An intelligent scheduling method and system for roadside parking assisted by a berth robot

Abstract

The present invention provides an intelligent scheduling method and system for roadside parking assisted by a berth robot, which relates to the technical field of intelligent parking. By obtaining multiple parking spaces and classifying and marking them according to the parking space size and type. Combining the marking information of multiple parking space types, the parking space slope information and the road information in the parking space neighborhood to conduct parking complexity analysis, predicting the parking level of the first driver, and combining the marking information of multiple parking space sizes, the first parking level prediction index and multiple complexity indexes to generate a first recommended parking space. Generating a first navigation route according to the first recommended parking space, and guiding the vehicle to park according to the first navigation route by the berth robot. It solves the technical problem in the prior art that the berth robot does not consider the difference in the parking level of drivers, resulting in too difficult recommended parking spaces and poor user experience. It achieves the technical effect of matching the driver's parking level with the parking space complexity, thereby improving the parking success rate and user experience.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent parking, and particularly to an intelligent parking scheduling method and system assisted by a berth robot for roadside parking. Background Art

[0002] In the field of intelligent parking, as an important tool to improve parking efficiency, a berth robot usually relies on parking space availability information for parking space recommendation. The advantage of this method is that it can provide real-time information on available parking spaces and help drivers quickly find parking positions. However, existing technologies mainly focus on the availability status of parking spaces and fail to fully consider the individual differences and parking capabilities of drivers, resulting in too high difficulty in recommending parking spaces and poor user parking experience.

[0003] There is a technical problem in the existing technology that the berth robot does not consider the differences in drivers' parking levels, resulting in too high difficulty in recommending parking spaces and poor user experience. Summary of the Invention

[0004] This application provides an intelligent parking scheduling method and system assisted by a berth robot for roadside parking to solve the technical problem in the existing technology that the berth robot does not consider the differences in drivers' parking levels, resulting in too high difficulty in recommending parking spaces and poor user experience.

[0005] In view of the above problems, this application provides an intelligent parking scheduling method and system assisted by a berth robot for roadside parking.

[0006] In the first aspect of this application, an intelligent parking scheduling method assisted by a berth robot for roadside parking is provided. The method includes: the berth robot connected to the first roadside parking area acquires a plurality of parking spaces, classifies and marks them according to the parking space size and type, and generates a plurality of parking space size marking information and a plurality of parking space type marking information; acquires the regional digital map of the first roadside parking area, wherein the parking space slope information and the parking space neighborhood road information are marked in the regional digital map; combines the plurality of parking space type marking information, the parking space slope information and the parking space neighborhood road information to analyze the parking complexity of the plurality of parking spaces, and generates a plurality of complexity indicators; when a first vehicle drives into the first roadside parking area, predicts the parking level of the first driver and generates a first parking level prediction indicator; combines the plurality of parking space size marking information, the first parking level prediction indicator and the plurality of complexity indicators for parking space matching to generate a first recommended parking space; generates a first navigation route according to the first recommended parking space, and the berth robot performs parking guidance according to the first navigation route.

[0007] In a second aspect of the present application, there is provided an intelligent scheduling system for roadside parking assisted by a berth robot, the system comprising: a parking space classification module configured to connect to the berth robots in a first roadside parking area to obtain a plurality of parking spaces, classify and label them according to the parking space size and type, and generate a plurality of parking space size label information and a plurality of parking space type label information; a digital map acquisition module configured to acquire the regional digital map of the first roadside parking area, wherein the regional digital map is marked with parking space slope information and parking space neighborhood road information; a parking complexity analysis module configured to perform a parking complexity analysis on the plurality of parking spaces in combination with the plurality of parking space type label information, the parking space slope information, and the parking space neighborhood road information, and generate a plurality of complexity indicators; a parking level prediction module configured to predict the parking level of a first driver when a first vehicle enters the first roadside parking area, and generate a first parking level prediction indicator; a parking space matching module configured to perform parking space matching in combination with the plurality of parking space size label information, the first parking level prediction indicator, and the plurality of complexity indicators, and generate a first recommended parking space; a navigation route generation module configured to generate a first navigation route according to the first recommended parking space, and perform parking guidance according to the first navigation route by the berth robot.

