Urban digital twin intelligent parking service management platform and method

Abstract

The invention discloses an urban digital twin intelligent parking service management method and system, a parking lot cloud server obtains a parking guide request instruction, the parking guide request instruction comprises a parking user license plate number and a vehicle type size, a parking lot ID code is sent to a user side, the user side receives and scans the parking lot ID code, and the parking lot ID code is sent to the parking lot cloud server; unoccupied parking space information of the corresponding parking lot is obtained; matching is carried out according to the unoccupied parking space information and the vehicle type size, and a first matching grade sorting table is generated; obtaining path parking space parking information of each unoccupied parking space in the first matching grade sorting table, and performing primary consideration on the first matching grade sorting table to obtain a second matching grade sorting table; and obtaining the average parking time length of the user, carrying out secondary consideration on the second matching grade sorting table, generating parking guidance information, and outputting the parking guidance information to the user side, thereby improving the accuracy of parking space matching and the parking experience of the user.

Description

Technical Field

[0001] The present invention relates to the field of smart parking, and in particular to an urban digital twin smart parking service management platform and method. Background Art

[0002] In recent years, with the continuous growth of urban motor vehicle ownership and the advancement of smart city construction, parking difficulties have become increasingly prominent. While existing parking technology has evolved from traditional manual management to intelligent guidance, existing parking guidance systems suffer from numerous technical flaws. Their single-distance decision-making model fails to accurately match vehicle size to parking spaces and struggles to predict the dynamic movement of vehicles along the route. They also lack comprehensive consideration of user parking duration and destination location, resulting in a disconnect between parking solutions and actual needs. Furthermore, systems generally lack user profiling capabilities, hindering personalized service for different users, especially first-time visitors. These technical shortcomings collectively contribute to low parking space utilization and poor user experience. There is an urgent need to establish a multi-dimensional intelligent decision-making system to improve the accuracy and intelligence of parking services. Furthermore, existing technologies lack a scientific basis for weighting various decision factors, often relying on fixed empirical values. This makes it difficult to adapt to the actual operational characteristics and dynamic needs of different parking lots, leading to discrepancies between recommended results and actual needs.

[0003] Therefore, this field needs a smart parking service management platform and method that can build a multi-dimensional intelligent decision-making model to realize parking space size adaptability analysis, neighboring vehicle dynamic prediction, user behavior profile recognition and optimal weight allocation functions, so as to solve the shortcomings of existing parking technology in terms of accuracy, personalization and intelligence. Summary of the Invention

[0004] The technical problem solved by the present invention is that existing parking technologies lack analysis of the matching degree between vehicle size and parking space, are unable to predict the departure time of vehicles and pedestrians in path parking spaces, ignore the influence of user parking time characteristics and destination location and crowd interference factor coefficient, and are difficult to provide users with parking solutions that truly meet their travel needs, resulting in users having to spend a long time searching for suitable parking spaces.

[0005] To solve the above technical problems, the present invention provides the following technical solution: a city digital twin smart parking service management method, the method comprising:

[0006] (1) The parking lot cloud server obtains a parking guidance request instruction, which includes the parking user's license plate number and vehicle size, and sends a parking lot ID code to the user terminal. The user terminal receives the parking lot ID code and scans it to obtain the available parking space information of the corresponding parking lot;

[0007] (2) matching the available parking space information with the vehicle model size to generate a first matching level ranking table;

[0008] (3) obtaining the path parking space information of each vacant parking space in the first matching level ranking table, and performing a consideration on the first matching level ranking table to obtain a second matching level ranking table;

[0009] (4) obtaining the average parking time of the user, performing a secondary consideration on the second matching level ranking table, generating parking guidance information, and outputting it to the user terminal;

[0010] The vacant parking space information includes the vacant parking space number, the distance between the vacant parking space and the exit, and the size of the vacant parking space;

[0011] The parking guidance information includes a target parking space, a target parking space number, and a vehicle parking path;

[0012] The path parking space parking information includes the path parking space, the path parking space number and the path parking space locking start timestamp;

[0013] The path parking spaces are all parking spaces that the parking user passes through on the path from the initial position to each vacant parking space in the first matching level ranking table.

