Smart city parking guidance system and method based on Internet of Things

Abstract

The invention provides a smart city parking guidance system and method based on the Internet of Things, and the method comprises the steps: constructing the road network association information between parking regions, and determining the availability index of each parking region through combining all the road network association information with the occupation characteristics of parking spaces in a target parking lot in a parking peak period; planning a confidence driving route of the user vehicle according to the current position of the user vehicle, the change trend between the short-time change characteristics of the parking space occupation of each parking zone and the availability index of each parking zone; when the user vehicle arrives at a target parking zone calibrated by the confidence driving route, determining a target unoccupied parking space of the user vehicle based on the spatial distribution characteristics of each unoccupied parking space in the target parking zone and the attribute difference relationship between the unoccupied parking spaces; and the Internet of Things control center sends the target unoccupied parking space information to a vehicle-mounted navigation system of the user vehicle. According to the scheme, confidence guidance of the user vehicle is realized, and the accuracy of parking guidance is further improved.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent transportation Internet of Things, and more specifically, to a smart city parking guidance system and method based on the Internet of Things. Background Art

[0002] With the acceleration of urbanization, the development of Internet of Things technology has provided new possibilities for intelligent transportation systems. By connecting various transportation infrastructures and vehicles, real-time data collection and exchange can be realized, thereby improving traffic efficiency and safety. On this basis, intelligent transportation Internet of Things application services have emerged. Intelligent transportation Internet of Things application services can not only provide real-time vehicle information, but also predict traffic trends through data analysis, provide users with personalized travel suggestions and optimized routes, reduce congestion, and improve the overall intelligence level of urban transportation. Among them, in the smart city parking guidance application, the smart city parking guidance system relies on a variety of cutting-edge technologies to effectively realize an intelligent and convenient parking experience.

[0003] In existing smart city parking guidance applications, smart city parking guidance usually plans parking based on the Internet of Things technology. First, sensors are deployed in parking lots to monitor parking space occupancy and transmit them to a central server. Then, the central server creates a real-time updated parking database based on the uploaded data, showing the available parking spaces in each parking lot. Finally, the smart city parking guidance system recommends the best parking plan based on the available parking spaces. However, existing smart city parking guidance systems often rely on current parking data for zoning guidance, but it is difficult to dynamically adapt to the road network capacity of the parking zone (i.e., the maximum number of vehicles that the road network where the parking zone is located can accommodate), parking space distribution, and changes in vehicle position, resulting in the inability to provide confidence guidance for user vehicles, thereby reducing the accuracy of parking guidance. Therefore, how to achieve confidence guidance for user vehicles and improve the accuracy of parking guidance has become a difficult problem faced by the industry. Summary of the invention

[0004] The present application provides a smart city parking guidance system and method based on the Internet of Things, which can achieve confident guidance of user vehicles and thus improve the accuracy of parking guidance.

[0005] In a first aspect, the present application provides a smart city parking guidance method based on the Internet of Things, comprising the following steps: Obtain the road network data of each parking partition in the target parking lot; The road network data of each parking partition is uploaded to the Internet of Things control center, and the Internet of Things control center constructs the road network association information between each parking partition based on all the road network data, and determines the availability index of each parking partition through all the road network association information combined with the occupancy characteristics of parking spaces during the peak parking period of the target parking lot; Determine the short-term change characteristics of parking spaces in each parking partition when the user vehicle enters the entrance of the target parking lot, and plan a confident driving route for the user vehicle based on the current position of the user vehicle, the change trend between the short-term change characteristics, and the availability index of each parking partition; When the user vehicle arrives at the target parking partition marked by the trusted driving route, the parking space attributes of each vacant parking space in the target parking partition are obtained from the Internet of Things control center, and the target vacant parking space of the user vehicle is determined based on the spatial distribution characteristics of each vacant parking space in the target parking partition and the attributes of each parking space; The Internet of Things control center sends the target vacant parking space information to the vehicle navigation system of the user's vehicle, and guides the user's vehicle to the target vacant parking space.

[0006] In some embodiments, the Internet of Things control center constructs the road network association information between each parking partition based on all road network data, specifically including: Obtaining road network data of each parking partition in the target parking lot from the IoT control center; Establishing a road network connection diagram of each parking partition based on the road network data of each parking partition; Calculate the travel cost of each road network connection diagram according to the historical traffic flow data and current traffic flow value of each road section in each road network connection diagram; The road network association information between each parking partition is determined based on each road network connection diagram and its corresponding travel cost.

[0007] In some embodiments, the availability index of each parking partition is determined by combining all road network association information with the occupancy characteristics of parking spaces in the target parking lot during the peak parking period, specifically including: Extracting the occupancy characteristics of parking spaces in the target parking lot during the peak parking period from the historical parking data of the target parking lot, wherein the occupancy characteristics include the occupancy characteristics of each parking partition; Aggregate and analyze the occupancy characteristics of each parking partition to generate a dynamic usage value of parking spaces in each parking partition during the peak parking period of the target parking lot; Based on all the road network association information and the dynamic usage value of parking spaces in each parking partition, the availability index of each parking partition is calculated.

