Intelligent passing system capable of automatically generating parking guidance and charging preferences

Abstract

The invention discloses an intelligent passing system capable of automatically generating parking guidance and charging preferences. The intelligent passing system comprises a system interface and integration module, a data acquisition module, a data processing and storage module, an intelligent passing solution algorithm module, a user interaction module, a dynamic feedback and model optimization module and a safety and privacy protection module. According to the invention, the data of the entrance and exit system, the guiding system, the charging system and the preferential system are collected, the parking lot and user travel conditions are monitored in real time, an optimal parking guiding route and a charging preferential scheme are provided by adopting an intelligent passing solution algorithm and are accurately pushed to different users in real time, intelligent distribution and guiding of parking spaces are realized, and the user experience is improved. The time for vehicle owners to find parking spaces is shortened, the overall use efficiency of the parking lot is improved, personalized charging preferences are provided according to the charging requirements of the vehicle owners, the charging cost of the vehicle owners is reduced, the utilization rate of the charging piles is increased, and intelligent passing in the true sense is achieved.

Description

Technical Field

[0001] The present invention relates to the field of smart traffic technology, and in particular to a smart traffic system that automatically generates parking guidance and charging discounts. Background Art

[0002] Most of the entrance and exit systems, guidance systems, charging systems and discount systems on the market do not have interoperable information data, forming information islands. They are all simple subsystem functional applications and cannot analyze, organize and utilize the data well, and then use the analyzed data to positively serve users. They cannot truly realize smart passage. For this reason, this application proposes a smart passage system that automatically generates parking guidance and charging discounts. Summary of the invention

[0003] Based on the technical problems existing in the background technology, the present invention proposes a smart traffic system that automatically generates parking guidance and charging discounts.

[0004] The present invention proposes a smart traffic system for automatically generating parking guidance and charging discounts, including a system interface and integration module, a data acquisition module, a data processing and storage module, a smart traffic solution algorithm module, a user interaction module, a dynamic feedback and model optimization module, and a security and privacy protection module;

[0005] The data acquisition module and the system interface are connected to the integration module and the data processing and storage module, the intelligent traffic solution algorithm module is connected to the user interaction module, the data processing and storage module, and the dynamic feedback and model optimization module, the security and privacy protection module is connected to the dynamic feedback and model optimization module and the data processing and storage module, the dynamic feedback and model optimization module is connected to the user interaction module, and the system interface is connected to the integration module with the discount system, charging system, entrance and exit system, guidance system and vehicle user-end recording system.

[0006] Preferably, the smart traffic solution algorithm module includes a data analysis module, an optimization algorithm module and a push and navigation module, and the optimization algorithm module is connected to the data analysis module and the push and navigation module;

[0007] The data analysis module includes a vehicle distribution analysis module, a parking space utilization prediction module and a user behavior analysis module.

[0008] Preferably, the system interface and integration module are connected and communicated with the preferential system, charging system, entrance and exit system and guidance system through the API communication protocol. The preferential system is used to collect currently valid preferential activities and applicable conditions. The charging system is used to collect the location, available status, queue status and charging rate of charging piles. The entrance and exit system is used to collect vehicle entry and exit time and license plate information. The guidance system is used to collect real-time parking space status and number of vacant parking spaces in the parking lot. The vehicle user terminal recording system is used to record the user's historical parking behavior, frequency and travel time;

[0009] The data acquisition module is used to collect data collected by the discount system, charging system, entrance and exit system, guidance system and vehicle user-end recording system, and the collected data is transmitted to the data processing and storage module. The data processing and storage module is used to clean the collected data to remove invalid data, process abnormal values, and integrate the data to establish a global view. The processed data is stored in a database.