[0014] As a preferred solution of the urban digital twin smart parking service management method described in the present invention, the method of generating a first matching level ranking table by matching the vacant parking space information with the vehicle model size includes:

[0015] Obtaining the size of available parking spaces in the parking lot and the size of the vehicle type in the parking guidance request instruction;

[0016] Dividing the vehicle size of the parking guidance request by the size of the available parking space to obtain a size ratio;

[0017] comparing the size ratio to a first size threshold and a second size threshold;

[0018] If the size ratio is greater than the first size threshold and less than the second size threshold, it is recorded as a first-level match;

[0019] If the size ratio is smaller than the first size threshold, it is recorded as a secondary match;

[0020] If the size ratio is greater than the second size threshold, it is recorded as a third-level match;

[0021] The vehicle size corresponding to the user terminal is bound to the matching level of each vacant parking space number and sorted in ascending order to obtain the first matching level sorting table.

[0022] As a preferred solution of the urban digital twin smart parking service management method described in the present invention, the method of considering the first matching level ranking table once to obtain the second matching level ranking table includes:

[0023] Obtaining the specific path parking space number and path parking space locking start timestamp of each vacant parking space corresponding to the first matching level ranking table, and obtaining the instruction request timestamp of the parking guidance request instruction generated by the user terminal;

[0024] Subtract the path parking space locking start timestamp from the instruction request timestamp to obtain the time difference;

[0025] Performing a walking simulation between the parking spaces on the path and the user's preset destination location to obtain the user's walking path and the time required for walking;

[0026] Subtract the required time from the time difference to obtain the relative time displacement, subtract the relative time displacement from the preset relative time displacement threshold to obtain the size of the crowd interference factor coefficient, add the size of the crowd interference factor coefficient to the first matching level ranking table, and obtain the second matching level ranking table after one consideration.

[0027] As a preferred solution of the urban digital twin smart parking service management method described in the present invention, the method of obtaining the average parking time of users at the user terminal, performing a secondary consideration on the second matching level ranking table, and outputting the parking guidance information includes:

[0028] Collecting the path distance from each parking space to the exit and the corresponding average parking time in the parking space, and obtaining a first distance threshold, a second distance threshold, a first time threshold, and a second time threshold for the path distance and the corresponding average parking time in the parking space through a clustering algorithm;

[0029] Divide the parking spaces in the parking lot into three parking space categories: short time zone, medium time zone and long time zone according to the first distance threshold, the second distance threshold, the first time threshold and the second time threshold;

[0030] Obtaining an average parking time of users at the user terminal, and obtaining a parking space classification corresponding to the average parking time of users;

[0031] Compare the parking space classification corresponding to the vacant parking space with the parking space classification corresponding to the average parking time of the user to obtain a distance matching value;

[0032] The distance matching value is added to the second matching level ranking table to obtain the guided parking information after secondary consideration.

[0033] As a preferred solution of the urban digital twin smart parking service management method described in the present invention, wherein: the user terminal receives and scans the parking lot ID code, and obtaining the average parking time includes:

[0034] When the parking user does not arrive at the current parking lot for the first time, the user terminal receives and scans the parking lot ID code to obtain the average parking time of the user terminal corresponding to the parking lot ID;

[0035] When the parking user arrives at the current parking lot for the first time, the user terminal receives and scans the parking lot ID code, does not display the average parking time of the user terminal corresponding to the parking lot ID, obtains the historical order information of the parking user, and calculates the corresponding user's average parking time.

[0036] As a preferred solution of the urban digital twin smart parking service management method described in the present invention, the method for obtaining the average parking time of the user terminal and obtaining the parking space classification corresponding to the average parking time of the user is:

[0037] Obtaining an average parking time of users, and comparing the average parking time of users with a first time threshold and a second time threshold;

[0038] If the average parking time of the user is less than the first time threshold, the parking space of the corresponding parking user is classified as a short-time zone;

[0039] If the average parking time of the user is greater than the first time threshold and less than the second time threshold, the parking space of the corresponding parking user is classified as the middle time zone;

[0040] If the average parking time of the user is greater than the second time threshold, the parking space of the corresponding parking user is classified as a long-time zone.

