Gate machine integrated control system and method based on artificial intelligence

Abstract

The invention discloses a gate machine integrated control system and method based on artificial intelligence, and belongs to the technical field of gate machine integrated control. The method comprises the following steps: S10, predicting the ability of a person to pass through to pass through a gate independently; s20, according to the motion characteristics of the next person waiting for passing, the following passing coefficient of the next person waiting for passing is predicted; s30, the central controller controls the transmission ratio of the adjustable speed reducer according to the ability, predicted in the step S10, of the person to pass through the gate alone and the trailing passing coefficient, predicted in the step S20, of the next person to pass through; and S40, the time for the central controller to control the adjustable speed reducer to recover to the standard state is determined. Compared with a high-definition camera, the infrared detection module and the photoelectric sensing module are arranged, so that the data processing efficiency is higher, the effect of monitoring personnel to pass through in real time can be well achieved, and it is guaranteed that the prediction result is more accurate.

Description

Technical Field

[0001] This invention relates to the field of integrated gate control technology, specifically to an integrated gate control system and method based on artificial intelligence. Background Technology

[0002] A turnstile is a channel blocking device (channel management equipment) mainly used in urban rail transit management and fare gate systems to manage pedestrian flow and regulate pedestrian access.

[0003] Existing toll gate systems cannot predict a pedestrian's ability to pass through the gate alone when managing and regulating pedestrian access. This can lead to situations where pedestrians with limited individual gate-passing ability fail to pass through the gate, the gate closes, or a person gets trapped in the gate. This reduces the efficiency of the toll gate system and the user experience. Furthermore, existing toll gate systems cannot effectively predict tailgating, which can result in pedestrians with children being unable to pass through the gate themselves after the child has passed, or increasing the risk of injury from the gate. These dangerous incidents occur frequently, reducing the security of the toll gate system. Summary of the Invention

[0004] The purpose of this invention is to provide an integrated control system and method for turnstiles based on artificial intelligence, so as to solve the problems mentioned in the background art.

[0005] To address the aforementioned technical problems, the present invention provides the following technical solution: an integrated gate control method based on artificial intelligence, the method comprising: S10: After the gate successfully recognizes the authorization information, the central controller controls the photoelectric sensing module and the infrared detection module to determine the height, shape and movement speed of the person to be passed. Based on the determination results, the ability of the person to be passed to pass through the gate alone is predicted. The central controller is electrically connected to the infrared detection module, the photoelectric sensing module and the adjustable reducer respectively. S20: Based on the movement characteristics of the next person waiting to pass, predict the following passage coefficient of the next person waiting to pass. The next person waiting to pass refers to the person who passes through the gate immediately after the person waiting to pass passes through the gate. S30: The central controller controls the transmission ratio of the adjustable reducer based on the individual passage capacity of the person waiting to pass through the gate as predicted in S10, and the following passage coefficient of the next person waiting to pass through as predicted in S20. S40: Determine the time required for the central controller to restore the adjustable reducer to its standard state.

[0006] Furthermore, S10 includes: S101: After the gate successfully recognizes the authorization information, the central controller controls the photoelectric sensing module and infrared detection module to start working. The photoelectric sensing module is a through-beam photoelectric sensor, and multiple photoelectric sensing modules are installed inside the gate. The infrared detection module is a passive infrared detector, located inside the gate near the gate entrance. The installation tilt angle of the infrared detection module is α={(π / 2)+(β / 2)+arctan[(Hh) / L]}. A ray is drawn eastward from the infrared detection module as the origin; this ray serves as the baseline for the installation tilt angle. The detection distance of the infrared detection module is √(L). 2 +x 2 ), where β represents the detection angle value of the infrared detection module, H represents the maximum height value that the gate allows to pass for free, h represents the vertical distance value of the infrared detection module from the ground, L represents half the width value of the gate channel, π=180°, x represents the shortest distance from the installation position of the infrared detection module to the gate entrance, and √ represents the square root. The infrared detection module is used to determine the height characteristics of the person to be passed. Compared with directly using the image information collected by the high-definition camera to measure the height of the person to be passed, it reduces the data processing of the image and is more efficient. S102: When the photoelectric sensor module located at the gate entrance senses the person waiting to pass, the central controller controls the gate wings to open. When the photoelectric sensor module located directly above the gate wings senses the person waiting to pass, the movement time t of the person to pass from the gate entrance to the gate wings is determined. Combined with the distance s from the gate entrance to the gate wings, the movement speed of the person to pass is determined. The determined movement speed = s / t. The photoelectric sensing module is used to acquire the light image set generated when the person to be passed moves from the gate entrance to the gate wing. The acquired light image set is matched with the Internet technology. Based on the matching results, the morphological characteristics of the person to be passed are determined. The light image set refers to the collection of light images collected by the photoelectric sensing module. The morphological characteristics refer to the volume of the means of passage and the items carried by the person to be passed when passing through the gate. The means of passage include wheelchairs, canes, strollers, etc. When the photoelectric sensor module at the entrance of the gate senses a person waiting to pass, the infrared detection module detects the infrared heat energy emitted by the person. If the infrared detection module can detect the change in infrared heat radiation energy, it means that the height of the person waiting to pass is ≥ H, and the height characteristic value y = H is considered to be the person's height. If the infrared detection module cannot detect the change in infrared heat radiation energy, it means that the height of the person waiting to pass is < H, and the height characteristic value y = u is considered to be the person's height. u represents the height value of the person waiting to pass obtained from the acquired light image set. The height value corresponding to the highest point of the light image of the person waiting to pass is taken as the height value of the person waiting to pass. S103: Based on the height, morphological characteristics, and movement speed of the person to be passed determined in S102, predict the person's ability to pass through the turnstile alone. The specific prediction formula is as follows: When the volume f of the object carried by the person to be passed is less than or equal to the standard volume F: S=α*(y / H)+η*<(2s / t´) / (s / t)>+τ*[(Σε i ) / m]; When the volume f of the object carried by the person waiting to pass is greater than the standard volume F: S={α*(y / H)+η*<(2s / t´) / (s / t)>+τ*[(Σε i ) / m]}*(f / F); Where α, η, and ε all represent proportionality coefficients, and α + η + ε = 1, t´ represents the dwell time of the gate wing in the open state under standard conditions, i = 1, 2, ..., m represents the number corresponding to the morphological feature, m represents the total number of numbers, Σε i =ε1+ε2+…+ε m When the person waiting to pass has the morphological characteristics numbered i, ε i =0, when the person to be passed does not have the morphological characteristics of number i. i =1, when (2s / t´) / (s / t)≥1, <(2s / t´) / (s / t)>=1, when (2s / t´) / (s / t)<1, <(2s / t´) / (s / t)>=(2s / t´) / (s / t), where S represents the ability value of a person waiting to pass through the gate alone.

