Barrier gate intelligent management method and device based on Internet of Things

Abstract

The invention relates to the technical field of barrier gate management, in particular to an intelligent barrier gate management method and device based on the Internet of Things. Comprising vehicle information passing verification, barrier gate equipment monitoring analysis and the like, vehicle identity information, vehicle owner identity and authority and vehicle safety conditions are subjected to multi-dimensional verification, comprehensive and precise vehicle passing verification of a target park is achieved, and it is ensured that the vehicle passing verification process is efficient and accurate; when the abnormal operation analysis of the barrier gate equipment in the target park is carried out, the abnormal operation condition analysis is carried out on the gate rod, the motor and the vehicle identification system, so that whether the barrier gate equipment operation risk exists in the target park is judged, and the potential barrier gate operation risk can be accurately identified through the analysis mode; potential faults and accident risks can be found and early warned in time, the response timeliness rate of abnormal operation of the barrier gate is improved to a certain extent, and the influence on normal operation of the barrier gate in the target park is reduced.

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: 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.

[0003] 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

[0004] 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.

[0005] The purpose of the present invention can be achieved through the following technical solutions: In the first aspect, the present invention provides an intelligent management method for a gate based on the Internet of Things, comprising: Step 1: Capture vehicle information: Capture the vehicle information of the target park through the vehicle identification system of the target park gate.

[0006] Step 2: Vehicle information access verification: The captured target park vehicle information is verified for accessibility. If the verification passes, the vehicle is allowed to enter the target park and the gate bar is automatically lifted. Otherwise, the vehicle is prohibited from entering the target park, the gate bar remains closed, and a corresponding warning is issued.

[0007] 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.

[0008] Step 4: Identify risky barrier equipment. When it is determined that there is a risk of barrier equipment operation in the target park, identify the specific risky equipment and provide corresponding early warning feedback.

[0009] In a second aspect, the present invention provides an intelligent gate management device based on the Internet of Things, comprising: The vehicle information capture module is used to capture the vehicle information of the target park through the vehicle identification system of the target park gate.

[0010] 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 judged to be allowed to enter the target park, and the gate bar is automatically lifted. Otherwise, the vehicle is judged to be prohibited from entering the target park, the gate bar remains closed, and a corresponding warning is issued.

[0011] 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 any barrier equipment operation risk in the target park. If so, it will issue an early warning and feedback.

[0012] The risk barrier equipment identification module is used to identify specific operating risk equipment when it is determined that there is abnormal operation of barrier equipment in the target park, and to provide corresponding early warning feedback.

[0013] 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.

[0014] Beneficial effects of the present invention: 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.

[0015] 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.

[0016] 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

[0017] The present invention will be further described below in conjunction with the accompanying drawings.

[0018] Figure 1 It is a schematic flow chart of the method of the present invention.

[0019] Figure 2 It is a connection diagram of the device modules of the present invention.

[0020] Figure 3 It is a logical schematic diagram of the vehicle information passability verification of the target park of the present invention.

[0021] Figure 4 It is a logical schematic diagram of the target park risk barrier equipment identification of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0023] See also Figure 1 As shown, the first aspect of the present invention provides an intelligent management method for a gate based on the Internet of Things, comprising: Step 1: Capture vehicle information: Capture the vehicle information of the target park through the vehicle identification system of the target park gate.

[0024] Exemplarily, the vehicle identification system is an intelligent high-definition camera, which is responsible for collecting vehicle images, including visual information such as license plate, vehicle model, body color, and vehicle brand logo.

[0025] Step 2: Vehicle information access verification: The captured target park vehicle information is verified for accessibility. If the verification passes, the vehicle is allowed to enter the target park and the gate bar is automatically lifted. Otherwise, the vehicle is prohibited from entering the target park, the gate bar remains closed, and corresponding early warning feedback is given.

[0026] Specifically, see Figure 3 As shown, the passability verification of the captured target park vehicle information includes: performing vehicle identity information verification, owner identity and authority verification, and vehicle safety status verification on the captured target park vehicle information respectively. If the vehicle identity information verification, owner identity and authority verification, and vehicle safety status verification are all passed, then the vehicle passability verification is determined to be passed, otherwise it is determined that the vehicle passability verification has failed.

[0027] Specifically, the vehicle identity information verification includes: The vehicle images in the captured target park vehicle information are processed based on image processing technology to obtain the license plate number, vehicle appearance features and vehicle physical features.

[0028] 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.