[0041] As a preferred solution of the urban digital twin smart parking service management method described in the present invention, the method for comparing the parking space classification corresponding to the vacant parking spaces with the parking space classification corresponding to the average parking time of the users to obtain the distance matching value is:

[0042] The short time zone priority value is smaller than the medium time zone priority value, and the medium time zone priority value is smaller than the long time zone priority value;

[0043] Obtain the parking space classification corresponding to the parking user and the parking space classification corresponding to the vacant parking spaces;

[0044] If the parking space classification is consistent, the distance matching value corresponding to the vacant parking space is 1;

[0045] If the parking space classifications are inconsistent, compare the priority values ​​of the available parking spaces and the parking space classifications of the parking users;

[0046] If the priority value of the parking space category corresponding to the vacant parking space is smaller than the priority value of the parking space category of the parking user, the distance matching value is 0.6;

[0047] If the priority value of the parking space category corresponding to the vacant parking space is greater than the priority value of the parking space category of the parking user, the distance matching value is 0.2.

[0048] As a preferred solution of the urban digital twin smart parking service management method described in the present invention, a method for performing a walking simulation on the parking spaces along the path and the destination location preset by the parking user to obtain the user's walking path and the time required for walking includes:

[0049] The parking space on the path is used as the starting point, and the position of the parking lot exit corresponding to the user's preset travel destination is locked. The position of the parking lot exit is used as the end point, and walking is selected as the arrival method. The user's walking path and walking time are obtained through the navigation software.

[0050] As an optimal solution of the urban digital twin smart parking service management method described in the present invention, the following is described: obtaining the size of the crowd interference factor coefficient obtained by the first consideration, the size of the distance matching value obtained by the second consideration and the matching level corresponding to each vacant parking space, marking the third-level matching as an unselectable parking space, setting the matching level weighting coefficient weighting coefficient to 0.5, the distance matching value size weighting coefficient to 0.3, and the crowd interference factor size weighting coefficient to 0.2, performing weighted calculation on the crowd interference factor coefficient size, distance matching value size and matching level of the remaining selectable vacant parking spaces, obtaining the recommended parking coefficient of each vacant parking space, selecting the vacant parking space with the largest recommended parking coefficient, and outputting the guided parking information corresponding to the vacant parking space with the largest recommended parking coefficient, and the user parks according to the guided parking information.

[0051] An urban digital twin smart parking service management platform, including a data collaborative processing module, an intelligent decision-making matching module, and a user interaction and execution module;

[0052] The data collaborative processing module is used to collect parking lot vacant space information, user terminal request data and path parking space dynamic information in real time;

[0053] The intelligent decision-making matching module is used to comprehensively consider the available parking spaces, the size of the parking spaces, the number of pedestrians on the parking paths, and the average parking time of users to obtain the optimal parking space selection;

[0054] The user interaction and execution module is used to push parking guidance information to the user terminal and lock the target parking space.

[0055] The beneficial effects of the present invention are as follows: through a multi-dimensional parking space matching algorithm and a dynamic hierarchical sorting mechanism, accurate matching and intelligent recommendation of parking resources are achieved. The initial screening is based on the matching degree between the vehicle model size and the parking space size to ensure the physical adaptability of the vehicle and the parking space; then secondary optimization is performed in combination with the usage status of the path parking spaces, effectively avoiding parking interference caused by the entry and exit of adjacent vehicles and pedestrians. By establishing a hierarchical evaluation system, the recommendation strategy can be dynamically adjusted according to multiple factors such as vehicle characteristics and parking space status, thereby improving the accuracy of parking space matching and the user parking experience.

[0056] Through user behavior analysis, personalized prediction and proactive service of parking needs are achieved. Parking areas are intelligently divided according to users' historical parking time data, and three optimizations are performed in combination with destination route planning, so that the recommended parking spaces can meet both parking time requirements and travel convenience. It can predict users' parking needs and provide a new service paradigm for smart parking management. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A schematic diagram of the basic process of a city digital twin smart parking service management method provided for one embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0059] Example 1, referring to the figure, is an embodiment of the present invention, which provides a city digital twin smart parking service management method, the method comprising:

[0060] (1) The parking lot cloud server obtains a parking guidance request instruction, which includes the parking user's license plate number and vehicle size, and sends a parking lot ID code to the user terminal. The user terminal receives the parking lot ID code and scans it to obtain the available parking space information of the corresponding parking lot;

[0061] (2) matching the available parking space information with the vehicle model size to generate a first matching level ranking table;

