A method, system and electronic equipment for highway toll processing

Abstract

This invention discloses a highway tolling processing method, system, and electronic device, comprising: receiving a tolling request from a road network node tolling terminal relative to a target vehicle, and sending a request to a data platform to obtain the target vehicle's driving information; receiving driving location information fed back by the data platform and road network node information along the route after verification according to request verification rules; reconstructing the target vehicle's driving path based on a pre-constructed road network model vector map, the driving location information, and the road network node information along the route, to obtain the reconstructed driving path; determining the tolling result of the target vehicle based on the reconstructed driving path and a pre-set toll rate benchmark, and feeding it back to the road network node tolling terminal. The above technical solution ensures the accuracy of the target vehicle's location result, improves the accuracy of path reconstruction, and enhances the success rate and accuracy of online tolling.

Description

Technical Field

[0001] This invention relates to the field of route reconstruction technology, and in particular to a highway toll processing method, system and electronic equipment. Background Technology

[0002] With the nationwide implementation of the elimination of provincial toll stations, my country's expressway network toll collection system uses gantry markers at key locations to mark routes and then reconstructs the travel route within the expressway network model based on the gantry marker information to calculate and collect tolls. Among these, entrance, gantry, and exit are the three core elements of the toll route, making gantry data particularly important.

[0003] Currently, online toll calculation on highways relies on gantry identification information along vehicle routes. When gantry malfunctions or signal interference occurs, critical data loss can easily occur, leading to missed or incorrect tolling, or the inability to effectively reconstruct gantry identification within the highway network model. In such cases, route reconstruction fails, and online toll calculation can only be based on the minimum amount, compromising the accuracy of route reconstruction and the success rate and accuracy of online toll calculation. Summary of the Invention

[0004] This invention provides a highway toll processing method, system, and electronic equipment to solve problems such as route reconstruction failure and online toll calculation errors caused by missing or mislabeled gantry markers. It ensures the accuracy of target vehicle location results, improves the accuracy of route reconstruction, and enhances the success rate and accuracy of online toll calculation.

[0005] In a first aspect, embodiments of this disclosure provide a highway toll processing method, including:

[0006] After receiving the billing request from the toll terminal at the road network node relative to the target vehicle, a request to obtain the driving information of the target vehicle is sent to the data platform.

[0007] Receive the vehicle positioning information fed back by the data platform and the route road network node information fed back after verification according to the request verification rules;

[0008] Based on the pre-constructed road network model vector map, the vehicle positioning information, and the information of the road network nodes along the route, the driving path of the target vehicle is reconstructed to obtain the reconstructed driving path.

[0009] Based on the driving recovery path and the preset rate benchmark, the driving toll result of the target vehicle is determined and fed back to the toll terminal of the road network node.

[0010] Secondly, embodiments of this disclosure provide a highway toll processing system, including:

[0011] The request sending module is used to send a request to the data platform to obtain the driving information of the target vehicle after receiving the billing request from the toll terminal of the road network node relative to the target vehicle.

[0012] The feedback information receiving module is used to receive the vehicle positioning information fed back by the data platform and the route road network node information fed back after verification according to the request verification rules;

[0013] The path restoration module is used to restore the driving path of the target vehicle based on the pre-constructed road network model vector map, the vehicle positioning information, and the information of the road network nodes along the route, and obtain the restored driving path.

[0014] The billing result determination module is used to determine the billing result of the target vehicle based on the driving restoration path and the preset rate benchmark, and then feed it back to the road network node toll terminal.

[0015] Thirdly, embodiments of this disclosure provide an electronic device, including:

[0016] At least one processor; and

[0017] A memory that is communicatively connected to at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by at least one processor, such that the at least one processor is able to execute the highway toll processing method provided in the first aspect embodiment described above.

[0019] This invention discloses a highway tolling processing method, system, and electronic device. Upon receiving a tolling request from a road network node tolling terminal relative to a target vehicle, the system sends a request to a data platform to obtain the target vehicle's driving information. It receives driving location information from the data platform and, after verification according to request validation rules, route road network node information. Based on a pre-constructed road network model vector map, the driving location information, and the route road network node information, the system reconstructs the target vehicle's driving path to obtain the reconstructed path. Based on the reconstructed path and a pre-set toll rate benchmark, the system determines the target vehicle's tolling result and sends it back to the road network node tolling terminal. In this technical solution, the data platform stores driving location information provided by the BeiDou system and route road network node information provided by the gantry system. Combining these two types of data effectively ensures the accuracy of the target vehicle's location result. By using the two types of information fed back by the data platform to reconstruct the target vehicle's driving path, the accuracy of path reconstruction is improved, thereby increasing the success rate and accuracy of online tolling.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a highway toll processing method provided in Embodiment 1 of the present invention;

[0023] Figure 2 This is a flowchart of a highway toll processing method provided in Embodiment 2 of the present invention;

[0024] Figure 3 This is a schematic diagram illustrating the determination rules for road network nodes involved in a highway toll processing method provided in Embodiment 2 of the present invention;

[0025] Figure 4A This is an example illustration of disconnected abnormal nodes involved in a highway toll processing method provided in Embodiment 2 of the present invention;

[0026] Figure 4B This is an example illustration of the reverse abnormal node involved in a highway toll processing method provided in Embodiment 2 of the present invention;

[0027] Figure 4C This is an example illustration of the abnormal detour node involved in a highway tolling processing method provided in Embodiment 2 of the present invention;

[0028] Figure 4D This is an example illustration of the comparison rules between preceding and following nodes involved in a highway toll processing method provided in Embodiment 2 of the present invention;

[0029] Figure 4E This is an example illustration of the multi-node determination rules involved in a highway toll processing method provided in Embodiment 2 of the present invention;

[0030] Figure 4F This is another example illustration of the multi-node determination rules involved in a highway toll processing method provided in Embodiment 2 of the present invention;

[0031] Figure 4GThis is an example illustration of the unsuccessful refund rule involved in a highway toll processing method provided in Embodiment 2 of the present invention;

[0032] Figure 4H This is an example illustration of the path splitting rules involved in a highway tolling processing method provided in Embodiment 2 of the present invention;

