features; According to the authorized license plate information stored in the cloud database, the vehicle feature information associated with the authorized license plate and the allowable range of vehicle pass dimensions stipulated in the target park, it is determined whether the license plate number, vehicle appearance features and vehicle physical features have been verified. If it is determined that the license plate number, vehicle appearance features and vehicle physical features have all been verified, then the vehicle identity authentication is determined to have passed, otherwise it is determined that the vehicle identity authentication has failed.

3. The intelligent management method of gate based on Internet of Things according to claim 2 is characterized in that: The process of determining whether the license plate number, vehicle appearance features and vehicle physical features have been verified is as follows: Preprocess the captured target park vehicle image, and use the license plate recognition algorithm to recognize the preprocessed license plate image to obtain the license plate number. If the license plate number exists in the authorized license plate information, the license plate recognition verification is judged to be passed. Otherwise, if the license plate number does not exist in the authorized license plate information, the license plate recognition verification is judged to be failed. When the license plate recognition verification is passed, the vehicle appearance feature information is extracted through image recognition technology, including the vehicle model classification code, body color and vehicle brand logo, and the extracted vehicle appearance feature information is compared with the vehicle appearance feature information associated with the license plate in the vehicle appearance feature information associated with the authorized license plate stored in the cloud database, and the vehicle model classification code similarity, body color similarity, and vehicle brand logo similarity are evaluated and analyzed, and cumulative calculations are performed at the same time to obtain the vehicle appearance feature similarity coefficient, and the vehicle appearance feature similarity coefficient is compared with the preset vehicle appearance feature similarity coefficient threshold. If the vehicle appearance feature similarity coefficient is greater than or equal to the vehicle appearance feature similarity coefficient, it is judged that the vehicle appearance feature verification is passed, otherwise if the vehicle appearance feature similarity coefficient is less than the vehicle appearance feature similarity coefficient, it is judged that the vehicle appearance feature verification is not passed; When the vehicle's appearance features pass, the vehicle's physical features are extracted through image recognition technology. If the vehicle's physical features are within the allowable range of vehicle pass dimensions specified by the target park, the vehicle's physical features are judged to have passed the verification. Conversely, if the vehicle's physical features are outside the allowable range of vehicle pass dimensions specified by the target park, the vehicle's physical features are judged to have failed the verification.

4. The intelligent management method of gate based on Internet of Things according to claim 3 is characterized in that: The process of evaluating and analyzing the similarity of vehicle type classification codes, vehicle body color similarity, and vehicle brand logo similarity is as follows: Extract the vehicle model classification code. If the vehicle model classification code is exactly the same as the vehicle model classification code associated with the license plate in the vehicle appearance feature information associated with the authorized license plate stored in the cloud database, then set the vehicle model classification code similarity to 1. Otherwise, if the vehicle model classification code is different from the vehicle model classification code associated with the license plate in the vehicle appearance feature information associated with the authorized license plate stored in the cloud database, then set the vehicle model classification code similarity to 0. Extract the RGB value of the car body color, recorded as , extract the RGB value of the vehicle body color associated with the license plate from the vehicle appearance feature information associated with the authorized license plate stored in the cloud database, and record it as , the color difference value is calculated by the Euclidean distance formula, and then the body color similarity is obtained according to the body color similarity calculation formula; The vehicle brand logo area is extracted, and the feature points of the vehicle brand logo area are extracted using the feature matching algorithm SIFT in image recognition. Based on the BF algorithm, the extracted vehicle brand logo feature points are matched with the vehicle brand logo feature points associated with the license plate in the vehicle appearance feature information associated with the authorized license plate stored in the cloud database. The number of successfully matched feature points is obtained by counting, and the vehicle brand logo similarity is calculated.

5. The intelligent management method of gate based on Internet of Things according to claim 1 is characterized in that: The verification of the identity and authority of the vehicle owner includes: The facial image of the owner is obtained from the captured target park vehicle image, and the facial feature vector of the owner is identified through the facial recognition algorithm, which is recorded as , where n is the number of feature dimensions, Represents the eigenvalue of each dimension and extracts the eigenvector of the associated owner’s facial image stored in the cloud database , according to the formula Calculate the similarity between the captured owner's facial image and the associated owner's facial image ; Comparing the similarity between the captured facial image of the vehicle owner and the associated facial image of the vehicle owner with a preset similarity threshold, if the similarity between the captured facial image of the vehicle owner and the associated facial image of the vehicle owner is greater than or equal to the preset similarity threshold, it is determined that the identity verification of the vehicle owner has been passed; conversely, if the similarity between the captured facial image of the vehicle owner and the associated facial image of the vehicle owner is less than the preset similarity threshold, it is determined that the identity verification of the vehicle owner has not been passed; The current vehicle information capture time point is extracted and compared with the authorized time period interval associated with the license plate in the authorized time period information associated with the authorized license plate stored in the cloud database. If the current vehicle information capture time point is within the authorized time period interval, it is determined that the vehicle access permission verification has passed; otherwise, the current time point is within the authorized time period interval, it is determined that the vehicle access permission verification has passed.

6. The intelligent management method of gate based on Internet of Things according to claim 1 is characterized in that: The vehicle safety status is verified, including: The vehicle is inspected for prohibited items by an X-ray scanner. If no prohibited items are detected, the prohibited items detection result identification value is set to 1, otherwise the prohibited items detection result identification value is set to 0; Extract the historical vehicle traffic records within the set historical period stored in the cloud database, and obtain the number of vehicle violations within the set historical period and the interval between two adjacent violations. Calculate the average interval between two adjacent violations to obtain the average interval between violations. Calculate the vehicle behavior safety factor , where e is a natural constant, n is the number of vehicle violations, It is expressed as the average time between violations; The vehicle safety status coefficient is obtained by multiplying the prohibited items detection result identification value and the vehicle behavior safety factor. If it is greater than or equal to the preset vehicle safety status coefficient threshold, the vehicle safety status verification is determined to have passed; otherwise, the vehicle safety status verification is determined to have failed.

