Vehicle passing method and device based on barrier gate system, medium and program product

Abstract

The invention discloses a vehicle passing method and device based on a barrier gate system, a medium and a program product, and relates to the field of intelligent traffic control. According to the method, the opportunity passing windows available in the future are actively identified and quantified by monitoring the real-time traffic flow information of the main circulation channel, and the step converts the decision basis from passive waiting of the upstream gate into accurate prediction of the passing capacity of the downstream main road. Then, the theoretical vehicle accommodation capacity calculated based on the window is used as an insurmountable reverse constraint condition to determine the target release number of the upstream gate group. According to the release mode pulled by the downstream demand, it is ensured that the traffic flow released when the gate is opened each time is matched with the actual bearing capacity of the main circulation channel, confluence point conflicts and impact on main road traffic caused by blind release are avoided, and the overall operation stability and safety of a traffic system are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent traffic control, and in particular to a vehicle passing method based on a barrier gate system, a device, a medium and a program product. BACKGROUND

[0002] In the traffic management of super-large underground garages, a common topology is that multiple parallel entrance lanes pass through their respective gates and finally converge into a one-way main circulating channel.

[0003] To ensure that they do not cause conflicts at the convergence point, while avoiding impacting the existing stable traffic flow in the main circulating channel. By deploying sensors (such as ground magnetic coils or video detectors) at key locations in the convergence area or the main circulating channel, the speed and density of the traffic flow are monitored in real time by sensors arranged downstream of the main circulating channel, and the maximum acceptable inflow rate that the current main channel can safely absorb is dynamically calculated based on this. Subsequently, the control system will distribute this total inflow rate index to each upstream entrance gate according to a pre-set fairness algorithm (such as polling or equal division principle).

[0004] However, in complex traffic environments where traffic density changes dramatically and the number of queued vehicles at each gate differs significantly, the fixed timing of the isochronous polling release method will be insufficient in adaptability. In the related art, when some gates accumulate a large number of queued vehicles while other gates are relatively idle, the system still allocates the same release time to each gate according to the pre-set period. This mechanical time allocation cannot respond to the dynamically changing actual demand distribution, resulting in a long-term accumulation of vehicles at some gates that cannot be dispersed in time, while another part of the gates frequently obtains more release time than the actual need, leading to a decline in overall dispersal efficiency due to unbalanced resource allocation. SUMMARY

[0005] The present application provides a vehicle passing method based on a barrier gate system, a device, a medium and a program product, for improving the passing efficiency and operational stability of underground garage traffic nodes.

[0006] In a first aspect, the application provides a vehicle passing method based on a barrier system, which comprises: after monitoring the number of queued vehicles in an entry lane area corresponding to a target barrier of a barrier group, identifying an opportunity passing window in a future preset time period based on real-time traffic flow information of a main circulating channel, the opportunity passing window being a time window in which the main circulating channel can accommodate a preset vehicle queue unit in the future preset time period; calculating a theoretical vehicle accommodation capacity based on the time span of the opportunity passing window; taking the theoretical vehicle accommodation capacity as a reverse constraint condition, determining a target release quantity from the queued vehicles of the barrier group that matches the theoretical vehicle accommodation capacity, and marking the queued vehicles of the target release quantity as marked queued vehicles; according to a preset polling strategy, allocating the opportunity passing window to the target barrier of the marked queued vehicles in the barrier group; when detecting that the opportunity passing window is reached, controlling the target barrier to release the marked queued vehicles when the opportunity passing window is reached; the target barrier is one or more of the barrier group.

[0007] By adopting the above technical solution, the application actively identifies and quantifies the available opportunity passing window by monitoring the real-time traffic flow information of the main circulating channel, which changes the basis of decision-making from passive waiting of the upstream barrier to accurate prediction of the carrying capacity of the downstream main road. Subsequently, the theoretical vehicle accommodation capacity calculated based on the window is taken as an insurmountable reverse constraint condition to determine the target release quantity of the upstream barrier group. This release mode driven by downstream demand ensures that the vehicle flow released by each barrier opening matches the actual carrying capacity of the main circulating channel, avoiding conflicts at the convergence point and impacts on the main road traffic caused by blind release, and improving the overall operation stability and safety of the traffic system.

[0008] In combination with some embodiments of the first aspect, in some embodiments, the step of identifying the opportunity passing window in the future preset time period based on the real-time traffic flow information of the main circulating channel specifically comprises: based on the vehicle passing timestamp data collected by the vehicle detection sensor deployed at the preset key section of the main circulating channel, calculating the time interval distribution between adjacent vehicles and identifying the inter-vehicle gap with a time interval greater than a preset safe convergence interval; based on the duration of the inter-vehicle gap and a standard passing time reference value of the lane converging from the target barrier, screening out effective inter-vehicle gaps whose duration is sufficient to accommodate at least one preset vehicle queue unit; calculating the expected time window of the inter-vehicle gap reaching the convergence point by forward calculating the propagation delay time of the start time and end time of the effective inter-vehicle gap; and obtaining the opportunity passing window by superimposing all time windows expected to reach the convergence point within the future preset time period.

[0009] By adopting the technical solution, the vehicle inter-gap larger than the safety interval is identified by analyzing the vehicle passing timestamp data, and the expected passing time window accurate to seconds is obtained by combining the vehicle propagation delay from the gate to the confluence point and performing forward calculation. This method not only identifies the physically existing gap, but more importantly, it predicts the accurate timing of the gap reaching the confluence point. This enables the system to plan ahead and convert the originally discrete and random inter-vehicle gaps into a series of determined and schedulable valuable time resources. By superimposing all the expected time windows, the system can obtain a global view of the total receiving capacity of the main road in the future period of time, providing a data basis for subsequent vehicle accommodation capacity calculation and efficient release quantity allocation, and improving the predictability and scientificity of decision-making.

[0010] In combination with some embodiments of the first aspect, in some embodiments, the step of determining the target release quantity matching the theoretical vehicle accommodation capacity from the queuing vehicles of the gate group as a reverse constraint condition specifically comprises: in the case where the total number of queuing vehicles is determined to be less than or equal to the theoretical vehicle accommodation capacity, determining the total number of queuing vehicles as the target release quantity; in the case where the total number of queuing vehicles is determined to be greater than the theoretical vehicle accommodation capacity, calculating the release quota of the target gate based on the historical passing priority weight and the current number of queuing vehicles of the target gate; and distributing the queuing vehicles according to the release quota of the target gate, and determining the distribution result as the target release quantity, wherein the distribution result satisfies that the total number of distributed vehicles is not greater than the theoretical vehicle accommodation capacity.

[0011] By adopting the technical solution, when the number of queuing vehicles is greater than the accommodable number of resources, the release quota is calculated based on the historical passing priority weight and the current number of queuing vehicles of each gate. The historical passing priority weight ensures long-term fairness and avoids long-term neglect of some gates, and the current number of queuing vehicles reflects the immediate relief pressure, so that the system can quickly respond and relieve the congestion point. Through this multi-dimensional and dynamic quota allocation method, the system can regulate and control the traffic pressure of the entire gate group under limited passing resources, ensuring the overall relief efficiency and taking into account the service fairness of each entrance.

[0012] In some embodiments of the first aspect, before the step of monitoring the number of queued vehicles in the entrance lane area of the target gate corresponding to the gate group, the method further comprises: calculating a standard passage time benchmark value of the target gate based on physical topology parameters of the gate group, the standard passage time benchmark value being the theoretical shortest time for a vehicle to reach a convergence point from the target gate via a standard route, the physical topology parameters including straight-line distances of each gate to the convergence point, lane widths, and path turning angles; determining a gate load capacity of the target gate according to historical traffic volume data of the target gate and the standard passage time benchmark value, the gate load capacity of the target gate being the maximum number of released vehicles per unit time of the target gate; determining an initial priority weight coefficient of the target gate based on a ranking of the gate load capacity of the target gate in the gate group; and obtaining a dynamic performance score by calculating a deviation degree of a released quota corresponding to a historical released quota instruction of the target gate from an actual number of released vehicles in a preset evaluation period.