[0033] Figure 5A This is an example illustration of the mislabeling removal process involved in a highway tolling processing method provided in Embodiment 2 of the present invention;

[0034] Figure 5B This is another example illustration of the mislabeling removal process involved in a highway tolling processing method provided in Embodiment 2 of the present invention;

[0035] Figure 5C This is another example illustration of the mislabeling removal process involved in the highway tolling processing method provided in Embodiment 2 of the present invention;

[0036] Figure 5D This is another example illustration of the mislabeling removal process involved in the highway tolling processing method provided in Embodiment 2 of the present invention;

[0037] Figure 6 This is a schematic diagram of the structure of a highway toll processing system provided in Embodiment 3 of the present invention;

[0038] Figure 7 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0039] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0040] It should be noted that the terms "first," "second," and "target," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0041] Example 1

[0042] Figure 1 This is a flowchart of a highway toll processing method provided in Embodiment 1 of the present invention. This embodiment is applicable to the scenario of reconstructing the route of vehicles traveling on highways and calculating tolls online. This method can be executed by a highway toll processing system, which can be implemented in hardware and / or software.

[0043] Vehicles traveling on highways are charged using either a Manual Toll Collection (MTC) system or an Electronic Toll Collection (ETC) system. The MTC system uses a 5.8GHz composite toll card (CPC card) as the toll medium, achieving "provincial-specific tolling, unified collection"; the ETC system uses an Onboard Unit (OBU) and an ETC user card as the toll medium, achieving "provincial-specific tolling, provincial-specific collection".

[0044] For ETC vehicles, when an ETC vehicle passes through a gantry, the gantry system reads the basic vehicle information stored in the OBU, calculates the toll based on the corresponding road segment toll rate of this gantry, generates a transaction record, and promptly uploads the transaction data to the Ministry's Internet Center platform and the provincial center system.

[0045] For CPC vehicles, a CPC card is issued at the entrance lane, and the CPC card records the vehicle's basic information. When a CPC vehicle passes through the gantry, the gantry system reads the basic vehicle information recorded in the CPC card, calculates the toll, writes it into the CPC card, serves as the basis for exit toll collection, and generates a CPC card passage record.

[0046] The highway tolling processing method provided in this embodiment achieves high-precision route reconstruction without affecting the existing tolling system, making highway tolling more reasonable and accurate.

[0047] like Figure 1 As shown, the method includes:

[0048] S101. After receiving the billing request from the road network node toll terminal relative to the target vehicle, send a request to the data platform to obtain the target vehicle's driving information.

[0049] In this embodiment, the road network node toll terminal can be understood as a device installed at the entrance and exit of a toll station, used to identify target vehicles and issue toll requests for those vehicles. The target vehicle can be understood as the vehicle closest to the road network node toll terminal in the current direction of travel. The distance between the target vehicle and the road network node toll terminal is no more than twenty meters. The toll request can be understood as a toll request triggered at the highway exit when the target vehicle is about to leave the highway. The data platform can be understood as a database server used to store driving data of various vehicles on the highway, enabling data storage and retrieval. The driving information retrieval request can be understood as a request to retrieve the driving path information of the target vehicle stored in the data platform.

[0050] Specifically, when a target vehicle exits the highway at an exit, it is captured by the toll collection terminal at the highway exit node. The terminal identifies the target vehicle's basic information using a CPC card or ETC card and triggers an online toll collection request for the target vehicle. After receiving the toll collection request from the toll collection terminal, the highway online toll collection system sends a request to the data platform to obtain the target vehicle's driving information based on the request parameters.

[0051] The request parameters are shown in the table below:

[0052]

[0053]

[0054] Among them, attributes represent various attribute information of the vehicle, description can be understood as the Chinese description of the vehicle attribute information, type is the character type that records the vehicle attribute information, basic information is the detailed determination information of the vehicle attributes, and all attribute information in the table above is mandatory information included in the road network node information.

[0055] For example, a vehicle information retrieval request can be sent based on the following code:

[0056]

[0057] S102, Receive the vehicle positioning information fed back by the data center and the route road network node information fed back after verification according to the request verification rules.

[0058] In this embodiment, vehicle positioning information can be understood as the positioning information obtained by locating the target vehicle based on the BeiDou system, stored in the data platform. The request verification rule can be understood as the rule used to verify whether the basic information of the target vehicle stored in the data platform is correct and logically consistent. The route network node information can be understood as the gantry data collected by the gantry system based on the gantry system and capable of being verified by the request verification rule, stored in the data platform.

[0059] Specifically, the data platform stores vehicle positioning information obtained from the BeiDou system and road network node information obtained from the gantry system. Upon receiving a request for vehicle information from a target vehicle, the data platform responds by retrieving and feeding back the vehicle's driving information. It also receives the vehicle positioning information and the road network node information verified according to the request validation rules, which are then passed.

[0060] The request verification rules include: verification of the vehicle's passage time sequence through road network nodes, verification of a vehicle's single passage at the toll station entrance, and verification of the information items contained in the information of the road network nodes passed through; and verification that the information version of the road network node information fed back by the data platform meets the set feedback conditions.

[0061] The information on road network nodes includes the transit time of the target vehicle through the road network nodes and the node information of the road network nodes, which include highway gantries and highway toll stations.

[0062] The vehicle passage time sequence verification can be understood as verifying the passage time sequence of target vehicles collected from two adjacent road network nodes in the same direction of travel. If the passage time collected by the next node in the same direction of travel is earlier than the passage time collected by the previous node, it can be determined that the road network node information is abnormal and cannot pass the verification.

[0063] A vehicle inspection at a tollbooth entrance can be understood as querying the road network node information along the route to the tollbooth entrance to determine if the target vehicle has only appeared once at that node. If the target vehicle appears two or more times at the tollbooth entrance, it can be determined that there is an anomaly in the road network node information along the route, and the inspection will fail.