[0008] In some embodiments, planning a confident driving route of the user vehicle based on the current position of the user vehicle, the change trend between various short-term change characteristics, and the availability index of each parking partition specifically includes: Obtaining the current position of the user's vehicle; Determine the change trends between the various short-term change features, and extract multiple candidate parking partitions from all parking partitions based on all the change trends; For each candidate parking partition, determining the driving effectiveness of the user vehicle to the candidate parking partition according to the availability index and the short-term change characteristics of the candidate parking partition, and then obtaining the driving effectiveness of the user vehicle to each candidate parking partition; Extracting a target parking partition from all candidate parking partitions based on the driving effectiveness of each candidate parking partition; A confident driving route of the user vehicle is planned based on the spatial position of the target parking partition and the current position of the user vehicle.

[0009] In some embodiments, determining the target vacant parking space for the user vehicle based on the spatial distribution characteristics of each vacant parking space in the target parking partition and the attributes of each parking space specifically includes: Obtain the spatial distribution characteristics of each vacant parking space in the target parking partition; Quantifying the parking space attributes of each vacant parking space to obtain a quantized attribute value of each vacant parking space; Determine the attribute difference value between each vacant parking space in the target parking partition according to the quantitative attribute value of each vacant parking space; Extracting the vacant parking space corresponding to the largest attribute difference value from all attribute difference values ​​as the candidate vacant parking space, and then obtaining multiple candidate vacant parking spaces; For each candidate vacant parking space, a multi-factor evaluation is performed on the spatial distribution characteristics and parking space attributes corresponding to the candidate vacant parking space to obtain an availability score for each candidate vacant parking space; A candidate vacant parking space corresponding to the maximum availability score is extracted from all candidate vacant parking spaces as the target vacant parking space for the user vehicle.

[0010] In some embodiments, the method further includes: dividing the target parking lot into a plurality of parking zones by using a location zoning method.

[0011] In some embodiments, the road network data of each parking partition is uploaded to the Internet of Things control center via a message queue telemetry transmission protocol.

[0012] In a second aspect, the present application provides a smart city parking guidance system based on the Internet of Things, including: An acquisition module is used to acquire the road network data of each parking partition in the target parking lot; A processing module is used to upload the road network data of each parking partition to the Internet of Things control center, and the Internet of Things control center constructs the road network association information between each parking partition according to all the road network data, and determines the availability index of each parking partition by combining all the road network association information with the occupancy characteristics of parking spaces during the peak parking period of the target parking lot; The processing module is further used to determine the short-term change characteristics of parking spaces in each parking partition when the user vehicle enters the entrance of the target parking lot, and plan a confident driving route for the user vehicle according to the current position of the user vehicle, the change trend between the various short-term change characteristics and the availability index of each parking partition; The processing module is further configured to obtain, when the user vehicle arrives at the target parking partition marked by the trusted driving route, the parking space attributes of each vacant parking space in the target parking partition from the Internet of Things control center, and determine the target vacant parking space for the user vehicle based on the spatial distribution characteristics of each vacant parking space in the target parking partition and the attributes of each parking space; The execution module is used for the Internet of Things control center to send the target vacant parking space information to the vehicle navigation system of the user's vehicle, so as to guide the user's vehicle to the target vacant parking space.

[0013] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory stores a code, and the processor is configured to obtain the code and execute the above-mentioned smart city parking guidance method based on the Internet of Things.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned smart city parking guidance method based on the Internet of Things.

[0015] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects: In the IoT-based smart city parking guidance system and method provided by the present application, first, the road network data of each parking partition in the target parking lot is obtained; secondly, the road network data of each parking partition is uploaded to the IoT control center, and the IoT control center constructs the road network association information between each parking partition based on all the road network data, and determines the availability index of each parking partition through all the road network association information combined with the occupancy characteristics of the parking spaces in the target parking lot during the peak parking period; further, the short-term change characteristics of the parking occupancy of each parking partition when the user vehicle enters the entrance of the target parking lot are determined, and the current position of the user vehicle is used to calculate the availability index of each parking partition. , the changing trends among various short-term changing characteristics and the availability index of each parking partition are used to plan a confident driving route for the user vehicle; then, when the user vehicle arrives at the target parking partition marked by the confident driving route, the parking space attributes of each vacant parking space in the target parking partition are obtained from the Internet of Things control center, and the target vacant parking space for the user vehicle is determined based on the spatial distribution characteristics of each vacant parking space in the target parking partition and the attributes of each parking space; finally, the Internet of Things control center sends the target vacant parking space information to the on-board navigation system of the user vehicle to guide the user vehicle to the target vacant parking space.