[0013] By adopting the above technical solutions, the initial priority weight coefficient reflecting the innate dredging capacity of the gate is determined based on the physical topology and the historical traffic volume data, laying a foundation for fairness. By introducing the dynamic performance score feedback mechanism, the deviation between the planned release quantity and the actual release quantity is continuously compared to evaluate the performance of each gate in real operation. The initial weight and the dynamic score are weighted to calculate the final priority weight, which not only reflects the static physical properties, but also reflects the dynamic execution efficiency and reliability in real time. This mechanism can automatically adjust the weight of the gate with poor traffic, thereby realizing self-optimization of the priority.

[0014] In some embodiments of the first aspect, the step of calculating the theoretical vehicle capacity based on the time span of the opportunity passage window specifically comprises: obtaining a standard merging duration required by a single vehicle to merge into the main circulation channel from the target gate, adding the standard merging duration to a preset minimum safe vehicle headway to form a single-vehicle merging time unit; and dividing the opportunity passage window by the length of the single-vehicle merging time unit to obtain the maximum integer value of vehicles that can be accommodated by the opportunity passage window as the theoretical vehicle capacity.

[0015] By adopting the technical scheme, the application combines the standard merging time length and the minimum safe vehicle headway to construct a single-vehicle merging time unit as a core measurement unit. The unit scientifically quantifies the time resources that a vehicle needs to occupy to safely and completely complete the merging action, and its composition considers not only the movement process of the vehicle itself but also the necessary condition for maintaining a safe distance with the front and rear vehicle flow. Based on this accurate measurement unit, through simple division and rounding operations, the system can quickly convert an abstract time window into a specific and operable theoretical vehicle capacity integer value, making the conversion process from time resources to vehicle quantity safe and efficient.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after the step of taking the theoretical vehicle capacity as a reverse constraint condition, determining a target release quantity matching the theoretical vehicle capacity from the queuing vehicles of the gate group and marking the queuing vehicles of the target release quantity as marked queuing vehicles, the method further comprises: acquiring vehicle identity information of the marked queuing vehicles through a license plate recognition system, and querying a parking space reservation database to determine whether the marked queuing vehicles have a reserved parking area; in the case where it is determined that the marked queuing vehicles have a reserved parking area, calculating an estimated driving time from the corresponding gate to the reserved parking area via the main circulating channel; based on the estimated driving time and the real-time parking space occupancy rate of the reserved parking area, determining a target parking space for the marked queuing vehicles from multiple vacant parking spaces, and providing a parking space guiding route to the client corresponding to the marked queuing vehicles.

[0017] By adopting the technical scheme, after completing the gate release decision, the application further extends the service to the parking guidance link after the vehicle enters the main circulating channel. By acquiring the vehicle identity through the license plate recognition and associating the reservation information, the system can know the final destination of the vehicle in advance. Based on this, the system not only calculates the estimated driving time but also actively determines a specific target parking space in combination with the real-time parking space occupancy rate of the target area. This end-to-end service from the entrance to the parking space connects the originally isolated gate passage management and the internal management of the parking lot, providing a deterministic passage solution for the driver. This reduces the confusion and invalid detours of the driver after entering the garage to find a parking space, eliminates the internal traffic disturbance caused by finding a parking space, improves the smoothness of the main circulating channel, further consolidates and improves the overall technical effect of the gate passage efficiency optimization, and improves the overall service experience and satisfaction of the user.

[0018] In some embodiments of the first aspect, in some embodiments, the step of determining the target parking space for the marked queuing vehicle from the plurality of vacant parking spaces based on the estimated driving time and the real-time parking space occupancy of the reserved parking area, and providing the parking guidance route to the client corresponding to the marked queuing vehicle, specifically comprises: identifying a congested road section based on the monitored traffic density and speed of the plurality of target road sections in the main circulation channel, wherein the speed of the congested road section is lower than a preset speed threshold and the traffic density is higher than a preset density threshold; dividing the main circulation channel into a plurality of traffic state intervals based on the position distribution and influence range of the congested road section and determining the traffic fluency scores of the plurality of traffic state intervals; calculating the corrected estimated time of arrival at each candidate area based on the traffic state interval passed by the marked queuing vehicle to the target parking space; setting the parking area with the shortest corrected estimated time and the highest real-time parking space occupancy as the current recommended target area; determining the target parking space for the marked queuing vehicle from the plurality of vacant parking spaces in the current recommended target area, and sending the guidance information including the recommended target area and the optimal driving path to the client corresponding to the marked queuing vehicle.

[0019] By using the above technical solution, the application actively identifies the congested road section by monitoring the traffic density and speed of the main road in real time, and divides the entire road network into traffic state intervals with different fluency scores based thereon, thereby generating a real-time traffic situation map. When calculating the arrival time, instead of using the fixed average speed, the corrected estimated time is dynamically calculated according to the real-time scores of the intervals passed by the vehicle path. Furthermore, when recommending the target area, the system adopts a multi-objective optimization strategy combining the shortest time and the highest occupancy. This method ensures that the user is recommended to select the actual target area in combination with the real-time traffic and parking space occupancy, realizes the transition from the static shortest path to the dynamic optimal path, and improves the traffic efficiency and parking success rate of the vehicle in the field.

[0020] In the second aspect, the application provides a device, comprising: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code comprises computer instructions, and the one or more processors invoke the computer instructions to make the device execute the method described in the first aspect and any possible implementation manner of the first aspect.

[0021] In the third aspect, the application provides a computer program product containing instructions, which, when the computer program product is run on a device, makes the device execute the method described in the first aspect and any possible implementation manner of the first aspect.

[0022] In a fourth aspect, the present application provides a computer-readable storage medium comprising instructions which, when executed on a device, cause the device to perform the method as described in the first aspect and any possible implementation of the first aspect.

[0023] The one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Since the future available traffic capacity of the downstream main loop channel is used as a reverse constraint condition for the number of upstream gate releases, the technical problem of traffic conflicts and traffic shocks caused by the disconnection between released traffic flow and main road carrying capacity due to the use of fixed timing or equalization strategy in the prior art is effectively solved, thereby realizing the coordination of gate release and main road traffic flow, improving the utilization efficiency of road resources and the traffic efficiency of the entire traffic node under the premise of ensuring traffic safety and stability.

[0024] 2. Since the historical traffic priority and the current queue number are used to dynamically calculate the release quota of each gate in resource competition, the resource allocation imbalance problem of long-term congestion in some gates and idle resources in some resources caused by the inability of mechanical time allocation to respond to dynamic demand distribution in the prior art is effectively solved, thereby realizing quick response and relief of emergency queue pressure under limited traffic opportunities, taking into account the fairness of long-term service, and making the traffic resource allocation more reasonable and efficient.

[0025] 3. Since the optimal driving path from the entrance to the predetermined idle parking space is planned and provided for the vehicle before it passes through the gate, the technical problem of internal congestion and reduced traffic efficiency caused by disordered detours due to the search for parking spaces after the vehicle enters the garage is effectively solved, the invalid driving time of the vehicle in the field is reduced, and the technical effects of improving user experience and the running order and stability of the garage interior are achieved. BRIEF DESCRIPTION OF DRAWINGS

[0026] Figure 1 is a flowchart of a vehicle traffic method based on a gate system in the embodiments of the present application; Figure 2 is another flowchart of a vehicle traffic method based on a gate system in the embodiments of the present application; Figure 3 is an exemplary hardware structure diagram of a device in the embodiments of the present application. DETAILED DESCRIPTION

[0027] The terminology used in the following embodiments of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application. As used in the description of the application and the appended claims, the singular forms "a," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It also will be understood that the term "and / or," as used herein, refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0028] Hereinafter, the terms "first", "second" are used only for the purpose of description, and cannot be understood as implying or suggesting relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, the meaning of "a plurality of" is two or more, unless otherwise specified.