[0064] The verification of information items contained in the road network node information can be understood as a check to see if each information item can be matched one-to-one with the vehicle's basic information when the vehicle is captured at each road network node. If the information items contained in the road network node information cannot be matched, it can be determined that the road network node information is abnormal and cannot pass the verification. The information items contained in the road network node information can include information items that are the same as the requested parameters, such as the target vehicle's license plate number, license plate color, entry time, exit time, and mode of transportation.

[0065] For example, in the information of passing road network nodes collected at one road network node, the color corresponding to vehicle A is 3 (white). In the information of passing road network nodes collected at another road network node, the color corresponding to vehicle A is 11 (green). It can be clearly determined that the information items contained in the information of passing road network nodes cannot be matched. Therefore, the information of passing road network nodes is abnormal and cannot pass the verification.

[0066] The data platform feeds back information on road network nodes that meets the set feedback conditions. The driving information stored in the data platform is updated in real time. Each time a gantry collects data from passing vehicles, the road network node information stored in the data platform is updated, with each update corresponding to a new version. The set feedback conditions can be understood as the preset conditions under which the data platform feeds back the road network node information; for example, the information version of the road network node information could be the most recently updated version.

[0067] For example, when providing feedback on the road network node information for a target vehicle, it is determined whether the road network node information is the latest version. If it is not the latest version of the road network node information, that version of the road network node information is not provided to ensure the accuracy of reconstructing the target vehicle's travel path.

[0068] S103. Based on the pre-constructed road network model vector map, vehicle positioning information, and road network node information, the driving path of the target vehicle is reconstructed to obtain the reconstructed driving path.

[0069] In this embodiment, the road network model vector diagram can be understood as a directed graph model established based on the various road network nodes of the highway, which is a simulation and reconstruction of the highway structure. The driving path reconstruction can be understood as the reconstruction of the target vehicle's driving route on the highway as shown on the road network model vector diagram.

[0070] Specifically, on the pre-constructed road network model vector map, the driving direction of the target vehicle, which road network node it passes through, and whether the two road network nodes it passes through are adjacent road network nodes are analyzed based on the driving positioning information and road network node information fed back by the data platform. Based on the analysis results, the driving path of the target vehicle is reconstructed to obtain the driving path of the target vehicle determined on the road network model vector map.

[0071] S104. Based on the driving restoration path and the preset rate benchmark, determine the driving tolling result of the target vehicle and feed it back to the road network node tolling terminal.

[0072] In this embodiment, the preset rate benchmark can be understood as the pre-set toll standard between two road network nodes. It can be understood that the toll standard is related to the vehicle type and is not related to the distance between the two road network nodes. For example, the preset rate benchmark from road network node A to road network node B is: 3 yuan for passenger cars, 5 yuan for buses, and 10 yuan for trucks.

[0073] The tolling result can be understood as the sum of the tolls of all road network nodes that the vehicle passes through in the driving route, that is, the total cost required for the vehicle to travel on the highway.

[0074] Specifically, based on the vehicle restoration path and the pre-set toll rate benchmark between two road network nodes, the tolling result of the target vehicle between each two road network nodes is determined. The tolling results between each two road network nodes are added together to calculate the total tolling result of the vehicle restoration path. Based on the response parameters, the tolling result is fed back to the tolling terminal of the road network node.

[0075] The response parameters are shown in the table below:

[0076]

[0077]

[0078] For example, the trip billing result can be returned based on the following code:

[0079]

[0080] It is understood that the highway toll processing method provided in this embodiment is an online tolling method, which does not limit the toll collection method. The toll can be settled at the toll terminal of the road network node, or the toll can be settled at the account bound to the vehicle after the target vehicle leaves the highway. This embodiment does not limit the toll collection method.

[0081] In this embodiment, after receiving a billing request from the road network node toll terminal relative to the target vehicle, a request to obtain the target vehicle's driving information is sent to the data platform. The system receives driving location information from the data platform and, after verification according to the request verification rules, path information from the road network nodes. Based on a pre-constructed road network model vector map, the driving location information, and the path information, the system reconstructs the target vehicle's driving path to obtain the reconstructed path. Based on the reconstructed path and a pre-set rate benchmark, the system determines the target vehicle's billing result and sends it back to the road network node toll terminal. In this technical solution, the data platform stores driving location information provided by the BeiDou system and path information from the gantry system. Combining these two types of data effectively ensures the accuracy of the target vehicle's location result. By using the two types of information from the data platform to reconstruct the target vehicle's driving path, the accuracy of path reconstruction is improved, thus increasing the success rate and accuracy of online billing.

[0082] As a first optional embodiment of the embodiments, based on the above embodiments, this first optional embodiment further optimizes and adds: when the path restoration of the target vehicle fails, the driving tolling result of the target vehicle is determined according to the set minimum fee standard and fed back to the road network node tolling terminal.

[0083] In this embodiment, the minimum toll standard can be understood as the minimum total toll amount for the target vehicle along the possible route from the entrance toll station to the exit toll station on the highway. Specifically, if the path node information for the entrance toll station cannot be found in the road network node information, the entrance toll station closest to the first path node is selected as the target vehicle's entrance toll station.

[0084] Specifically, when it is detected that a complete path reconstruction for the target vehicle cannot be performed or the path reconstruction result is unreasonable, it is determined that the path reconstruction for the target vehicle has failed. The toll is calculated based on the path with the lowest toll standard among all the drivable paths of the target vehicle on the highway from the entrance toll station to the exit toll station. The toll calculation result of the target vehicle is determined and fed back to the toll terminal of the road network node.

[0085] It is understandable that there are multiple routes between the entrance toll station and the exit toll station for the target vehicle, and the toll standards for each route are different. The route with the lowest toll standard among the multiple routes is selected for calculation, rather than the route with the shortest travel distance.

[0086] Example 2

[0087] Figure 2This is a flowchart of a highway toll processing method provided in Embodiment 2 of the present invention. This embodiment is a further optimization of any of the above embodiments and can be applied to the situation of reconstructing the route of vehicles traveling on highways and calculating tolls online. This method can be executed by a highway toll processing system, which can be implemented in hardware and / or software.

[0088] like Figure 2 As shown, the method includes:

[0089] S201. After receiving the billing request from the toll collection terminal of the road network node relative to the target vehicle, send a request to the data center to obtain the driving information of the target vehicle.