[0029] It should be noted that the above process of determining whether the license plate number, vehicle appearance features and vehicle physical features have been verified is as follows: The captured target park vehicle image is preprocessed, and the preprocessed license plate image is recognized by the license plate recognition algorithm 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 successful. Otherwise, if the license plate number does not exist in the authorized license plate information, the license plate recognition verification is judged to be unsuccessful.

[0030] It should be noted that the captured images of target park vehicles are preprocessed, including grayscale processing to convert color images into grayscale images to reduce the amount of data; filtering algorithms are used to remove image noise and enhance image clarity; edge detection, contour extraction and other technologies are used to determine the position of the license plate in the image based on the characteristics of the license plate (such as specific shape, color distribution, etc.), and tilt correction is performed to improve the accuracy of license plate recognition.

[0031] When the license plate recognition verification passes, 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. At the same time, cumulative calculations are performed to obtain a vehicle appearance feature similarity coefficient, and the vehicle appearance feature similarity coefficient is compared with a 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, then the vehicle appearance feature verification is judged to have passed. Conversely, if the vehicle appearance feature similarity coefficient is less than the vehicle appearance feature similarity coefficient, then the vehicle appearance feature verification is judged to have failed.

[0032] Specifically, the process of evaluating and analyzing the similarity of vehicle model 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; conversely, 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.

[0033] 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 , through the Euclidean distance formula Calculate the color difference value , and then according to the body color similarity calculation formula Get the body color similarity, where Expressed as the preset maximum allowed color difference threshold.

[0034] The vehicle brand identification area is extracted, and the feature points of the vehicle brand identification area are extracted using the feature matching algorithm SIFT in image recognition. Based on the BF algorithm, the extracted vehicle brand identification feature points are matched with the vehicle brand identification 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 recorded as , according to the formula Calculate the vehicle brand logo similarity, where It is expressed as the total number of vehicle brand identification feature points, It is represented by the total number of brand identification feature points associated with the license plate in the vehicle appearance feature information associated with the authorized license plate stored in the cloud database.

[0035] 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.

[0036] It should be noted that the physical characteristics of a vehicle refer to the physical dimensions of the vehicle such as length, width, and height.

[0037] Specifically, the vehicle owner's identity and authority verification 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 ; The similarity between the captured facial image of the vehicle owner and the associated facial image of the vehicle owner is compared 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 authentication of the vehicle owner has passed. Otherwise, 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 authentication of the vehicle owner has failed.

[0038] 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.

[0039] Specifically, the vehicle safety status verification includes: The vehicle is inspected for prohibited items through 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.

[0040] 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 value of the average interval between violations. Calculate the vehicle behavior safety factor , where e is a natural constant, It is represented by the number of vehicle violations, A value expressed as the average time between violations.

[0041] It should be noted that violations include but are not limited to: speeding, illegal parking, and overloading.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] Specifically, the barrier device operation data includes barrier rod operation data, motor operation data and vehicle identification system operation data.

[0047] 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.

[0048] 2. The motor is the power source of the gate equipment, providing power support for the rise and fall of the gate bar. Its operating status directly determines whether the gate bar can be raised and lowered normally. If the motor fails, such as the motor winding short circuit or open circuit, the motor will not be able to operate, and the gate bar will remain in the current position, making it impossible for vehicles to pass normally, seriously affecting the entrance and exit management of the park.

[0049] 3. The vehicle identification system is the core component of the barrier equipment to realize automatic vehicle management. It determines whether the vehicle has the right to enter the park by identifying the vehicle license plate number or other identity identification, and transmits the identification result to the control system to control the opening and closing of the barrier. If the vehicle identification system fails, such as low recognition accuracy, inability to recognize license plates, etc., it will cause vehicles to be unable to enter and exit the park normally, causing traffic congestion and affecting the normal order of the park.

[0050] Exemplarily, the gate lever operation data includes: rise and fall time deviation, rise and fall smoothness, and rise and fall balance.