[0029] Please refer to Figure 1 , a flowchart of one embodiment of a vehicle passing method based on a barrier system in the present application.

[0030] S101, after monitoring the number of queued vehicles in the entrance lane area corresponding to the target gate of the gate group, identify the opportunity passing window in the future preset time period based on the real-time traffic flow information of the main circulating channel.

[0031] Among them, "gate group" means a set composed of multiple entrance barriers, for example, A, B and C three entrance gates of an underground garage together constitute a gate group.

[0032] This step is triggered after the device senses the queue status at each gateway. The device first processes the continuous vehicle pass timestamp data from the vehicle detection sensors deployed at the pre-set key sections of the main loop channel in parallel. The core task of the device is not simply to count the traffic, but to obtain the distribution of the actual time interval between adjacent vehicles (i.e. the headway) from these timestamp sequences by calculating the difference between adjacent timestamps. Subsequently, the device compares each calculated time interval with a pre-set "safe merging interval" threshold, which is the minimum time requirement to ensure that the merging vehicle and the vehicle flow before and after the main road can maintain a safe driving distance. All time intervals greater than this threshold are identified as potential "inter-vehicle gaps". The device further filters out those gaps that, after deducting the "standard passing time reference value" (i.e. the time spent by a vehicle from the gateway to the merging point) of the merging lane from each target gateway, still have a sufficient duration to accommodate at least one pre-set vehicle queue unit (e.g. a standard length car safely passing) to define "effective inter-vehicle gaps". Finally, the device calculates the delay time required for these effective inter-vehicle gaps to propagate to the merging point according to their position on the main road and the current average speed of the main road, and calculates the forward starting and ending time of the gap to obtain the expected time window of the gap to reach the merging point. The device superimposes and merges all the time windows expected to reach the merging point within a pre-set time period on the time axis, and finally forms one or more discrete "opportunity passing windows" that can be allocated.

[0033] In some embodiments, the value of the safe merging interval can be associated with environmental factors or road conditions. For example, the device can receive data from environmental sensors such as illumination sensors, road surface wetness detectors. In the case of insufficient light at night or low visibility (such as smoke), the device will automatically increase the pre-set safe merging interval value by a fixed safety margin (e.g. 0.5 seconds) to compensate for the decreased perception and reaction ability of the driver. Similarly, when detecting wet road surface, the device will also adjust the threshold value of the safe merging interval accordingly, considering the lengthening of the braking distance.

[0034] In some other embodiments, the value of the safe merging interval can also be dynamically determined by the device according to the real-time average vehicle speed of the main loop channel. While monitoring the vehicle timestamp, the device also acquires the average driving speed of the main loop channel in the upstream area of the merging point in real time, and through an internally stored function or lookup table, the function maps the average speed of the main road to a corresponding "safe merging interval" value. For example, when the speed is higher than 60 km / h, the safe interval is set to 3 seconds; when the speed is between 40-60 km / h, the safe interval is set to 2.5 seconds; and when the speed is lower than 40 km / h, the safe interval is set to 2 seconds. When performing this step, the device first queries the current main road speed, dynamically acquires the currently most suitable safe merging interval value, and then uses this dynamic value to screen the vehicle gap.

[0035] It can be understood that other ways can also be used to determine the safe merging interval, such as the safe merging interval can also be related to the type of vehicle to be merged. In the case where the device can be associated to the vehicle registration information through license plate recognition or judge the vehicle type through image recognition, the device can set different safe merging intervals for different types of vehicles, which is not limited here.

[0036] In some embodiments, the opportunity passing window in the future preset time period can be identified by the real-time traffic flow information based on the main circulation channel in various ways. Optionally, the device first acquires the time stamp data of each passing vehicle continuously through the vehicle detection sensor deployed at the preset key section of the main circulation channel; then, the device performs differential calculation on the time stamp data to obtain a distribution sequence containing the time intervals between adjacent vehicles, and screens all the inter-vehicle gaps with time intervals greater than the preset safe merging interval; then, the device further screens the effective inter-vehicle gaps with sufficient duration to accommodate at least one preset vehicle queue unit (such as a standard car) to safely merge based on the duration of each inter-vehicle gap and the standard passing time reference value from the target gate to the merging point; finally, the device calculates the propagation delay time of the starting and ending time of each effective inter-vehicle gap according to the distance from its current position to the merging point and the average speed of the main road, so as to obtain the expected time window of the gap reaching the merging point, and combines all the expected time windows reaching the merging point in the future preset time period to form the final opportunity passing window. Optionally, the device can also use a prediction method based on a traffic flow model: first, the device calibrates a macroscopic traffic flow model (such as a cellular automaton model) using historical and real-time traffic flow data (such as density, speed, and flow); then, the device runs the model to simulate and predict the traffic flow state of the main circulation channel at the merging point location in the future preset time period; finally, the device identifies the time periods with low traffic density below the preset threshold and lasting for a certain duration in the simulation results, and defines these time periods as the opportunity passing window. It can be understood that other ways can also be used to identify the opportunity passing window, which are not limited here.

[0037] S102, calculate the theoretical vehicle accommodation capacity based on the time span of the opportunity passing window.

[0038] After the device obtains one or more opportunity traffic windows and their respective time spans from the output of step S101, it obtains two key time parameters from the internal configuration parameters: the standard merging duration required for a single vehicle to merge from the target gate into the main circulating channel, and the preset minimum safe headway set to ensure the safety distance within a vehicle platoon. The device adds these two time parameters to form a composite single-vehicle merging time unit, which precisely defines the additional opportunity window duration consumed by each additional vehicle in a continuously released vehicle platoon. Subsequently, the device divides the total time span of the opportunity traffic windows identified in step S101 by the single-vehicle merging time unit. Since the number of vehicles must be an integer, the device performs a floor operation on the division result to ensure that the calculation result does not exceed the actual time constraints. When there are multiple discrete opportunity traffic windows, the device performs the above calculation process for each window separately, then accumulates the capacity of each window to obtain the total theoretical vehicle capacity within the entire future preset time period. The device also takes into account the difference in time consumption between the first vehicle and subsequent vehicles, as the first vehicle requires the full standard merging duration, while subsequent vehicles mainly consume the safe headway. Therefore, in the precise calculation, the device separately handles the time consumption of the first vehicle, and divides the remaining time according to the safe headway.

[0039] It can be understood that other ways can also be used to implement this step, such as dynamically adjusting the standard merging duration according to the real-time obtained queuing vehicle type information, setting a longer merging time for large vehicles and a shorter time for small vehicles, or dynamically adjusting the preset minimum safe headway according to the current main circulating channel traffic density, which is not limited here.

[0040] In some embodiments, the device, after completing each vehicle release operation, accurately measures the total consumption time from the start of merging to the complete entry of the last vehicle in the actual released vehicle platoon through the sensors deployed at the merging point, and compares the actual consumption time with the expected consumption time calculated based on the theoretical vehicle capacity to calculate an execution efficiency ratio. The device maintains a sequence of efficiency ratios within a sliding window and calculates the weighted average value as the current execution efficiency coefficient, which reflects the degree of deviation between theoretical calculation and actual execution under the current traffic environment and driver behavior pattern. In subsequent execution of this step, the device multiplies the original theoretical capacity value obtained by the time span division by the execution efficiency coefficient, and then performs a floor operation to obtain a theoretical vehicle capacity that is closer to the actual execution capacity after being corrected by historical data.