[0008] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0009] The method provided in the embodiment of the present application obtains a plurality of parking spaces by connecting to the berth robots in the first roadside parking area, classifies and labels them according to the parking space size and type, and generates a plurality of parking space size label information and a plurality of parking space type label information, and acquires the regional digital map of the first roadside parking area, wherein the regional digital map is marked with parking space slope information and parking space neighborhood road information. Perform a parking complexity analysis on the plurality of parking spaces in combination with the plurality of parking space type label information, the parking space slope information, and the parking space neighborhood road information, and generate a plurality of complexity indicators. When a first vehicle enters the first roadside parking area, predict the parking level of the first driver and generate a first parking level prediction indicator. Perform parking space matching in combination with the plurality of parking space size label information, the first parking level prediction indicator, and the plurality of complexity indicators, and generate a first recommended parking space. Generate a first navigation route according to the first recommended parking space, and perform parking guidance according to the first navigation route by the berth robot. It achieves the technical effect of matching the driver's parking level with the parking space complexity, thereby improving the parking success rate and user experience. Description of the Drawings

[0010] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0011] Figure 1 It is a schematic flowchart of an intelligent scheduling method for roadside parking assisted by a berth robot provided by this application;

[0012] Figure 2 It is a schematic structural diagram of an intelligent scheduling system for roadside parking assisted by a berth robot provided by this application.

[0013] Explanation of reference numerals: The parking space classification module 11, the digital map acquisition module 12, the parking complexity analysis module 13, the parking level prediction module 14, the parking space matching module 15, the navigation route generation module 16. Detailed implementation manners

[0014] This application provides an intelligent scheduling method and system for roadside parking assisted by a berth robot, which is used to solve the technical problem in the prior art that the berth robot does not consider the difference in the parking levels of drivers, resulting in too high difficulty in recommending parking spaces and poor user experience. It achieves the technical effect of matching according to the driver's parking level and the complexity of the parking space, thereby improving the parking success rate and user experience.

[0015] Next, the technical solutions in the present invention will be clearly and completely described with reference to the drawings. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments of the present invention. It should be understood that the present invention is not limited by the example embodiments described here. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention. Additionally, it should be noted that for the convenience of description, only the parts related to the present invention are shown in the drawings rather than all.

[0016] Embodiment 1, as Figure 1 shown, this application provides an intelligent scheduling method for roadside parking assisted by a berth robot, and the method includes:

[0017] The berth robot connected to the first roadside parking area acquires multiple parking spaces, classifies and marks them according to the parking space size and type, and generates multiple parking space size marking information and multiple parking space type marking information.

[0018] Specifically, a parking robot is an intelligent automation device with advanced sensors, cameras, and image recognition technologies, capable of real-time monitoring of parking space information in a parking area. It connects to the berth robots in the first roadside parking area through wireless networks or other communication methods to obtain real-time information on multiple available parking spaces in the area. The berth robots classify and label multiple available parking spaces based on two key features: parking space size and parking space type. The parking space size refers to obtaining the actual size of each parking space, including parameters such as length, width, and height, which is used to determine whether the parking space is suitable for different types of vehicles, such as small cars or large SUVs. The parking space types include perpendicular parking spaces, parallel parking spaces, and angled parking spaces. Perpendicular parking spaces are usually arranged on both sides of the parking lot, with relatively simple entry and exit, and are easier to park, suitable for novice drivers. Parallel parking spaces require higher driving skills, especially on narrow streets, where drivers need to make multiple fine-tuning maneuvers in a smaller space, increasing the difficulty. Angled parking spaces are easier to park than parallel parking spaces, but still need to pay attention to the size of the surrounding space to ensure that the vehicle can enter smoothly. By classifying and labeling the size and type of each parking space in detail, the berth robots generate multiple parking space size label information and multiple parking space type label information. By classifying and labeling the parking spaces, the characteristics of each parking space can be accurately evaluated, providing more accurate parking recommendations for users and enhancing the user's parking experience.

[0019] Obtain the regional digital map of the first roadside parking area, where the regional digital map is marked with parking space slope information and parking space neighborhood road information.

[0020] Specifically, the first roadside parking area is scanned by sensors such as GPS and lidar, and combined with reliable data sources, such as professional map service providers, to obtain the regional digital map of the first roadside parking area. The regional digital map is marked with parking space slope information and parking space neighborhood road information. The parking space slope information refers to the degree of inclination of each parking space relative to the horizontal plane. The parking space neighborhood road information includes the road conditions around the parking space, such as the width of the road, traffic flow, and whether there are obstacles, etc., which can evaluate the difficulty of parking near a specific parking space. By obtaining the regional digital map, the berth robot's understanding ability of the parking environment is improved, and safer and more convenient parking recommendations are also provided for drivers.