[0007] Furthermore, S20 includes: S201: When the photoelectric sensor module located at the gate wing senses a person waiting to pass through, if the photoelectric sensor module located between the gate entrance and the gate wing can collect the light image, the motion characteristics of the next person waiting to pass through are determined based on the collected real-time light image. If the photoelectric sensor module located between the gate entrance and the gate wing fails to collect the light image, it means that the next person waiting to pass through has no motion characteristics. At this time, the average motion speed V of the next person waiting to pass through is 0, and the motion tendency value k is 0. S202: Determine the motion characteristics of the next person waiting to pass based on the real-time optical image acquired in S201. The motion characteristics include average speed and motion tendency. The specific determination method is as follows: If the real-time optical image shows that the person waiting to pass is moving forward, it indicates that the movement tendency of the person waiting to pass is to follow the person waiting to pass through the gate. In this case, the movement tendency value of the person waiting to pass is k=(2 / t´)*{∑ceil[(Lj＋1 -L j ) / L j ]}*T, where j=1,2,…,[(2 / t´)-1] / T represents the number corresponding to the number of times the real-time optical image is acquired, T represents the time consumed by the photoelectric sensing module to acquire the optical image in a single time, [(2 / t´)-1] / T represents the total number of times the real-time optical image is acquired, L j L represents the forward distance value of the next person waiting to pass as displayed in the real-time light and shadow set acquired in the j-th acquisition. j+1 This represents the forward distance of the next person waiting to pass in the real-time optical image captured in the (j+1)th acquisition. ceil() represents the rounding up function, where 0 < L. j＋1 <2*L j ; If the real-time optical image shows that the next person waiting to pass is always stationary, it means that the next person's movement tendency is to scan the code to pass through the gate. At this time, the movement tendency value of the next person waiting to pass is k = 0. Based on the collected real-time light images, the average speed of the next person waiting to pass is determined using the distance-time-velocity formula. S203: Based on the movement characteristics of the next person waiting to pass determined in S202, predict the following passage coefficient of the next person waiting to pass. The specific prediction formula is as follows: G = k * [V / (2s / t')]; Where k represents the motion tendency value corresponding to the next person waiting to pass, V represents the determined average motion speed of the next person waiting to pass, and G represents the predicted tail-passing coefficient of the next person waiting to pass.

[0008] The tailgating coefficient refers to the probability that, after the gate opens, the next person waiting to pass will follow another person waiting to pass through the gate during the same period the gate is open.

[0009] Furthermore, the specific method for controlling the transmission ratio of the adjustable reducer in S30 is as follows: When G≤ set threshold, the central controller controls the transmission ratio of the adjustable reducer based on the predicted ability of the person to pass through the gate alone in S103. The central controller controls the transmission ratio of the adjustable reducer to be set to =(2t / t´)*d. When G > the set threshold, the central controller controls the transmission ratio of the adjustable reducer based on the individual's ability to pass through the gate independently as predicted in S103 and the following passage coefficient of the next individual as predicted in S203. The central controller controls the transmission ratio of the adjustable reducer to be set as ={{2t+<(s / `V)-t>} / t´}*d, where d represents the transmission ratio of the adjustable reducer module under standard conditions. When (s / `V)-t≥0, <(s / `V)-t>=(s / `V)-t, and when (s / `V)-t<0, <(s / `V)-t>=0.

[0010] Based on the individual's ability to pass through the turnstile alone and the following pass coefficient of the next individual, it is determined whether the next individual is a person who is evading ticket inspection by tailgating. This method can accurately identify those who evade ticket inspection and effectively identify those who are exempt from ticket inspection by tailgating, thereby improving system security while ensuring the efficiency of turnstile passage.

[0011] Furthermore, the specific method for S40 to determine the time for the central controller to control the adjustable reducer to return to the standard state is as follows: When G ≤ the set threshold, the time for the central controller to control the adjustable reducer to return to the standard state is the time when the photoelectric sensor module located at the gate exit senses the passage of the person waiting to pass. When G > the set threshold, the time for the central controller to control the adjustable reducer to return to the standard state is the time when the photoelectric sensor module at the gate exit senses the next person waiting to pass through.

[0012] An integrated control system for turnstiles based on artificial intelligence, the system comprising a turnstile individual throughput capacity prediction module, a tail-following throughput coefficient prediction module, a transmission ratio determination module, and a state recovery module; The gate's individual passage capability prediction module is used to determine the height, shape, and movement speed of the person to be passed by the central controller after the gate successfully recognizes the authorization information. Based on the determination results, the module predicts the individual's ability to pass through the gate alone and transmits the predicted individual's ability to pass through the gate alone to the transmission ratio determination module. The tail-following pass coefficient prediction module is used to predict the tail-following pass coefficient of the next person to pass based on the motion characteristics of the next person to pass, and transmit the predicted tail-following pass coefficient of the next person to pass to the transmission ratio determination module and the state recovery module. The transmission ratio determination module is used to receive the capacity value transmitted by the gate's individual passage capacity prediction module and the tail passage coefficient transmitted by the tail passage coefficient prediction module. Based on the received information, the central controller is used to control the transmission ratio of the adjustable reducer. The state recovery module is used to receive the tail-following coefficient transmitted by the tail-following coefficient prediction module. Based on the received information and the time when the person waiting to pass or the next person waiting to pass passes through the photoelectric sensing module located at the gate exit, the central controller determines the time for the adjustable reducer to return to the standard state.

