A parking lot wireless communication method and device for facilitating traffic diversion

Abstract

The present application relates to the technical field of parking lot traffic diversion, and specifically to a parking lot wireless communication method and device that facilitates traffic diversion. The method includes: evaluating the guideability of each waiting-to-park vehicle at the current moment; comparing the difference in direction between the total number of waiting-to-park vehicles in the neighborhood of each waiting-to-park vehicle at the current moment and the previous moment and the vehicle to be parked, and correcting the guideability in combination with the number of all waiting-to-park vehicles in the neighborhood of each waiting-to-park vehicle at the current moment; analyzing the difference in the range between the peak corresponding moment in each weak fluctuation interval in each cluster and the peak corresponding moment in all weak fluctuation intervals, and determining the weak fluctuation distance in combination with the time interval between the peaks of all weak fluctuation intervals, and guiding the waiting-to-park vehicles in combination with the guideability correction value. The present application solves the problem of ineffective guidance caused by recommending parking spaces to vehicles in congested areas during busy periods, thereby improving the overall operating efficiency of multi-story parking lots.

Description

Technical Field

[0001] The present application relates to the technical field of parking lot traffic diversion, and in particular to a parking lot wireless communication method and device for facilitating traffic diversion. Background Art

[0002] With the increasing popularity of cars, parking difficulties are becoming increasingly severe. Efficient parking management and orderly operation are becoming increasingly important. Currently, smart parking lots combine smartphones and the LoRa communication network to transmit parking space information to users, utilize LoRa wireless communication to locate vehicles within the parking lot, and implement automated and intelligent functions such as automatic toll collection. This has alleviated operational pressures on parking lots to a certain extent.

[0003] However, due to the complex obstructions inherent in parking lots and the presence of communication interference from pedestrians, vehicles, and various devices, LoRa communication is inevitably subject to certain interference. Furthermore, the frequent changes in parking space and vehicle location information can lead to network congestion and signal interference, which in turn can cause data confusion and untimely updates in control devices, ultimately reducing vehicle guidance efficiency. Traditional methods typically determine parking difficulty based on parking space information and area congestion, and recommend parking spaces in descending order. However, during busy hours, this method may recommend parking spaces to vehicles in congested areas, preventing them from moving to their designated spaces. This results in vacant spaces and ineffective guidance, failing to alleviate congestion and reducing parking lot efficiency. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a parking lot wireless communication method and device that facilitates traffic diversion. The technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present application provides a parking lot wireless communication method for facilitating traffic diversion, the method comprising the following steps:

[0006] Real-time acquisition of the positioning data and communication delay of each vehicle waiting to be parked in the parking lot;

[0007] The period between the time each waiting vehicle enters the parking lot and the current moment is recorded as the parking period. All communication delays within the parking period of each waiting vehicle are fitted, and the period between all adjacent troughs on the fitted curve is used as the fluctuation interval. The steerability of each waiting vehicle at the current moment is evaluated by analyzing the difference in positioning data of each vehicle between the current moment and the previous moment, combined with the number of all fluctuation intervals and the average length of all fluctuation intervals within the parking period. The direction difference between the total number of waiting vehicles in the neighborhood of each waiting vehicle at the current moment and the previous moment is compared with the direction of the waiting vehicle to be parked to determine a correction parameter, and the steerability is corrected based on the number of all waiting vehicles in the neighborhood of each waiting vehicle at the current moment.

[0008] Based on the peak values ​​of all fluctuation intervals during the parking period of each waiting vehicle, weak fluctuation intervals are screened from all fluctuation intervals. All weak fluctuation intervals of each waiting vehicle are clustered. The difference in the range of the peak value of each weak fluctuation interval within each cluster is analyzed compared with the peak value of all weak fluctuation intervals. The weak fluctuation distance of each waiting vehicle at the current moment is determined by combining the time intervals between the peak values ​​of all weak fluctuation intervals of each waiting vehicle.

[0009] Based on the guidance degree correction value and weak fluctuation distance of each vehicle waiting to be parked at the current moment, the guidance priority of each vehicle waiting to be parked at the current moment is determined, and the vehicles waiting to be parked are guided.

[0010] Preferably, the guideability of each vehicle to be parked at the current moment is the result of a forward fusion of the difference in positioning data of each vehicle between the current moment and the previous moment, the number of all fluctuation intervals within the parking period of each vehicle, and the average level of the length of all fluctuation intervals.

[0011] Preferably, the correction parameter is determined by:

[0012] Traverse all the common vehicles waiting to be parked in the neighborhood of vehicle i at the current moment and the previous moment, calculate the angle between the line connecting vehicle i and the common vehicles k waiting to be parked in its neighborhood at each moment and the north direction of the geographical location, calculate the difference in angle between vehicle i and the common vehicles k waiting to be parked in its neighborhood at the current moment compared to the previous moment, and take the cumulative sum of the differences in angles between vehicle i and all the common vehicles waiting to be parked at the previous moment as the correction parameter for vehicle i at the current moment, traverse all vehicles waiting to be parked at the current moment, and obtain the correction parameter for each vehicle waiting to be parked at the current moment.