[0021] Combine the multiple parking space type label information, the parking space slope information, and the parking space neighborhood road information to perform a parking complexity analysis on the multiple parking spaces, and generate multiple complexity indicators.

[0022] Specifically, taking multiple parking space type marking information as basic data, the initial parking difficulty is evaluated. Then, the slope information of the parking spaces is analyzed, and combined with the road information in the parking space area, factors such as the width, traffic flow, and visibility of the adjacent roads are evaluated, and a comprehensive analysis of the parking complexity of multiple parking spaces is carried out to generate multiple complexity indicators. By obtaining the complexity indicators of the parking spaces, the parking difficulty of each parking space can be reflected, providing a basis for subsequent parking space recommendations, enabling the berthing robot to provide more accurate and personalized parking suggestions according to the skill levels of different drivers, and enhancing the user's parking experience.

[0023] Furthermore, combining the multiple parking space type marking information, the slope information of the parking spaces, and the road information in the parking space area, a parking complexity analysis is carried out on the multiple parking spaces to generate multiple complexity indicators, including: initializing the parking complexity according to the multiple parking space type marking information to generate multiple initial difficulty indicators; analyzing the impact of the slope information of the parking spaces on parking to establish a slope impact weight; analyzing the impact of the road information in the parking space area on parking to establish a neighborhood road impact weight; and using the slope impact weight and the neighborhood road impact weight to perform weighted compensation on the multiple initial difficulty indicators to generate the multiple complexity indicators.

[0024] Specifically, first, according to the multiple parking space type marking information, the parking complexity of each parking space is initialized. For example, perpendicular parking spaces are given a lower difficulty, while parallel parking spaces are marked as high difficulty, and angled parking spaces are marked as medium difficulty to reflect the driving skills required, obtaining multiple initial difficulty indicators. Then, the slope information of the parking spaces is analyzed to evaluate the inclination degree of each parking space, quantify the impact of the slope on the parking difficulty, and establish a slope impact weight. The larger the inclination angle of the parking space slope, the higher the impact weight. An analysis of the impact of the road information in the parking space area on parking is carried out. The road information in the parking space area includes the road environment in the area and the traffic flow in the area. The road environment in the area refers to the environmental information near the parking space. For example, when there are obstacles (such as other vehicles, street lights, etc.) near the parking space, it will limit the parking space, increase the parking difficulty, and the impact weight is large. The traffic flow in the area includes the vehicle flow and pedestrian flow around the parking space. When the vehicle flow and pedestrian flow are large, it will also directly affect the safety and convenience of parking. Based on the analysis results of the road environment in the area and the traffic flow in the area, a neighborhood road impact weight is established to ensure that the complexity of the parking environment can be comprehensively reflected. Finally, the obtained slope impact weight and neighborhood road impact weight are applied to the initial difficulty indicators, and the multiple initial difficulty indicators are processed through weighted compensation to generate multiple complexity indicators. The multiple complexity indicators can provide a comprehensive and accurate evaluation of the parking difficulty of the parking spaces for the berthing robot, optimize the parking space recommendations, and improve the user's parking experience and satisfaction.

[0025] Furthermore, conduct a parking impact analysis on the parking space slope information and establish a slope impact weight, including: collecting multiple historical parking record datasets corresponding to parking spaces of the same type, where any one historical parking record dataset includes multiple parking records corresponding to the same parking space slope label; calculating the average parking time based on the multiple parking records in the multiple historical parking record datasets, statistically analyzing the proportion of data with parking time exceeding the average parking time and a deviation greater than a preset deviation, generating multiple proportion coefficients, and establishing a first mapping relationship table between the multiple proportion coefficients and the parking space slope labels; inputting the parking space slope information into the first mapping relationship table, obtaining the proportion coefficient corresponding to the parking space slope label with the highest similarity to the parking space slope information, and generating the slope impact weight.