[0013] Furthermore, the gate's individual passage capability prediction module includes a passage authorization information identification unit, a movement speed determination unit, a morphological feature determination unit, a height feature value determination unit, and a gate's individual passage capability prediction unit; The authorization information identification unit identifies the authorization information of the person to be passed, and the central controller controls the photoelectric sensing module and infrared detection module to start working based on the successful identification information; When the photoelectric sensor module located at the gate entrance senses a person waiting to pass, the central controller controls the gate wings to open. When the photoelectric sensor module located directly above the gate wings senses a person waiting to pass, it determines the movement time of the person from the gate entrance to the gate wings. Combining the distance from the gate entrance to the gate wings, it determines the movement speed of the person waiting to pass and transmits the determined movement speed to the gate's individual passage capacity prediction unit. The morphological feature determination unit uses a photoelectric sensing module to acquire a set of light images generated when a person to be passed moves from the gate entrance to the gate wing. The acquired light image set is matched using Internet technology. Based on the matching result, the morphological features of the person to be passed are determined, and the determined morphological features are transmitted to the gate's individual passage capacity prediction unit. When the photoelectric sensing module at the gate entrance senses a person waiting to pass, the height characteristic value determination unit detects the infrared heat energy emitted by the person. Based on the changes in infrared heat radiation energy that the infrared detection module can detect, the height characteristic value of the person is determined and transmitted to the gate's individual passage capacity prediction unit. The gate individual passage capability prediction unit receives the motion speed transmitted by the motion speed determination unit, the morphological features transmitted by the morphological feature determination unit, and the height feature value transmitted by the height feature value determination unit. Based on the received information, it predicts the individual's ability to pass through the gate individually and transmits the predicted individual's ability to pass through the gate individually to the transmission ratio determination module.

[0014] Furthermore, the tail-following pass coefficient prediction module includes a judgment unit, a motion feature determination unit, and a tail-following pass coefficient prediction unit; When the photoelectric sensor module located at the gate wing senses the passage of a person waiting to pass, the judgment unit determines whether the next person waiting to pass has motion characteristics based on whether the photoelectric sensor module located between the gate entrance and the gate wing can collect light images. If it is determined that the next person waiting to pass has no motion characteristics, it means that the average speed and motion tendency value of the next person waiting to pass are both 0. The collected light images are then transmitted to the motion characteristic determination unit, and the determined average speed and motion tendency value are transmitted to the tail passage coefficient prediction unit. The motion characteristic determination unit receives the optical image transmitted by the judgment unit, determines the motion tendency value and average motion speed value of the next person to pass based on the collected optical image, and transmits the determined motion tendency value and average motion speed to the tail passing coefficient prediction unit. The tail-following coefficient prediction unit receives the average motion speed and motion tendency value transmitted by the judgment unit, as well as the motion tendency value and average motion speed transmitted by the motion characteristic determination unit. Based on the received information, it predicts the tail-following coefficient of the next person to pass and transmits the predicted tail-following coefficient to the transmission ratio determination module.

[0015] Furthermore, the transmission ratio determination module includes a comparison unit, a first determination unit, and a second determination unit; The comparison unit receives the predicted tail-following coefficient transmitted by the tail-following coefficient prediction unit, compares the received tail-following coefficient with a set threshold, and transmits the comparison result to the first determination unit and the second determination unit. The first determining unit receives the comparison result of the tail passing coefficient ≤ set threshold transmitted by the comparison unit, and the capability value transmitted by the gate's individual passing capability prediction unit. The central controller controls the transmission ratio of the adjustable reducer according to the received capability value. The second determining unit receives the comparison result of the tail passage coefficient > set threshold transmitted by the comparison unit, the capability value transmitted by the gate single passage capability prediction unit, and the tail passage coefficient transmitted by the tail passage coefficient prediction unit. The central controller controls the transmission ratio of the adjustable reducer based on the received capability value and tail passage coefficient.

[0016] Furthermore, the state recovery module receives the predicted tail-following coefficient transmitted by the tail-following coefficient prediction unit. When the received tail-following coefficient is less than or equal to a set threshold, the central controller controls the adjustable reducer to return to the standard state when the photoelectric sensor at the gate exit senses the passage of the person waiting to pass. When the received tail-following coefficient is greater than the set threshold, the central controller controls the adjustable reducer to return to the standard state when the photoelectric sensor at the gate exit senses the passage time of the next person waiting to pass.

[0017] Compared with the prior art, the beneficial effects achieved by the present invention are: 1. This invention utilizes an infrared detection module and a photoelectric sensing module to determine the height, morphological characteristics, and movement speed of the person to be passed. Based on this information, it predicts the person's ability to pass through the gate independently. In this process, the infrared detection module and the photoelectric sensing module have higher data processing efficiency compared to high-definition cameras, enabling real-time monitoring of the person to be passed and ensuring more accurate prediction results.

[0018] 2. This invention utilizes a photoelectric sensing module to collect the optical image of the next person waiting to pass through in real time between the gate entrance and the gate wing. Based on the real-time collected optical image, the motion characteristics of the next person waiting to pass through are determined. Based on the determination result, the tailing coefficient of the next person waiting to pass through is predicted. Combined with the predicted ability of the person waiting to pass through the gate alone, it can accurately identify the tailing person and judge the relationship between the people waiting to pass through. Based on the judgment result, the gate can adjust the speed ratio of the adjustable reducer, which helps to reduce the occurrence of people being trapped by the gate and further improves the safety of the system.