7. The intelligent gate management method based on the Internet of Things according to claim 1 is characterized in that: The steps of analyzing the gate lever operation abnormality index are as follows: Extract the lifting and lowering time deviation, lifting and lowering smoothness, and lifting and lowering balance corresponding to each gate lever lifting and lowering operation within the set time interval, and construct a three-dimensional gate lever operation evaluation space. The three dimensions correspond to the lifting and lowering time deviation, lifting and lowering smoothness, and lifting and lowering balance, respectively. The lifting and lowering time deviation, lifting and lowering smoothness, and lifting and lowering balance corresponding to each gate lever lifting and lowering operation within the set time interval are mapped to the corresponding dimensions of the gate lever operation evaluation space according to a preset ratio. According to the preset allowable lifting and lowering time deviation interval, allowable lifting and lowering smoothness interval, and allowable lifting and lowering balance interval, a normal operating area is delineated in the three-dimensional gate lever operation evaluation space, and the number of times the corresponding points of all gate lever lifting and lowering operations within the set time interval fall outside the normal operating area is counted, and the ratio is calculated with the total number of gate lever lifting and lowering operations within the set time interval to obtain the gate lever operation abnormality index within the set time interval; The steps of analyzing the abnormal operation index of the vehicle identification system are as follows: Obtain the clarity, contrast, and exposure of the images collected by each vehicle recognition system within a set time interval, and record them as respectively. At the same time, extract the reasonable clarity threshold, reasonable contrast interval, and reasonable exposure interval of the images collected by the license plate recognition system in the standard high-quality state from the cloud database, calculate the upper and lower limits of the reasonable contrast interval by averaging, and obtain a reasonable reference contrast. The absolute value difference between the reasonable reference contrast and the upper limit of the reasonable contrast interval is used as the reasonable deviation contrast threshold. Similarly, obtain a reasonable reference exposure and a reasonable deviation exposure threshold according to the reasonable exposure interval. The clarity of the images collected by each vehicle identification system within the set time interval is calculated by difference with the reasonable clarity threshold, and the difference result is calculated by ratio with the reasonable clarity threshold, thereby summing up the ratio results of the images collected by all vehicle identification systems to obtain a total value of one, and similarly, obtaining total values ​​of two and three according to contrast and exposure; The total value two and the total value three are converted into lengths according to a certain ratio, and an ellipse is constructed with the lengths of the total value two and the total value three as the major axis and the minor axis respectively; the total value one is converted into lengths according to a certain ratio, and a circle is constructed with the length of the total value one as the radius, and the center of the circle is overlapped with the center of the ellipse, and then the value of the area where the ellipse and the center of the circle do not overlap is identified and marked as the abnormality index of the vehicle identification system within the set time interval.

8. The intelligent gate management method based on the Internet of Things according to claim 1 is characterized in that: The specific equipment for identifying operational risks is as follows: Extract and analyze the target park gate lever operation abnormality index, motor operation abnormality index, and vehicle identification system operation abnormality index, and then compare them with the preset target park gate lever operation abnormality index threshold, motor operation abnormality index threshold, and vehicle identification system operation abnormality index threshold respectively; If the target park gate lever operation abnormality index is greater than the gate lever operation abnormality index threshold, it is determined that the operation risk device is the gate lever; If the motor operation abnormality index of the target park is greater than the motor operation abnormality index threshold, the operation risk device is judged to be a motor; If the target park vehicle identification system operation abnormality index is greater than the vehicle identification system operation abnormality index threshold, the operation risk device is judged to be the vehicle identification system.

9. An intelligent gate management device based on the Internet of Things, used to execute the intelligent gate management method based on the Internet of Things as claimed in any one of claims 1 to 8, characterized in that: include: A vehicle information capture module is used to capture vehicle information of the target park through a vehicle information capture device at the target park gate; The vehicle information access verification module is used to verify the accessibility of the captured target park vehicle information. If the verification is passed, the vehicle is determined to be allowed to enter the target park, and the gate bar is automatically raised. Otherwise, the vehicle is determined to be prohibited from entering the target park, the gate bar remains closed, and a corresponding warning is issued; The barrier equipment monitoring and analysis module is used to monitor the barrier equipment in the target park, obtain the operating data of the barrier equipment in the target park, analyze the abnormal operation of the barrier equipment in the target park, and determine whether there is a barrier equipment operation risk in the target park. If so, it will issue an early warning and feedback; The risk barrier equipment identification module is used to identify specific risky equipment when it is determined that there are barrier equipment operating abnormally in the target park, and provide corresponding early warning feedback; The cloud database is used to store authorized license plate information, vehicle feature information associated with the authorized license plate, and the allowable range of vehicle traffic dimensions specified in the target park, store feature vectors of associated vehicle owner facial images, store authorized traffic time period information associated with authorized license plates, store historical vehicle traffic records within a set historical period, store standard raising and lowering durations corresponding to normal raising and lowering operations of gate levers, store reference angle difference indicators and gravity component force difference indicators corresponding to normal raising and lowering operations of gate levers, store standard current, standard voltage, and standard speed under normal operating conditions of the motor, and store reasonable clarity thresholds, reasonable contrast ranges, and reasonable exposure ranges of images collected by the license plate recognition system under standard high-quality conditions.

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