[0026] Specifically, extract the historical parking record data of multiple parking spaces of the same type from the parking management system or the cloud database to form multiple historical parking record datasets. Each dataset in the multiple historical parking record datasets contains multiple parking records corresponding to different time periods with the same parking space slope label. The parking space slope label is a label used to identify and classify the inclination degree of each parking space relative to the horizontal plane, which can reflect the slope characteristics of the parking space, such as flat, slightly inclined, moderately inclined, and steep, etc. Then, based on the multiple historical parking record datasets, calculate the average parking time for the multiple parking records, that is, calculate the average of the time required for vehicles to park in the parking spaces with a specific slope, and obtain the average required parking time. Then, set a preset deviation threshold, compare the multiple parking records with the average parking time, and statistically analyze the proportion of parking records that exceed the parking average and are greater than the preset deviation, and identify the situations where the parking time is abnormal under the influence of the slope. For example, if the average parking time of a certain parking space is 10 minutes and the preset deviation is 5 minutes, then any parking time exceeding 15 minutes will be regarded as a deviation greater than the preset deviation. Generate multiple proportion coefficients based on the proportion data to reflect the actual impact of different slopes on the parking time, and establish a first mapping relationship table between the proportion coefficients and the parking space slope data to ensure that the abnormal proportion of parking time corresponding to each slope can be quickly queried. Finally, input the parking space slope information into this first mapping relationship table, automatically obtain the proportion coefficient corresponding to the slope label with the highest similarity to the input slope information, thereby generating the slope impact weight, and accurately quantifying the impact degree of the parking space slope on the stability of the parking time and the parking behavior. The same method is also applicable to the neighborhood road analysis. Analyze the historical parking records of the neighborhood roads, calculate the influence coefficients related to the road environment and traffic flow, and the accurate and reliable neighborhood road impact weight can be obtained. By obtaining the accurate slope impact weight and neighborhood road impact weight, further supplement the evaluation of the parking complexity, and provide more accurate and reliable decision-making support for parking management.

[0027] When the first vehicle enters the first roadside parking area, predict the parking level of the first driver and generate a first parking level prediction index.

[0028] Further, when the first vehicle enters the first roadside parking area, predict the parking level of the first driver and generate a first parking level prediction index, including: connecting to the parking record database, extracting the historical parking records of the first driver; counting the historical parking success rate, historical parking time, and historical parking space type ratio of the first driver according to the historical parking records; inputting the historical parking success rate, historical parking time, and historical parking space type ratio into the parking level evaluation model for evaluation and analysis, and generating the first parking level prediction index.

[0029] Specifically, when the first vehicle enters the first roadside parking area, the berth robot connects to the parking record database and extracts the historical parking records of the driver of the first vehicle. The historical parking records include the parking performance of the driver in different parking spaces before. Then, statistical analysis is performed on the historical parking records to obtain the historical parking success rate, historical parking time, and historical parking space type ratio of the first driver. The historical parking success rate refers to the ratio of the number of times the first driver successfully parks to the total number of attempts to park. The historical parking time is the time required for each parking of the first driver's historical parking. The historical parking space type ratio refers to the usage ratio of the first driver's horizontal parking spaces, vertical parking spaces, and inclined parking spaces. Finally, the historical parking success rate, historical parking time, and historical parking space type ratio are used as input data and input into the parking level evaluation model for comprehensive analysis. The parking level evaluation model calculates the weights of these indicators through machine learning or statistical analysis methods to generate the required first parking level prediction index. The first parking level prediction not only reflects the overall parking level of the first driver but also provides data support for subsequent parking space recommendations, ensuring that more suitable parking suggestions can be provided for them, improving parking efficiency and user satisfaction.

[0030] Further, inputting the historical parking success rate, historical parking time, and historical parking space type ratio into the parking level evaluation model for evaluation and analysis to generate the first parking level prediction index, including: connecting the parking level evaluation model to the evaluation sample library. Among them, the evaluation sample library includes multiple groups of evaluation samples. Any group of evaluation samples includes a parking success rate sample, a parking time sample, and a parking space type ratio sample, as well as corresponding evaluation identification information marked with a parking level prediction index; inputting the historical parking success rate, the historical parking time, and the historical parking space type ratio into the parking level evaluation model, calling the evaluation sample library through the parking level evaluation model, traversing the multiple groups of evaluation samples for sample matching, and generating the first parking level prediction index.