[0013] Preferably, the correcting the steerability comprises:

[0014] Correction value of the navigability of vehicle i at the current moment The expression is: Where, represents the guideability of the parking vehicle i at the current moment; represents the correction parameter of the vehicle i waiting to be parked at the current moment; represents the number of all vehicles waiting to park in the neighborhood of vehicle i at the current moment; Represents the normalization function.

[0015] Preferably, screening out weak fluctuation intervals from all fluctuation intervals includes:

[0016] The peak values ​​of all fluctuation intervals within the parking period of each vehicle to be parked are used as input to the threshold segmentation algorithm, and the segmentation threshold is output. The fluctuation intervals with peak values ​​less than or equal to the segmentation threshold are regarded as weak fluctuation intervals.

[0017] Preferably, the metric distance in the process of clustering all weak fluctuation intervals of each vehicle to be parked is the similarity between all communication delays in each weak fluctuation interval and all communication delays in the remaining weak fluctuation intervals.

[0018] Preferably, the expression of the weak fluctuation distance of each vehicle to be parked at the current moment is: Where, represents the weak fluctuation distance of the parking vehicle i at the current moment; represents the cumulative sum of the time intervals between the peaks of all weak fluctuation intervals of the vehicle i waiting to be parked at the current moment; represents the value at the current moment of the peak value in the j-th weak fluctuation interval of the vehicle i to be parked; It represents the range of the peak corresponding to the moment in all weak fluctuation intervals in the cluster where the j-th weak fluctuation interval of the vehicle i to be parked is located at the current moment; represents the total number of weak fluctuation intervals of vehicle i waiting to be parked at the current moment.

[0019] Preferably, the guidance priority of each vehicle to be parked at the current moment is a normalized value of the sum of the guidance degree correction value and the weak fluctuation distance degree of each vehicle to be parked at the current moment.

[0020] Preferably, the guiding of the parked vehicles includes:

[0021] Arrange the guidance priorities of all vehicles waiting to be parked in the parking lot at the current moment in descending order, and park the vehicles according to the arrangement results.

[0022] In a second aspect, an embodiment of the present application further provides a parking lot wireless communication device for facilitating traffic diversion, comprising a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-mentioned parking lot wireless communication methods for facilitating traffic diversion.

[0023] This application has at least the following beneficial effects:

[0024] The present application constructs a steerability correction value by analyzing changes in the positioning information and communication delay information of a waiting vehicle, and combining it with the density and relative position changes of vehicles around the waiting vehicle. The steerability correction value can more accurately reflect the steerability of the waiting vehicle in the current environment, that is, the probability of the vehicle being successfully guided out of the congested area. This value can prioritize vehicles in relatively normal areas, improve the guidance success rate, and thus improve the operating efficiency of the entire parking lot. Furthermore, the present application constructs a weak fluctuation distance by analyzing the temporal distribution characteristics of the peak value of the weak fluctuation interval. This is used to distinguish whether a vehicle is in the center or periphery of the congested area. Combined with the steerability correction value, the vehicle guidance priority is determined. This can prioritize guiding vehicles in the periphery of the congestion with good steerability, quickly clearing the congested area, reducing vehicle waiting time in queues, effectively alleviating congestion, and improving the operating efficiency of the multi-story parking lot. The present application solves the problem of ineffective guidance caused by recommending parking spaces to vehicles in congested areas during busy hours. By prioritizing vehicles in the periphery of the congestion with good steerability to park, the congested area is cleared more quickly, improving the overall operating efficiency of the multi-story parking lot. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0026] Figure 1 A flowchart of a wireless communication method for parking lots that facilitates traffic diversion is provided in accordance with one embodiment of the present application;

[0027] Figure 2 A schematic diagram of the guidance priority extraction process provided for one embodiment of the present application. DETAILED DESCRIPTION

[0028] To further illustrate the technical means and effectiveness of this application's implementation of the intended invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effectiveness of a parking lot wireless communication method and device for facilitating traffic flow control proposed in this application. In the following description, references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0029] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0030] The following describes in detail a specific solution of a parking lot wireless communication method and device for facilitating traffic diversion provided by the present application with reference to the accompanying drawings.

[0031] See also Figure 1 , which shows a flowchart of a parking lot wireless communication method for facilitating traffic diversion provided by an embodiment of the present application, the method comprising the following steps:

[0032] Step S1: Acquire the positioning data and communication delay of each vehicle to be parked in the parking lot in real time.