[0019] 3. This invention controls the transmission ratio of the adjustable reducer based on the movement time of the people waiting to pass from the gate entrance to the gate wing, reducing the amount of data processing. This ensures that people with low individual gate passage capacity and no accompanying persons can safely pass through the gate, and also ensures that people with low individual gate passage capacity and those following can safely pass through the gate together, further improving the system's safety and the gate's working efficiency. Attached Figure Description

[0020] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram of the workflow of an integrated gate control system and method based on artificial intelligence according to the present invention; Figure 2This is a schematic diagram illustrating the working principle of an integrated gate control system and method based on artificial intelligence according to the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Please see Figure 1 and Figure 2 This invention provides a technical solution: an integrated control method for turnstiles based on artificial intelligence, the method comprising: S10: After the gate successfully recognizes the authorization information, the central controller controls the photoelectric sensing module and the infrared detection module to determine the height, shape and movement speed of the person to be passed. Based on the determination results, the ability of the person to be passed to pass through the gate alone is predicted. The central controller is electrically connected to the infrared detection module, the photoelectric sensing module and the adjustable reducer respectively. S10 includes: S101: After the gate successfully recognizes the authorization information, the central controller controls the photoelectric sensing module and infrared detection module to start working. The photoelectric sensing module is a through-beam photoelectric sensor, and multiple photoelectric sensing modules are installed inside the gate. The infrared detection module is a passive infrared detector, located inside the gate near the gate entrance. The installation tilt angle of the infrared detection module is α={(π / 2)+(β / 2)+arctan[(Hh) / L]}. A ray is drawn eastward from the infrared detection module as the origin; this ray serves as the baseline for the installation tilt angle. The detection distance of the infrared detection module is √(L). 2 +x 2 ), where β represents the detection angle value of the infrared detection module, H represents the maximum height value that the gate allows to pass for free, h represents the vertical distance value of the infrared detection module from the ground, L represents half the width value of the gate channel, π=180°, x represents the shortest distance from the installation position of the infrared detection module to the gate entrance, and √ represents the square root. The infrared detection module is used to determine the height characteristics of the person to be passed. Compared with directly using the image information collected by the high-definition camera to measure the height of the person to be passed, it reduces the data processing of the image and is more efficient. S102: When the photoelectric sensor module located at the gate entrance senses the person waiting to pass, the central controller controls the gate wings to open. When the photoelectric sensor module located directly above the gate wings senses the person waiting to pass, the movement time t of the person to pass from the gate entrance to the gate wings is determined. Combined with the distance s from the gate entrance to the gate wings, the movement speed of the person to pass is determined. The determined movement speed = s / t. The photoelectric sensing module is used to acquire the light image set generated when the person to be passed moves from the gate entrance to the gate wing. The acquired light image set is matched with the Internet technology. Based on the matching results, the morphological characteristics of the person to be passed are determined. The light image set refers to the collection of light images collected by the photoelectric sensing module. The morphological characteristics refer to the volume of the means of passage and the items carried by the person to be passed when passing through the gate. The means of passage include wheelchairs, canes, strollers, etc. When the photoelectric sensor module at the entrance of the gate senses a person waiting to pass, the infrared detection module detects the infrared heat energy emitted by the person. If the infrared detection module can detect the change in infrared heat radiation energy, it means that the height of the person waiting to pass is ≥ H, and the height characteristic value y = H is considered to be the person's height. If the infrared detection module cannot detect the change in infrared heat radiation energy, it means that the height of the person waiting to pass is < H, and the height characteristic value y = u is considered to be the person's height. u represents the height value of the person waiting to pass obtained from the acquired light image set. The height value corresponding to the highest point of the light image of the person waiting to pass is taken as the height value of the person waiting to pass. S103: Based on the height, morphological characteristics, and movement speed of the person to be passed determined in S102, predict the person's ability to pass through the turnstile alone. The specific prediction formula is as follows: When the volume f of the object carried by the person to be passed is less than or equal to the standard volume F: S=α*(y / H)+η*<(2s / t´) / (s / t)>+τ*[(Σε i ) / m]; When the volume f of the object carried by the person waiting to pass is greater than the standard volume F: S={α*(y / H)+η*<(2s / t´) / (s / t)>+τ*[(Σε i ) / m]}*(f / F); Where α, η, and ε all represent proportionality coefficients, and α + η + ε = 1, t´ represents the dwell time of the gate wing in the open state under standard conditions, i = 1, 2, ..., m represents the number corresponding to the morphological feature, m represents the total number of numbers, Σε i =ε1+ε2+…+ε m When the person waiting to pass has the morphological characteristics numbered i, ε i=0, when the person to be passed does not have the morphological characteristics of number i. i =1, when (2s / t´) / (s / t)≥1, <(2s / t´) / (s / t)>=1, when (2s / t´) / (s / t)<1, <(2s / t´) / (s / t)>=(2s / t´) / (s / t), where S represents the ability value of a person waiting to pass through the gate alone.