[0031] Specifically, the parking level evaluation model is connected to the evaluation sample library, which is a dataset containing multiple groups of parking behavior samples and is used to support the analysis and prediction of the parking level evaluation model. Each group of evaluation samples in the evaluation sample library includes specific parking success rate samples, parking time samples, and parking space type proportion samples. In addition, each group of samples also has a corresponding marked parking level prediction index, that is, the corresponding evaluation identification information, which reflects the parking performance and ability of different drivers under specific conditions. When the berth robot receives the historical parking data of the first driver, the historical parking success rate, the historical parking time, and the historical parking space type proportion are integrated into input data and input into the parking level evaluation model. The parking level evaluation model traverses and matches each group of evaluation samples in the evaluation sample library according to the input data by calling the evaluation sample library connected to it, compares with each group of samples in the sample library, and identifies samples similar to the historical parking success rate, historical parking time, and historical parking space type proportion. Through sample matching, the parking level evaluation model can find the sample group closest to the historical parking behavior of the first driver. Furthermore, according to the matched evaluation sample group, the corresponding parking level prediction index is obtained, and the generated first parking level prediction index provides necessary information for the berth robot, enabling it to recommend the most suitable parking space according to the actual parking level of the first driver, improving the parking experience and efficiency.

[0032] Combining the multiple parking space size marking information, the first parking level prediction index, and the multiple complexity indexes for parking space matching to generate the first recommended parking space.

[0033] Specifically, based on the multiple parking space size marking information including the specific sizes of multiple parking spaces, combined with the first parking level prediction index reflecting the parking ability of the first driver and the multiple complexity indexes reflecting the parking difficulty of each parking space, a comprehensive matching of the parking space for the vehicle driven by the first driver is carried out, the suitability of each parking space is comprehensively evaluated, and the first recommended parking space is generated. The first recommended parking space can ensure that the recommended parking space matches the vehicle size, and the recommended parking space not only suits the driver's parking level but also provides sufficient convenience and safety in the current environment, meeting the driver's needs and improving the user's parking experience and efficiency.

[0034] Further, perform parking space matching by combining the multiple parking space size marking information, the first parking level prediction index, and the multiple complexity indexes to generate a first recommended parking space, including: collecting and identifying an image of the first vehicle through a camera in the first roadside parking area to generate first vehicle size information; comparing the first vehicle size information with the multiple parking space size marking information to locate a first set of matching parking spaces; based on the multiple complexity indexes, obtaining a first set of matching parking space complexity indexes corresponding to the first set of matching parking spaces; and performing parking space adaptation analysis and screening by combining the first set of matching parking space complexity indexes and the first parking level prediction index to generate the first recommended parking space.

[0035] Specifically, first, collect an image of the first vehicle through a camera in the first roadside parking area, and automatically identify the shape and size of the first vehicle based on the collected vehicle image using image recognition technology to obtain the first vehicle size information. The first vehicle size information includes key size parameters such as the vehicle length, width, and height. Then, compare the first vehicle size information with the multiple parking space size marking information. The size marking information of each parking space includes specific requirements for the vehicle size and vehicle type suitable for that parking space. Through the comparison, locate a first set of matching parking spaces that meet the first vehicle size. The first set of matching parking spaces includes all the parking spaces in the first roadside parking area suitable for the vehicle to park. Based on multiple complexity indexes obtained from the analysis of the parking space slope, neighborhood road conditions, and historical parking records, assign corresponding matching parking space complexity indexes to each matching parking space in the first set of matching parking spaces to form a first set of matching parking space complexity indexes. The first set of matching parking space complexity indexes reflects the parking difficulty of each matching parking space in terms of slope and neighborhood road conditions, ensuring that the actual parking conditions can be considered when recommending parking spaces. Finally, perform parking space adaptation analysis and screening according to the combination of the first set of matching parking space complexity indexes and the first parking level prediction index of the first driver, comprehensively analyze the driver's parking ability and the complexity of the matching parking spaces, identify the most suitable parking space for the first driver, and generate a first recommended parking space, ensuring that the recommended parking space not only meets the vehicle size requirements but also suits the driver's parking level, thereby improving parking efficiency and user satisfaction.

[0036] Furthermore, combining the first matching parking space complexity index set and the first parking level prediction index for parking space adaptation analysis and screening to generate the first recommended parking space includes: establishing a mapping relationship between parking space complexity and parking level; performing adapted parking space complexity analysis on the first parking level prediction index according to the mapping relationship between parking space complexity and parking level to generate an adapted complexity interval; connecting the berth robot to obtain the parking space usage status information of the first roadside parking area, and combining the adapted complexity interval to match parking spaces to generate the first recommended parking space.