[0041] S103, taking the theoretical vehicle accommodating capacity as a reverse constraint condition, determining a target release quantity matching the theoretical vehicle accommodating capacity from the queuing vehicles of the gate group and marking the queuing vehicles of the target release quantity as marked queuing vehicles.

[0042] This step is performed after obtaining the theoretical vehicle accommodating capacity. The device sets the theoretical vehicle accommodating capacity as the upper limit of the total resources for this distribution, and then sums up the total number of queuing vehicles of all gates and compares it with the accommodating capacity. When the total number of queuing vehicles is less than or equal to the theoretical vehicle accommodating capacity, it indicates that the traffic resources are sufficient, and the device directly determines all queuing vehicles as target release vehicles and marks them as marked queuing vehicles. At this time, there is no resource competition. When the total number of queuing vehicles is greater than the theoretical vehicle accommodating capacity, the device starts a multi-factor weight-based competition distribution mechanism, which needs to calculate the release quota of each gate with queuing vehicles. The device first obtains the historical traffic priority weight of each gate, which takes into account the physical topological position, historical traffic volume, load capacity and other long-term factors of the gate. Then the device obtains the current real-time queuing vehicle quantity of each gate, which reflects the immediate pressure of the relief demand. The device multiplies the priority weight of each gate with its current queuing quantity to obtain the comprehensive weight value of the gate through a preset distribution function, and then normalizes the comprehensive weight values of all gates. Finally, the theoretical vehicle accommodating capacity is distributed to each gate according to the normalized weight proportion. The device performs integer processing on the distribution result and ensures that the sum of the quotas of all gates does not exceed the theoretical vehicle accommodating capacity, and then selects the corresponding number of vehicles from the front end of the queue of each gate according to the release quota obtained by the gate to mark them.

[0043] In some embodiments, the target number of released vehicles from the queue of vehicles at the group of gates can be determined in various ways. Optionally, the device uses a standard distribution algorithm based on weight proportion. First, it is determined whether the total number of vehicles in the queue at all gates exceeds the theoretical vehicle capacity. If not, all vehicles in the queue are determined as the target number of released vehicles. If yes, the weight distribution process is entered. The device calculates the distribution weight value for each gate with vehicles in the queue, which is equal to the historical priority weight of the gate multiplied by the current number of vehicles in the queue. Then, the device calculates the sum of all gate weight values as the denominator, multiplies the theoretical vehicle capacity by each gate weight value, and divides by the sum of the weights to obtain the initial quota for each gate. Finally, the device rounds down the quota for each gate and allocates the remaining vehicle slots according to the size of the quota priority to ensure that the total distribution number is equal to the theoretical vehicle capacity. Optionally, the device can use a dynamic distribution strategy based on queue length priority. This strategy not only considers the number of vehicles in the queue but also focuses on the waiting time of the vehicles. The device first calculates the cumulative waiting time of the head vehicle at each gate. Then, the waiting time is used as an emergency indicator and combined with the historical priority weight to obtain a dynamic priority. Next, the device allocates the release slots to each gate in order of dynamic priority from high to low. Each time the device allocates a certain number of vehicles to the gate with the highest priority until the queue pressure is relieved or the theoretical capacity is exhausted. This way can effectively prevent vehicles at some gates from waiting for a long time. It can be understood that other ways can also be used to implement the distribution process, such as differentiated distribution combined with vehicle type information to provide priority passage for vehicles carrying special goods or with time sensitivity, or dynamically adjusting the distribution weight of each gate according to the real-time congestion status of different regions of the main loop channel. Here, no limitation is made.

[0044] It can be understood that the device is pre-set with a judgment threshold representing the severity of congestion, for example, the number of vehicles in the queue at a gate exceeds 25 vehicles or the waiting time of the head vehicle exceeds 5 minutes. When the device detects that a gate reaches the threshold, the distribution algorithm switches from the regular fair distribution mode to the emergency relief mode. In the emergency relief mode, the device allocates a large proportion (such as 70%-90%) of the current theoretical vehicle capacity to the gate with the most severe congestion, while the gates that do not reach the threshold are allocated a small amount of quota or not allocated temporarily. In this way, the system can quickly reduce the queue length at the most severe congestion point and make it fall below the safety level. The device continuously monitors the congestion status of each gate. When the originally severe congestion gate is relieved, if the congestion degree of other gates rises to the threshold at this time, the system will transfer the concentrated allocation target to the new congestion point. Only when the congestion degree of all gates is below the pre-set threshold, the system will return to the regular fair distribution mode based on weight proportion.

[0045] In some embodiments, the historical traffic priority weight is not a static parameter. Optionally, the device can pre-calculate a standard passage time benchmark for each target intersection based on pre-stored physical topology parameters of the intersection group. The physical topology parameters include geometric information such as the straight-line distance from each intersection to the main circulation channel merging point, the lane width, and the path turning angle. The device calculates the theoretical shortest time for a vehicle to travel from the target intersection to the merging point via the standard route under ideal conditions using a kinematic model, which is the standard passage time benchmark. Then, the device determines the load capacity of the target intersection based on the historical traffic volume data of the intersection and the standard passage time benchmark calculated above. Specifically, the device analyzes the maximum number of released vehicles per unit time for the intersection in the historical data, and calibrates it in combination with the standard passage time benchmark to obtain a load capacity index that quantitatively represents the maximum traffic efficiency of the intersection. Then, the device sorts all target intersections in the intersection group based on their load capacities, and determines an initial priority weight coefficient for each target intersection according to the sorting result. Generally, the higher the load capacity of an intersection, the higher the initial priority weight coefficient assigned to it, which reflects its physical potential for traffic relief. At the same time, the device continuously performs feedback evaluation. Within a preset evaluation period (e.g., the past one hour), the device counts the total quota corresponding to the historical release quota instructions received by the target intersection in the period, and compares it with the actual number of vehicles passing through the intersection in the period to calculate the deviation between the two, thereby obtaining a dynamic performance score. The score reflects the execution efficiency and reliability of the intersection in actual operation, and the smaller the deviation, the higher the score. Finally, the device calculates the final "historical traffic priority weight" for the current distribution period by weighting the static initial priority weight coefficient determined above and the real-time updated dynamic performance score according to the time sequence. For example, a weighted average formula is used: final weight = a * initial priority weight coefficient + (1-a) * dynamic performance score, where a is an adjustable weight factor.

[0046] Optionally, the device continuously counts and records the cumulative historical traffic volume of each target intersection in the intersection group within a preset long statistical period (e.g., the past week or month). The device calculates the total traffic volume of the intersection group in the statistical period. The device divides the cumulative historical traffic volume of each target intersection by the total traffic volume to obtain a normalized proportion value, and directly uses this proportion value as the historical traffic priority weight of the target intersection in the next period. For example, if A and B intersections pass 7000 and 3000 vehicles respectively in a week, the total is 10000, then the weights of A and B intersections are 0.7 and 0.3 respectively. It can be understood that other ways can be used to calculate the historical traffic priority weight, such as assigning weights based on the statistics of historical average queue length or average waiting time, which are not limited here.

[0047] S104, assign the opportunity passing window to the target gate of the marked queuing vehicle in the gate group according to the preset polling strategy.

[0048] After determining the released vehicles of each gate in step S103, the device checks how many gates have marked queuing vehicles. If only one gate has vehicles to be released, the opportunity passing window is naturally assigned to the only gate. When multiple gates have marked queuing vehicles at the same time, the device starts the preset polling strategy for arbitration. The device maintains a dynamic polling state table, which records the current polling position, cumulative service time, priority weight and other information of each gate. According to the specific type of the polling strategy, the device will use different decision logic. For example, under the weighted polling strategy, the device will assign each gate a service opportunity proportional to its weight, and the gate with higher weight will get more window assignment opportunities in a polling cycle. The device also considers the time characteristics of the opportunity passing window, including the start time, duration, etc., to ensure that the selected target gate can complete vehicle release within the specified time. After determining the target gate, the device binds the opportunity passing window to the target gate and updates the relevant records in the polling state table to prepare for the next window assignment. The device also handles special cases, such as when a gate in the polling sequence should get a window but temporarily has no vehicle to be released, the device will skip that gate and assign the window to the next valid gate in the sequence.