[0051] It should be noted that the reasons for selecting the rise and fall time deviation, rise and fall smoothness, and rise and fall balance as the gate operation data are: 1. The rise and fall time of the gate bar has a standard setting, and the rise and fall time deviation refers to the difference between the actual rise and fall time and the standard time. In scenes with heavy traffic such as morning and evening rush hours in the target park, if the actual rise and fall time of the gate bar is too long, the waiting time of vehicles will increase, which will seriously reduce the traffic efficiency and cause traffic congestion; if it is too short, the mechanical parts of the equipment may be subjected to excessive impact, accelerate wear, and affect the service life of the equipment. By monitoring the rise and fall time deviation, problems in equipment operation efficiency can be discovered in time, and measures can be taken to optimize and ensure the rapid passage of vehicles. 2. Smooth rise and fall of the gate bar is the basic condition for vehicles to pass through the gate smoothly. If the gate bar is stuck, shaken, or other unsmooth conditions during the rise and fall process, it will prolong the time for the vehicle to pass, especially when multiple vehicles pass continuously, which is easy to cause vehicle collisions and other accidents, giving the owner a bad traffic experience. By monitoring the smoothness of the rise and fall, mechanical problems of the gate bar operation can be discovered and solved in time, the smoothness and safety of vehicle traffic can be improved, and user satisfaction can be enhanced. 3. It is very important for the gate bar to maintain a good balance during the rise and fall process. If the gate bar is unbalanced, it may hit vehicles or pedestrians due to the shift of the center of gravity during the fall process, causing safety accidents, especially in crowded areas, where the consequences are more serious. By monitoring the rise and fall balance, the gate bar balance problem can be discovered in time, and adjustment measures can be taken, such as calibrating the center of gravity, adjusting the counterweight, etc., to eliminate safety hazards and ensure the safety of personnel and vehicles.

[0052] Exemplarily, the motor operation data includes: current deviation rate, voltage deviation rate, temperature balance index, and speed deviation rate.

[0053] It should be noted that the reasons for selecting the current deviation rate, voltage deviation rate, temperature balance index, and speed deviation rate as 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, the motor current should be maintained within a certain range under normal circumstances. When vehicles pass frequently or the gate pole encounters obstacles, the motor load increases and the current increases accordingly. By monitoring the current deviation rate, the load changes of the motor can be understood in real time. 2. Stable voltage is the 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 insulation material will be easily damaged; if the voltage is too low, the motor torque will decrease, the speed will decrease, and it may even fail to start. It will also cause the current to increase, increasing the risk of motor overload. By monitoring the voltage deviation rate, voltage abnormalities can be discovered in time, and voltage stabilization measures can be taken to ensure that the motor works at a suitable 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 does not dissipate heat well, such as when the cooling fan fails or the vent is blocked, the temperature will rise rapidly. By monitoring the temperature balance index, you can intuitively understand the heat dissipation status of the motor. 4. The motor speed directly determines the rising and falling speed of the gate bar, which in turn affects the traffic efficiency of the gate. The speed deviation rate indicates the deviation between the actual motor speed and the standard speed. In scenarios with large traffic volume, if the motor speed is too low, the gate bar rises and falls slowly, which will cause vehicles to queue and wait, reducing traffic efficiency; if the speed is too high, the gate bar may rise and fall too quickly, increasing mechanical shock and affecting the life of the equipment. By monitoring the speed deviation rate, it can ensure that the motor runs at an appropriate speed, ensure that the gate bar rises and falls normally according to the design requirements, and improve the overall operation efficiency of the gate.

[0054] Exemplarily, the vehicle identification system operation data includes: clarity, contrast, and exposure.

[0055] It should be noted that the reasons for choosing clarity, contrast, and exposure as the operating data of the vehicle identification system are: 1. Clarity is directly related to whether the vehicle identification system can clearly present the license plate characters and vehicle appearance details. High-definition images allow the recognition algorithm to more accurately capture the character information on the license plate and reduce recognition errors caused by image blur. In complex lighting environments or when the vehicle is driving fast, if the image clarity is insufficient, the license plate characters may appear blurred at the edges and the strokes may stick together, making it difficult for the recognition system to make accurate judgments. By monitoring the clarity, the camera parameters can be adjusted in time or the image can be pre-processed to ensure the accuracy of license plate recognition and ensure smooth entry and exit of vehicles in the park. 2. Contrast determines the degree of difference between different areas in the image (such as the license plate and the background, the vehicle outline and the surrounding environment). Appropriate contrast can make the license plate more prominent in the image, making it easier for the recognition system to separate the license plate from the complex background. If the contrast is low, the image will appear gray, and the boundary between the license plate and the background will be blurred, increasing the difficulty of recognition. High-contrast images can clearly display the color, texture and other features of the license plate, which helps the recognition system to accurately analyze the license plate information, thereby effectively improving the success rate of vehicle recognition and maintaining the normal traffic order at the entrance and exit of the park. 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 too bright, causing the license plate to reflect and some character information to be lost; at night or in 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 scenes to ensure that clear and recognizable vehicle images can be collected at any time, providing a reliable data basis for accurate license plate recognition and improving the stability and adaptability of the vehicle recognition system.