[0090] S202, Receive the vehicle positioning information fed back by the data center and the route road network node information fed back after verification according to the request verification rules.

[0091] S203. Based on the latitude and longitude coordinates of road network nodes in the road network model vector diagram, convert the vehicle positioning information into road network node conversion information.

[0092] In this embodiment, the latitude and longitude coordinates of a node can be understood as the latitude and longitude coordinates of a road network node in a highway determined under a unified coordinate system. The road network node conversion information can be understood as converting the driving positioning information obtained from BeiDou positioning into positioning information corresponding to the road network node information obtained by the gantry under the road network structure.

[0093] Specifically, by combining the latitude and longitude coordinates of road network nodes in the road network model vector map with the node positions determined by BeiDou positioning and the target vehicle positions, the driving positioning information of the target vehicle is converted in the road network model vector map, forming road network node conversion information based on the road network model vector map and corresponding to the information of the road network nodes it passes through.

[0094] S204. Merge road network node conversion information and path road network node information to obtain target road network node information.

[0095] In this embodiment, the target road network node information can be understood as the overall road network node information of each road network node corresponding to the target vehicle in the road network model vector diagram, including road network node transformation information and path road network node information.

[0096] Specifically, in the data platform, the road network node transformation information and the information of the road network nodes along the route are merged to obtain the overall road network node information corresponding to each road network node in the road network model vector diagram. The overall road network node information of each target vehicle can be used as the target road network node information.

[0097] It is understandable that the target road network node information includes the vehicle transaction records corresponding to each road network node. Vehicle transaction records can include various transaction information such as license plate records, gantry records, and billing records.

[0098] S205. Based on the target road network node information and the set path restoration strategy, restore the driving path of the target vehicle to obtain the driving restoration path.

[0099] Among them, the road network model vector diagram is pre-constructed based on the road network nodes in the highway road network.

[0100] In this embodiment, the set path restoration strategy can be understood as a pre-set reference strategy for restoring the path of the target vehicle.

[0101] Specifically, in the vector map of the road network model constructed based on the road network nodes in the highway road network, the target road network node information corresponding to each node of the target vehicle is compared with the pre-set path restoration strategy to determine whether the target road network node information called in the data platform is logically consistent, and the driving path of the target vehicle is restored based on the set path restoration strategy to obtain the driving restoration path of the target vehicle.

[0102] S206. Based on the driving route reconstruction and the preset rate benchmark, determine the driving tolling result of the target vehicle and feed it back to the road network node tolling terminal.

[0103] In this embodiment, after receiving a billing request from the road network node toll terminal relative to the target vehicle, a request to obtain the target vehicle's driving information is sent to the data platform; the driving location information fed back by the data platform and the route road network node information fed back after verification according to the request verification rules are received; based on the latitude and longitude coordinates of the road network nodes in the road network model vector map, the driving location information is converted into road network node conversion information; the road network node conversion information and the route road network node information are merged to obtain the target road network node information; based on the target road network node information and the set path restoration strategy, the driving path of the target vehicle is restored to obtain the driving restoration path; based on the driving restoration path and the preset rate benchmark, the driving billing result of the target vehicle is determined and fed back to the road network node toll terminal. The above technical solution converts the vehicle positioning information obtained by the BeiDou system and merges it with the road network node information obtained by the gantry system. This makes the target vehicle information corresponding to each road network node a unified target road network node information. By calling the target road network node information, the target vehicle information corresponding to each road network node can be directly obtained, effectively improving the efficiency of obtaining the corresponding information of each road network node in the data platform. Combined with a pre-set path restoration strategy, the path of the target vehicle is restored, reducing the possibility of path restoration failure and improving the efficiency, accuracy and success rate of path restoration, thus ensuring the accuracy and success rate of online billing.

[0104] As a first optional embodiment of the embodiments, based on the above embodiments, this first optional embodiment further optimizes and adds specific steps to restore the driving path of the target vehicle according to the target road network node information and the set path restoration strategy, and obtain the driving path restoration path, including:

[0105] a1) Arrange the road network nodes included in the target road network node information in order of vehicle passage time to form a road network node sequence.

[0106] In this embodiment, the road network node sequence can be understood as a sequence of road network nodes arranged according to the passing time order of the target vehicles.

[0107] Specifically, the target road network node information includes the vehicle transaction records corresponding to each road network node. The system queries the target vehicle to determine the time when the target vehicle passes through each road network node. The road network nodes passed by the target vehicle are arranged in order of their passing times to form a road network node sequence.

[0108] b1) For two adjacent road network nodes in the road network node sequence, the two road network nodes are determined according to the target road network node information of the two road network nodes and the set road network node determination rules.

[0109] In this embodiment, the established road network node determination rule can be understood as a rule used to determine whether there are any abnormalities in the road network node sequence and to identify road network nodes that have abnormalities.

[0110] Specifically, for two adjacent road network nodes in the road network node sequence, the target road network node information of the two road network nodes is compared to determine whether the comparison result is logical. Combined with the set road network node judgment rules, a judgment is made to determine whether there is an anomaly in the road network node sequence. If there is an anomaly, the abnormal road network node is identified.

[0111] For example, the road network node sequence formed by arranging the target vehicles according to their passing times is (node ​​A, node B, node C). Node A and node B are adjacent road network nodes, and node B and node C are adjacent road network nodes. Based on the target road network node information of the two adjacent road network nodes, the two road network nodes are determined according to the set road network node determination rules.

[0112] It is understandable that, during the determination process based on the road network node determination rules, auxiliary determination can be made by adding supplementary points between two adjacent road network nodes.

[0113] c1) Based on the judgment results, identify the missing and incorrectly marked road network nodes in the target vehicle's journey, and perform missing node replacement and incorrect node removal processing.

[0114] In this embodiment, a missing road network node can be understood as a road network node through which a target vehicle passed, but which was not marked or recorded. A mismarked road network node can be understood as a road network node through which a target vehicle did not pass, but which was marked or recorded.