[0010] Preferably, the vehicle distribution analysis module is used to analyze the proportion of fuel vehicles and new energy vehicles, and the specific steps are: obtaining the license plate number, vehicle model and entry and exit time of the vehicle according to the collected data, combining the parking distribution of vehicles in the parking lot, and counting the proportion of fuel vehicles and new energy vehicles. The formula used is:

[0011] The parking space utilization rate prediction module is used to predict the busy time period, vacant status and peak usage period of the parking lot. The specific steps are: obtaining the parking space status data (free / occupied) of the parking lot in real time according to the collected data, collecting historical parking data (including vehicle entry and exit time, parking space number, parking duration), obtaining the usage records of charging piles (start time, end time, charging duration, number of queues), and predicting the number of free parking spaces and peak usage of charging piles in the future based on historical data. The formula used is:

[0012] The user behavior analysis module is used to analyze the user's historical behavior and predict the user's possible parking location and charging needs. The specific steps are: obtaining the user's parking record (including the user's license plate number, entry and exit time, parking time, parking space used, whether a charging pile is used, charging time and charging fee) and the user's behavior preference data (including travel frequency, travel time (weekdays, weekends), parking lot selection preference) according to the collected data, using a clustering algorithm to group the users, divide them into groups with different parking / charging behavior characteristics, build a user behavior prediction model, and predict possible parking areas and charging needs based on the user's historical data;

[0013] The specific steps for building a user behavior prediction model are as follows:

[0014] S101: extracting a behavioral feature vector of each user, including average parking duration, parking frequency, preferred parking area, whether a charging pile is frequently used, and charging duration;

[0015] S102: Use K-Means clustering algorithm to group users based on their row and position features to obtain user groups with similar behaviors;

[0016] S103: predicting the parking area and charging demand of the new user based on the feature mean of the user group;

[0017] S104: If the user preference is unclear, a collaborative filtering algorithm can be used for further prediction. The collaborative filtering algorithm uses the user behavior vector to calculate the similarity between users during prediction. Based on the similarity between users, the collaborative filtering algorithm recommends parking areas and charging stations related to other similar users. The similarity calculation formula is:

[0018] A and B represent the feature vectors of two users (including the frequency of use of parking areas and the usage of charging piles), AB represents the dot product of vectors A and B, ||A||||B|| represents the length of each vector, and the value of Sim(A,B) is 1 if the two vectors are exactly the same, 0 if the two vectors are completely unrelated, and -1 if the two vectors are completely opposite.

[0019] Preferably, the optimization algorithm module is used to generate the shortest time path by combining the parking lot layout, the current parking space status and the vehicle position. If the user has fixed preferences, the frequently used parking spaces are recommended first. If the parking lot is busy, the nearest empty parking space is recommended based on the real-time parking space data.

[0020] The formula used to calculate the shortest time path is as follows:

[0021] f(n)=g(n)+h(n), where f(n) is the estimated shortest path from the starting point to the target, g(n) is the actual distance from the starting point to the current node n, and h(n) is the heuristic estimated cost from the current node n to the target node.

[0022] Where (x1, y1) and (x2, y2) are the coordinates of the current node and the target node respectively;

[0023] The optimization algorithm module also provides charging solutions based on the idle status of charging piles, user preferences and charging needs, allocates idle charging piles to users in advance, predicts peak periods, and notifies users of queues in advance. At the same time, it recommends suitable promotional activities based on user characteristics (parking frequency, whether it is a new energy vehicle).

[0024] Preferably, the push and navigation module is used to push the information of parking spaces, charging plans, promotional activities and navigation routes to the user interaction module in real time through the vehicle terminal, and personnel can view it through the user interaction module.

[0025] Preferably, the dynamic feedback and model optimization module is used to collect user satisfaction scores for recommended routes, parking spaces, and charging solutions, and record user manual changes to recommended parking spaces, adjust the algorithm weight of the smart traffic algorithm module based on user historical behavior, and re-input the feedback data into the algorithm module to update the smart traffic solution algorithm module.