[0037] Specifically, with the help of the technical capabilities of professional technicians combined with practical experience, a mapping relationship between parking space complexity and parking level is established. The mapping relationship between parking space complexity and parking level can effectively associate the complexity of parking spaces with the parking ability of drivers. By establishing the mapping relationship between parking space complexity and parking level, the berth robot can clarify which complexity-level parking spaces are suitable for drivers of different levels. Then, the first parking level prediction index is compared with the set mapping relationship between parking space complexity and parking level. According to the first parking level prediction index of the first driver, that is, according to the user's parking level, the complexity level of the parking space adapted to the first driver is analyzed. By analyzing, the complexity range in which the first driver can park safely and smoothly is clarified to form an adapted complexity interval. Finally, connect the berth robot to obtain the parking space usage status information of the first roadside parking area in real time. The parking space usage status information includes the occupancy and availability status of each current parking space. According to the parking space usage status information, combined with the adapted complexity area, parking spaces are matched to obtain the first recommended parking space. During the matching process, if there are available parking spaces that meet the adapted complexity interval, these parking spaces will be recommended first. If all the parking spaces that meet the conditions are occupied, other parking spaces with the smallest difference from the adapted complexity interval are selected as recommended parking spaces to ensure that the driver can find a suitable parking space. Through comprehensive analysis and matching, parking spaces that meet the driver's level are recommended, meeting the driver's personalized needs, improving the user's parking experience, and ensuring that the recommended parking spaces are both safe and convenient.

[0038] Generate a first navigation route according to the first recommended parking space, and the berth robot performs parking guidance according to the first navigation route.

[0039] Specifically, after obtaining the first recommended parking space, based on the first recommended parking space information, using map data and positioning information, the optimal path from the current vehicle position to the recommended parking space is determined, that is, the first navigation route. The first navigation route not only considers the geographical location of the parking space, but also takes into account road conditions, traffic flow, and other potential obstacles to ensure the safety and efficiency of the path. After receiving the first navigation route information, the berth robot provides real-time parking guidance according to the navigation route, including steering prompts, driving speed control, and coordination of parking actions, safely guiding the driver to the first recommended parking space to ensure the smooth completion of the parking operation and improve the user's parking experience and satisfaction.

[0040] Embodiment 2, based on the same inventive concept as the method for intelligent scheduling of roadside parking assisted by a berth robot in the foregoing embodiment, as Figure 2 shown, the present application provides a system for intelligent scheduling of roadside parking assisted by a berth robot, wherein the system includes:

[0041] A parking space classification module 11, which is used to connect the berth robots in the first roadside parking area to obtain multiple parking spaces, classify and mark them according to the parking space size and type, and generate multiple parking space size marking information and multiple parking space type marking information.

[0042] A digital map acquisition module 12, which is used to acquire the regional digital map of the first roadside parking area, wherein the regional digital map is marked with parking space slope information and parking space neighborhood road information.

[0043] A parking complexity analysis module 13, which is used to combine the multiple parking space type marking information, the parking space slope information, and the parking space neighborhood road information to perform parking complexity analysis on the multiple parking spaces and generate multiple complexity indicators.

[0044] A parking level prediction module 14, which is used to predict the parking level of the first driver when the first vehicle enters the first roadside parking area and generate a first parking level prediction index.

[0045] A parking space matching module 15, which is used to combine the multiple parking space size marking information, the first parking level prediction index, and the multiple complexity indicators to perform parking space matching and generate a first recommended parking space.

[0046] A navigation route generation module 16, which is used to generate a first navigation route according to the first recommended parking space and perform parking guidance according to the first navigation route through the berth robot.

[0047] Further, the parking complexity analysis module 13 is configured to perform the following steps: initialize the parking complexity according to the multiple parking space type marking information to generate multiple initialization difficulty indicators; perform a parking impact analysis on the parking space slope information to establish a slope impact weight; perform a parking impact analysis on the parking space neighborhood road information to establish a neighborhood road impact weight; and perform weighted compensation on the multiple initialization difficulty indicators with the slope impact weight and the neighborhood road impact weight to generate the multiple complexity indicators.