[0016] It can be seen that the present application can realize the confidence guidance of user vehicles, thereby improving the accuracy of parking guidance; firstly, the road network association information between each parking partition is constructed according to the road network data of each parking partition to show the traffic association relationship between the parking partitions, so as to dynamically adjust the vehicle flow direction, optimize the allocation of parking resources, and avoid excessive congestion in some partitions; secondly, the availability index of each parking partition is determined through all the road network association information combined with the occupancy characteristics of the parking spaces during the peak parking period of the target parking lot, so as to provide the degree of influence of the changes in the road network capacity, parking space distribution and vehicle position in each parking partition, thereby optimizing the utilization rate of parking resources, improving the efficiency of traffic management, and reducing the influence of the dynamic changes in the road network capacity, parking space distribution and vehicle position; further, through the current position of the user's vehicle, the changing trend between the various short-term change characteristics and each The confidence driving route of the user's vehicle is planned based on the availability index of each parking partition to ensure that the user's vehicle quickly reaches the target parking partition, thereby greatly reducing the driving time of the vehicle in the parking lot, thereby avoiding traffic congestion and road bottlenecks; then, the target vacant parking space for the user's vehicle is determined based on the spatial distribution characteristics of each vacant parking space in the target parking partition and the attributes of each parking space, so that the car owner can quickly find the target vacant parking space, thereby reducing the time spent on queuing and searching for parking spaces and obtaining a parking space that is more in line with the car owner's parking preference, thereby providing confidence guidance for the user's vehicle; finally, the Internet of Things control center sends the target vacant parking space information to the on-board navigation system of the user's vehicle, and guides the user's vehicle to the target vacant parking space; in summary, the technical solution provided by the present application can achieve confidence guidance for user vehicles, thereby improving the accuracy of parking guidance. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is an exemplary flow chart of a parking guidance method for a smart city based on the Internet of Things according to some embodiments of the present application; Figure 2 is an exemplary flow chart for determining road network association information according to some embodiments of the present application; Figure 3 is an exemplary flow chart of determining a trusted driving route according to some embodiments of the present application; Figure 4 is a schematic diagram of the structure of a smart city parking guidance system based on the Internet of Things according to some embodiments of the present application; Figure 5 It is a structural diagram of a computer device for implementing a smart city parking guidance method based on the Internet of Things as shown in some embodiments of the present application. DETAILED DESCRIPTION

[0018] In order to better understand the technical solution of the present application, the technical solution of the present application will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0019] refer to Figure 1 , which is an exemplary flow chart of a smart city parking guidance method based on the Internet of Things according to some embodiments of the present application. The smart city parking guidance method 100 based on the Internet of Things mainly includes the following steps: In step 101, the road network data of each parking partition in the target parking lot is obtained.

[0020] It should be noted that in the present application, the target parking lot is divided into multiple parking zones by the orientation zoning method, for example, four parking zones of east, west, south and north, and the parking zones represent parking areas of different spaces in the target parking lot.

[0021] In specific implementation, the road network data of each parking partition in the target parking lot can be obtained through the parking lot management database. The road network data includes the road section information, intersection node information and the traffic weight of each road section of the parking partition. In this embodiment, the road network data represents a collection of various information related to the road network in the parking partition. By obtaining the road network data of the parking partition, the parking characteristics of different parking partitions can be effectively managed, and conditions can be provided for the precise parking of subsequent car owners.

[0022] In step 102, the road network data of each parking partition is uploaded to the Internet of Things control center, and the Internet of Things control center constructs the road network association information between each parking partition based on all the road network data, and determines the availability index of each parking partition through all the road network association information combined with the occupancy characteristics of parking spaces during peak parking hours in the target parking lot.

[0023] In specific implementation, the road network data of each parking partition can be uploaded to the Internet of Things control center through the message queue telemetry transmission protocol. In addition, in other embodiments, other methods can be used to upload the road network data of each parking partition to the Internet of Things control center, such as Wi-Fi, Bluetooth, which are not limited here. The Internet of Things control center refers to a control center responsible for collecting and processing data from various Internet of Things devices (such as sensors, smart meters, cameras, smart appliances, etc.).

[0024] In some embodiments, reference Figure 2 As shown, this figure is an exemplary flow chart of determining road network association information according to some embodiments of the present application. In this embodiment, the Internet of Things control center constructs road network association information between each parking partition according to all road network data, which can be implemented by the following steps: First, in step 1021, the road network data of each parking partition in the target parking lot is obtained from the Internet of Things control center; Secondly, in step 1022, a road network connection diagram of each parking partition is established based on the road network data of each parking partition; Then, in step 1023, the travel cost of each road network connection diagram is calculated based on the historical traffic flow data and the current traffic flow value of each road section in each road network connection diagram; Finally, in step 1024, the road network association information between each parking partition is determined based on each road network connection diagram and its corresponding travel cost.

[0025] In the specific implementation, first, the road network data of each parking partition in the target parking lot is obtained from the Internet of Things control center; secondly, based on the road network data of each parking partition, a road network connection diagram of each parking partition is established, that is, the road network data of each parking partition is imported into the traffic simulation micro open system (SUMO) in the traffic simulation tool, and the SUMO outputs the road network connection diagram of each parking partition. In addition, other traffic simulation tools can also be used for processing, for example, the MATSim tool, which is not limited here. The nodes in the road network connection diagram represent intersections or parking entrances, and the edges represent road sections; further, according to the historical traffic flow data and current traffic flow values ​​of each road section in each road network connection diagram, the travel cost of each road network connection diagram is calculated, that is, the data of each road in each road network connection diagram are retrieved from the historical database of the target parking lot. The historical traffic flow data and current traffic flow values ​​of each road section are collected. For each road network connection diagram, the average traffic flow value of the historical traffic flow data of all road sections in the road network connection diagram is calculated, and the ratio of the current traffic flow value to the average traffic flow value is used as the travel cost of the road network connection diagram, thereby obtaining the travel cost of each road network connection diagram; finally, the road network association information between each parking partition is determined based on each road network connection diagram and its corresponding travel cost, that is: for every two road network connection diagrams, the absolute difference of the corresponding travel costs is calculated to obtain the road network association value, and the road network association value is used to represent the road network association information of the two road network connection diagrams, thereby obtaining the road network association information between each parking partition. In addition, in other embodiments, other calculation methods can also be used to calculate the road network association information between each parking partition, which is not limited here.