[0026] Preferably, the security and privacy protection module is used to encrypt the transmission of user information and transaction data, strictly control the access rights of different roles, monitor data flow in real time, and prevent malicious attacks.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] The present invention collects data from the entrance and exit system, guidance system, charging system and discount system, monitors the parking lot and user travel conditions in real time, and adopts a smart traffic solution algorithm to provide the optimal parking guidance route and charging discount plan, and accurately pushes them to different users in real time, thereby realizing the intelligent allocation and guidance of parking spaces, reducing the time car owners spend looking for parking spaces, and improving the overall utilization efficiency of parking lots. It also provides personalized charging discounts based on the charging needs of car owners, reduces the charging costs of car owners, and increases the utilization rate of charging piles. At the same time, it provides real-time parking navigation for car owners, reduces the time of getting lost and searching in the parking lot, and realizes truly smart traffic. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A connection block diagram of a smart traffic system for automatically generating parking guidance and charging discounts proposed by the present invention;

[0030] Figure 2 A block diagram of a smart traffic solution algorithm module in a smart traffic system for automatically generating parking guidance and charging discounts proposed by the present invention;

[0031] Figure 3 This is a flow chart of a smart traffic system for automatically generating parking guidance and charging discounts proposed by the present invention. DETAILED DESCRIPTION

[0032] The present invention will be further explained below in conjunction with specific embodiments.

[0033] Example

[0034] Reference Figure 1-3,This embodiment proposes a smart traffic system for automatically generating parking guidance and charging discounts, including a system interface and integration module, a data acquisition module, a data processing and storage module, a smart traffic solution algorithm module, a user interaction module, a dynamic feedback and model optimization module, and a security and privacy protection module;

[0035] The smart traffic solution algorithm module includes a data analysis module, an optimization algorithm module, and a push and navigation module. The optimization algorithm module is connected with the data analysis module and the push and navigation module.

[0036] The data analysis module includes a vehicle distribution analysis module, a parking space utilization prediction module, and a user behavior analysis module;

[0037] The vehicle distribution analysis module is used to analyze the proportion of fuel vehicles and new energy vehicles. The specific steps are: obtain the vehicle license plate number, model and entry and exit time according to the collected data, and combine the parking distribution of vehicles in the parking lot to calculate the proportion of fuel vehicles and new energy vehicles. The formula used is:

[0038] The parking space utilization rate prediction module is used to predict the busy time period, vacant status and peak usage period of the parking lot. The specific steps are: obtain the parking space status data (free / occupied) of the parking lot in real time according to the collected data, collect historical parking data (including vehicle entry and exit time, parking space number, parking time), obtain the usage records of charging piles (start time, end time, charging time, number of queues), and predict the number of free parking spaces and peak usage of charging piles in the future based on historical data. The formula used is:

[0039] The user behavior analysis module is used to analyze the user's historical behavior and predict the user's possible parking location and charging needs. The specific steps are: obtain the user's parking record (including the user's license plate number, entry and exit time, parking time, parking space used, whether to use a charging pile, charging time and charging fee) and the user's behavior preference data (including travel frequency, travel time (weekdays, weekends), parking lot selection preference) based on the collected data, use clustering algorithms to group users, divide them into groups with different parking / charging behavior characteristics, build a user behavior prediction model, and predict possible parking areas and charging needs based on user historical data;

[0040] The specific steps for building a user behavior prediction model are as follows:

[0041] S101: extracting a behavioral feature vector of each user, including average parking duration, parking frequency, preferred parking area, whether a charging pile is frequently used, and charging duration;

[0042] S102: Use K-Means clustering algorithm to group users based on their row and position features to obtain user groups with similar behaviors;

[0043] S103: predicting the parking area and charging demand of the new user based on the feature mean of the user group;

[0044] S104: If the user preference is unclear, a collaborative filtering algorithm can be used for further prediction. The collaborative filtering algorithm uses the user behavior vector to calculate the similarity between users during prediction. Based on the similarity between users, the collaborative filtering algorithm recommends parking areas and charging stations related to other similar users. The similarity calculation formula is:

[0045] A and B represent the feature vectors of two users (including the usage frequency of the parking area and the usage of the charging pile), AB represents the dot product of vectors A and B, ||A||||B|| represents the length of each vector, and the value of Sim(A,B) is 1 if the two vectors are exactly the same, 0 if the two vectors are completely unrelated, and -1 if the two vectors are completely opposite.