[0023] S20: Based on the movement characteristics of the next person waiting to pass, predict the following passage coefficient of the next person waiting to pass. The next person waiting to pass refers to the person who passes through the gate immediately after the person waiting to pass passes through the gate. S20 includes: S201: When the photoelectric sensor module located at the gate wing senses a person waiting to pass through, if the photoelectric sensor module located between the gate entrance and the gate wing can collect the light image, the motion characteristics of the next person waiting to pass through are determined based on the collected real-time light image. If the photoelectric sensor module located between the gate entrance and the gate wing fails to collect the light image, it means that the next person waiting to pass through has no motion characteristics. At this time, the average motion speed V of the next person waiting to pass through is 0, and the motion tendency value k is 0. S202: Determine the motion characteristics of the next person waiting to pass based on the real-time optical image acquired in S201. The motion characteristics include average speed and motion tendency. The specific determination method is as follows: If the real-time optical image shows that the person waiting to pass is moving forward, it indicates that the movement tendency of the person waiting to pass is to follow the person waiting to pass through the gate. In this case, the movement tendency value of the person waiting to pass is k=(2 / t´)*{∑ceil[(L j＋1 -L j ) / L j ]}*T, where j=1,2,…,[(2 / t´)-1] / T represents the number corresponding to the number of times the real-time optical image is acquired, T represents the time consumed by the photoelectric sensing module to acquire the optical image in a single time, [(2 / t´)-1] / T represents the total number of times the real-time optical image is acquired, L j L represents the forward distance value of the next person waiting to pass as displayed in the real-time light and shadow set acquired in the j-th acquisition. j+1 This represents the forward distance of the next person waiting to pass in the real-time optical image captured in the (j+1)th acquisition. ceil() represents the rounding up function, where 0 < L. j＋1 <2*L j ; If the real-time optical image shows that the next person waiting to pass is always stationary, it means that the next person's movement tendency is to scan the code to pass through the gate. At this time, the movement tendency value of the next person waiting to pass is k = 0. Based on the collected real-time light images, the average speed of the next person waiting to pass is determined using the distance-time-velocity formula. S203: Based on the movement characteristics of the next person waiting to pass determined in S202, predict the following passage coefficient of the next person waiting to pass. The specific prediction formula is as follows: G = k * [V / (2s / t')]; Where k represents the motion tendency value corresponding to the next person waiting to pass, V represents the determined average motion speed of the next person waiting to pass, and G represents the predicted tail-passing coefficient of the next person waiting to pass.

[0024] The tailgating coefficient refers to the probability that, after the gate opens, the next person waiting to pass will follow another person waiting to pass through the gate during the same period the gate is open.

[0025] S30: The central controller controls the transmission ratio of the adjustable reducer based on the individual passage capacity of the person waiting to pass through the gate as predicted in S10, and the following passage coefficient of the next person waiting to pass through as predicted in S20. The specific method for controlling the transmission ratio of the adjustable reducer using S30 is as follows: When G≤ set threshold, the central controller controls the transmission ratio of the adjustable reducer based on the predicted ability of the person to pass through the gate alone in S103. The central controller controls the transmission ratio of the adjustable reducer to be set to =(2t / t´)*d. When G > the set threshold, the central controller controls the transmission ratio of the adjustable reducer based on the individual's ability to pass through the gate independently as predicted in S103 and the following passage coefficient of the next individual as predicted in S203. The central controller controls the transmission ratio of the adjustable reducer to be set as ={{2t+<(s / `V)-t>} / t´}*d, where d represents the transmission ratio of the adjustable reducer module under standard conditions. When (s / `V)-t≥0, <(s / `V)-t>=(s / `V)-t, and when (s / `V)-t<0, <(s / `V)-t>=0.

[0026] Based on the individual's ability to pass through the turnstile alone and the following pass coefficient of the next individual, it is determined whether the next individual is a person who is evading ticket inspection by tailgating. This method can accurately identify those who evade ticket inspection and effectively identify those who are exempt from ticket inspection by tailgating, thereby improving system security while ensuring the efficiency of turnstile passage.

[0027] S40: Determine the time required for the central controller to restore the adjustable reducer to its standard state.

[0028] The specific method by which S40 determines the time for the central controller to restore the adjustable reducer to its standard state is as follows: When G ≤ the set threshold, the time for the central controller to control the adjustable reducer to return to the standard state is the time when the photoelectric sensor module located at the gate exit senses the passage of the person waiting to pass. When G > the set threshold, the time for the central controller to control the adjustable reducer to return to the standard state is the time when the photoelectric sensor module at the gate exit senses the next person waiting to pass through.

[0029] An integrated control system for turnstiles based on artificial intelligence, the system includes a turnstile individual throughput capacity prediction module, a tailing throughput coefficient prediction module, a transmission ratio determination module, and a state recovery module; The gate's individual passage capability prediction module is used to determine the height, shape, and movement speed of the person to be passed by the central controller after the gate successfully recognizes the authorization information. Based on the determination results, the module predicts the individual's ability to pass through the gate alone and transmits the predicted individual's ability to pass through the gate alone to the transmission ratio determination module. The turnstile's individual passage capacity prediction module includes a passage authorization information recognition unit, a movement speed determination unit, a morphological feature determination unit, a height feature value determination unit, and a turnstile's individual passage capacity prediction unit. The authorization information recognition unit identifies the authorization information of the person to be passed. Based on the successful recognition information, the central controller controls the photoelectric sensing module and infrared detection module to start working. When the photoelectric sensor module located at the gate entrance senses a person waiting to pass, the central controller controls the gate wings to open. When the photoelectric sensor module located directly above the gate wings senses a person waiting to pass, it determines the time it takes for the person to move from the gate entrance to the gate wings. Combining this with the distance from the gate entrance to the gate wings, it determines the speed of the person and transmits the determined speed to the gate's individual throughput capacity prediction unit. The morphological feature determination unit uses a photoelectric sensing module to acquire the light image set generated when the person to be passed moves from the gate entrance to the gate wing. The acquired light image set is matched using Internet technology. Based on the matching result, the morphological features of the person to be passed are determined and transmitted to the gate's individual passage capacity prediction unit. When the photoelectric sensing module at the entrance of the gate senses a person waiting to pass, the infrared detection module detects the infrared heat energy emitted by the person. Based on the changes in infrared heat radiation energy that the infrared detection module can detect, the height characteristic value of the person is determined and transmitted to the gate's individual passage capacity prediction unit. The turnout individual passage capability prediction unit receives the motion speed transmitted by the motion speed determination unit, the morphological characteristics transmitted by the morphological characteristics determination unit, and the height characteristic values ​​transmitted by the height characteristic value determination unit. Based on the received information, it predicts the individual's ability to pass through the turnout individually and transmits the predicted individual's ability to pass through the turnout individually to the transmission ratio determination module.