[0026] It should be noted that the road network connection diagram in this embodiment represents a topological diagram model established based on the road structure within the parking partition; the traffic flow value in this embodiment represents a quantitative indicator of the vehicle traffic conditions on each road section in the parking partition; the traffic cost in this embodiment represents the traffic cost of vehicles passing through the parking partition. By determining the traffic cost, the traffic efficiency of different road sections inside the parking lot can be accurately reflected, providing intelligent decision support for the parking guidance system; the road network association information in this application represents the association information of the road network characteristics between different parking partitions, and the road network association information is represented by the road network association value, and the road network association value represents the degree of association between the road network characteristics between different parking partitions. By determining the road network association information, the traffic relationship between the parking partitions is effectively displayed. The road network association information can effectively and dynamically adjust the vehicle flow direction, optimize the allocation of parking resources, and avoid excessive congestion in certain partitions.

[0027] In some embodiments, the following steps may be used to determine the availability index of each parking partition by combining all road network association information with the occupancy characteristics of parking spaces in the target parking lot during the peak parking period, namely: Extracting the occupancy characteristics of parking spaces in the target parking lot during the peak parking period from the historical parking data of the target parking lot, wherein the occupancy characteristics include the occupancy characteristics of each parking partition; Aggregate and analyze the occupancy characteristics of each parking partition to generate a dynamic usage value of parking spaces in each parking partition during the peak parking period of the target parking lot; Based on all the road network association information and the dynamic usage value of parking spaces in each parking partition, the availability index of each parking partition is calculated.

[0028] In the specific implementation, first, the occupancy characteristics of the parking spaces in the target parking lot during the peak parking period are extracted from the historical parking data of the target parking lot through the data acquisition system, and the occupancy characteristics include the average occupancy rate of the parking spaces, the occupancy change trend of the parking spaces, and the frequency of use of the parking spaces; secondly, the occupancy characteristics of each parking partition are aggregated through the weighted average in the statistical analysis method to obtain the dynamic usage value of the parking spaces in each parking partition during the peak parking period of the target parking lot. In addition, regression analysis can also be used to calculate the dynamic usage value, which is not limited here; finally, according to all the road network association information and the dynamic usage values ​​of the parking spaces in each parking partition, the availability index of each parking partition is calculated, that is: the road network association value corresponding to each road network association information is extracted, the standard deviation of all the road network association values ​​is calculated, and the standard deviation calculation result is used as the road network restriction factor. For each parking partition, the road network restriction factor and the availability index of the parking partition are multiplied, and the product calculation result is used as the availability index of the parking partition, thereby obtaining the availability index of each parking partition.

[0029] It should be noted that the occupancy characteristics in the present application represent the usage status characteristics of parking spaces in a parking partition. The occupancy characteristics mainly describe the average occupancy rate of parking spaces, the changing trend of parking space occupancy, and the frequency of parking space use. Through these characteristics, the availability of parking partitions or parking spaces can be accurately evaluated, and the resource scheduling and parking planning of parking lots can be optimized; the dynamic usage value in the present embodiment represents the dynamic change index of parking space usage in a parking partition, that is, the larger the dynamic usage value, the greater the dynamic change of parking space usage in the parking partition, and vice versa; the road network restriction factor in the present embodiment represents the traffic impact value brought by the road network structure in the parking lot; the availability index in the present application represents the quantitative parameter of the availability of vacant parking spaces in a parking partition, that is, the larger the availability index, the greater the availability of vacant parking spaces in the parking partition, and vice versa. The availability index reflects the current parking space utilization rate, vacancy level, and accessibility of parking spaces in a certain period of time. By determining the availability index, more accurate parking guidance services can be provided to car owners, the utilization rate of parking resources can be optimized, and the efficiency of traffic management can be effectively improved.

[0030] In step 103, the short-term change characteristics of the parking spaces in each parking partition when the user vehicle enters the entrance of the target parking lot are determined, and a confident driving route of the user vehicle is planned based on the current position of the user vehicle, the change trends among the various short-term change characteristics, and the availability index of each parking partition.

[0031] In some embodiments, determining the short-term change characteristics of parking spaces in each parking partition when the user vehicle enters the entrance of the target parking lot can be achieved by using the following steps, namely: Determine the vehicle diversion coefficient of each parking partition when the user vehicle enters the entrance of the target parking lot; Based on the dynamic monitoring module of the Internet of Things control center, the vehicle entry and exit events in each parking partition are continuously collected to generate a time series of the vehicle entry and exit rate in each parking partition; A parking partition is selected as the selected parking partition, and a short-term trend analysis is performed on the time series corresponding to the selected parking partition to obtain the initial short-term change characteristics of the parking space occupied by the user vehicle in the selected parking partition when entering the entrance of the target parking lot; The initial short-term variation characteristic is modified based on the vehicle diversion coefficient of the selected parking partition, thereby obtaining the short-term variation characteristic of the parking space occupied by the user vehicle in the selected parking partition when entering the entrance of the target parking lot; Continue to determine the short-term change characteristics of the parking spaces in the remaining parking partitions at the entrance of the target parking lot, and then obtain the short-term change characteristics of the parking spaces in each parking partition when the user vehicle enters the entrance of the target parking lot.