[0046] The optimization algorithm module is used to generate the shortest time path by combining the parking lot layout, current parking space status and vehicle location. Users have fixed preferences, and their frequently used parking spaces are recommended first. If the parking lot is busy, the nearest empty parking space is recommended based on real-time parking space data.

[0047] The formula used to calculate the shortest time path is as follows:

[0048] f(n)=g(n)+h(n), where f(n) is the estimated shortest path from the starting point to the target, g(n) is the actual distance from the starting point to the current node n, and h(n) is the heuristic estimated cost from the current node n to the target node.

[0049] Where (x1, y1) and (x2, y2) are the coordinates of the current node and the target node respectively;

[0050] The optimization algorithm module also provides charging solutions based on the idle status of charging piles, user preferences and charging needs, allocates idle charging piles to users in advance, predicts peak hours, and informs users of queues in advance. At the same time, it recommends suitable discount activities based on user characteristics (parking frequency, whether it is a new energy vehicle).

[0051] The data acquisition module and the system interface are connected to the integration module and the data processing and storage module, the intelligent traffic solution algorithm module is connected to the user interaction module, the data processing and storage module and the dynamic feedback and model optimization module, the security and privacy protection module is connected to the dynamic feedback and model optimization module and the data processing and storage module, the dynamic feedback and model optimization module is connected to the user interaction module, the system interface and the integration module are connected to the preferential system, the charging system, the entrance and exit system, the guidance system and the vehicle user end recording system, wherein the system interface and the integration module are connected and communicated with the preferential system, the charging system, the entrance and exit system and the guidance system through the API communication protocol, the preferential system is used to collect the current effective preferential activities and applicable conditions, the charging system is used to collect the location, available status, queuing status and charging rate of the charging pile, the entrance and exit system is used to collect the vehicle entry and exit time and license plate information, the guidance system is used to collect the real-time parking space status and the number of vacant parking spaces in the parking lot, and the vehicle user end recording system is used to record the user's historical parking behavior, frequency and travel time;

[0052] The data collection module is used to collect data collected by the preferential system, charging system, entrance and exit system, guidance system and vehicle user end recording system, and transmit the collected data to the data processing and storage module. The data processing and storage module is used to clean the collected data to remove invalid data, process abnormal values, integrate the data, establish a global view, and store the processed data through the database;

[0053] The push and navigation module is used to push information about parking spaces, charging plans, promotions and navigation routes to the user interaction module in real time through the vehicle terminal, and personnel can view it through the user interaction module;

[0054] The dynamic feedback and model optimization module is used to collect user satisfaction scores on recommended routes, parking spaces, and charging solutions, and record the user's manual changes to recommended parking spaces. The algorithm weight of the smart traffic algorithm module is adjusted according to the user's historical behavior, and the feedback data is re-entered into the algorithm module to update the smart traffic solution algorithm module.

[0055] The security and privacy protection module is used to encrypt the transmission of user information and transaction data, strictly control the access rights of different roles, monitor data flow in real time, and prevent malicious attacks; this embodiment collects data from the entrance and exit system, guidance system, charging system and discount system, monitors the parking lot and user travel conditions in real time, and adopts a smart passage solution algorithm to provide the optimal parking guidance route and charging discount plan, and pushes them to different users in real time and accurately, thereby realizing the intelligent allocation and guidance of parking spaces, reducing the time for car owners to find parking spaces, and improving the overall utilization efficiency of parking lots. It also provides personalized charging discounts according to the charging needs of car owners, reduces the charging costs of car owners, and increases the utilization rate of charging piles. At the same time, it provides real-time parking navigation for car owners, reduces the time of getting lost and searching in the parking lot, and realizes truly smart passage.