[0062] (3) obtaining the path parking space information of each vacant parking space in the first matching level ranking table, and performing a consideration on the first matching level ranking table to obtain a second matching level ranking table;

[0063] (4) obtaining the average parking time of the user, performing a secondary consideration on the second matching level ranking table, generating parking guidance information, and outputting it to the user terminal;

[0064] The vacant parking space information includes the vacant parking space number, the distance between the vacant parking space and the exit, and the size of the vacant parking space;

[0065] The parking guidance information includes a target parking space, a target parking space number, and a vehicle parking path;

[0066] The path parking space parking information includes the path parking space, the path parking space number and the path parking space locking start timestamp;

[0067] The path parking spaces are all parking spaces that the parking user passes through on the path from the initial position to each vacant parking space in the first matching level ranking table.

[0068] The method of generating a first matching ranking table by matching the vacant parking space information with the vehicle model size includes:

[0069] Obtaining the size of available parking spaces in the parking lot and the size of the vehicle type in the parking guidance request instruction;

[0070] Dividing the vehicle size of the parking guidance request by the size of the available parking space to obtain a size ratio;

[0071] comparing the size ratio to a first size threshold and a second size threshold;

[0072] If the size ratio is greater than the first size threshold and less than the second size threshold, it is recorded as a first-level match;

[0073] If the size ratio is smaller than the first size threshold, it is recorded as a secondary match;

[0074] If the size ratio is greater than the second size threshold, it is recorded as a third-level match;

[0075] The vehicle size corresponding to the user terminal is bound to the matching level of each vacant parking space number and sorted in ascending order to obtain the first matching level sorting table.

[0076] In this implementation, an intelligent vehicle model-parking space size matching algorithm enables precise screening and hierarchical recommendation of parking space resources. The algorithm calculates the proportional relationship between vehicle model size and parking space size, and uses a preset dual-threshold comparison mechanism to classify the matching results into three levels. This ultimately generates an ordered list of parking space recommendations. This effectively addresses the mismatch between vehicle model and parking space size in traditional parking systems, avoiding parking difficulties caused by undersized spaces or waste of resources caused by oversized spaces, thereby improving parking space utilization efficiency and the user parking experience. Furthermore, this hierarchical ranking mechanism provides a reliable initial data foundation for subsequent route parking space analysis and parking duration considerations, making the entire parking guidance process more scientific and rational.

[0077] The method of obtaining a second matching rank ranking table by considering the first matching rank ranking table once includes:

[0078] Obtaining the specific path parking space number and path parking space locking start timestamp of each vacant parking space corresponding to the first matching level ranking table, and obtaining the instruction request timestamp of the parking guidance request instruction generated by the user terminal;

[0079] Subtract the path parking space locking start timestamp from the instruction request timestamp to obtain the time difference;

[0080] Performing a walking simulation between the parking spaces on the path and the user's preset destination location to obtain the user's walking path and the time required for walking;

[0081] Subtract the required time from the time difference to obtain the relative time displacement, subtract the relative time displacement from the preset relative time displacement threshold to obtain the size of the crowd interference factor coefficient, add the size of the crowd interference factor coefficient to the first matching level ranking table, and obtain the second matching level ranking table after one consideration.

[0082] In this implementation, dynamic route parking space analysis and time displacement calculations enable spatiotemporal optimization of parking space selection. Real-time access to route parking spaces is captured, combined with intelligent analysis of the user's walking time to the destination. The pedestrian interference factor coefficient is then calculated to assess the spatiotemporal convenience of parking space use. This method effectively predicts the degree of alignment between the departure times of vehicles and pedestrians at route parking spaces and the user's activity schedule, avoiding congestion caused by impending departures and improving the smoothness of the parking process.

[0083] The method of obtaining the average parking duration of users at the user terminal, performing a secondary consideration on the second matching level ranking table, and outputting parking guidance information includes:

[0084] Collecting the path distance from each parking space to the exit and the corresponding average parking time in the parking space, and obtaining a first distance threshold, a second distance threshold, a first time threshold, and a second time threshold for the path distance and the corresponding average parking time in the parking space through a clustering algorithm;

[0085] Divide the parking spaces in the parking lot into three parking space categories: short time zone, medium time zone and long time zone according to the first distance threshold, the second distance threshold, the first time threshold and the second time threshold;

[0086] Obtaining an average parking time of users at the user terminal, and obtaining a parking space classification corresponding to the average parking time of users;

[0087] Compare the parking space classification corresponding to the vacant parking space with the parking space classification corresponding to the average parking time of the user to obtain a distance matching value;

[0088] The distance matching value is added to the second matching level ranking table to obtain the guided parking information after secondary consideration.