[0049] In some embodiments, the polling allocation of opportunity passage windows can be implemented in various ways: optionally, the device adopts a fairness allocation mechanism based on deficit polling, which maintains a deficit counter for each gate to track the amount of service it should have but has not yet obtained, and at the beginning of the polling cycle, the device increases the credit value of each gate's deficit counter according to its historical passage priority weight, when it is necessary to allocate an opportunity passage window, the device selects the gate with the largest deficit counter value and an empty queue as the target gate, and after the window allocation is completed, the device subtracts the amount of resources consumed by this service (which can be the length of time or the number of vehicles) from the deficit counter of the gate, which ensures that the long-term allocation fairness can be maintained even in the case of uneven window size. Optionally, the device can implement an intelligent allocation strategy based on window matching degree, which evaluates the time span of the upcoming opportunity passage window and calculates the matching degree with the total passage time required by the vehicles queued in each gate, and the device will preferentially allocate the window to the gate whose required passage time is closest to the window length to maximize the utilization efficiency of time resources, for example, when an 8-second opportunity window appears, the device will choose the A gate which requires 7 seconds of passage time over the B gate which requires 12 seconds of passage time, because the A gate can make more full use of the window without wasting time, and at the same time, the device also considers whether the remaining time after window allocation is sufficient to accommodate the minimum release requirements of other gates. It can be understood that other ways can also be used to implement this polling allocation process, such as introducing a dynamic weight adjustment mechanism based on machine learning to automatically optimize the polling weights of each gate according to historical allocation results and traffic flow change patterns, or implementing preemptive scheduling combined with emergency vehicle detection signals, which are not limited here.

[0050] In some embodiments, the device does not immediately allocate each opportunity passage window after receiving a series of windows, but first analyzes the distribution pattern of these windows on the time axis and identifies the time interval characteristics between adjacent windows. When the device finds that the interval time between two adjacent windows is less than the time required for a complete gate switching cycle (for example, less than 3 seconds) and the interval itself cannot be effectively utilized, the device will logically combine the two windows into a larger continuous window for unified allocation. In addition, the device also implements a sticky session mechanism, that is, when a gate obtains the allocation right of a window and starts to release, if a new opportunity window appears during its release process or immediately after its release is completed, the gate will have the priority to continue using the window without the need to participate in polling competition again. Only when the gate's continuous window occupation time exceeds the preset upper limit or its queue to be released is completely empty, the control right will be transferred to other gates in the polling sequence. This strategy improves the overall time utilization efficiency and system throughput by reducing unnecessary gate switching times.

[0051] S105, when detecting the arrival of the opportunity passing window, controlling the target gate to release the marked queued vehicles when the opportunity passing window arrives.

[0052] After the window allocation is completed in S104, the device waits for an accurate timing to perform the physical release operation. Specifically, the real-time scheduler inside the device continuously monitors the system clock and compares it with the start time of the opportunity passing window determined in S104. When the system time reaches the predetermined window start time, the device sends an opening instruction to the controller of the target gate in advance by a preset compensation time (usually a few hundred milliseconds, used to offset communication delay and mechanical response time). When the gate barrier rises, the device activates the vehicle detection sensor array located behind the gate and starts real-time monitoring and counting of the passing vehicles. The device maintains an internal counter, and each time the detection sensor identifies a complete vehicle (from the front entering to the tail leaving the detection area) passing, the counter value increases by 1. The device compares the current count value with the target release quantity determined in S103 in real time, and when the two are equal, it immediately sends a closing instruction to the gate controller.

[0053] In the embodiments of the present application, since the technical feature of determining the target release quantity by using the opportunity passing window based on the real-time traffic flow prediction of the main circulation channel and taking the theoretical vehicle capacity of the opportunity passing window as a reverse constraint condition, the resource allocation imbalance problem in the prior art caused by the fixed timing or equal division strategy, which cannot respond to the dynamic changes in traffic demand, resulting in part of the gate vehicle backlog and part of the resource idling, is effectively solved, and the utilization rate of the available gap of the main circulation channel is improved under the premise of ensuring the convergence safety and not impacting the stable traffic flow of the main road, and the comprehensive dredging efficiency of the entire gate group and the dynamic adaptability of the system are improved.

[0054] In the above embodiments, the device can achieve the technical effect of optimizing vehicle convergence and improving gate passing efficiency by using the gate release strategy based on the reverse constraint of the opportunity window of the main circulation channel. However, in actual application, the released vehicles may travel in disorder and at low speed after entering the main circulation channel due to unfamiliarity with the road conditions or searching for parking spaces, forming new and unpredictable internal congestion points, thereby destroying the stability of the traffic flow of the main circulation channel and affecting the accuracy of the prediction of subsequent opportunity passing windows. The vehicle passing method based on the gate system can solve this technical problem by combining a dynamic parking space guidance strategy based on real-time road conditions, thereby eliminating internal traffic disturbances caused by searching for parking spaces and further improving the gate passing efficiency.

[0055] Please refer to Figure 2 , which is another flowchart of the vehicle passing method based on the gate system in the embodiments of the present application.

[0056] S201, after monitoring the number of queued vehicles in the entrance lane area corresponding to the target gate in the gate group, identify the opportunity passing window in the future preset time period based on the real-time traffic flow information of the main circulating channel.

[0057] S202, calculate the theoretical vehicle accommodation capacity based on the time span of the opportunity passing window.

[0058] S203, take the theoretical vehicle accommodation capacity as a reverse constraint condition, determine the target release quantity from the queued vehicles of the gate group, and mark the queued vehicles of the target release quantity as marked queued vehicles.

[0059] Steps S201-S203 are similar to steps S101-S103 in the embodiment shown in Figure 1 The steps S101-S103 in the embodiment shown in

[0060] S204, obtain the vehicle identity information of the marked queued vehicles through the license plate recognition system, and query the parking space reservation database to determine whether the marked queued vehicles have a reserved parking area.

[0061] The execution timing of this step is after some queued vehicles are marked as to-be-released in step S203, as the starting link of the parking space guidance. The device first activates the license plate recognition system deployed in front of the target gate, and collects images of the "marked queued vehicles" at the front end of the queue. Through the built-in image processing algorithm, the collected images are preprocessed, the license plate is located, the characters are segmented, and the characters are recognized, and finally the string format license plate number of the vehicle is output as its vehicle identity information. After obtaining the license plate number, the device takes the license plate number as the query keyword and initiates a query request to the internal or external parking space reservation database. The database performs a matching operation and returns a Boolean value or a record containing detailed information. The device receives and parses the return result to determine whether the marked queued vehicle has the right to reserve parking. The determination result will be used as the basis for determining whether the subsequent step (such as S205) is triggered.

[0062] S205, in the case where it is determined that the marked queued vehicle has a reserved parking area, calculate the estimated driving time from the corresponding gate to the reserved parking area via the main circulating channel.

[0063] After confirming the vehicle's eligibility at S204, the device obtains the geographical coordinates of the "corresponding gate" where the vehicle is currently located and the "reservation parking area" from the internal stored digital map of the parking lot. Then, the device calls the built-in path planning algorithm (such as A* or Dijkstra algorithm) to calculate the shortest physical path from the gate coordinate to the reservation area coordinate on the road network graph represented by the digital map, and obtains the total length of the path. Then, the device reads a preset "standard driving speed" parameter from the system configuration, which represents the average speed of the vehicle when driving smoothly in the main circulation channel of the garage. Finally, the device divides the total length of the shortest path calculated by the "standard driving speed" to obtain the initial "estimated driving time".