[0033] LoRa wireless communication is a low-power wide area network technology primarily used for long-distance wireless communication, featuring long transmission distances, low power consumption, and strong anti-interference capabilities. Therefore, this embodiment employs LoRa wireless communication for communication between vehicles in a parking lot. Specifically, when a vehicle enters the parking lot, the user terminal device establishes a connection with the parking lot management system and obtains a parking lot map. The indoor positioning system within the parking lot vehicle management system collects real-time positioning data for each parked vehicle in the parking lot, with a data collection frequency of m. The indoor positioning system includes LoRa base stations and user terminal devices. N indoor LoRa base stations are deployed on the roof of the parking lot, with a spacing of P meters between each two LoRa base stations. The communication range of these LoRa base stations covers the entire parking lot. The user terminal device periodically transmits LoRa signals. The indoor LoRa base station that receives the signal records the precise time of signal arrival and calculates the user terminal's coordinates. Simultaneously, data output by the user terminal device's internal inertial sensor is collected to implement integrated navigation positioning.

[0034] Use the LoRa base station transmission network delay test tool in the parking lot management system to collect the communication delay of each parked vehicle in the parking lot in real time, and set the communication delay collection frequency to n.

[0035] The wireless sensor network in the parking lot vehicle management system is used to obtain the occupancy information of each parking space. A geomagnetic sensor is set up in each parking space. All geomagnetic sensors are connected through the Lora communication network to form a low-power, long-distance transmission wireless sensor network structure. The parking space occupancy information is collected, transmitted and stored through the wireless sensor network.

[0036] It should be noted that the values ​​of data acquisition frequency m, number of Lora communication nodes N, interval P between Lora communication nodes, and communication delay acquisition frequency n are all set manually. In this embodiment, the value of data acquisition frequency m is 0.2 Hz, the value of number of Lora communication nodes N is 50, the interval between Lora communication nodes is 15, and the value of communication delay acquisition frequency n is 0.5 s. The implementer can also set them according to the specific situation. This embodiment does not impose any special restrictions.

[0037] Step S2: The period between the time each waiting-to-park vehicle begins entering the parking lot and the current moment is recorded as the parking period. All communication delays within the parking period of each waiting-to-park vehicle are fitted, and the period between all adjacent troughs on the fitting curve is used as the fluctuation interval. The guidability of each waiting-to-park vehicle at the current moment is evaluated by analyzing the difference in positioning data of each vehicle between the current moment and the previous moment, combined with the number of all fluctuation intervals and the average level of the length of all fluctuation intervals within the parking period. The difference in direction between the total number of waiting-to-park vehicles in the neighborhood of each waiting-to-park vehicle at the current moment and the previous moment is compared to determine a correction parameter, and the guidability is corrected based on the number of all waiting-to-park vehicles in the neighborhood of each waiting-to-park vehicle at the current moment.

[0038] Generally speaking, the better the network communication environment, the smaller and more stable the Bluetooth communication latency. In this case, selecting a vehicle with a low and relatively stable Bluetooth communication latency for communication can quickly transmit parking information to that vehicle. However, parking lots are often underground and have complex internal environments. Communication equipment between vehicles and between vehicles and pedestrians may affect each other, resulting in inconsistent and high Bluetooth communication latency for most vehicles in the parking lot. In this case, using traditional methods to select vehicles in congested locations may result in ineffective guidance.

[0039] For vehicles in congested sections of a parking lot, the closer they are to the center of the congestion, the more likely they are to be unable to move or move a shorter distance because the surrounding vehicles are affected by the congestion. Furthermore, vehicles in the center of the congestion are subject to greater interference from surrounding communication equipment, and the latency will be higher for a period of time. Furthermore, when other vehicles and pedestrians pass by around the congested area, their communication equipment will also have a certain impact on the vehicle's Bluetooth communication latency.

[0040] Specifically, the closer vehicles are to the center of the congestion, the greater their communication latency will be when affected. However, due to the presence of a certain distance, such as when several vehicles are in a congestion, a vehicle in the central area is surrounded by other vehicles. Passing vehicles and pedestrians are therefore farther away from the vehicle, resulting in a weaker or even no impact, affecting only vehicles outside the congestion. Furthermore, the shorter the duration of the interference, the shorter the duration of the interference. When vehicles or pedestrians pass by the vehicle currently waiting to park, its latency data shows a rapid increase at a higher value. When the surrounding vehicles and pedestrians leave, the latency data shows a rapid decrease, resulting in shorter-duration interference fluctuations and fewer interference fluctuations compared to vehicles outside the congestion. Conversely, vehicles farther away from the congestion area, such as those on the outer edge of the congestion area, have lower communication latency compared to vehicles in the congestion center. They are more susceptible to interference from passing vehicles and pedestrians, experience longer interference, and experience longer-lasting interference fluctuations.

[0041] Based on the above analysis, we determine whether the current vehicle is in a congested area by analyzing the changes in the positioning data and communication delay of each vehicle between the time the vehicle enters the parking lot and the current time. For ease of presentation, the period between the time the vehicle enters the parking lot and the current time is referred to as the parking period. The specific analysis process is as follows:

[0042] (1) This embodiment fits all communication delays during the parking period of each waiting vehicle in chronological order, and uses the time period between all adjacent troughs on the fitting curve as the fluctuation interval. If the current waiting vehicle is in the center of the congestion area, then when a vehicle passes by, the interference fluctuation caused to the waiting vehicle is shorter due to the longer distance, that is, the fluctuation interval is shorter. Conversely, if the current waiting vehicle is outside the congestion area, when other vehicles pass by, the interference time is longer, and the interference fluctuation mutation lasts longer.