[0089] In this implementation, personalized dynamic allocation of parking space resources is achieved through an intelligent zoning algorithm based on user parking behavior. Cluster analysis technology is used to establish a correlation model between parking time and parking space location, and the parking lot is scientifically divided into three functional areas: short-time zone, medium-time zone and long-time zone. The most suitable parking space type is then automatically matched according to the user's historical parking data. This can accurately predict the user's parking time requirements, avoid the waste of resources caused by short-time parking users occupying long-time parking spaces, and prevent long-time parking users from being forced to move their cars frequently, thereby improving the overall turnover efficiency of the parking lot. The user's average parking time characteristics are intelligently compared with the parking space zoning characteristics, and a quantitative distance matching value is generated to evaluate the suitability of the parking space, so that the recommendation results meet the user's actual needs and take into account the parking lot management requirements. The parking space recommendation strategy can be automatically optimized according to the user type, improving the parking experience of individual users and realizing the global optimization configuration of parking lot resources.

[0090] The user terminal receives and scans the parking lot ID code, and obtains the average parking time, including:

[0091] When the parking user does not arrive at the current parking lot for the first time, the user terminal receives and scans the parking lot ID code to obtain the average parking time of the user terminal corresponding to the parking lot ID;

[0092] When the parking user arrives at the current parking lot for the first time, the user terminal receives and scans the parking lot ID code, does not display the average parking time of the user terminal corresponding to the parking lot ID, obtains the historical order information of the parking user, and calculates the corresponding user's average parking time.

[0093] In this implementation, an intelligent user parking behavior analysis system is used to achieve accurate and personalized parking duration predictions, and a personal parking portrait is established based on the user's historical parking data. For old users, the historical parking data of the parking lot is directly called, and for new users, the parking duration characteristics are inferred by analyzing their historical order information across parking lots. This breaks through the limitation of traditional parking systems that adopt a unified recommendation strategy for all users, and can provide differentiated services based on the parking habits of different users, improving the accuracy of parking duration predictions and the adaptability of parking space recommendations. The combination of parking lot ID code identification and historical order data is used to ensure that both old and new users can obtain services that match their parking characteristics, solving the problem of missing first-time user data. By mining the user's activity records in other parking lots, the cross-site migration application of parking behavior characteristics is realized, providing a continuously optimized data foundation for the smart parking system, making the parking recommendation service more intelligent and humane.

[0094] The method for obtaining the average parking time of the user terminal and obtaining the parking space classification corresponding to the average parking time of the user is:

[0095] Obtaining an average parking time of users, and comparing the average parking time of users with a first time threshold and a second time threshold;

[0096] If the average parking time of the user is less than the first time threshold, the parking space of the corresponding parking user is classified as a short-time zone;

[0097] If the average parking time of the user is greater than the first time threshold and less than the second time threshold, the parking space of the corresponding parking user is classified as the middle time zone;

[0098] If the average parking time of the user is greater than the second time threshold, the parking space of the corresponding parking user is classified as a long-time zone.

[0099] In this implementation, a dual-threshold grading algorithm is used to achieve accurate classification of parking users and intelligent matching of parking space resources. The first time threshold and the second time threshold are used to divide users into three categories: short-time zone, medium-time zone and long-time zone. A scientific and reasonable parking demand grading system is constructed, which can automatically allocate the most suitable parking area according to the actual parking time characteristics of the user. It not only avoids the waste of resources caused by short-time parking users occupying long-time parking spaces, but also solves the problem of long-time parking users being forced to move their cars frequently, thereby improving the parking space utilization rate and user satisfaction.