[0048] Further, the parking complexity analysis module 13 is further configured to perform the following steps: collect multiple historical parking record datasets corresponding to the same type of parking space, where any one of the historical parking record datasets includes multiple parking records corresponding to the same parking space slope marking; calculate the average parking time based on the multiple parking records in the multiple historical parking record datasets, count the proportion of data that exceeds the average parking time and has a deviation greater than a preset deviation, generate multiple proportion coefficients, and establish a first mapping relationship table between the multiple proportion coefficients and the parking space slope marking; input the parking space slope information into the first mapping relationship table, and obtain the proportion coefficient corresponding to the parking space slope marking with the highest similarity to the parking space slope information to generate the slope impact weight.

[0049] Further, the parking level prediction module 14 is configured to perform the following steps: connect to the parking record database to extract the historical parking records of the first driver; count the historical parking success rate, historical parking time, and historical parking space type proportion of the first driver according to the historical parking records; input the historical parking success rate, historical parking time, and historical parking space type proportion into the parking level evaluation model for evaluation and analysis to generate the first parking level prediction index.

[0050] Further, the parking level prediction module 14 is further configured to perform the following steps: the parking level evaluation model is connected to an evaluation sample library, where the evaluation sample library includes multiple groups of evaluation samples, and any one of the evaluation samples includes a parking success rate sample, a parking time sample, and a parking space type proportion sample, as well as an evaluation identification information marked with a parking level prediction index; input the historical parking success rate, the historical parking time, and the historical parking space type proportion into the parking level evaluation model, and call the evaluation sample library through the parking level evaluation model to traverse the multiple groups of evaluation samples for sample matching to generate the first parking level prediction index.

[0051] Further, the parking space matching module 15 is configured to perform the following steps: collect and identify an image of the first vehicle through a camera in the first roadside parking area to generate first vehicle size information; compare the first vehicle size information with the multiple parking space size marking information to locate a first set of matching parking spaces; based on the multiple complexity indicators, obtain a first set of matching parking space complexity indicators corresponding to the first set of matching parking spaces; perform parking space adaptation analysis and screening by combining the first set of matching parking space complexity indicators and the first parking level prediction indicator to generate the first recommended parking space.

[0052] Further, the parking space matching module 15 is further configured to perform the following steps: establish a mapping relationship between parking space complexity and parking level; perform adapted parking space complexity analysis on the first parking level prediction indicator according to the mapping relationship between parking space complexity and parking level to generate an adapted complexity interval; connect to the berth robot, obtain the parking space usage status information of the first roadside parking area, and perform matching of the parking space in combination with the adapted complexity interval to generate the first recommended parking space.

[0053] The foregoing are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

[0054] This specification and the drawings are merely exemplary descriptions of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and deformations to the present application without departing from the scope of the present application. Thus, if these modifications and deformations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and deformations.

Claims

1. An intelligent scheduling method for roadside parking assisted by a berth robot, characterized in that Including: The berth robot connected to the first roadside parking area acquires multiple parking spaces, classifies and marks them according to the parking space size and type, and generates multiple parking space size marking information and multiple parking space type marking information; Acquire the regional digital map of the first roadside parking area, wherein the parking space slope information and the parking space neighborhood road information are marked in the regional digital map; Combine the multiple parking space type marking information, the parking space slope information and the parking space neighborhood road information to analyze the parking complexity of the multiple parking spaces, and generate multiple complexity indicators; When the first vehicle drives into the first roadside parking area, predict the parking level of the first driver and generate the first parking level prediction indicator; Combine the multiple parking space size marking information, the first parking level prediction indicator and the multiple complexity indicators for parking space matching to generate the first recommended parking space; Generate the first navigation route according to the first recommended parking space, and perform parking guidance by the berth robot according to the first navigation route; Combine the multiple parking space type marking information, the parking space slope information and the parking space neighborhood road information to analyze the parking complexity of the multiple parking spaces, and generate multiple complexity indicators, including: Perform parking complexity initialization according to the multiple parking space type marking information to generate multiple initialization difficulty indicators; Perform parking impact analysis on the parking space slope information to establish a slope impact weight; Perform parking impact analysis on the parking space neighborhood road information to establish a neighborhood road impact weight; Use the slope impact weight and the neighborhood road impact weight to perform weighted compensation on the multiple initialization difficulty indicators to generate the multiple complexity indicators; Perform parking impact analysis on the parking space slope information to establish a slope impact weight, including: Collect multiple historical parking record data sets corresponding to parking spaces of the same type, where any one historical parking record data set includes multiple parking records corresponding to the same parking space slope mark; Based on multiple parking records in the multiple historical parking record data sets, calculate the average parking time respectively, count the proportion of data that exceeds the average parking time and the deviation is greater than the preset deviation, generate multiple proportion coefficients, and establish a first mapping relationship table between the multiple proportion coefficients and the parking space slope mark; Input the parking space slope information into the first mapping relationship table, and obtain the proportion coefficient corresponding to the parking space slope mark with the highest similarity to the parking space slope information to generate the slope impact weight; When the first vehicle drives into the first roadside parking area, predict the parking level of the first driver and generate the first parking level prediction indicator, including: Connect to the parking record database and extract the historical parking records of the first driver; Statistically calculate the historical parking success rate, historical parking time and historical parking space type proportion of the first driver according to the historical parking records; Input the historical parking success rate, historical parking time and historical parking space type proportion into the parking level evaluation model for evaluation analysis to generate the first parking level prediction indicator.