[0032] In the specific implementation, first, the vehicle diversion coefficient of each parking partition is determined when the user vehicle enters the entrance of the target parking lot, that is: for each parking partition, the ratio of the number of vehicles entering the parking partition to the number of vehicles leaving the parking partition when the user vehicle enters the entrance of the target parking lot is used as the vehicle diversion coefficient of the parking partition, and then the vehicle diversion coefficient of each parking partition when the user vehicle enters the entrance of the target parking lot is obtained; secondly, the vehicle entry and exit events in each parking partition are continuously collected through the dynamic monitoring module of the Internet of Things control center to generate a time series of vehicle entry and exit rates in each parking partition; further, a parking partition is selected as the selected parking partition, and the short-term trend analysis of the time series corresponding to the selected parking partition is performed through the exponential smoothing in the time series analysis method to obtain the initial short-term change characteristics of the parking space occupancy of the selected parking partition when the user vehicle enters the entrance of the target parking lot. In addition, In other embodiments, the sliding average in the time series analysis method can also be used to perform short-term trend analysis on the time series corresponding to the selected parking partition, which is not limited here; then, the initial short-term change characteristics are corrected based on the vehicle diversion coefficient of the selected parking partition to obtain the short-term change characteristics of the parking space in the selected parking partition when the user vehicle enters the target parking lot entrance, that is, the correction expression is: short-term change characteristics = initial short-term change characteristics + (1 + vehicle diversion coefficient); finally, the short-term change characteristics of the parking spaces in the remaining parking partitions at the target parking lot entrance are further determined by the determination method of "correcting the initial short-term change characteristics based on the vehicle diversion coefficient of the selected parking partition to obtain the short-term change characteristics of the parking space in the selected parking partition when the user vehicle enters the target parking lot entrance", thereby obtaining the short-term change characteristics of the parking spaces in each parking partition when the user vehicle enters the target parking lot entrance.

[0033] It should be noted that the vehicle diversion coefficient in this embodiment represents the coefficient of the degree to which the parking partition is affected by the vehicle flow diversion; the time series in this embodiment represents an ordered data set of changes in the vehicle entry and exit rates in the parking partition recorded in chronological order; the initial short-term change characteristics in this embodiment represent the short-term trend information of the dynamic changes in the occupancy of the initial parking partition; the short-term change characteristics in this application represent the short-term trend information of the dynamic changes in the occupancy of the parking partition. The short-term change characteristics can reflect the parking occupancy dynamics of the parking partition in real time. By analyzing the short-term change characteristics, car owners can know the occupancy trends of certain parking partitions in advance, quickly select suitable parking partitions, reduce the time of finding parking spaces, and thus improve the overall parking efficiency.

[0034] In some embodiments, reference Figure 3As shown in FIG. 1 , this figure is an exemplary flow chart of determining a confident driving route according to some embodiments of the present application. In this embodiment, the confident driving route of the user vehicle is planned by the current position of the user vehicle, the change trend between various short-term change characteristics, and the availability index of each parking partition, which can be implemented by the following steps: First, in step 1031, the current position of the user's vehicle is obtained; Next, in step 1032, the change trends among the various short-term change features are determined, and a plurality of candidate parking partitions are extracted from all parking partitions based on all the change trends; Further, in step 1033, for each candidate parking partition, the driving effectiveness of the user vehicle to the candidate parking partition is determined according to the availability index and the short-term change characteristic of the candidate parking partition, thereby obtaining the driving effectiveness of the user vehicle to each candidate parking partition; Then, in step 1034, a target parking partition is extracted from all the candidate parking partitions based on the driving effectiveness of each candidate parking partition; Finally, in step 1035, a confident driving route of the user vehicle is planned based on the spatial position of the target parking partition and the current position of the user vehicle.

[0035] In the specific implementation, first, the current position of the user's vehicle is obtained through GPS; secondly, the change trend between each short-time change feature is determined, and multiple candidate parking partitions are extracted from all parking partitions based on all the change trends, that is: for every two short-time change features, the absolute difference between the two short-time change features is calculated to obtain the change trend between the two short-time change features, and then the change trend between each short-time change feature is obtained, and the parking partition corresponding to the maximum change trend is extracted from all parking partitions as a candidate parking partition, and multiple candidate parking partitions are obtained, that is, one change trend corresponds to two short-time change features, one short-time change feature corresponds to one parking partition, and then one change trend corresponds to two parking partitions; further, for each candidate parking partition, the driving effectiveness of the user's vehicle to the candidate parking partition is determined according to the availability index and the short-time change feature of the candidate parking partition, that is: for each candidate parking partition, the availability of the candidate parking partition is calculated by weighted summation. The driving effectiveness of the user vehicle to the candidate parking partition is calculated by using the short-term variation characteristics and the short-term variation characteristics to obtain the driving effectiveness of the user vehicle to the candidate parking partition. In addition, in other embodiments, other calculation methods can be used to calculate the driving effectiveness of the user vehicle to the candidate parking partition, which is not limited here. Then, based on the driving effectiveness of each candidate parking partition, the target parking partition is extracted from all the candidate parking partitions, that is, the candidate parking partition corresponding to the maximum driving effectiveness is extracted from all the candidate parking partitions as the target parking partition. Finally, based on the spatial position of the target parking partition and the current position of the user vehicle, the single-source shortest path algorithm (Dijkstra) in the path search algorithm is used to plan the confident driving route of the user vehicle, that is, the current position of the user vehicle is used as the starting point and the spatial position of the target parking partition is used as the end point. The driving route of the shortest path is searched out by the Dijkstra algorithm, and the driving route of the shortest path is used as the confident driving route of the user vehicle.