[0100] The method for comparing the parking space classification corresponding to the vacant parking space with the parking space classification corresponding to the average parking time of the user to obtain the distance matching value is:

[0101] The short time zone priority value is smaller than the medium time zone priority value, and the medium time zone priority value is smaller than the long time zone priority value;

[0102] Obtain the parking space classification corresponding to the parking user and the parking space classification corresponding to the vacant parking spaces;

[0103] If the parking space classification is consistent, the distance matching value corresponding to the vacant parking space is 1;

[0104] If the parking space classifications are inconsistent, compare the priority values ​​of the available parking spaces and the parking space classifications of the parking users;

[0105] If the priority value of the parking space category corresponding to the vacant parking space is smaller than the priority value of the parking space category of the parking user, the distance matching value is 0.6;

[0106] If the priority value of the parking space category corresponding to the vacant parking space is greater than the priority value of the parking space category of the parking user, the distance matching value is 0.2.

[0107] In this implementation, the optimal allocation and dynamic adjustment of parking resources are achieved through the intelligent parking space matching priority algorithm. Based on the priority setting of short time zone, medium time zone and long time zone, by quantitatively calculating the distance matching value, the adaptability of each vacant parking space to the user's parking needs can be accurately evaluated, and the parking space classification priority is intelligently matched with the user's parking characteristics. When the parking space classification is completely consistent with the user's needs, the highest matching value is given, when it is partially matched, the medium matching value is given, and when it is not matched, the lowest matching value is given, thereby ensuring that the recommendation results not only meet the user's actual parking needs, but also effectively improve the overall operation efficiency of the parking lot, and realize the precise and optimized allocation of parking resources.

[0108] The method of performing walking simulation on the parking spaces on the path and the destination location preset by the parking user to obtain the user's walking path and the time required for walking includes:

[0109] The parking space on the path is used as the starting point, and the position of the parking lot exit corresponding to the user's preset travel destination is locked. The position of the parking lot exit is used as the end point, and walking is selected as the arrival method. The user's walking path and walking time are obtained through the navigation software.

[0110] In this implementation, the optimal movement line matching between parking spaces and destinations is achieved through intelligent path planning and walking time estimation algorithms. With the path parking space as the starting point and the destination exit as the end point, the navigation software is called to simulate the walking path, accurately calculating the user's expected walking time and optimal walking route. It can automatically avoid complex routes and obstacle areas, improving the practicality of parking services and user experience.

[0111] Obtain the size of the crowd interference factor coefficient obtained from the first consideration, the size of the distance matching value obtained from the second consideration, and the matching level corresponding to each vacant parking space, mark the third-level matching as an unselectable parking space, set the matching level weighting coefficient to 0.5, the distance matching value weighting coefficient to 0.3, and the crowd interference factor weighting coefficient to 0.2, perform weighted calculation on the crowd interference factor coefficient, distance matching value, and matching level of the remaining selectable vacant parking spaces, obtain the recommended parking coefficient of each vacant parking space, select the vacant parking space with the largest recommended parking coefficient, and output the guided parking information corresponding to the vacant parking space with the largest recommended parking coefficient, and the user parks according to the guided parking information.

[0112] In this implementation, a multi-dimensional weighted decision-making model and intelligent recommendation algorithm have enabled scientific and optimized parking space selection. The system comprehensively considers three key indicators: parking space matching level, distance adaptability, and pedestrian interference factors. Through quantitative calculation, it derives a comprehensive recommendation coefficient for each parking space, automatically eliminating substandard spaces and recommending the optimal option. A comprehensive evaluation system has been established that encompasses spatial adaptability, time rationality, and pedestrian comfort. This intelligently balances these various influencing factors, ensuring that recommended spaces meet vehicle size requirements and are appropriately distributed for user parking durations, while avoiding crowded areas. Ultimately, the system provides users with the optimal parking solution that truly meets their individual needs, achieving precise and intelligent parking resource allocation.

[0113] An urban digital twin smart parking service management platform, including a data collaborative processing module, an intelligent decision-making matching module, and a user interaction and execution module;

[0114] The data collaborative processing module is used to collect parking lot vacant space information, user terminal request data and path parking space dynamic information in real time;

[0115] The intelligent decision-making matching module is used to comprehensively consider the available parking spaces, the size of the parking spaces, the number of pedestrians on the parking paths, and the average parking time of users to obtain the optimal parking space selection;

[0116] The user interaction and execution module is used to push parking guidance information to the user terminal and lock the target parking space.