2. The intelligent scheduling method for roadside parking assisted by a berth robot according to claim 1, wherein, Input the historical parking success rate, historical parking time, and historical proportion of parking space types into the parking level evaluation model for evaluation and analysis to generate the first parking level prediction indicator, including: The parking level evaluation model is connected to the evaluation sample library, where the evaluation sample library includes multiple groups of evaluation samples. Any group of evaluation samples includes a parking success rate sample, a parking time sample, and a proportion of parking space types sample, as well as evaluation identification information marked with the parking level prediction indicator; Input the historical parking success rate, the historical parking time, and the historical proportion of parking space types into the parking level evaluation model. The parking level evaluation model calls the evaluation sample library, traverses the multiple groups of evaluation samples for sample matching, and generates the first parking level prediction indicator.

3. The intelligent scheduling method for roadside parking assisted by a berth robot according to claim 2, wherein, Combine the multiple parking space size marking information, the first parking level prediction indicator, and the multiple complexity indicators for parking space matching to generate the first recommended parking space, including: Collect and identify an image of the first vehicle through a camera in the first roadside parking area to generate the first vehicle size information; Compare the first vehicle size information with the multiple parking space size marking information to locate the first matching parking space set; Based on the multiple complexity indicators, obtain the first matching parking space complexity indicator set corresponding to the first matching parking space set; Combine the first matching parking space complexity indicator set and the first parking level prediction indicator for parking space adaptation analysis and screening to generate the first recommended parking space.

4. The intelligent scheduling method for roadside parking assisted by a berth robot according to claim 3, wherein Combine the first matching parking space complexity indicator set and the first parking level prediction indicator for parking space adaptation analysis and screening to generate the first recommended parking space, including: Establish a mapping relationship between parking space complexity and parking level; According to the mapping relationship between parking space complexity and parking level, perform an analysis of the adaptation of the parking space complexity for the first parking level prediction indicator to generate an adaptation complexity interval; Connect the berth robot, obtain the parking space usage status information of the first roadside parking area, and combine it with the adaptation complexity interval for parking space matching to generate the first recommended parking space.

5. An intelligent scheduling system for roadside parking assisted by a berth robot, characterized in that, Steps for implementing the method according to any one of claims 1 to 4, including: A parking space classification module, which is used to connect to the berth robot in the first roadside parking area to obtain multiple parking spaces, and classify and mark them according to the parking space size and parking space type to generate multiple parking space size marking information and multiple parking space type marking information; A digital map acquisition module, which is used to acquire the regional digital map of the first roadside parking area, where the regional digital map is marked with parking space slope information and parking space neighborhood road information; A parking complexity analysis module, which is used to combine the multiple parking space type marking information, the parking space slope information, and the parking space neighborhood road information to perform parking complexity analysis on the multiple parking spaces to generate multiple complexity indicators; Parking level prediction module, which is used to predict the parking level of the first driver when the first vehicle drives into the first roadside parking area and generate a first parking level prediction index; Parking space matching module, which is used to perform parking space matching by combining the multiple parking space size marking information, the first parking level prediction index and the multiple complexity indexes to generate a first recommended parking space; Navigation route generation module, which is used to generate a first navigation route according to the first recommended parking space and guide the vehicle to park according to the first navigation route by the berth robot.

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