[0043] It should be noted that there are many commonly used fitting methods. In this embodiment, the polynomial function fitting method is used to fit the communication delay. In actual application, as other implementation methods, the implementer may also adopt other fitting methods such as the least squares fitting method based on the specific situation. Regarding the selection of the fitting method, this embodiment does not impose any special restrictions.

[0044] Among them, the polynomial function fitting method is a well-known technology, and the specific process of fitting the data is not described in detail.

[0045] (2) Furthermore, in this embodiment, by analyzing the difference in positioning data of each vehicle between the current moment and the previous moment, and combining the number of all fluctuation intervals and the average level of the length of all fluctuation intervals within the parking period, the guideability of each vehicle to be parked at the current moment is evaluated, specifically:

[0046] As an implementation method, in this embodiment, the difference in positioning data of each vehicle between the current moment and the previous moment, the number of all fluctuation intervals within the parking period of each vehicle, and the average level of the length of all fluctuation intervals are forward fused to serve as the guideability of each vehicle to be parked at the current moment.

[0047] It should be noted that there are many methods for measuring differences between data. In this embodiment, the absolute value of the difference between the positioning data of each vehicle at the current moment and the previous moment is used as the positioning data difference between the current moment and the previous moment. In actual applications, as other implementations, implementers may also use other methods for measuring differences between data, such as the square of the difference or the ratio, based on specific circumstances. This embodiment does not impose any special restrictions on the method used to measure the difference between data. In this embodiment, all content related to measuring differences between data uses the method of taking the absolute value of the difference.

[0048] It should be noted that there are many ways to measure the average level of data. In this embodiment, the mean of all fluctuation interval lengths is used as the average level of all fluctuation interval lengths. In actual application, as other implementation methods, implementers can also use other methods to measure the average level of data, such as the geometric mean, and this embodiment does not impose any special restrictions.

[0049] Furthermore, it should be understood that forward fusion refers to combining two or more indicators through addition or multiplication to obtain a comprehensive indicator, thereby more comprehensively and accurately assessing a phenomenon or problem. This fusion method is not limited to simple arithmetic operations and can also include more complex statistical models and analysis methods. Implementers can choose according to their specific circumstances and this embodiment does not impose any special restrictions.

[0050] Preferably, as an implementation method, in this embodiment, the difference in positioning data of each vehicle between the current moment and the previous moment, the number of all fluctuation intervals in the parking period of each vehicle, and the average level of the length of all fluctuation intervals are added together to serve as the guideability of each vehicle to be parked at the current moment. In actual application, as another implementation method, the implementer may also multiply the difference in positioning data of each vehicle between the current moment and the previous moment, the number of all fluctuation intervals in the parking period of each vehicle, and the average level of the length of all fluctuation intervals to serve as the guideability of each vehicle to be parked at the current moment.

[0051] Furthermore, based on the guidance degree of each waiting-for-parking vehicle at the current moment, it can be understood that the guidance degree is used to evaluate the probability of successfully guiding the waiting-for-parking vehicle within the parking lot. The greater the difference between the positioning data of the current waiting-for-parking vehicle at the current moment and the previous moment, the greater the distance the current waiting-for-parking vehicle has moved from the previous moment to the current moment, indicating that the vehicle is in a relatively less congested area and has more room to move. The probability of successfully guiding the current waiting-for-parking vehicle is higher, and thus the corresponding guidance degree is greater. At the same time, the greater the number of fluctuation intervals and the longer the fluctuation intervals, the greater the interference the current waiting-for-parking vehicle is experiencing during the communication process. This situation usually occurs outside the congested area. The probability of successfully guiding the current waiting-for-parking vehicle is higher, and thus the corresponding guidance degree is greater.

[0052] On the contrary, if the difference between the positioning data of the current vehicle to be parked between the current moment and the previous moment is smaller, it means that the distance moved by the current vehicle to be parked from the previous moment to the current moment is shorter, which indicates that the vehicle may be in a more congested area with limited movement space, and the probability of successfully guiding the current vehicle to be parked is lower, so the corresponding guideability is smaller; at the same time, if the number of fluctuation intervals is smaller and the length of the fluctuation interval is shorter, this may indicate that the current vehicle to be parked is less interfered with during the communication process, but if this situation occurs in the center of the congested area, it may be because the vehicle can hardly move and the surrounding environment is relatively stable, but the probability of successfully guiding the current vehicle to be parked is still low, so the corresponding guideability is also smaller.