[0056] Specifically, 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.

[0057] Specifically, the steps of analyzing the gate lever operation abnormality index are as follows: 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 extracted to 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.

[0058] It should be noted that the analysis steps of the deviation of the lifting and lowering time corresponding to each gate lever lifting and lowering operation within the set time interval are as follows: an electronic timing instrument is set on the gate machine, and the gate lever lifting start time point and gate lever lowering end time point corresponding to each gate lever lifting and lowering operation within the set time interval are obtained in real time, and the lifting and lowering time corresponding to each gate lever lifting and lowering operation within the set time interval is obtained by difference calculation, and the standard lifting and lowering time corresponding to the normal lifting and lowering operation of the gate lever stored in the cloud database is extracted, and the lifting and lowering time corresponding to each gate lever lifting and lowering operation within the set time interval is averaged, and the average lifting and lowering time corresponding to the gate lever lifting and lowering operation within the set time interval is obtained. The lifting and lowering time deviation corresponding to each gate lever lifting and lowering operation within the set time interval is calculated.

[0059] It should be noted that the analysis steps for the lifting and lowering smoothness corresponding to each gate lever lifting and lowering operation within the set time interval are as follows: a speed sensor is set on the gate machine to obtain the speed of each gate lever lifting and lowering operation at each monitoring time point within the set time interval in real time, and the speed curve corresponding to each gate lever lifting and lowering operation within the set time interval is drawn with the time monitoring point as the horizontal coordinate and the speed as the vertical coordinate. At the same time, an upper reference line and a lower reference line for the speed of the gate lever lifting and lowering operation are made in the curve, and each monitoring time point between the upper reference line and the lower reference line is recorded as a normal time point, otherwise it is recorded as an abnormal time point, thereby obtaining the number of abnormal time points and the duration of each abnormal time point by statistics, and the duration of each abnormal time point is accumulated and calculated to obtain the total abnormal duration, through Calculate the smoothness of each gate lever raising and lowering operation within a set time interval; It should be noted that the analysis steps of the lifting and lowering balance corresponding to each gate lever lifting and lowering operation within the set time interval are as follows: an angle sensor is set on the gate machine to obtain the angles of the left and right ends and the center position of each gate lever lifting and lowering operation at each monitoring time point within the set time interval in real time, and the angle difference index is used to calculate the angle difference between the left and right ends and the center position of the gate lever. Calculate the angle difference index of each gate lever raising and lowering operation at each monitoring time point within a set time interval; A gravity sensor is installed on the gate machine to obtain the gravity force along the rotation direction of the left and right ends of the gate bar at each monitoring time point during each gate bar lifting and lowering operation within the set time interval in real time. Calculate the gravity force difference index of each gate lever raising and lowering operation at each monitoring time point within a set time interval; The angle difference index and gravity component difference index of each gate lever lifting and lowering operation at each monitoring time point within the set time interval are summed up and calculated respectively, and the total angle difference index and gravity component difference index of each gate lever lifting and lowering operation within the set time interval are obtained. The reference angle difference index and gravity component difference index corresponding to the normal lifting and lowering operation of the gate lever are extracted from the cloud database. The lifting and lowering balance of each gate lever lifting and lowering operation within a set time interval is calculated.

[0060] Specifically, 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 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 as the height to construct a vertebral entity. A hollow sphere is constructed inside the vertebral body with the length of the temperature balance index as the radius. The solid volume value of the vertebral body is extracted and marked as the motor operation abnormality index.

[0061] It should be noted that the analysis steps of the current deviation rate, voltage deviation rate and speed deviation rate are as follows: extract the motor working current, working voltage and actual speed at each monitoring time point within the set time interval, and perform difference calculations with the standard current, standard voltage and standard speed of the motor under normal operating conditions stored in the cloud database to obtain the motor working current difference, working voltage difference and actual speed difference at each monitoring time point within the set time interval; perform ratio calculations on the motor working current difference, working voltage difference and actual speed difference at each monitoring time point within the set time interval and the standard current, standard voltage and standard speed to obtain the current deviation rate, voltage deviation rate and speed deviation rate at each monitoring time point within the set time interval; and perform sum calculations to obtain the total current deviation rate, total voltage deviation rate and total speed deviation rate within the set time interval as the current deviation rate, voltage deviation rate and speed deviation rate of the motor within the set time interval.