[0056] In this embodiment, when in use, the communication between the smart access system and the preferential system, charging system, entrance and exit system and guidance system is realized through the system interface and the integrated module. The preferential system is used to collect currently valid preferential activities and applicable conditions. The charging system is used to collect the location, available status, queuing status and charging rate of the charging pile. The entrance and exit system is used to collect vehicle entry and exit time and license plate information. The guidance system is used to collect real-time parking space status and the number of vacant parking spaces in the parking lot. The vehicle user-side recording system is used to record the user's historical parking behavior, frequency and travel time. The data acquisition module is used to collect data collected by the preferential system, charging system, entrance and exit system, guidance system and vehicle user-side recording system, and the collected data is transmitted to the data processing and storage module. The data processing and storage module is used to clean the collected data to remove invalid data, process abnormal values, and integrate the data to establish a global view. The processed data is stored through the database, and the processed data is transmitted to the smart access solution algorithm module. The smart access solution algorithm module The vehicle distribution analysis module is used to analyze the proportion of fuel vehicles and new energy vehicles. The parking space utilization prediction module is used to predict the busy time period, vacant status and peak usage period of the parking lot. The user behavior analysis module is used to analyze the user's historical behavior and predict the user's possible parking location and charging needs. The analysis results are transmitted to the optimization algorithm module. The optimization algorithm module generates the shortest time path based on the analysis results combined with the parking lot layout, current parking space status and vehicle location, and provides charging optimization solutions based on the idle status of the charging pile, user preferences and charging needs. The generated solution information is transmitted to the user interaction module through the push and navigation module. Personnel can view it through the user interaction module. Personnel can perform operations through the information displayed in the user interaction module. The intelligent traffic solution algorithm is used to provide the optimal parking guidance route and charging preferential solutions through the collected data, and push them to different users in real time and accurately, so as to realize the intelligent allocation and guidance of parking spaces, reduce the time for car owners to find parking spaces, reduce the charging costs of car owners, and increase the utilization rate of charging piles.

[0057] In addition, during use, the dynamic feedback and model optimization modules are used to collect users' satisfaction scores on recommended routes, parking spaces, and charging plans, and record users' manual changes to recommended parking spaces. The algorithm weights of the smart traffic algorithm module are adjusted based on users' historical behaviors, and the feedback data is re-entered into the algorithm module to update the smart traffic solution algorithm module. Through continuous optimization, the accuracy and efficiency of the algorithm are improved.

[0058] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A smart traffic system that automatically generates parking guidance and charging discounts, characterized in that: It includes system interface and integration module, data acquisition module, data processing and storage module, intelligent traffic solution algorithm module, user interaction module, dynamic feedback and model optimization module, and security and privacy protection module; The data acquisition module and the system interface are connected to the integration module and the data processing and storage module, the intelligent traffic solution algorithm module is connected to the user interaction module, the data processing and storage module, and the dynamic feedback and model optimization module, the security and privacy protection module is connected to the dynamic feedback and model optimization module and the data processing and storage module, the dynamic feedback and model optimization module is connected to the user interaction module, and the system interface is connected to the integration module with the discount system, charging system, entrance and exit system, guidance system and vehicle user-end recording system.

2. The intelligent traffic system for automatically generating parking guidance and charging discounts according to claim 1 is characterized in that: The smart traffic solution algorithm module includes a data analysis module, an optimization algorithm module and a push and navigation module, and the optimization algorithm module is connected with the data analysis module and the push and navigation module; The data analysis module includes a vehicle distribution analysis module, a parking space utilization prediction module and a user behavior analysis module.

3. The intelligent traffic system for automatically generating parking guidance and charging discounts according to claim 1 is characterized in that: The system interface and integration module are connected and communicated with the preferential system, charging system, entrance and exit system and guidance system through the API communication protocol. The preferential system is used to collect the currently valid preferential activities and applicable conditions. The charging system is used to collect the location, available status, queue status and charging rate of the charging piles. The entrance and exit system is used to collect the vehicle entry and exit time and license plate information. The guidance system is used to collect the real-time parking space status and the number of vacant parking spaces in the parking lot. The vehicle user terminal recording system is used to record the user's historical parking behavior, frequency and travel time; The data acquisition module is used to collect data collected by the discount system, charging system, entrance and exit system, guidance system and vehicle user-end recording system, and the collected data is transmitted to the data processing and storage module. The data processing and storage module is used to clean the collected data to remove invalid data, process abnormal values, and integrate the data to establish a global view. The processed data is stored in a database.