[0053] (3) Furthermore, in this embodiment, the difference in direction between the total number of vehicles to be parked in the neighborhood of each vehicle to be parked at the current moment and the previous moment and the vehicle to be parked is compared to determine a correction parameter. This correction parameter is then combined with the number of all vehicles to be parked in the neighborhood of each vehicle to be parked at the current moment to correct the guideability. Specifically, the correction parameter is:

[0054] In this embodiment, a neighborhood is divided with each vehicle to be parked at each moment as the center, and all common vehicles to be parked in the neighborhood of vehicle to be parked i at the current moment and the previous moment are traversed. The angle between the line connecting vehicle to be parked i and the common vehicles to be parked k in its neighborhood at each moment and the north direction of the geographical location is calculated. The difference in angle between vehicle to be parked i and the common vehicles to be parked k in its neighborhood at the current moment and the previous moment is calculated. The sum of the differences in angles between vehicle to be parked i and all common vehicles to be parked at the previous moment is used as the correction parameter of vehicle to be parked i at the current moment. All vehicles to be parked at the current moment are traversed to obtain the correction parameter of each vehicle to be parked at the current moment.

[0055] It should be noted that, as an implementation method, the neighborhood construction process described in this embodiment is: a circular area formed with each vehicle to be parked at each moment as the center and a radius of r is used as the neighborhood of the vehicle to be parked. In this embodiment, the value of r is 15m. In actual application, as other implementation methods, the implementer can also set it according to the specific situation. This embodiment does not impose any special restrictions.

[0056] It should be noted that the shared vehicles waiting to be parked refer to the same vehicles waiting to be parked that are in the neighborhood of each vehicle waiting to be parked at the current moment and the previous moment.

[0057] Furthermore, as an implementation method, in this embodiment, the guideability correction value of the vehicle i to be parked at the current moment is The expression is: Where, represents the guideability of the parking vehicle i at the current moment; represents the correction parameter of the vehicle i waiting to be parked at the current moment; represents the number of all vehicles waiting to park in the neighborhood of vehicle i at the current moment; Represents the normalization function.

[0058] Based on the steerability correction value of vehicle i at the current moment, it can be understood that the steerability correction value is a further adjustment to the original steerability value. Taking into account the impact of the vehicle's surrounding environment on Bluetooth communication, the steerability correction value can more accurately reflect the vehicle's steerability in the current environment, that is, the probability of the vehicle being successfully guided out of the congested area. The larger the number of all vehicles in the neighborhood of the current vehicle at the current moment, the smaller the correction parameter, indicating that there are many other vehicles around the current vehicle, but the relative positions of these vehicles have not changed much, indicating that the current vehicle is in the center of congestion or in an area with limited communication. Therefore, lowering the steerability correction value can reduce the priority of guiding the current vehicle, because the current vehicle is in a congested area, and the probability of successfully guiding it may be low.

[0059] On the contrary, if the number of all vehicles waiting to be parked in the neighborhood of the current vehicle to be parked at the current moment is smaller and the relative positions of these vehicles to be parked vary greatly, then the correction parameter will be larger, which means that there are not many other vehicles around the current vehicle to be parked, or the relative positions of the surrounding vehicles vary greatly, indicating that the current vehicle to be parked may be in a relatively unobstructed area or a location with good communication conditions. In this case, increasing the guidance correction value can increase the priority of guiding the current vehicle to be parked, because the current vehicle to be parked is in a relatively unobstructed area or a location with good communication conditions, and the probability of successful guidance may be higher. Such vehicles are more likely to respond to guidance instructions quickly and leave the current area smoothly, thereby improving the operating efficiency of the entire parking lot.

[0060] Thus, this embodiment constructs a guidance degree correction value by analyzing the communication delay fluctuation range and positioning data changes after the vehicle enters the parking lot, and determines whether the vehicle is in a congested area, thereby guiding the vehicle more effectively and improving the operation efficiency of the parking lot.

[0061] Step S3: Based on the peak values ​​of all fluctuation intervals within the parking period of each vehicle to be parked, weak fluctuation intervals are screened out from all fluctuation intervals, and all weak fluctuation intervals of each vehicle to be parked are clustered. The difference in the range between the peak value corresponding to the moment in each weak fluctuation interval within each cluster and the peak value corresponding to the moment in all weak fluctuation intervals within each cluster is analyzed. Combined with the time interval between the peak values ​​of all weak fluctuation intervals of each vehicle to be parked, the weak fluctuation distance of each vehicle to be parked at the current moment is determined.

[0062] With the growing demand for parking spaces, many parking lots have adopted multi-story designs to accommodate more vehicles, alleviating parking shortages to some extent. However, this multi-story structure also presents new challenges. In multi-story parking lots, the increased time required to park vehicles leads to increased signal interference between vehicles. This interference can cause vehicles in congested areas on one floor to be affected by communications with vehicles on other floors, resulting in Bluetooth communication delay data characteristics similar to those of vehicles farther away from the congested area being affected by passing vehicles. Therefore, relying solely on the above method for judgment may lead to vehicles in congested areas being mistakenly prioritized, resulting in ineffective guidance.