[0117] Through the multi-dimensional parking space matching algorithm and dynamic hierarchical sorting mechanism, accurate matching and intelligent recommendation of parking resources are achieved. The initial screening is carried out based on the matching degree between the vehicle model size and the parking space size to ensure the physical adaptability of the vehicle and the parking space; then the secondary optimization is carried out in combination with the usage status of the parking spaces on the path, which effectively avoids parking interference caused by the entry and exit of neighboring vehicles and pedestrians. By establishing a hierarchical evaluation system, the recommendation strategy can be dynamically adjusted according to multiple factors such as vehicle characteristics and parking space status, thereby improving the accuracy of parking space matching and user parking experience. Through user behavior analysis, personalized prediction and proactive service of parking needs are achieved. The parking area is intelligently divided according to the user's historical parking time data, and three optimizations are carried out in combination with the destination path planning, so that the recommended parking spaces can meet both parking time requirements and travel convenience. It can predict user parking needs and provide a new service paradigm for smart parking management.

[0118] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a fully hardware embodiment, a fully software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium may be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0119] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. Urban digital twin smart parking service management method, characterized by: include: (1) The parking lot cloud server obtains a parking guidance request instruction, which includes the parking user's license plate number and vehicle size, and sends a parking lot ID code to the user terminal. The user terminal receives the parking lot ID code and scans it to obtain the available parking space information of the corresponding parking lot; (2) matching the available parking space information with the vehicle model size to generate a first matching level ranking table; (3) obtaining the path parking space information of each vacant parking space in the first matching level ranking table, and performing a consideration on the first matching level ranking table to obtain a second matching level ranking table; (4) obtaining the average parking time of the user, performing a secondary consideration on the second matching level ranking table, generating parking guidance information, and outputting it to the user terminal; The vacant parking space information includes the vacant parking space number, the distance between the vacant parking space and the exit, and the size of the vacant parking space; The parking guidance information includes a target parking space, a target parking space number, and a vehicle parking path; The path parking space parking information includes the path parking space, the path parking space number and the path parking space locking start timestamp; The path parking spaces are all parking spaces that the parking user passes through on the path from the initial position to each vacant parking space in the first matching level ranking table.

2. The urban digital twin smart parking service management method according to claim 1, characterized in that: The method of generating a first matching ranking table by matching the vacant parking space information with the vehicle model size includes: Obtaining the size of available parking spaces in the parking lot and the size of the vehicle type in the parking guidance request instruction; Dividing the vehicle size of the parking guidance request by the size of the available parking space to obtain a size ratio; comparing the size ratio to a first size threshold and a second size threshold; If the size ratio is greater than the first size threshold and less than the second size threshold, it is recorded as a first-level match; If the size ratio is smaller than the first size threshold, it is recorded as a secondary match; If the size ratio is greater than the second size threshold, it is recorded as a third-level match; The vehicle size corresponding to the user terminal is bound to the matching level of each vacant parking space number and sorted in ascending order to obtain the first matching level sorting table.

3. The urban digital twin smart parking service management method according to claim 2, characterized in that: The method of obtaining a second matching rank ranking table by considering the first matching rank ranking table once includes: Obtaining the specific path parking space number and path parking space locking start timestamp of each vacant parking space corresponding to the first matching level ranking table, and obtaining the instruction request timestamp of the parking guidance request instruction generated by the user terminal; Subtract the path parking space locking start timestamp from the instruction request timestamp to obtain the time difference; Performing a walking simulation between the parking spaces on the path and the user's preset destination location to obtain the user's walking path and the time required for walking; Subtract the required time from the time difference to obtain the relative time displacement, subtract the relative time displacement from the preset relative time displacement threshold to obtain the size of the crowd interference factor coefficient, add the size of the crowd interference factor coefficient to the first matching level ranking table, and obtain the second matching level ranking table after one consideration.

4. The urban digital twin smart parking service management method according to claim 3, characterized in that: The method of obtaining the average parking duration of users at the user terminal, performing a secondary consideration on the second matching level ranking table, and outputting parking guidance information includes: Collecting the path distance from each parking space to the exit and the corresponding average parking time in the parking space, and obtaining a first distance threshold, a second distance threshold, a first time threshold, and a second time threshold for the path distance and the corresponding average parking time in the parking space through a clustering algorithm; Divide the parking spaces in the parking lot into three parking space categories: short time zone, medium time zone and long time zone according to the first distance threshold, the second distance threshold, the first time threshold and the second time threshold; Obtaining an average parking time of users at the user terminal, and obtaining a parking space classification corresponding to the average parking time of users; Compare the parking space classification corresponding to the vacant parking space with the parking space classification corresponding to the average parking time of the user to obtain a distance matching value; The distance matching value is added to the second matching level ranking table to obtain the guided parking information after secondary consideration.