[0030] The tail-following pass coefficient prediction module is used to predict the tail-following pass coefficient of the next person to pass based on the motion characteristics of the next person to pass, and transmit the predicted tail-following pass coefficient of the next person to pass to the transmission ratio determination module and the state recovery module. The tail-following pass coefficient prediction module includes a judgment unit, a motion feature determination unit, and a tail-following pass coefficient prediction unit. When the photoelectric sensor module located at the gate wing senses the passage of a person waiting to pass, the judgment unit determines whether the next person waiting to pass has motion characteristics based on whether the photoelectric sensor module located between the gate entrance and the gate wing can collect light images. If it is determined that the next person waiting to pass has no motion characteristics, it means that the average movement speed and movement tendency value of the next person waiting to pass are both 0. The collected light images are then transmitted to the motion characteristic determination unit, and the determined average movement speed and movement tendency value are transmitted to the tail passage coefficient prediction unit. The motion characteristic determination unit receives the optical image transmitted by the judgment unit, determines the motion tendency value and average motion speed value of the next person to pass based on the acquired optical image, and transmits the determined motion tendency value and average motion speed to the tail passing coefficient prediction unit. The tail-following coefficient prediction unit receives the average motion speed and motion tendency value transmitted by the judgment unit, as well as the motion tendency value and average motion speed transmitted by the motion characteristic determination unit. Based on the received information, it predicts the tail-following coefficient of the next person to pass and transmits the predicted tail-following coefficient to the transmission ratio determination module.

[0031] The transmission ratio determination module is used to receive the capacity value transmitted by the gate's individual throughput capacity prediction module and the tail throughput coefficient transmitted by the tail throughput coefficient prediction module. Based on the received information, the central controller is used to control the transmission ratio of the adjustable reducer. The transmission ratio determination module includes a comparison unit, a first determination unit, and a second determination unit; The comparison unit receives the predicted tail-following coefficient transmitted by the tail-following coefficient prediction unit, compares the received tail-following coefficient with a set threshold, and transmits the comparison result to the first determination unit and the second determination unit. The first determining unit receives the comparison result of the tail passage coefficient ≤ set threshold transmitted by the comparison unit, as well as the capability value transmitted by the gate's individual passage capability prediction unit. The central controller controls the transmission ratio of the adjustable reducer based on the received capability value. The second determining unit receives the comparison result of the tailing passage coefficient > set threshold transmitted by the comparison unit, the capacity value transmitted by the gate single passage capacity prediction unit, and the tailing passage coefficient transmitted by the tailing passage coefficient prediction unit. The central controller controls the transmission ratio of the adjustable reducer based on the received capacity value and tailing passage coefficient.

[0032] The state recovery module receives the tail-following coefficient transmitted by the tail-following coefficient prediction module. Based on the received information and the time it takes for the person waiting to pass or the next person waiting to pass to pass through the photoelectric sensor module located at the gate exit, the central controller determines the time for the adjustable reducer to return to the standard state.

[0033] The state recovery module receives the predicted tail-following coefficient transmitted by the tail-following coefficient prediction unit. When the received tail-following coefficient is less than or equal to the set threshold, the central controller controls the adjustable reducer to return to the standard state when the photoelectric sensor at the gate exit senses the passage of the person waiting to pass. When the received tail-following coefficient is greater than the set threshold, the central controller controls the adjustable reducer to return to the standard state when the photoelectric sensor at the gate exit senses the passage time of the next person waiting to pass.

[0034] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0035] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An integrated gate control method based on artificial intelligence, characterized in that: The method includes: S10: After the gate successfully recognizes the authorization information, the central controller controls the photoelectric sensing module and the infrared detection module to determine the height, shape and movement speed of the person to be passed. Based on the determination results, the ability of the person to be passed to pass through the gate alone is predicted. The central controller is electrically connected to the infrared detection module, the photoelectric sensing module and the adjustable reducer respectively. S20: Based on the movement characteristics of the next person waiting to pass, predict the following pass coefficient of the next person waiting to pass; S30: The central controller controls the transmission ratio of the adjustable reducer based on the individual passage capacity of the person waiting to pass through the gate as predicted in S10, and the following passage coefficient of the next person waiting to pass through as predicted in S20. S40: Determine the time required for the central controller to restore the adjustable reducer to its standard state.