[0036] It should be noted that the changing trend in the present application represents the dynamic change difference between the short-term parking occupancy characteristics of different parking partitions; the candidate parking partition in the present embodiment represents the pre-selected parking partition; the driving effectiveness in the present embodiment represents the suitability index of the user's vehicle driving to the candidate parking partition, and the driving effectiveness reflects the superiority of selecting the candidate parking partition as the target parking partition; the confident driving route in the present application represents the optimal driving path of the user's vehicle from the current position to the target parking partition, and the planning of the confident driving route can ensure that the user's vehicle quickly reaches the target parking partition, so as to greatly reduce the driving time of the vehicle in the parking lot, thereby avoiding traffic congestion and road bottlenecks, and reducing the driver's ineffective driving when looking for parking spaces.

[0037] In step 104, when the user vehicle arrives at the target parking zone marked by the trusted driving route, the parking space attributes of each vacant parking space in the target parking zone are obtained from the Internet of Things control center, and the target vacant parking space of the user vehicle is determined based on the spatial distribution characteristics of each vacant parking space in the target parking zone and the attributes of each parking space.

[0038] In specific implementation, when the user vehicle arrives at the target parking zone marked by the trusted driving route, the parking space attributes of each vacant parking space in the target parking zone are obtained from the Internet of Things control center. The parking space attributes include distance attributes (the distance from the vacant parking space to the elevator entrance), parking space size attributes, and parking space fee attributes. In this application, parking space attributes represent a combination of various feature information of parking spaces. Parking space attributes are used to describe the characteristics of each parking space and provide relevant parking guidance and decision-making basis for the vehicle.

[0039] In some embodiments, the target vacant parking space for the user vehicle may be determined based on the spatial distribution characteristics of each vacant parking space in the target parking partition and the attributes of each parking space by using the following steps, namely: Obtain the spatial distribution characteristics of each vacant parking space in the target parking partition; Quantifying the parking space attributes of each vacant parking space to obtain a quantized attribute value of each vacant parking space; Determine the attribute difference value between each vacant parking space in the target parking partition according to the quantitative attribute value of each vacant parking space; Extracting the vacant parking space corresponding to the largest attribute difference value from all attribute difference values ​​as the candidate vacant parking space, and then obtaining multiple candidate vacant parking spaces; For each candidate vacant parking space, a multi-factor evaluation is performed on the spatial distribution characteristics and parking space attributes corresponding to the candidate vacant parking space to obtain an availability score for each candidate vacant parking space; A candidate vacant parking space corresponding to the maximum availability score is extracted from all candidate vacant parking spaces as the target vacant parking space for the user vehicle.

[0040] In the specific implementation, first, the spatial distribution characteristics of all the vacant parking spaces in the target parking partition are obtained through sensors; secondly, the parking space attributes of each vacant parking space are quantized to obtain the quantized attribute value of each vacant parking space, that is: for each vacant parking space, the distance attribute, the parking space size attribute, and the parking space fee attribute are extracted from the parking space attributes of the vacant parking space, and the distance attribute, the parking space size attribute, and the parking space fee attribute are weighted and summed according to the parking preference of the vehicle user to obtain the quantized attribute value of the vacant parking space, and then the quantized attribute value of each vacant parking space is obtained, wherein the weight of each attribute can be set between 0 and 1 according to the parking preference of the vehicle user, which will not be repeated here; further, the attribute difference value between each vacant parking space in the target parking partition is determined according to the quantized attribute value of each vacant parking space, that is: for every two vacant parking spaces, the absolute difference between the quantized attribute values ​​corresponding to the two vacant parking spaces is calculated to obtain the two vacant parking spaces. The attribute difference values ​​between the available parking spaces are obtained, and then the attribute difference values ​​between the available parking spaces in the target parking partition are obtained; further, the available parking space corresponding to the largest attribute difference value is extracted from all the attribute difference values ​​as the candidate available parking space, and then a plurality of candidate available parking spaces are obtained; then, for each candidate available parking space, the spatial distribution characteristics and parking space attributes corresponding to the candidate available parking space are evaluated by multiple factors to obtain the availability score of each candidate available parking space, that is: for each candidate available parking space, the spatial distribution characteristics and parking space attributes corresponding to the candidate available parking space are usually evaluated by multiple factors through the hierarchical analysis method to obtain the availability score of each candidate parking space. In addition, the spatial distribution characteristics and parking space attributes corresponding to the candidate available parking spaces can also be evaluated by multiple factors through the weighted summation method, which is not limited here; finally, the candidate available parking space corresponding to the largest availability score is extracted from all the candidate available parking spaces as the target available parking space for the user vehicle.

[0041] It should be noted that the spatial distribution characteristics in this application represent the spatial location information of the vacant parking spaces; the quantitative attribute values ​​in this embodiment represent the values ​​that quantitatively describe the attributes of multiple parking spaces, and the parking preference of vehicle users can be analyzed to the maximum extent by determining the quantitative attribute values; the attribute difference relationship in this embodiment represents the attribute deviation relationship between different vacant parking spaces, which can be specifically characterized by the attribute deviation value, wherein the attribute deviation value represents the attribute deviation amount between different vacant parking spaces; the candidate vacant parking spaces in this embodiment represent the pre-selected vacant parking spaces; the availability score in this application represents the numerical indicator of the availability of vacant parking spaces, that is, The larger the availability score, the greater the availability of the vacant parking space, and vice versa. The availability score helps the system to prioritize multiple candidate parking spaces and recommend the most suitable parking space to the user. The target vacant parking space in this application represents the target parking space for the user's vehicle, that is, the optimal parking space recommended to the car owner by the intelligent parking guidance system. By determining the target vacant parking space, the car owner can avoid unnecessary waste of time and safety hazards, especially in busy parking lots. The car owner can quickly find a vacant parking space, thereby reducing the time spent on queuing and searching for parking spaces and obtaining a parking space that better meets the car owner's parking preferences.