[0062] It should be noted that the analysis steps of the motor temperature balance index are as follows: extract the motor temperature at each monitoring time point within the set time interval, calculate the average value thereof, obtain the average motor temperature within the set time interval, extract the maximum and minimum values ​​from the motor temperature at each monitoring time point within the set time interval, and calculate the motor temperature balance index. The motor temperature balance index within the set time interval is calculated.

[0063] Specifically, 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.

[0064] It should be noted that the process of obtaining the clarity, contrast and exposure of the images collected by each vehicle recognition system within the set time interval is as follows: the Canny operator is used to calculate the gradient amplitude of each pixel in the images collected by each vehicle recognition system within the set time interval, and the gradient amplitudes of all pixels are averaged 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.

[0065] The grayscale value of each pixel in the image collected by each vehicle identification system within the set time interval is obtained, and the maximum grayscale value and the minimum grayscale value are extracted therefrom. The difference between the maximum grayscale value and the minimum grayscale value, and the sum of the maximum grayscale value and the minimum grayscale value are calculated in ratio, and the ratio calculation result is used as the contrast of the image collected by each vehicle identification system.

[0066] The grayscale values ​​of all pixels in the images collected by each vehicle recognition system are averaged, and the average calculation result is used as the exposure of the images collected by each vehicle recognition system.

[0067] Step 4: Identify risky barrier equipment. When it is determined that there is a risk of barrier equipment operation in the target park, identify the specific risky equipment and provide corresponding early warning feedback.

[0068] Specifically, see Figure 4 As shown, the identification of specific operation risk equipment 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.

[0069] It should be noted that the specific abnormal operation device may be one or more of a gate lever, a motor, and a vehicle identification system.

[0070] In a specific embodiment, when analyzing the abnormal operation of the target park barrier equipment, the present invention analyzes the abnormal operation of the gate rod, motor, and vehicle identification system, and then determines whether there is a barrier equipment operation risk in the target park. This analysis method can accurately identify potential barrier operation risks, timely discover and warn of potential faults and accident risks, to a certain extent improve the timeliness of response to barrier operation abnormalities, and reduce the impact on the normal operation of the target park barrier.

[0071] See also Figure 2 As shown, the second aspect of the present invention provides an intelligent gate management device based on the Internet of Things, comprising: The vehicle information capturing module captures the vehicle information of the target park through the vehicle information capturing device of the target park gate.

[0072] The vehicle information access verification module verifies the accessibility of the captured target park vehicle information. If the verification passes, the vehicle is allowed to enter the target park and the gate bar is automatically lifted. Otherwise, the vehicle is prohibited from entering the target park, the gate bar remains closed, and a corresponding warning is issued.

[0073] The barrier equipment monitoring and analysis module monitors the barrier equipment in the target park, obtains the operating data of the barrier equipment in the target park, analyzes the abnormal operation of the barrier equipment in the target park, and determines whether there is any barrier equipment operation risk in the target park. If so, it will issue an early warning and feedback.

[0074] The risk barrier equipment identification module identifies the specific operating risk equipment when it is determined that there is abnormal operation of barrier equipment in the target park, and provides corresponding early warning feedback.

[0075] The cloud database stores the authorized license plate information, the vehicle feature information associated with the authorized license plate, and the allowable range of vehicle passage dimensions specified in the target park, stores the feature vector of the associated vehicle owner's facial image, stores the authorized passage time period information associated with the authorized license plate, stores the historical vehicle passage records within a set historical period, stores the standard raising and lowering time corresponding to the normal raising and lowering operation of the gate lever, stores the reference angle difference index and the gravity component force difference index corresponding to the normal raising and lowering operation of the gate lever, stores the standard current, standard voltage, and standard speed under the normal operating state of the motor, and stores the reasonable clarity threshold, reasonable contrast range, and reasonable exposure range of the image collected by the license plate recognition system under the standard high-quality state.

[0076] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all 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, owner identity and authority, and vehicle safety status of the captured vehicle information in 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 the vehicle passability verification is determined to have 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; 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 vehicle owner's identity and authority verification 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 verification of the vehicle safety status includes: 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 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.

8. The intelligent management method of gate based on Internet of Things according to claim 7 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 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. 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.

9. 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.

10. 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 9, 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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