2. The integrated gate control method based on artificial intelligence according to claim 1, characterized in that: S10 includes: S101: After the gate successfully recognizes the authorization information, the central controller controls the photoelectric sensing module and infrared detection module to start working. The photoelectric sensing module is a through-beam photoelectric sensor, and multiple photoelectric sensing modules are installed inside the gate. The infrared detection module is a passive infrared detector, located inside the gate near the gate entrance. The installation tilt angle of the infrared detection module is α={(π / 2)+(β / 2)+arctan[(Hh) / L]}, and the detection distance of the infrared detection module is √(L). 2 +x 2 ), where β represents the detection angle value of the infrared detection module, H represents the maximum height value that the gate allows to pass for free, h represents the vertical distance value of the infrared detection module from the ground, L represents half the width value of the gate channel, π=180°, x represents the shortest distance from the installation position of the infrared detection module to the gate entrance, and √ represents the square root. S102: When the photoelectric sensor module located at the gate entrance senses the person waiting to pass, the central controller controls the gate wings to open. When the photoelectric sensor module located directly above the gate wings senses the person waiting to pass, the movement time t of the person to pass from the gate entrance to the gate wings is determined. Combined with the distance s from the gate entrance to the gate wings, the movement speed of the person to pass is determined. The determined movement speed = s / t. The photoelectric sensing module is used to acquire the light image set generated when the person to be passed moves from the gate entrance to the gate wing. The acquired light image set is matched with the Internet technology. Based on the matching results, the morphological characteristics of the person to be passed are determined. The light image set refers to the collection of light images collected by the photoelectric sensing module. The morphological characteristics refer to the volume of the means of passage and the items carried by the person to be passed when passing through the gate. When the photoelectric sensor module at the entrance of the gate senses a person waiting to pass, the infrared detection module detects the infrared heat energy emitted by the person. If the infrared detection module can detect the change in infrared heat radiation energy, it means that the height of the person waiting to pass is ≥ H, and the height characteristic value y = H is considered to be the person's height. If the infrared detection module cannot detect the change in infrared heat radiation energy, it means that the height of the person waiting to pass is < H, and the height characteristic value y = u is considered to be the person's height. u represents the height value of the person waiting to pass obtained from the acquired light image set. The height value corresponding to the highest point of the light image of the person waiting to pass is taken as the height value of the person waiting to pass. S103: Based on the height, morphological characteristics, and movement speed of the person to be passed determined in S102, predict the person's ability to pass through the turnstile alone. The specific prediction formula is as follows: When the volume f of the object carried by the person to be passed is less than or equal to the standard volume F: S=α*(y / H)+η*<(2s / t´) / (s / t)>+τ*[(In i ) / m]; When the volume f of the object carried by the person waiting to pass is greater than the standard volume F: S={α*(y / H)+η*<(2s / t´) / (s / t)>+τ*[(In i ) / m]}*(f / F); Where α, η, and ε all represent proportionality coefficients, and α + η + ε = 1, t´ represents the dwell time of the gate wing in the open state under standard conditions, i = 1, 2, ..., m represents the number corresponding to the morphological feature, m represents the total number of numbers, Σε i =ε1+ε2+…+ε m When the person waiting to pass has the morphological characteristics numbered i, ε i =0, when the person to be passed does not have the morphological characteristics of number i. i =1, when (2s / t´) / (s / t)≥1, <(2s / t´) / (s / t)>=1, when (2s / t´) / (s / t)<1, <(2s / t´) / (s / t)>=(2s / t´) / (s / t), where S represents the ability value of a person waiting to pass through the gate alone.

3. The integrated gate control method based on artificial intelligence according to claim 2, characterized in that: S20 includes: S201: When the photoelectric sensor module located at the gate wing senses a person waiting to pass through, if the photoelectric sensor module located between the gate entrance and the gate wing can collect the light image, the motion characteristics of the next person waiting to pass through are determined based on the collected real-time light image. If the photoelectric sensor module located between the gate entrance and the gate wing fails to collect the light image, it means that the next person waiting to pass through has no motion characteristics. At this time, the average motion speed V of the next person waiting to pass through is 0, and the motion tendency value k is 0. S202: Determine the motion characteristics of the next person waiting to pass based on the real-time optical image acquired in S201. The motion characteristics include average speed and motion tendency. The specific determination method is as follows: If the real-time optical image shows that the person waiting to pass is moving forward, it indicates that the movement tendency of the person waiting to pass is to follow the person waiting to pass through the gate. In this case, the movement tendency value of the person waiting to pass is k=(2 / t´)*{∑ceil[(L j＋1 -L j ) / L j ]}*T, where j=1,2,…,[(2 / t´)-1] / T, represents the number corresponding to the number of times the real-time optical image is acquired, T represents the time consumed by the photoelectric sensing module to acquire the optical image in a single acquisition, [(2 / t´)-1] / T represents the total number of times the real-time optical image is acquired, L j L represents the forward distance value of the next person waiting to pass as displayed in the real-time light and shadow set acquired in the j-th acquisition. j+1 This represents the forward distance of the next person waiting to pass in the real-time optical image captured in the (j+1)th acquisition. ceil() represents the rounding up function, where 0 < L. j＋1 <2*L j ; If the real-time optical image shows that the next person waiting to pass is always stationary, it means that the next person's movement tendency is to scan the code to pass through the gate. At this time, the movement tendency value of the next person waiting to pass is k = 0. Based on the collected real-time light images, the average speed of the next person waiting to pass is determined using the distance-time-velocity formula. S203: Based on the movement characteristics of the next person waiting to pass determined in S202, predict the following passage coefficient of the next person waiting to pass. The specific prediction formula is as follows: G = k * [V / (2s / t')]; Where k represents the motion tendency value of the next person waiting to pass, V represents the determined average motion speed of the next person waiting to pass, and G represents the predicted tail-passing coefficient of the next person waiting to pass.

4. The integrated gate control method based on artificial intelligence according to claim 3, characterized in that: The specific method for controlling the transmission ratio of the adjustable reducer in S30 is as follows: When G≤ set threshold, the central controller controls the transmission ratio of the adjustable reducer based on the predicted ability of the person to pass through the gate alone in S103. The central controller controls the transmission ratio of the adjustable reducer to be set to =(2t / t´)*d; When G > the set threshold, the central controller controls the transmission ratio of the adjustable reducer based on the individual's ability to pass through the gate independently as predicted in S103 and the following passage coefficient of the next individual as predicted in S203. The central controller controls the transmission ratio of the adjustable reducer to be set as ={{2t+<(s / `V)-t>} / t´}*d, where d represents the transmission ratio of the adjustable reducer module under standard conditions. When (s / `V)-t≥0, <(s / `V)-t>=(s / `V)-t, and when (s / `V)-t<0, <(s / `V)-t>=0.

5. The integrated gate control method based on artificial intelligence according to claim 4, characterized in that: The specific method for S40 to determine the time for the central controller to restore the adjustable reducer to the standard state is as follows: When G ≤ the set threshold, the time for the central controller to control the adjustable reducer to return to the standard state is the time when the photoelectric sensor module located at the gate exit senses the passage of the person waiting to pass. When G > the set threshold, the time for the central controller to control the adjustable reducer to return to the standard state is the time when the photoelectric sensor module at the gate exit senses the next person waiting to pass through.