[0063] When a vehicle to be parked is located in a congested area, if a vehicle passes by on the upper or lower floor, it will have a certain impact on the communication quality. However, due to the certain distance between floors, the interference from vehicles on the upper and lower floors is relatively weaker than the interference from vehicles on the same floor. In addition, the vehicle to be parked and the vehicles around it will be subject to similar interference from vehicles on the upper and lower floors. Due to the current congestion, the moving distance of congested vehicles on the same floor is smaller, while vehicles on the upper and lower floors may be in a relatively unobstructed state. This impact may first act on vehicles on the periphery of the congested area, then gradually affect vehicles in the center of the congested area, and finally affect vehicles on the periphery of the congested area again. Therefore, there are certain differences in the time when vehicles in different congested positions are affected. Specifically, for the impact of a single vehicle on the upper and lower floors, vehicles on the periphery of the congestion will be affected earlier or later, while vehicles in the center of the congestion will be affected by vehicles on the upper and lower floors at a relatively central time.

[0064] To quantify the communication interference of the same vehicle on the upper and lower floors on the blocked vehicles, in this embodiment, based on the peak values ​​of all fluctuation intervals during the parking period of each waiting vehicle, weak fluctuation intervals are screened from all fluctuation intervals. All weak fluctuation intervals of each waiting vehicle are clustered. The difference in the range between the peak time of each weak fluctuation interval within each cluster and the peak time of all weak fluctuation intervals is analyzed. Combined with the time interval between the peak values ​​of all weak fluctuation intervals of each waiting vehicle, the weak fluctuation distance of each waiting vehicle at the current moment is determined. Specifically, it is:

[0065] (1) In this embodiment, in order to identify and analyze the communication interference pattern between vehicles on different floors in a parking lot, the peak values ​​of all fluctuation intervals during the parking period of each vehicle to be parked are used as the input of a threshold segmentation algorithm, and a segmentation threshold is output. The fluctuation intervals with peak values ​​less than or equal to the segmentation threshold are regarded as weak fluctuation intervals. All weak fluctuation intervals of each vehicle to be parked are clustered to obtain a plurality of clusters. In the clustering process, the metric distance is the similarity between all communication delays in each weak fluctuation interval and all communication delays in the remaining weak fluctuation intervals. In this embodiment, the cutoff distance is set to 3, and the number of clusters is determined by the elbow method. The clustering method adopts the DPC density clustering algorithm. The implementer can also set the cutoff distance and the number of clusters according to the specific situation, or adopt other clustering algorithms. This embodiment does not impose a special number restriction.

[0066] It should be noted that there are many commonly used threshold segmentation algorithms. In this embodiment, the maximum inter-class variance algorithm is used to classify peaks. In actual application, as other implementation methods, implementers can also choose other methods based on specific circumstances. This embodiment does not impose any special restrictions.

[0067] In addition, it should be noted that there are many methods for calculating the similarity between data groups. In this embodiment, cosine similarity is used to calculate the metric distance. In actual application, as other implementation methods, implementers can also use the reciprocal of the Euclidean distance or other methods of measuring similarity based on specific circumstances. This embodiment does not impose any special restrictions on the selection of the similarity measurement method.

[0068] Among them, the elbow method, DPC density clustering algorithm, maximum inter-class variance algorithm and cosine similarity calculation method are all well-known technologies, and their specific principles are not repeated here.

[0069] (2) Furthermore, in order to more accurately assess the distance of the waiting vehicle from the center of the congestion and thus more effectively guide the vehicle, this embodiment analyzes the difference between the peak time corresponding to each weak fluctuation interval in each cluster and the peak time corresponding to all weak fluctuation intervals, and combines the time intervals between the peaks of all weak fluctuation intervals of each waiting vehicle to be parked to determine the weak fluctuation distance of each waiting vehicle at the current moment, so as to distinguish whether the waiting vehicle is in the center or the periphery of the congestion area, thereby determining the vehicle that should be guided first, thereby effectively alleviating the congestion. Specifically:

[0070] As an implementation method, in this embodiment, the weak fluctuation distance of the vehicle i to be parked at the current moment is The expression is: Where, represents the cumulative sum of the time intervals between the peaks of all weak fluctuation intervals of the vehicle i waiting to be parked at the current moment; represents the value at the current moment of the peak value in the j-th weak fluctuation interval of the vehicle i to be parked; It represents the range of the peak corresponding to the moment in all weak fluctuation intervals in the cluster where the j-th weak fluctuation interval of the vehicle i to be parked is located at the current moment; represents the total number of weak fluctuation intervals of vehicle i waiting to be parked at the current moment.