4. The intelligent traffic system for automatically generating parking guidance and charging discounts according to claim 2 is characterized in that: The vehicle distribution analysis module is used to analyze the proportion of fuel vehicles and new energy vehicles. The specific steps are: obtain the license plate number, vehicle model and entry and exit time of the vehicle according to the collected data, and combine the parking distribution of vehicles in the parking lot to count the proportion of fuel vehicles and new energy vehicles. The formula used is: The parking space utilization rate prediction module is used to predict the busy time period, vacant status and peak usage period of the parking lot. The specific steps are: obtaining the parking space status data of the parking lot in real time according to the collected data, collecting historical parking data, obtaining the usage records of the charging piles, and predicting the number of vacant parking spaces and the peak usage of charging piles in the future based on the historical data. The formula used is: The user behavior analysis module is used to analyze the user's historical behavior and predict the user's possible parking location and charging needs. The specific steps are: obtaining the user's parking record and the user's behavior preference data based on the collected data, using a clustering algorithm to group the users, dividing them into groups with different parking / charging behavior characteristics, building a user behavior prediction model, and predicting possible parking areas and charging needs based on the user's historical data; The specific steps for building a user behavior prediction model are as follows: S101: extracting a behavioral feature vector of each user, including average parking duration, parking frequency, preferred parking area, whether a charging pile is frequently used, and charging duration; S102: Use K-Means clustering algorithm to group users based on their row and position features to obtain user groups with similar behaviors; S103: predicting the parking area and charging demand of the new user based on the feature mean of the user group; S104: If the user preference is unclear, a collaborative filtering algorithm can be used for further prediction. The collaborative filtering algorithm uses the user behavior vector to calculate the similarity between users during prediction. Based on the similarity between users, the collaborative filtering algorithm recommends parking areas and charging stations related to other similar users. The similarity calculation formula is: A and B represent the feature vectors of two users, AB represents the dot product of vectors A and B, ||A||||B|| represents the length of each vector, and the value of Sim(A,B) is 1 if the two vectors are exactly the same, 0 if the two vectors are completely unrelated, and -1 if the two vectors are completely opposite.

5. The intelligent traffic system for automatically generating parking guidance and charging discounts according to claim 1 is characterized in that: The optimization algorithm module is used to generate the shortest time path by combining the parking lot layout, current parking space status and vehicle location. Users have fixed preferences and their frequently used parking spaces are recommended first. If the parking lot is busy, the nearest empty parking space is recommended based on real-time parking space data. The formula used to calculate the shortest time path is as follows: f(n)=g(n)+h(n), where f(n) is the estimated shortest path from the starting point to the target, g(n) is the actual distance from the starting point to the current node n, and h(n) is the heuristic estimated cost from the current node n to the target node. Where (x1, y1) and (x2, y2) are the coordinates of the current node and the target node respectively; The optimization algorithm module also provides charging solutions based on the idle status of charging piles, user preferences and charging needs, allocates idle charging piles to users in advance, predicts peak periods, and notifies users of queues in advance. At the same time, it recommends suitable promotional activities based on user characteristics.

6. The intelligent traffic system for automatically generating parking guidance and charging discounts according to claim 1, characterized in that: The push and navigation module is used to push information about parking spaces, charging plans, promotions and navigation routes to the user interaction module in real time through the vehicle terminal, and personnel can view it through the user interaction module.

7. The intelligent traffic system for automatically generating parking guidance and charging discounts according to claim 1, characterized in that: The dynamic feedback and model optimization module is used to collect user satisfaction scores for recommended routes, parking spaces, and charging solutions, and record users' manual changes to recommended parking spaces, adjust the algorithm weights of the smart traffic algorithm module based on user historical behaviors, and re-input feedback data into the algorithm module to update the smart traffic solution algorithm module.

8. The intelligent traffic system for automatically generating parking guidance and charging discounts according to claim 1, characterized in that: The security and privacy protection module is used to encrypt the transmission of user information and transaction data, strictly control the access rights of different roles, monitor data flow in real time, and prevent malicious attacks.

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