6. An AI-based integrated gate control system applied to the AI-based integrated gate control method according to any one of claims 1-5, characterized in that: The system includes a gate individual throughput capacity prediction module, a tailing throughput coefficient prediction module, a transmission ratio determination module, and a state recovery module. The gate's individual passage capability prediction module is used to determine the height, shape, and movement speed of the person to be passed by the central controller after the gate successfully recognizes the authorization information. Based on the determination results, the module predicts the individual's ability to pass through the gate alone and transmits the predicted individual's ability to pass through the gate alone to the transmission ratio determination module. The tail-following pass coefficient prediction module is used to predict the tail-following pass coefficient of the next person to pass based on the motion characteristics of the next person to pass, and transmit the predicted tail-following pass coefficient of the next person to pass to the transmission ratio determination module and the state recovery module. The transmission ratio determination module is used to receive the capacity value transmitted by the gate's individual passage capacity prediction module and the tail passage coefficient transmitted by the tail passage coefficient prediction module. Based on the received information, the central controller is used to control the transmission ratio of the adjustable reducer. The state recovery module is used to receive the tail-following coefficient transmitted by the tail-following coefficient prediction module. Based on the received information and the time when the person waiting to pass or the next person waiting to pass passes through the photoelectric sensing module located at the gate exit, the central controller determines the time for the adjustable reducer to return to the standard state.

7. The integrated gate control system based on artificial intelligence according to claim 6, characterized in that: The gate's individual passage capability prediction module includes a passage authorization information identification unit, a movement speed determination unit, a morphological feature determination unit, a height feature value determination unit, and a gate's individual passage capability prediction unit. The authorization information identification unit identifies the authorization information of the person to be passed, and the central controller controls the photoelectric sensing module and infrared detection module to start working based on the successful identification information; When the photoelectric sensor module located at the gate entrance senses a person waiting to pass, the central controller controls the gate wings to open. When the photoelectric sensor module located directly above the gate wings senses a person waiting to pass, it determines the movement time of the person from the gate entrance to the gate wings. Combining the distance from the gate entrance to the gate wings, it determines the movement speed of the person waiting to pass and transmits the determined movement speed to the gate's individual passage capacity prediction unit. The morphological feature determination unit uses a photoelectric sensing module to acquire a set of light images generated when a person to be passed moves from the gate entrance to the gate wing. The acquired light image set is matched using Internet technology. Based on the matching result, the morphological features of the person to be passed are determined, and the determined morphological features are transmitted to the gate's individual passage capacity prediction unit. When the photoelectric sensing module at the gate entrance senses a person waiting to pass, the height characteristic value determination unit detects the infrared heat energy emitted by the person. Based on the changes in infrared heat radiation energy that the infrared detection module can detect, the height characteristic value of the person is determined and transmitted to the gate's individual passage capacity prediction unit. The gate individual passage capability prediction unit receives the motion speed transmitted by the motion speed determination unit, the morphological features transmitted by the morphological feature determination unit, and the height feature value transmitted by the height feature value determination unit. Based on the received information, it predicts the individual's ability to pass through the gate individually and transmits the predicted individual's ability to pass through the gate individually to the transmission ratio determination module.

8. The integrated gate control system based on artificial intelligence according to claim 7, characterized in that: The tail-following pass coefficient prediction module includes a judgment unit, a motion feature determination unit, and a tail-following pass coefficient prediction unit. When the photoelectric sensor module located at the gate wing senses the passage of a person waiting to pass, the judgment unit determines whether the next person waiting to pass has motion characteristics based on whether the photoelectric sensor module located between the gate entrance and the gate wing can collect light images. If it is determined that the next person waiting to pass has no motion characteristics, it means that the average speed and motion tendency value of the next person waiting to pass are both 0. The collected light images are then transmitted to the motion characteristic determination unit, and the determined average speed and motion tendency value are transmitted to the tail passage coefficient prediction unit. The motion characteristic determination unit receives the optical image transmitted by the judgment unit, determines the motion tendency value and average motion speed value of the next person to pass based on the collected optical image, and transmits the determined motion tendency value and average motion speed to the tail passing coefficient prediction unit. The tail-following coefficient prediction unit receives the average motion speed and motion tendency value transmitted by the judgment unit, as well as the motion tendency value and average motion speed transmitted by the motion characteristic determination unit. Based on the received information, it predicts the tail-following coefficient of the next person to pass and transmits the predicted tail-following coefficient to the transmission ratio determination module.

9. The integrated gate control system based on artificial intelligence according to claim 8, characterized in that: The transmission ratio determination module includes a comparison unit, a first determination unit, and a second determination unit; The comparison unit receives the predicted tail-following coefficient transmitted by the tail-following coefficient prediction unit, compares the received tail-following coefficient with a set threshold, and transmits the comparison result to the first determination unit and the second determination unit. The first determining unit receives the comparison result of the tail passing coefficient ≤ set threshold transmitted by the comparison unit, and the capability value transmitted by the gate's individual passing capability prediction unit. The central controller controls the transmission ratio of the adjustable reducer according to the received capability value. The second determining unit receives the comparison result of the tail passage coefficient > set threshold transmitted by the comparison unit, the capability value transmitted by the gate single passage capability prediction unit, and the tail passage coefficient transmitted by the tail passage coefficient prediction unit. The central controller controls the transmission ratio of the adjustable reducer based on the received capability value and tail passage coefficient.

10. The integrated gate control system based on artificial intelligence according to claim 9, characterized in that: The state recovery module receives the predicted tail-following coefficient transmitted by the tail-following coefficient prediction unit. When the received tail-following coefficient is less than or equal to a set threshold, the central controller controls the adjustable reducer to return to the standard state when the photoelectric sensor at the gate exit senses the passage of the person waiting to pass. When the received tail-following coefficient is greater than the set threshold, the central controller controls the adjustable reducer to return to the standard state when the photoelectric sensor at the gate exit senses the passage time of the next person waiting to pass.

Images