[0071] According to the weak fluctuation distance of each waiting-for-parking vehicle at the current moment, it can be understood that the weak fluctuation distance represents the relative position of the waiting-for-parking vehicle in the congested area of ​​the parking lot, which helps to determine the priority of vehicle guidance, thereby more effectively alleviating the congestion in the parking lot. Among them, if the cumulative sum of the time intervals between the peak corresponding times in all weak fluctuation intervals of waiting-for-parking vehicle i at the current moment is larger, it indicates that the waiting-for-parking vehicle i is more likely to be at the edge of the congestion area, and the corresponding weak fluctuation distance is larger, and a higher guidance priority should be set for the waiting-for-parking vehicle i. At the same time, if the value of the peak corresponding time in the j-th weak fluctuation interval of waiting-for-parking vehicle i at the current moment is larger than the range of the peak corresponding time in all weak fluctuation intervals in the cluster to which the j-th weak fluctuation interval belongs, it indicates that the peak corresponding time of the weak fluctuation interval is closer to the start and end time in its cluster, further confirming that the waiting-for-parking vehicle i is at the edge of the congestion, and the corresponding weak fluctuation distance is larger.

[0072] On the contrary, if the cumulative sum of the time intervals between the peak corresponding moments in all weak fluctuation intervals of vehicle i to be parked at the current moment is smaller, it indicates that vehicle i to be parked is more likely to be in the center of the congestion area, the corresponding weak fluctuation distance is smaller, and the guidance priority set for vehicle i to be parked should be relatively low; at the same time, if the value of the peak corresponding moment in the j-th weak fluctuation interval of vehicle i to be parked at the current moment is smaller than the difference in the range of the peak corresponding moments in all weak fluctuation intervals in the cluster where the j-th weak fluctuation interval is located, it means that the peak corresponding moment of the weak fluctuation interval is closer to the middle moment in its cluster, further confirming that vehicle i to be parked is closer to the center of the congestion, and the corresponding weak fluctuation distance is smaller.

[0073] To date, in multi-story parking lots, due to signal interference between floors, relying solely on communication delay fluctuations and positioning data changes to determine whether a vehicle is in a congested area may not be accurate enough. This embodiment analyzes the peak values ​​in all fluctuation intervals during the vehicle's parking period, screens out weak fluctuation intervals, and clusters them. It further analyzes the range differences at the corresponding moments of the peak values ​​within each weak fluctuation interval and the time intervals between peak values ​​to determine the vehicle's weak fluctuation distance. This indicator can more accurately reflect the vehicle's position relative to the center or edge of the congested area, thereby more effectively guiding vehicles, alleviating parking lot congestion, and improving the operating efficiency of multi-story parking lots.

[0074] Step S4: Based on the guidance degree correction value and weak fluctuation distance of each vehicle to be parked at the current moment, the guidance priority of each vehicle to be parked at the current moment is determined, and the vehicle to be parked is guided.

[0075] Based on the analysis of steps S2 and S3, the guidance priority of each vehicle to be parked at the current moment is determined based on the guidance degree correction value and weak fluctuation distance of each vehicle to be parked at the current moment, and the vehicles to be parked are guided. Specifically,

[0076] As an implementation mode, in this embodiment, the normalized value of the sum of the guideability correction value and the weak fluctuation distance of each vehicle to be parked at the current moment is used as the guidance priority of each vehicle to be parked at the current moment.

[0077] Preferably, the schematic diagram of the guidance priority extraction process provided in this embodiment is as follows: Figure 2 shown.

[0078] Furthermore, the guidance priorities of all vehicles waiting to be parked in the parking lot at the current moment are arranged in descending order, and a geomagnetic sensor is set up in each parking space. All geomagnetic sensors form a wireless sensor network structure that supports multiple communication modes. The occupancy information of each parking space is collected, transmitted and stored through the wireless sensor network. The occupancy information of each parking space is transmitted to the display screen of the corresponding vehicle waiting to be parked through the Lora module installed in the vehicle in the order of vehicle guidance priority using Lora wireless communication. The user manually selects the parking space he wants to park and clicks the start navigation button. The best path is planned in combination with the path planning algorithm to guide the corresponding vehicle waiting to be parked to the parking space for parking.

[0079] Among them, Lora wireless communication and path planning algorithm are well-known technologies, and their specific principles are not repeated here.

[0080] In complex environments like underground parking lots, factors like signal interference between vehicles can render traditional methods of selecting and guiding vehicles based on communication delay ineffective. This embodiment determines the guidance priority of each waiting vehicle based on the steerability correction value and the weak fluctuation distance. Parking space occupancy information is then transmitted to the corresponding vehicle's mobile phone terminal via a wireless sensor network, guiding the vehicle to the parking space. This method more accurately determines whether a vehicle is in a congested area, more effectively guides vehicles, and improves parking lot operation efficiency.

[0081] Based on the same inventive concept as the above method, an embodiment of the present application also provides a parking lot wireless communication device that facilitates traffic diversion, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-mentioned parking lot wireless communication methods that facilitate traffic diversion.