[0115] Specifically, if the determination result indicates an anomaly in the road network node sequence, the abnormal road network nodes are identified as either missing or mislabeled. If the abnormal road network node is a missing node, the road network nodes that vehicles passed through but were not marked are supplemented. By combining the road network model vector map with shortest path search, the optimal traversal of each road network node in the road network model vector map is achieved, and the road network node to be supplemented is determined. This is used for local matching and repair of missing labels, and the supplemented node is added to the appropriate position in the road network node sequence. If the abnormal road network node is a mislabeled node, the road network nodes that vehicles did not pass through but were marked are removed, and the mislabeled road network node is removed from the road network node sequence.

[0116] For example, based on the road network nodes present in the road network node sequence (node ​​A, node D, node N), combined with the road network model vector map, and based on the set road network node determination rules, a determination is made. The determination results show that the missing road network nodes include nodes B and C, and the incorrectly labeled road network node is node N. The missing road network nodes A and B are added to the road network node sequence, and the incorrectly labeled road network node N is removed from the road network node sequence. The processed road network node sequence is then determined to be (node ​​A, node B, node C, node D).

[0117] Understandably, in the process of filling in missing road network nodes, the location of the replacement point can be determined by the time interval between the passage times of the original two adjacent road network nodes. For example, if the time difference between node A and node D is one hour, then node B and node C are determined as replacement points between node A and node D. The time difference between node A and node D is evenly divided, and the time interval between each pair of adjacent road network nodes of nodes A, B, C, and D is determined to be twenty minutes. The passage time corresponding to each replacement point based on this time interval is then determined.

[0118] Understandably, for the same road network node sequence, missing node replacement and incorrect node removal can be performed simultaneously, and the road network node sequence can be updated.

[0119] d1) When the path generation conditions are met, the driving restoration path of the target vehicle is constructed based on the target road network nodes obtained after processing.

[0120] In this embodiment, the path generation conditions can be understood as conditions used to determine whether a path can be reconstructed based on the road network node sequence. The target road network node can be understood as the valid road network node existing in the road network node sequence after processing for missing and mislabeled road network nodes.

[0121] Specifically, after determining the road network node sequence according to the road network node determination rules, if it is determined that there are no abnormalities in the road network node sequence, or if the road network nodes with abnormalities have been processed accordingly, and the processed road network node sequence conforms to normal logic, then the path generation conditions are met. Based on each target road network node in the road network node sequence after the completion of missing marker replacement and mislabeling removal processing, the driving reconstruction path of the target vehicle is constructed.

[0122] Furthermore, Figure 3 This is a schematic diagram illustrating the determination rules for road network nodes involved in a highway tolling processing method provided in Embodiment 2 of the present invention. Figure 3 As shown, based on the target road network node information, the two road network nodes are determined according to the set road network node determination rules, including:

[0123] b11) Obtain the node codes and vehicle intervals of two road network nodes from the target road network node information.

[0124] In this embodiment, the node code can be understood as a code used to identify and distinguish each road network node, and the node code of each road network node is unique. It can also be understood as the time difference between the passing times of target vehicles recorded between two adjacent road network nodes.

[0125] Specifically, the target road network node information of two adjacent road network nodes in the road network node sequence is obtained, and the node codes and vehicle intervals of the two road network nodes in the target road network node information are obtained.

[0126] b12) Based on the road network model vector map, node codes, and vehicle interval time, the following determination is made:

[0127] b121) Determine whether the two network nodes are connected.

[0128] Specifically, it determines whether two adjacent road network nodes are connected, that is, whether the target vehicle can travel directly from the previous road network node to the next road network node.

[0129] b122) If connected, determine whether the two network nodes are encoded repeatedly; otherwise, determine that the two network nodes are disconnected abnormal nodes.

[0130] Specifically, if two adjacent road network nodes are connected, it means there is no abnormality in the judgment of whether the two road network nodes have connectivity, and the judgment is made on whether the node codes of the two adjacent road network nodes are duplicated; if two adjacent road network nodes are not connected, it means that the two adjacent road network nodes do not have connectivity, the target vehicle cannot travel directly from the previous road network node to the next road network node, and it is determined that the two adjacent road network nodes are abnormal nodes that are not connected to each other.

[0131] For example, Figure 4A This is an example illustration of disconnected abnormal nodes involved in a highway tolling processing method provided in Embodiment 2 of the present invention. Figure 4A As shown, the road network node sequence is (node ​​A, node B, node C, node D). Node A is connected to node B, node B is not connected to node C, and node C is not connected to node D. Therefore, it can be determined that node B and node C are mutually disconnected abnormal nodes, and node C and node D are mutually disconnected abnormal nodes. Thus, node C can be identified as a disconnected abnormal node.

[0132] (b123) If they are not duplicates, determine whether the two network nodes are reverse nodes; otherwise, determine that the two network nodes are duplicate abnormal nodes.

[0133] Specifically, each road network node has its corresponding node code. If the node codes of two adjacent road network nodes are not repeated, it means that there is no abnormality in judging whether the node codes of the two road network nodes are repeated. The judgment is made on whether the two adjacent road network nodes are reverse nodes. If the node codes of two adjacent road network nodes are repeated, it is determined that the two adjacent road network nodes are abnormal nodes with repeated nodes.

[0134] (b124) If it is not a reverse node, then jump to determine the time rationality of the two network nodes; otherwise, determine whether the two network nodes are connected in the opposite direction.

[0135] Specifically, on highways, there are two driving routes in opposite directions. Correspondingly, there are often two reverse road network nodes at the same location. If two adjacent road network nodes in the road network node sequence are not reverse nodes, it means there is no abnormality in the judgment of whether two road network nodes are reverse nodes, and the temporal rationality of the two road network nodes is judged; if two adjacent road network nodes are reverse nodes, it is determined whether the two adjacent road network nodes are connected in opposite directions.

[0136] b125) If the two network nodes are connected in reverse, then determine that the two network nodes are abnormal nodes in reverse connection; otherwise, determine that the two network nodes are abnormal nodes in reverse connection to each other, and determine the shortest path information of the two network nodes to update the abnormal nodes in reverse connection.