[0042] In step 105, the Internet of Things control center sends the target vacant parking space information to the vehicle navigation system of the user's vehicle to guide the user's vehicle to the target vacant parking space.

[0043] In some embodiments, the IoT control center sends the target vacant parking space information to the vehicle navigation system of the user's vehicle, and guides the user's vehicle to the target vacant parking space by the following steps, namely: When the user's vehicle obtains the target vacant parking space, the Internet of Things control center sends the target vacant parking space information to the on-board navigation system of the user's vehicle; The vehicle owner drives the user vehicle to the target vacant parking space according to the target vacant parking space information displayed by the vehicle navigation system.

[0044] In specific implementation, when the user's vehicle obtains the target vacant parking space, the Internet of Things control center obtains the target vacant parking space information through the integrated intelligent parking management system, and sends the target vacant parking space information to the user's vehicle's on-board navigation system. After receiving the pushed information, the on-board navigation system will display the exact location of the target vacant parking space, including the coordinates of the parking space, direction guidance, distance and other information. The car owner drives the user's vehicle to the target vacant parking space according to the target vacant parking space information displayed by the on-board navigation system, thereby completing parking efficiently.

[0045] In addition, in another aspect of the present application, in some embodiments, the present application provides a smart city parking guidance system based on the Internet of Things, referring to Figure 4 , which is a schematic diagram of the structure of a smart city parking guidance system based on the Internet of Things according to some embodiments of the present application. The smart city parking guidance system 200 based on the Internet of Things includes: an acquisition module 201, a processing module 202 and an execution module 203, which are respectively described as follows: Acquisition module 201, in this application, acquisition module 201 is mainly used to acquire road network data of each parking partition in the target parking lot; Processing module 202, in this application, processing module 202 is mainly used to upload the road network data of each parking partition to the Internet of Things control center, and the Internet of Things control center constructs the road network association information between each parking partition according to all the road network data, and determines the availability index of each parking partition by combining all the road network association information with the occupancy characteristics of parking spaces during the peak parking period of the target parking lot; The processing module 202 is further used to determine the short-term change characteristics of parking spaces in each parking partition when the user vehicle enters the entrance of the target parking lot, and plan a confident driving route of the user vehicle according to the current position of the user vehicle, the change trend between the short-term change characteristics and the availability index of each parking partition; In addition, the processing module 202 is further configured to obtain the parking space attributes of each vacant parking space in the target parking space from the Internet of Things control center when the user vehicle arrives at the target parking space marked by the trusted driving route, and determine the target vacant parking space of the user vehicle based on the spatial distribution characteristics of each vacant parking space in the target parking space and the attributes of each parking space; Execution module 203, in this application, execution module 203 is mainly used for the Internet of Things control center to send the target vacant parking space information to the vehicle navigation system of the user's vehicle, and guide the user's vehicle to the target vacant parking space.

[0046] In addition, the present application also provides a computer device, which includes a memory and a processor, the memory stores a code, and the processor is configured to obtain the code and execute the above-mentioned smart city parking guidance method based on the Internet of Things.

[0047] In some embodiments, reference Figure 5 , which is a schematic diagram of the structure of a computer device for implementing a smart city parking guidance method based on the Internet of Things according to some embodiments of the present application. The smart city parking guidance method based on the Internet of Things in the above embodiment can be Figure 5 The computer device 300 shown in the figure is implemented, and the computer device 300 includes at least one processor 301, a communication bus 302, a memory 303 and at least one communication interface 304.

[0048] The processor 301 may be a general-purpose central processing unit (CPU), or an application-specific integrated circuit (ASIC) or one or more for controlling the execution of the IoT-based smart city parking guidance method in the present application.

[0049] The communication bus 302 may be used to transmit information between the above-mentioned components.

[0050] The memory 303 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compressed optical disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 303 may exist independently and be connected to the processor 301 via the communication bus 302. The memory 303 may also be integrated with the processor 301.

[0051] The memory 303 is used to store the program code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the program code stored in the memory 303. The program code may include one or more software modules. The determination of the smart city parking guidance method based on the Internet of Things in the above embodiment can be implemented by the processor 301 and one or more software modules in the program code in the memory 303.

[0052] The communication interface 304 uses any transceiver or other device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0053] In a specific implementation, as an embodiment, a computer device may include multiple processors, each of which may be a single-CPU processor or a multi-CPU processor. The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0054] The above-mentioned computer device may be a general-purpose computer device or a special-purpose computer device. In a specific implementation, the computer device may be a desktop computer, a portable computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device or an embedded device. The embodiment of the present application does not limit the type of computer device.

[0055] In addition, the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned smart city parking guidance method based on the Internet of Things.