[0064] In some embodiments, the calculation of the estimated driving time can be implemented in various ways: optionally, the device uses a calculation method based on road segment accumulation. First, the device models the digital map of the garage as a graph composed of nodes (intersections) and weighted edges (road segments, weights are lengths). Then, the device runs the Dijkstra algorithm to find the shortest path from the starting gate node to the target area entrance node, which is composed of a series of consecutive road segments. Finally, the device traverses all road segments on the path, adds the lengths of each road segment to obtain the total distance, and then divides the total distance by the preset standard driving speed to obtain the estimated driving time. Optionally, the device can use a query method based on historical data statistics. The device maintains a historical driving time database inside, which stores the average driving time from each gate to each parking area in different time periods (such as weekday morning peak, flat peak, evening peak, weekend, etc.). When needed, the device first obtains the current system time and determines its time period type. Then, the device directly queries the corresponding historical average driving time in the database with "starting gate", "target area" and "time period type" as the combination key, and uses it as the estimated driving time for this calculation. This way has already implicitly considered the periodic congestion. It can be understood that other ways can also be used to implement the calculation, for example, different standard driving speeds are called according to the vehicle type (obtained by querying the license plate information) to set a lower speed for large vehicles to obtain a more accurate estimate, which is not limited here.

[0065] S206, based on the monitored traffic density and speed of multiple target road segments in the main circulation channel, identify the congestion road segment whose speed is lower than the preset speed threshold and whose traffic density is higher than the preset density threshold.

[0066] The device continuously receives real-time collected traffic data from sensors (such as geomagnetic coils, video detectors, microwave radars, etc.) deployed at each target section of the main circulation channel. For each target section, the device calculates the average speed of traffic and the average density of traffic in the current time window. Then, the device performs a double condition judgment on each section: it compares the real-time speed of traffic with the preset traffic threshold (e.g. 10 km / h), and compares the traffic density with the preset density threshold (e.g. more than 15 vehicles per 100 meters). Only when the speed of traffic of a section is "lower" than the traffic threshold, and its traffic density is "higher" than the density threshold, the section is officially marked as a "congested section" by the device.

[0067] S207, based on the location distribution and influence range of the congested sections, divide the main circulation channel into multiple traffic state intervals and determine the traffic fluency scores of the multiple traffic state intervals.

[0068] After identifying the specific congested sections in step S206, the device obtains the list of all congested sections and their accurate start and end positions output in step S206. Based on the locations of these congested sections, the device logically divides the main circulation channel. The congested sections themselves are defined as "congested intervals". The areas upstream of the congested sections where the speed is reduced due to vehicle queuing are defined as "slow-moving intervals" or "transition intervals". Other unaffected sections are defined as "smooth intervals". After completing the division of the intervals, the device calculates a "traffic fluency score" for each divided traffic state interval. The calculation of the score will integrate multiple indicators such as the average speed of traffic, traffic density, speed stability, etc. For example, a simple scoring function can be: score = (w1 * average speed) - (w2 * average density), where w1 and w2 are weight coefficients. Finally, the device outputs an interval map with scores covering the entire main circulation channel.

[0069] In some embodiments, the division and scoring of the traffic state interval can be achieved in various ways. Optionally, the device employs a dynamic division method based on clustering analysis. First, the device takes all the real-time data (location, speed, density) collected by sensors on the main circulation channel as a data set. Then, the device applies a one-dimensional clustering algorithm (such as K-Means or DBSCAN) to aggregate sensor data points that are adjacent in geographical location and similar in traffic parameters (speed, density) into the same cluster. Each generated cluster naturally constitutes a dynamic "traffic state interval". Finally, the device calculates the average speed and density of all data points in each cluster and substitutes them into a pre-set scoring function to obtain the smoothness score of the interval. This method can adaptively discover the natural boundaries of traffic states. Optionally, the device can employ a scoring method based on fixed segmentation. First, the device pre-divides the main circulation channel into a series of fixed-length short road segments (e.g. every 50 meters). Then, the device associates real-time sensor data with the fixed road segment it belongs to and calculates an independent smoothness score for each fixed road segment. Finally, the device checks adjacent fixed road segments with similar scores (e.g. score difference less than a certain threshold) and merges them into a larger traffic state interval. It can be understood that other ways can also be used to implement the process, such as introducing analysis of traffic wave theory to more accurately define the range of congestion influence by identifying the propagation of traffic waves, which is not limited here.

[0070] In some embodiments, the device can continuously collect and cache a series of discrete, instantaneous individual vehicle speed samples from sensors (such as video detectors or microwave radars) covering each divided traffic state interval within a pre-set calculation period (e.g. every 30 seconds), forming a speed sample set; then, the device performs statistical calculations on the speed sample set and obtains three key indicators in parallel: the average traffic speed of the interval, the traffic flow density of the interval, and the speed standard deviation representing traffic stability and quantifying the degree of speed fluctuation; finally, the device calls a set of pre-set weight coefficients (corresponding to speed, density, and stability, respectively) from the memory and applies a three-dimensional weighted scoring function, such as smoothness score = (speed weight * average speed) - (density weight * traffic flow density) - (stability weight * speed standard deviation), to calculate the final comprehensive score. By introducing the speed standard deviation as a negative penalty term, this method allows two intervals with similar average speed and density, but with frequent acceleration and deceleration, to obtain a significantly lower score due to their larger speed standard deviation, thus making the scoring results more comprehensive in reflecting the true traffic quality including driving comfort.

[0071] S208, based on the marked traffic state intervals that the queuing vehicle will pass through on the way to the target parking space, calculate the modified estimated time to reach each candidate area.

[0072] After generating the real-time traffic score map of the entire road network in S207, the device obtains the initial paths leading to each candidate area that were planned for the vehicle in S205. For each path, the device overlays it with the traffic state interval map output in S207 to determine which traffic state intervals the path will pass through in sequence. Then, the device iterates through each interval on the path to obtain the length of the interval and its corresponding "traffic smoothness score". The device maps the smoothness score of each interval to an "actual traffic speed" using a conversion function (e.g., a high score corresponds to a high speed, and a low score corresponds to a very low speed). Then, the device divides the length of the interval by its corresponding actual traffic speed to obtain the dynamic time required to pass through the interval. Finally, the device accumulates the dynamic times of all intervals on the path to obtain the "modified estimated time" to reach the candidate area from the current gate. The device performs this calculation for all candidate areas.

[0073] In some embodiments, the device calculates the specific speed value between the highest design speed (e.g., 30 km / h) and the lowest congestion speed (e.g., 2 km / h) of the road segment by linear interpolation according to the proportion of the current score in the full score (e.g., 100 points). A very high score will be mapped to a speed close to the highest design speed, and a very low score will be mapped to a speed close to the lowest congestion speed. Then, the device divides the length of the interval by its calculated actual traffic speed to obtain the dynamic time required to pass through the interval. Finally, the device accumulates the dynamic times of all intervals on the path to obtain the "modified estimated time" to reach the candidate area from the current gate. The device performs this calculation for all candidate areas.