[0137] Specifically, if two adjacent road network nodes in the road network node sequence are connected in reverse, it indicates that the two road network nodes are reverse-connected abnormal nodes; if two adjacent road network nodes are not connected in reverse, it is determined that the two road network nodes are reverse abnormal nodes at the same location and are opposite to each other. The shortest path information between the two road network nodes and other adjacent road network nodes is determined. Based on the shortest path information, the reverse abnormal nodes in the target vehicle's driving route are determined, and the reverse nodes corresponding to the abnormal nodes are updated to the path node sequence or the reverse abnormal nodes are removed from the road network node sequence.

[0138] For example, Figure 4B This is an example illustration of a reverse abnormal node involved in a highway toll processing method provided in Embodiment 2 of the present invention. Figure 4B As shown, if the road network node sequence is (node ​​A1, node B1, node B2, node C1), it is determined that road network node A1 and road network node B2 are reverse connected, road network node B1 and road network node B2 are not reverse connected, and road network node B2 and road network node C1 are reverse connected. Therefore, road network node A1 and road network node B2 are mutually reverse connected abnormal nodes, road network node B1 and road network node B2 are mutually reverse connected abnormal nodes, and road network node B2 and road network node C1 are mutually reverse connected abnormal nodes. Road network node B2 is determined to be the reverse connected abnormal node in this road network node sequence, and the road network node sequence is updated to (node ​​A1, node B1, node C1).

[0139] b126) Determine the time rationality of the two network nodes.

[0140] If two adjacent network nodes are not opposite nodes, the reasonableness of the time between the two network nodes is judged by combining the distance between the two network nodes.

[0141] (b127) If reasonable, the result is that the two network nodes are normal nodes; otherwise, the result is that the two network nodes are unreachable abnormal nodes.

[0142] Specifically, if the time interval between two road network nodes is reasonable, then the two adjacent road network nodes are determined to be normal nodes; if the time interval between two road network nodes is unreasonable, then the two adjacent road network nodes are determined to be unreachable abnormal nodes.

[0143] For example, if the distance between two adjacent road network nodes is 200km and the time interval between passing vehicles between the two adjacent road network nodes is 30min, and the vehicle speed is 400km / h, this is obviously unreasonable. Therefore, the two adjacent road network nodes are determined to be unreachable abnormal nodes.

[0144] In this embodiment, the determination of adjacent road network nodes may also include a loop restriction judgment. The road network nodes in the road network node sequence and the complementary points between adjacent road network nodes are traversed cyclically in the road network model vector diagram to determine whether there is a connectivity relationship between the complementary points and to determine whether there is a repeated loop. If there is a repeated loop, the two adjacent road network nodes corresponding to the complementary point with the loop are determined to be loop abnormal nodes.

[0145] For example, Figure 4C This is an example illustration of the abnormal detour node involved in a highway tolling processing method provided in Embodiment 2 of the present invention. Figure 4C As shown, intermediate supplementary points 1, 2, 3, 4, and 5 are added between node A and node B for auxiliary judgment. The system determines whether there is a loop between these intermediate supplementary points, specifically whether intermediate supplementary point 5 can connect to 1, 2, 3, and 4; whether intermediate supplementary point 4 can connect to 1, 2, and 3; whether intermediate supplementary point 3 can connect to 1 and 2; and whether intermediate supplementary point 2 can connect to 1. If a loop is found, the two adjacent road network nodes A and B corresponding to the supplementary point with the loop are identified as loop-abnormal nodes.

[0146] It is understandable that the type of abnormal node is determined based on the different driving conditions of the target vehicle on the highway, and this embodiment does not set any limitation on this.

[0147] In this embodiment, the road network node determination rules may also include rules for comparing preceding and following nodes, rules for determining multiple nodes, and rules for returning to the original state if the determination fails.

[0148] The comparison rule between preceding and following nodes can be understood as the rule for judging the next node and the node before it when there are many defects between two adjacent nodes. The multi-node judgment rule can be understood as the rule for judging the previous node and the node after it when two adjacent nodes are not connected. The backtracking rule for unsuccessful judgment can be understood as the rule for backtracking to the previous node and continuing the judgment when the judgment between two adjacent nodes fails.

[0149] For example, Figure 4D This is an example illustration of the comparison rules between preceding and following nodes involved in a highway toll processing method provided in Embodiment 2 of the present invention. Figure 4D As shown, the road network node sequence (node ​​A, node B, node C, node D, node E) is used for judgment based on the position of each road network node in the road network model vector map. When performing rule-based judgment on nodes C and D, there are several shortcomings. Therefore, rule-based judgment is performed on nodes B and D. If the path distance between nodes B and D is less than the path distance between nodes C and D, then node C is determined to be an abnormal node.

[0150] For example, Figure 4E This is an example illustration of the multi-node determination rules involved in a highway toll processing method provided in Embodiment 2 of the present invention. Figure 4E As shown, the road network node sequence (node ​​A, node B, node C, node D) is used to determine the location of each node in the road network model vector diagram. When performing rule-based judgment on nodes B and C, it is found that the two nodes are not connected. When performing rule-based judgment on nodes B and D, if nodes B and D are connected, node C is determined to be an abnormal node.

[0151] For example, Figure 4F This is another example illustration of the multi-node determination rules involved in a highway toll processing method provided in Embodiment 2 of the present invention. Figure 4F As shown, the road network node sequence (node ​​A, node B, node C, node D) is defined, where node A is the first road network node appearing in the target vehicle's travel path and is not an entrance toll station. The determination is made based on the position of each road network node in the road network model vector diagram. If, when performing rule checks on nodes A and B, it is found that the two nodes are not connected, and when performing rule checks on nodes A and C, they are also found to be not connected, while nodes B and C are connected, then node A is determined to be an abnormal node.

[0152] For example, Figure 4G This is an example illustration of the unsuccessful refund rule involved in a highway toll processing method provided in Embodiment 2 of the present invention. Figure 4GAs shown, the road network node sequence (node ​​A, node B, node C, node D, node E) is used to determine the connection based on the position of each node in the road network model vector diagram. If node C and node D are found to be disconnected when performing rule checks, and node C and node E are also found to be disconnected, the process reverts to node B. If node B and node D are connected, then node C is determined to be an abnormal node.