[0082] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0083] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0084] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A parking lot wireless communication method for facilitating traffic diversion, characterized in that: The method comprises the following steps: Real-time acquisition of the positioning data and communication delay of each vehicle waiting to be parked in the parking lot; The period between the time each waiting vehicle enters the parking lot and the current moment is recorded as the parking period. All communication delays within the parking period of each waiting vehicle are fitted, and the period between all adjacent troughs on the fitted curve is used as the fluctuation interval. The steerability of each waiting vehicle at the current moment is evaluated by analyzing the difference in positioning data of each vehicle between the current moment and the previous moment, combined with the number of all fluctuation intervals and the average length of all fluctuation intervals within the parking period. The direction difference between the total number of waiting vehicles in the neighborhood of each waiting vehicle at the current moment and the previous moment is compared with the direction of the waiting vehicle to be parked to determine a correction parameter, and the steerability is corrected based on the number of all waiting vehicles in the neighborhood of each waiting vehicle at the current moment. Based on the peak values ​​of all fluctuation intervals during the parking period of each waiting vehicle, weak fluctuation intervals are screened from all fluctuation intervals. All weak fluctuation intervals of each waiting vehicle are clustered. The difference in the range of the peak value of each weak fluctuation interval within each cluster is analyzed compared with the peak value of all weak fluctuation intervals. The weak fluctuation distance of each waiting vehicle at the current moment is determined by combining the time intervals between the peak values ​​of all weak fluctuation intervals of each waiting vehicle. Based on the guidance degree correction value and weak fluctuation distance of each vehicle waiting to be parked at the current moment, the guidance priority of each vehicle waiting to be parked at the current moment is determined, and the vehicles waiting to be parked are guided.

2. A parking lot wireless communication method for facilitating traffic diversion according to claim 1, characterized in that: The guideability of each vehicle to be parked at the current moment is the result of a forward fusion of the difference in positioning data of each vehicle between the current moment and the previous moment, the number of all fluctuation intervals within the parking period of each vehicle, and the average level of the length of all fluctuation intervals.

3. A parking lot wireless communication method for facilitating traffic diversion according to claim 1, characterized in that: The method for determining the correction parameters is: Traverse all the common vehicles waiting to be parked in the neighborhood of vehicle i at the current moment and the previous moment, calculate the angle between the line connecting vehicle i and the common vehicles k waiting to be parked in its neighborhood at each moment and the north direction of the geographical location, calculate the difference in angle between vehicle i and the common vehicles k waiting to be parked in its neighborhood at the current moment compared to the previous moment, and take the cumulative sum of the differences in angles between vehicle i and all the common vehicles waiting to be parked at the previous moment as the correction parameter for vehicle i at the current moment, traverse all vehicles waiting to be parked at the current moment, and obtain the correction parameter for each vehicle waiting to be parked at the current moment.

4. A parking lot wireless communication method for facilitating traffic diversion according to claim 1, characterized in that: The modifying of the steerability comprises: Correction value of the navigability of vehicle i at the current moment The expression is: Where, represents the guideability of the parking vehicle i at the current moment; represents the correction parameter of the vehicle i waiting to be parked at the current moment; represents the number of all vehicles waiting to park in the neighborhood of vehicle i at the current moment; Represents the normalization function.

5. A parking lot wireless communication method for facilitating traffic diversion as claimed in claim 1, characterized in that: The weak fluctuation intervals are screened out from all fluctuation intervals, including: The peak values ​​of all fluctuation intervals within the parking period of each vehicle to be parked are used as input to the threshold segmentation algorithm, and the segmentation threshold is output. The fluctuation intervals with peak values ​​less than or equal to the segmentation threshold are regarded as weak fluctuation intervals.

6. A parking lot wireless communication method for facilitating traffic diversion as claimed in claim 1, characterized in that: The metric distance in the process of clustering all weak fluctuation intervals of each vehicle to be parked is the similarity between all communication delays in each weak fluctuation interval and all communication delays in the remaining weak fluctuation intervals.

7. A parking lot wireless communication method for facilitating traffic diversion as claimed in claim 1, characterized in that: The expression of the weak fluctuation distance of each vehicle to be parked at the current moment is: Where, represents the weak fluctuation distance of the parking vehicle i at the current moment; represents the cumulative sum of the time intervals between the peaks of all weak fluctuation intervals of the vehicle i waiting to be parked at the current moment; represents the value at the current moment of the peak value in the j-th weak fluctuation interval of the vehicle i to be parked; It represents the range of the peak corresponding to the moment in all weak fluctuation intervals in the cluster where the j-th weak fluctuation interval of the vehicle i to be parked is located at the current moment; represents the total number of weak fluctuation intervals of vehicle i waiting to be parked at the current moment.

8. A parking lot wireless communication method for facilitating traffic diversion as claimed in claim 1, characterized in that: The guidance priority of each vehicle to be parked at the current moment is a normalized value of the sum of the guidance degree correction value and the weak fluctuation distance degree of each vehicle to be parked at the current moment.

9. A parking lot wireless communication method for facilitating traffic diversion as claimed in claim 1, characterized in that: The said guiding of parked vehicles includes: Arrange the guidance priorities of all vehicles waiting to be parked in the parking lot at the current moment in descending order, and park the vehicles according to the arrangement results.

10. A parking lot wireless communication device for facilitating traffic diversion, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the parking lot wireless communication method for facilitating traffic diversion as described in any one of claims 1 to 9 are implemented.

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