[0056] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0057] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A parking guidance method for a smart city based on the Internet of Things, characterized in that: The method comprises the following steps: Obtain the road network data of each parking partition in the target parking lot; The road network data of each parking partition is uploaded to the Internet of Things control center, and the Internet of Things control center constructs the road network association information between each parking partition based on all the road network data, and determines the availability index of each parking partition through all the road network association information combined with the occupancy characteristics of parking spaces during the peak parking period of the target parking lot; Determine the short-term change characteristics of parking spaces in each parking partition when the user vehicle enters the entrance of the target parking lot, and plan a confident driving route for the user vehicle based on the current position of the user vehicle, the change trend between the short-term change characteristics, and the availability index of each parking partition; When the user vehicle arrives at the target parking partition marked by the trusted driving route, the parking space attributes of each vacant parking space in the target parking partition are obtained from the Internet of Things control center, and the target vacant parking space of the user vehicle is determined based on the spatial distribution characteristics of each vacant parking space in the target parking partition and the attributes of each parking space; The Internet of Things control center sends the target vacant parking space information to the vehicle navigation system of the user's vehicle, and guides the user's vehicle to the target vacant parking space.

2. The method according to claim 1, characterized in that The Internet of Things control center constructs the road network association information between each parking partition based on all the road network data, specifically including: Obtaining road network data of each parking partition in the target parking lot from the IoT control center; Establishing a road network connection diagram of each parking partition based on the road network data of each parking partition; Calculate the travel cost of each road network connection diagram according to the historical traffic flow data and current traffic flow value of each road section in each road network connection diagram; The road network association information between each parking partition is determined based on each road network connection diagram and its corresponding travel cost.

3. The method according to claim 1, characterized in that The availability indicators of each parking partition are determined by combining all road network association information with the occupancy characteristics of parking spaces during peak hours in the target parking lot, including: Extracting the occupancy characteristics of parking spaces in the target parking lot during the peak parking period from the historical parking data of the target parking lot, wherein the occupancy characteristics include the occupancy characteristics of each parking partition; Aggregate and analyze the occupancy characteristics of each parking partition to generate a dynamic usage value of parking spaces in each parking partition during the peak parking period of the target parking lot; Based on all the road network association information and the dynamic usage value of parking spaces in each parking partition, the availability index of each parking partition is calculated.

4. The method according to claim 1, characterized in that Planning a confident driving route of the user's vehicle based on the current position of the user's vehicle, the changing trends between various short-term changing features, and the availability index of each parking partition specifically includes: Obtaining the current position of the user's vehicle; Determine the change trends between the various short-term change features, and extract multiple candidate parking partitions from all parking partitions based on all the change trends; For each candidate parking partition, determining the driving effectiveness of the user vehicle to the candidate parking partition according to the availability index and the short-term change characteristics of the candidate parking partition, and then obtaining the driving effectiveness of the user vehicle to each candidate parking partition; Extracting a target parking partition from all candidate parking partitions based on the driving effectiveness of each candidate parking partition; A confident driving route of the user vehicle is planned based on the spatial position of the target parking partition and the current position of the user vehicle.

5. The method according to claim 1, characterized in that Determining the target vacant parking space for the user vehicle based on the spatial distribution characteristics of each vacant parking space in the target parking partition and the attributes of each parking space specifically includes: Obtain the spatial distribution characteristics of each vacant parking space in the target parking partition; Quantifying the characteristics of each vacant parking space to obtain a quantized attribute value of each vacant parking space; Determine the attribute difference value between each vacant parking space in the target parking partition according to the quantitative attribute value of each vacant parking space; Extracting the vacant parking space corresponding to the largest attribute difference value from all attribute difference values ​​as the candidate vacant parking space, and then obtaining multiple candidate vacant parking spaces; For each candidate vacant parking space, a multi-factor evaluation is performed on the spatial distribution characteristics and parking space attributes corresponding to the candidate vacant parking space to obtain an availability score for each candidate vacant parking space; A candidate vacant parking space corresponding to the maximum availability score is extracted from all candidate vacant parking spaces as the target vacant parking space for the user vehicle.

6. The method according to claim 1, characterized in that Also includes: The target parking lot is divided into multiple parking zones by the orientation zoning method.

7. The method according to claim 1, characterized in that The road network data of each parking partition is uploaded to the IoT control center through the message queue telemetry transmission protocol.

8. A smart city parking guidance system based on the Internet of Things, characterized in that: include: An acquisition module is used to acquire the road network data of each parking partition in the target parking lot; A processing module is used to upload the road network data of each parking partition to the Internet of Things control center, and the Internet of Things control center constructs the road network association information between each parking partition according to all the road network data, and determines the availability index of each parking partition by combining all the road network association information with the occupancy characteristics of parking spaces during the peak parking period of the target parking lot; The processing module is further used to determine the short-term change characteristics of parking spaces in each parking partition when the user vehicle enters the entrance of the target parking lot, and plan a confident driving route for the user vehicle according to the current position of the user vehicle, the change trend between the various short-term change characteristics and the availability index of each parking partition; The processing module is further configured to obtain, when the user vehicle arrives at the target parking partition marked by the trusted driving route, the parking space attributes of each vacant parking space in the target parking partition from the Internet of Things control center, and determine the target vacant parking space for the user vehicle based on the spatial distribution characteristics of each vacant parking space in the target parking partition and the attributes of each parking space; The execution module is used for the Internet of Things control center to send the target vacant parking space information to the vehicle navigation system of the user's vehicle, so as to guide the user's vehicle to the target vacant parking space.

9. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a code, and the processor is configured to obtain the code and execute the IoT-based smart city parking guidance method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the parking guidance method for a smart city based on the Internet of Things is implemented.

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