[0074] In some embodiments, the process of calculating the actual travel speed based on the smoothness score can be implemented in various ways. Optionally, the device can adopt a mapping method based on piecewise lookup table. First, one or more lookup tables are pre-stored in the device, each of which divides the smoothness score into multiple non-overlapping numerical intervals and pre-sets a fixed representative travel speed for each interval. For example, one lookup table can define that the score [90, 100] corresponds to a speed of 30 km / h, the score [70, 89] corresponds to a speed of 20 km / h, the score [50, 69] corresponds to a speed of 12 km / h, and the score [0, 49] corresponds to a speed of 5 km / h. Then, when the device obtains the smoothness score of a specific interval, it retrieves the numerical interval to which the score belongs in the lookup table. Finally, the device directly reads and adopts the pre-set speed value corresponding to the interval as the "actual travel speed" of this road segment. Optionally, the device can adopt an adaptive model based on historical data regression analysis. First, the device continuously collects and stores a large amount of "three-tuple" data: (road segment ID, smoothness score, actual average travel speed) during operation. The actual average travel speed is directly measured by the sensor within the score calculation period. Then, the device periodically (e.g., every morning) performs regression analysis (such as linear regression or polynomial regression) on the accumulated historical data, fitting a mathematical formula for each road segment or each type of road segment that can predict the speed based on the score. Finally, when performing this step, the device directly substitutes the currently obtained smoothness score into the regression formula corresponding to the road segment and fitted most recently, to calculate a highly customized and self-learning "actual travel speed". It can be understood that other ways can also be used to implement the mapping process, which are not limited here.

[0075] It can be understood that the device can not only calculate the modified estimated time once, but also continuously perform rolling calculation for the vehicle in the background after sending the guidance information to the client. The device will update the current position of the vehicle on the path based on the real-time GPS position returned by the vehicle client (such as a mobile phone APP). Every short time period (e.g., 30 seconds), the device will recalculate the remaining modified estimated time from the current position to the target area based on the latest position of the vehicle and the latest traffic situation of the garage (the latest travel state interval and score). If the new calculation result is significantly different (e.g., more than 1 minute) from the time estimate previously sent to the user, the device will push an update information (e.g., "congestion ahead, estimated arrival time delayed by 2 minutes") to the client.

[0076] S209, setting the parking area with the shortest modified estimated time and the highest real-time parking occupancy rate as the current recommended target area.

[0077] The device now has a two-dimensional data list about all candidate regions, each of which is associated with two decision indicators: "corrected estimated time" and "real-time parking occupancy rate". The device starts a multi-objective optimization decision algorithm to screen the best region, including: the device screens the region with the shortest "corrected estimated time" from all candidate regions. If there is more than one region with the shortest time, the device further compares their "real-time parking occupancy rates" in these regions with the shortest time and selects the one with the highest occupancy rate. The finally selected region, i.e. the "current recommended target region", is set by the device.

[0078] In some embodiments, the setting of the current recommended target region can be achieved in various ways: optionally, the device adopts a decision-making method based on weighted scoring. First, the device normalizes the two indicators of each candidate region to eliminate the dimensional influence. Then, the device calculates the comprehensive score of each region by a weighted summation function, and the weight coefficient is pre-set to reflect the preference degree of "fast arrival" and "high occupancy rate". Finally, the device sets the candidate region with the highest comprehensive score as the current recommended target region. Optionally, the device can sort all candidate regions in ascending order according to the "corrected estimated time". Then, the device selects the first (i.e. the shortest time) region from the sorted list as the preliminary recommendation. Then, the device checks whether the real-time parking occupancy rate of the region is lower than a "maximum acceptable occupancy rate" threshold (e.g. 98%) to ensure that there is at least a vacancy. If the condition is met, it is directly set as the recommended target region; if not (i.e. almost full), the device gives up this region and examines the next region in the list, repeating the process until the first region that is both fast and not full is found. It can be understood that other ways can also be used to achieve the decision, for example, introducing user preferences (such as user historical preference to stop in B1 area) as an additional scoring item into the decision model, which is not limited here.

[0079] S210, determining a target parking space for the marked queuing vehicle from the multiple vacant parking spaces in the current recommended target region, and sending guidance information containing the recommended target region and the optimal driving path to the client corresponding to the marked queuing vehicle.

[0080] After the macro target area is determined in step S209, the device initiates a query to the parking lot management system, requesting a list of all "vacant parking spaces" within the "current recommended target area" and their coordinates. From the returned list, the device selects a "target parking space" according to pre-set preferred rules. This rule can be very simple, for example, selecting the closest vacant parking space to the entrance of the area to reduce the vehicle's invalid detours within the area. After determining the target parking space, the device begins to generate the final "guidance information". This information is a structured data packet, at least containing: the name of the recommended target area (such as "B2 area"), the exact number of the target parking space (such as "B2-058"), and a "best driving path" from the vehicle's current gate to the target parking space, which has been corrected by real-time traffic conditions (usually represented as a series of turn instructions or a navigation trajectory line). Finally, the device sends this guidance information data packet to the client bound to the vehicle through a wireless communication network (such as 4G / 5G or Wi-Fi).

[0081] In some embodiments, the determination of the target parking space and the sending of the guidance information can be achieved in various ways: optionally, the device uses an intelligent screening method based on parking space attributes to determine the target parking space. First, the device's obtained vacant parking space list contains not only coordinates but also parking space attributes, such as "standard parking space", "widened parking space", "charging pile parking space", etc. The device also queries the vehicle's attributes (such as vehicle size, whether it is a new energy vehicle) through the license plate information. Then, the device matches the most suitable parking space according to the vehicle's attributes, for example, preferentially selecting widened parking spaces for large SUVs and charging pile parking spaces for electric vehicles. Among all the matched parking spaces, the one closest to the entrance is selected as the target parking space. Optionally, the device uses a phased push strategy when sending guidance information. First, the device sends the macro guidance information containing the target area and the best path to the client, guiding the vehicle to the target area entrance. When the vehicle's GPS position shows that it has entered the target area, the device pushes the second-stage micro guidance information, i.e. detailed instructions from the area entrance to the specific target parking space (for example, "turn right 50 meters ahead, the target parking space is on your left"). This way can avoid information overload and let the driver focus more on the current road segment. It can be understood that other ways can also be used to implement this process, for example, considering user historical preferences when selecting the target parking space, which is not limited here.

[0082] In some embodiments, after the device determines a target parking space for the vehicle in S210, it sends an instruction to the parking lot management system to temporarily mark the status of the target parking space as "reserved" or "locked" in the database, and set a short validity time (e.g. 3-5 minutes). This lock state prevents the system from reassigning the parking space to other vehicles. At the same time, the physical indicator light (if any) of the parking space can turn to a specific color (e.g. yellow) indicating that it has been reserved. If the guided vehicle arrives and parks within the validity time, the lock state is released and the parking space becomes "occupied". If the vehicle does not arrive (e.g. leaves halfway) within the validity time, or the parking sensor detects an unauthorized vehicle parking, the device will immediately receive an abnormal event notification. At this time, the device will automatically re-execute the logic of S210 for the vehicle: select a new vacant parking space from the same area, update the guidance information, and push it to the user's client, while unlocking the previously incorrectly occupied parking space.

[0083] S211, assign an opportunity passage window to the target gate of the marked queuing vehicle in the gate group according to a preset polling strategy.

[0084] S212, when detecting the arrival of the opportunity passage window, control the target gate to release the marked queuing vehicle at the arrival of the opportunity passage window.

[0085] Steps S211-S212 are similar to steps S104-S105 in the embodiment shown in Figure 1 The embodiment of the present application is similar to steps S104-S105 in the embodiment shown in

[0086] In the embodiment of the present application, since the technical feature of combining gate release with dynamic parking space guidance based on the real-time congestion state of the main circulation channel is adopted, before the vehicle is released, the system has planned an optimal parking path for it to avoid internal congestion and has more accurate estimated time. This effectively solves the problem of traditional guidance systems that information lags, cannot avoid dynamic congestion in the field, and leads to secondary searching by vehicles and exacerbation of congestion, thereby shortening the driving time of vehicles in the field and improving user experience and the running stability of the main circulation channel.

[0087] The exemplary device 300 provided by the embodiment of the present application will be introduced below. Figure 3 is an exemplary hardware structure schematic diagram of the device 300 provided by the embodiment of the present application.