[0153] In this embodiment, the road network node determination rules may also include path splitting rules, which are pre-determined rules. If the time span or distance span between two adjacent road network nodes exceeds a preset threshold, and the two road network nodes are disconnected abnormal nodes, the road network node sequence can be split into multiple road network node sequences, and further other rule judgments and road network node processing can be performed.

[0154] For example, Figure 4H This is an example illustration of the path splitting rules involved in a highway tolling processing method provided in Embodiment 2 of the present invention. Figure 4H As shown, the road network node sequence (nodes A, B, C, D, E, and F) is determined based on the position of each node in the road network model vector diagram. When performing rule judgment on nodes C and D, it is determined that the time interval between vehicle passes between the two nodes is large and the two nodes are not connected or adjacent. In this case, it can be determined that this situation is due to two vehicle passes caused by certain scenarios. A segmentation is performed at the position of nodes C and D, so that nodes A, B, and C form a new road network node sequence, and nodes D, E, and F form a new road network node sequence. Rule judgments are then performed on each of these segments.

[0155] Furthermore, based on the judgment results, the missing and incorrectly marked road network nodes in the target vehicle's journey are identified, and missing node replacement and incorrect node removal are performed, including:

[0156] c11) Summarize the judgment results of each pair of adjacent road network nodes.

[0157] Specifically, the judgment results determined after judging each pair of adjacent road network nodes according to the set road network node judgment rules are summarized.

[0158] c12) Based on the disconnected and unreachable abnormal nodes in the judgment results, determine the missing road network nodes in the target vehicle's journey.

[0159] Specifically, the disconnected abnormal nodes and unreachable abnormal nodes determined based on the judgment results of two adjacent road network nodes are identified as the missing road network nodes of the target vehicle while it is traveling on the highway.

[0160] It is understood that the missing road network nodes are not limited to the two types of abnormal nodes mentioned above, but can be of many different types. This embodiment does not limit this.

[0161] It is understandable that the number of missing road network nodes between two adjacent road network nodes does not exceed a preset threshold. This preset threshold is determined based on the success rate of road network nodes in recording target vehicle identification. If the success rate is high, the threshold for missing road network nodes is low; conversely, if the success rate is low, the threshold is high.

[0162] c13) Based on the judgment results of duplicate abnormal nodes, mutually reversed abnormal nodes, and reverse connected abnormal nodes, determine the mislabeled road network nodes in the target vehicle's journey.

[0163] Specifically, duplicate abnormal nodes, mutually opposite abnormal nodes, and reverse connectivity abnormal nodes determined based on the judgment results of two adjacent road network nodes are identified as mislabeled road network nodes of the target vehicle while it is traveling on the highway.

[0164] It is understood that mislabeled road network nodes are not limited to the above-mentioned types of abnormal nodes, but can be of many different types. This embodiment does not limit this.

[0165] c14) The missing road network nodes are filled in according to the missing road network nodes and the filling rules, and the incorrectly labeled road network nodes are removed according to the incorrectly labeled road network nodes and the incorrectly labeled removal rules.

[0166] In this embodiment, the omission filling rule can be understood as the rule of adding omission points before and after the missing road network node in the road network node sequence. The mislabeling removal rule can be understood as the rule of removing mislabeled road network nodes from the road network node sequence.

[0167] For example, Figure 5A This is an example illustration of the misbilling removal process involved in a highway tolling processing method provided in Embodiment 2 of the present invention. Figure 5A As shown, the road network node sequence (node ​​A1, node A2, node B1, node B2, node C1) is defined. Nodes A1 and A2 are mutually reverse anomalous nodes, nodes B1 and B2 are mutually reverse anomalous nodes, and nodes B2 and C1 are mutually reverse connectivity anomalous nodes. Based on the positions of each road network node in the road network model vector diagram, nodes A2 and B2 are removed.

[0168] For example, Figure 5B This is another example illustration of the misbilling removal process involved in a highway tolling processing method provided in Embodiment 2 of the present invention. Figure 5B As shown, the road network node sequence (node ​​A, node B, node C, node D) is determined based on the position of each road network node in the road network model vector diagram. Specifically, when performing rule-based judgment on nodes B and C, they are determined to be disconnected abnormal nodes. When performing rule-based judgment on nodes C and D, they are determined to be disconnected abnormal nodes, while nodes B and D are connected. Therefore, node C is determined to be a disconnected abnormal node, i.e., a mislabeled road network node, and is thus removed.

[0169] For example, Figure 5C This is another example illustration of the misbilling removal process involved in a highway tolling processing method provided in Embodiment 2 of the present invention. Figure 5C As shown, nodes C1 and C2 are two road network nodes corresponding to an interchange. The road network node sequence (node ​​A1, node B1, node C2, node C1, node D1, node E1) is used to determine the connection between each node and its position in the road network model vector diagram. Rule-based judgment is applied to nodes B1 and C2, confirming they are connected. Then, rule-based judgment is applied to nodes C2 and C1, confirming they are not connected. Rule-based judgment is then applied to nodes C2 with nodes D1 and E1 respectively, confirming they are not connected to either node D1 or E1. Therefore, node C2 is determined to be a mislabeled road network node and is removed from the list.

[0170] For example, Figure 5D This is another example illustration of the misbilling removal process involved in a highway tolling processing method provided in Embodiment 2 of the present invention. Figure 5D As shown, a U-turn can be made from node B2 to node B1, and from node C1 to node C2, making this road segment a U-shaped road. The road network node sequence (node ​​A1, node B1, node C1, node C2, node D1) is used to determine the location of each node in the road network model vector diagram. Rule judgment is applied to nodes C1 and C2, finding them to be connected. Then, rule judgment is applied to nodes C2 and D1, finding them to be connected but with many intermediate points. This triggers rule judgment on nodes C1 and D1. It is determined that node C1 is directly connected to node D1, and the travel distance is much shorter than the distance from node C2 to node D1. Therefore, node C2 is identified as a mislabeled road network node and is removed from the list.