5. The urban digital twin smart parking service management method according to claim 4, characterized in that: The user terminal receives and scans the parking lot ID code, and obtains the average parking time, including: When the parking user does not arrive at the current parking lot for the first time, the user terminal receives and scans the parking lot ID code to obtain the average parking time of the user terminal corresponding to the parking lot ID; When the parking user arrives at the current parking lot for the first time, the user terminal receives and scans the parking lot ID code, does not display the average parking time of the user terminal corresponding to the parking lot ID, obtains the historical order information of the parking user, and calculates the corresponding user's average parking time.

6. The urban digital twin smart parking service management method according to claim 5, characterized in that: The method for obtaining the average parking time of the user terminal and obtaining the parking space classification corresponding to the average parking time of the user is: Obtaining an average parking time of users, and comparing the average parking time of users with a first time threshold and a second time threshold; If the average parking time of the user is less than the first time threshold, the parking space of the corresponding parking user is classified as a short-time zone; If the average parking time of the user is greater than the first time threshold and less than the second time threshold, the parking space of the corresponding parking user is classified as the middle time zone; If the average parking time of the user is greater than the second time threshold, the parking space of the corresponding parking user is classified as a long-time zone.

7. The urban digital twin smart parking service management method according to claim 6, characterized in that: The method for comparing the parking space classification corresponding to the vacant parking space with the parking space classification corresponding to the average parking time of the user to obtain the distance matching value is: The short time zone priority value is smaller than the medium time zone priority value, and the medium time zone priority value is smaller than the long time zone priority value; Obtain the parking space classification corresponding to the parking user and the parking space classification corresponding to the vacant parking spaces; If the parking space classification is consistent, the distance matching value corresponding to the vacant parking space is 1; If the parking space classifications are inconsistent, compare the priority values ​​of the available parking spaces and the parking space classifications of the parking users; If the priority value of the parking space category corresponding to the vacant parking space is smaller than the priority value of the parking space category of the parking user, the distance matching value is 0.6; If the priority value of the parking space category corresponding to the vacant parking space is greater than the priority value of the parking space category of the parking user, the distance matching value is 0.

2.

8. The urban digital twin smart parking service management method according to claim 7, characterized in that: The method of performing walking simulation on the parking spaces on the path and the destination location preset by the parking user to obtain the user's walking path and the time required for walking includes: The parking space on the path is used as the starting point, and the position of the parking lot exit corresponding to the user's preset travel destination is locked. The position of the parking lot exit is used as the end point, and walking is selected as the arrival method. The user's walking path and walking time are obtained through the navigation software.

9. The urban digital twin smart parking service management method according to claim 8, characterized in that: Obtain the size of the crowd interference factor coefficient obtained from the first consideration, the size of the distance matching value obtained from the second consideration, and the matching level corresponding to each vacant parking space, mark the third-level matching as an unselectable parking space, set the matching level weighting coefficient to 0.5, the distance matching value weighting coefficient to 0.3, and the crowd interference factor weighting coefficient to 0.2, perform weighted calculation on the crowd interference factor coefficient, distance matching value, and matching level of the remaining selectable vacant parking spaces, obtain the recommended parking coefficient of each vacant parking space, select the vacant parking space with the largest recommended parking coefficient, and output the guided parking information corresponding to the vacant parking space with the largest recommended parking coefficient, and the user parks according to the guided parking information.

10. An urban digital twin smart parking service management platform, characterized by: Applying the method described in any one of claims 1 to 9, the platform includes a data collaborative processing module, an intelligent decision matching module, and a user interaction and execution module; The data collaborative processing module is used to collect parking lot vacant space information, user terminal request data and path parking space dynamic information in real time; The intelligent decision-making matching module is used to comprehensively consider the available parking spaces, the size of the parking spaces, the number of pedestrians on the parking paths, and the average parking time of users to obtain the optimal parking space selection; The user interaction and execution module is used to push parking guidance information to the user terminal and lock the target parking space.

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