[0088] In some embodiments, the device 300 is a computer device or includes a computer device therein. The computer device includes a processor, a memory and a network interface connected by a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The database of the computer device is configured to store data. The network interface of the computer device is configured to communicate with other terminals or servers outside through a network connection. In some embodiments, the network interface can be a wired network interface, and in some embodiments, the network interface can also be a wireless network interface. The computer program is executed by the processor to implement the method in the embodiments of the present application.

[0089] Those skilled in the art can understand that, Figure 3 The structure shown in the above-mentioned figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. The above-described embodiments are only used to illustrate the technical solutions of the present application, but not limit the technical solutions thereof. Even though the technical solutions recorded in the foregoing embodiments have been described in detail, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements to some technical features thereof. The modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

[0090] In the above-described embodiments, according to the context, the term "when" can be interpreted as meaning "if" or "after" or "in response to determining" or "in response to detecting". Similarly, according to the context, the phrase "upon determining" or "if detecting (the stated condition or event)" can be interpreted as meaning "if determining" or "in response to determining" or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)".

[0091] In the above embodiments, all or part of the embodiments can be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. The computer can be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions can be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through wired (such as coaxial cable, optical fiber, digital subscriber line) or wireless (such as infrared, wireless, microwave, etc.) manner. The computer readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. integrated with one or more available media. The available media can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid state disk) and the like.

[0092] Those of ordinary skill in the art understand that all or part of the processes in the above embodiments can be implemented by a computer program to instruct the relevant hardware, which can be stored in a computer readable storage medium. The program can include the processes of the above method embodiments when executed. The aforementioned storage medium includes ROM or random access memory (RAM), magnetic disk or optical disk, and various media that can store program codes.

Claims

1. A vehicle passage method based on a barrier gate system, characterized in that, The method includes: After monitoring the number of queuing vehicles in the entrance lane area corresponding to the target gate in the gate group, an opportunity passage window within a future preset time period is identified based on the real-time traffic flow information of the main circulation channel. The opportunity passage window is the time window in the future preset time period in which the main circulation channel can accommodate the convergence of a preset vehicle queue unit. The target gate is one or more in the gate group. The theoretical vehicle capacity is calculated based on the time span of the opportunity passage window. Using the theoretical vehicle capacity as a reverse constraint, a target number of vehicles that match the theoretical vehicle capacity is determined from the queue of vehicles in the gate group, and the queue of vehicles with the target number of vehicles is marked as marked queue vehicles. The opportunity passage window is allocated to the target gate of the marked queuing vehicle in the gate group according to the preset polling strategy. When the opportunity window is detected, the target gate is controlled to release the marked queued vehicle upon arrival at the opportunity window.

2. The method according to claim 1, characterized in that, The step of identifying opportunity windows for passage within a preset time period based on real-time traffic flow information from the main circulation channel specifically includes: Based on the vehicle passing timestamp data collected by vehicle detection sensors deployed at the preset key sections of the main circulation channel, the time interval distribution between adjacent vehicles is calculated and the gaps in the workshop with time intervals greater than the preset safe merging interval are identified. Based on the duration of the workshop gap and the standard passage time reference value for merging into the lane from the target gate, effective workshop gaps with a duration sufficient to accommodate at least one preset vehicle queue unit are selected. The propagation delay time is calculated by extrapolating the start and end times of the effective workshop gap, thus obtaining the expected time window for the workshop gap to reach the confluence point; The opportunity passage window is obtained by superimposing all time windows within a preset future time period on the expected arrival time at the confluence point.

3. The method according to claim 1, characterized in that, The step of determining the target number of vehicles to be released from the queue of vehicles at the gate group, using the theoretical vehicle capacity as a reverse constraint, specifically includes: If the total number of vehicles in the queue is determined to be less than or equal to the theoretical vehicle capacity, the total number of vehicles in the queue shall be determined as the target release number. If the total number of vehicles in the queue is greater than the theoretical vehicle capacity, the release quota of the target gate is calculated based on the historical passage priority weight of the target gate and the current number of vehicles in the queue. The queued vehicles are allocated according to the release quota of the target gate, and the allocation result is determined as the target release quantity. The allocation result meets the condition that the total number of allocated vehicles is not greater than the theoretical vehicle capacity.

4. The method according to claim 3, characterized in that, Before the step of monitoring the number of queuing vehicles in the entrance lane area corresponding to the target gate in the gate group, the method further includes: The standard passage time reference value of the target gate is calculated based on the physical topology parameters of the gate group. The standard passage time reference value is the theoretical shortest time for a vehicle to reach the confluence point from the target gate via a standard route. The physical topology parameters include the straight-line distance from each gate to the confluence point, lane width, and path turning angle. The gate's load capacity is determined based on the historical traffic flow data of the target gate and the standard passage time benchmark value. The target gate's load capacity is the maximum number of vehicles that can pass through the target gate per unit time. The initial priority weight coefficient of the target gate is determined based on the ranking of the load capacity of the target gate in the gate group. The deviation between the release quota corresponding to the historical release quota instruction of the target gate and the actual number of vehicles released within the preset evaluation period is statistically analyzed to obtain a dynamic performance score. The initial priority weight coefficient and the dynamic performance score are weighted according to the time series to obtain the historical access priority weight.

5. The method according to claim 1, characterized in that, The step of calculating the theoretical vehicle capacity based on the time span of the opportunity passage window specifically includes: After obtaining the standard merging time required for a single vehicle to merge into the main circulation channel from the target gate, the standard merging time is added to the preset minimum safe headway to form a single vehicle merging time unit; Dividing the opportunity window by the length of the single vehicle merging time unit, the maximum integer value of the number of vehicles that the opportunity window can accommodate is obtained as the theoretical vehicle capacity.

6. The method according to claim 1, characterized in that, After the step of determining the target number of vehicles to be released from the queue of the gate group that matches the theoretical vehicle capacity as a reverse constraint and marking the queue of vehicles of the target number of vehicles to be released as marked queued vehicles, the method further includes: The vehicle identification information of the marked queued vehicles is obtained through the license plate recognition system, and the parking space reservation database is queried to determine whether the marked queued vehicles have reserved parking areas. If it is determined that the marked queued vehicles have reserved parking areas, calculate the estimated travel time from the corresponding gate to the reserved parking area via the main circulation channel; Based on the estimated travel time and the real-time parking space occupancy rate of the reserved parking area, a target parking space is determined from multiple vacant parking spaces for the marked queued vehicle, and a parking space guidance route is provided to the client corresponding to the marked queued vehicle.

7. The method according to claim 6, characterized in that, The step of determining a target parking space from multiple vacant parking spaces for the marked queued vehicles based on the estimated travel time and the real-time parking space occupancy rate of the reserved parking area, and providing parking space guidance routes to the client corresponding to the marked queued vehicles, specifically includes: Based on the monitored traffic density and speed of multiple target road segments within the main circulation channel, congested road segments with speeds below a preset traffic threshold and traffic density above a preset density threshold are identified. Based on the location distribution and impact range of the congested road sections, the main circulation channel is divided into multiple traffic status intervals and the traffic smoothness score of the multiple traffic status intervals is determined. Based on the traffic status intervals that the marked queued vehicles pass through to reach the target parking space, calculate the corrected estimated time to reach each candidate area. The parking area with the shortest correction prediction time and the highest real-time parking space occupancy rate is set as the current recommended target area; For the marked queued vehicles, a target parking space is determined from multiple vacant parking spaces in the currently recommended target area, and guidance information containing the recommended target area and the optimal driving route is sent to the client corresponding to the marked queued vehicles.

8. A device, characterized in that, The device includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, the one or more processors invoking the computer instructions to cause the device to perform the method as described in any one of claims 1-7.

9. A computer program product containing instructions, characterized in that, When the computer program product is run on the device, the device causes the device to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium comprising instructions, characterized in that, When the instructions are executed on the device, the device causes the device to perform the method as described in any one of claims 1-7.

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