[0171] c15) Obtain the sequence of road network nodes containing the processed target road network nodes.

[0172] Specifically, after processing the missing and mislabeled road network nodes according to the corresponding rules, the abnormal nodes in the road network node sequence are updated to obtain a processed and updated road network node sequence that is closer to the actual driving path of the target vehicle.

[0173] Furthermore, when the path generation conditions are met, the driving reconstruction path of the target vehicle is constructed based on the processed target road network nodes, including:

[0174] d11) After all road network nodes in the road network node sequence have been detected and node determination has been completed, it is determined that the path generation conditions are met.

[0175] In this embodiment, after judging all road network nodes in the road network node sequence, abnormal nodes are processed by either missing node replacement or incorrect node removal. After processing the abnormal nodes, if there are no more abnormal nodes in the road network node sequence, it is determined that the road network node sequence meets the path generation conditions.

[0176] d12) Obtain each target road network node included in the processed road network node sequence.

[0177] In this embodiment, it is determined that there are no abnormal nodes in the road network node sequence. Therefore, it can be determined that all road network nodes in the road network node sequence are normal nodes, i.e., target road network nodes. The target road network nodes included in the road network node sequence are obtained.

[0178] d13) Construct the driving restoration path of the target vehicle according to the passing time sequence of each target road network node.

[0179] In this embodiment, the driving path of the target vehicle is constructed based on all target road network nodes in the road network node sequence and the passing time order of the target vehicle at each target road network node, that is, the order of each target road network node in the path node sequence.

[0180] Example 3

[0181] Figure 6 This is a schematic diagram of the structure of a highway toll processing system provided in Embodiment 3 of the present invention. Figure 6 As shown, the system includes:

[0182] The request sending module 31 is used to send a request to the data platform to obtain the driving information of the target vehicle after receiving the billing request from the toll terminal of the road network node relative to the target vehicle.

[0183] The feedback information receiving module 32 is used to receive the driving positioning information fed back by the data platform and the route road network node information fed back after verification according to the request verification rules;

[0184] The path restoration module 33 is used to restore the driving path of the target vehicle based on the pre-constructed road network model vector map, the driving positioning information, and the information of the road network nodes along the route, so as to obtain the driving path restoration.

[0185] The billing result determination module 34 is used to determine the driving billing result of the target vehicle based on the driving restoration path and the preset rate benchmark, and feed it back to the road network node toll terminal.

[0186] This invention provides a highway toll processing system. The data stored in the data center combines vehicle positioning information provided by the BeiDou system and road network node information provided by the gantry system. Combining these two types of data effectively ensures the accuracy of the target vehicle's positioning results. By using the two types of information fed back by the data center, the driving path of the target vehicle is reconstructed, improving the accuracy of path reconstruction and increasing the success rate and accuracy of online toll calculation.

[0187] Optionally, the request verification rules include: verification of the vehicle's passage time sequence through road network nodes, verification of the vehicle's single passage at the toll station entrance, and verification of the information items contained in the information of the road network nodes.

[0188] The information version of the road network node information fed back by the data platform meets the set feedback conditions.

[0189] The road network node information includes the vehicle's transit time through the road network nodes and the node information of the road network nodes; the road network nodes include highway gantries and highway toll stations.

[0190] Optionally, the restore path acquisition module 33 includes:

[0191] The information conversion submodule is used to convert the driving positioning information into road network node conversion information based on the latitude and longitude coordinates of the road network nodes in the road network model vector map.

[0192] The information merging submodule is used to merge the road network node conversion information and the information of the road network nodes along the route to obtain the target road network node information;

[0193] The route restoration submodule is used to restore the driving path of the target vehicle based on the target road network node information and the set route restoration strategy, so as to obtain the driving restoration path.

[0194] The road network model vector diagram is pre-constructed based on the road network nodes in the highway network.

[0195] Optional, the path restoration submodule includes:

[0196] A node sequence forming unit is used to arrange each road network node included in the target road network node information according to the vehicle passage time order to form a road network node sequence;

[0197] The node information determination unit is used to determine the two adjacent road network nodes in the road network node sequence according to the target road network node information of the two road network nodes and the set road network node determination rules.

[0198] The missing and mislabeled processing unit is used to determine the missing and mislabeled road network nodes in the target vehicle's journey based on the judgment result, and to perform missing point supplementation processing and mislabeled point removal processing.

[0199] The path reconstruction unit is used to construct the driving reconstruction path of the target vehicle based on each target road network node obtained after processing, when the path generation conditions are met.

[0200] Optionally, the node information determination unit is specifically applied to:

[0201] Obtain the node codes and vehicle intervals of two road network nodes from the target road network node information;

[0202] Based on the road network model vector map, the node codes, and the vehicle interval time, the following determination is made:

[0203] Determine whether the two network nodes are connected;

[0204] If the two network nodes are connected, it is determined whether they are encoded repeatedly; otherwise, the result is that the two network nodes are disconnected abnormal nodes.

[0205] If they are not duplicates, then determine whether the two network nodes are reverse nodes; otherwise, the result is that the two network nodes are duplicate abnormal nodes.

[0206] If it is not a reverse node, then jump to determine the time rationality of the two network nodes; otherwise, determine whether the two network nodes are connected in the opposite direction.

[0207] If the two network nodes are connected in reverse, then the two network nodes are determined to be abnormal nodes in reverse connection; otherwise, the two network nodes are determined to be abnormal nodes in reverse connection, and the shortest path information of the two network nodes is determined to update the abnormal nodes in reverse connection.

[0208] Determine the reasonableness of the timing of the two network nodes;

[0209] If the result is reasonable, the two network nodes are determined to be normal nodes; otherwise, the two network nodes are determined to be unreachable abnormal nodes.

[0210] Optionally, the omission and mislabeling processing unit is specifically applied to:

[0211] Summarize the determination results of each pair of adjacent road network nodes;

[0212] Based on the disconnected and unreachable abnormal nodes in the judgment results, the missing road network nodes in the target vehicle's journey are determined;

[0213] Based on the judgment results of duplicate abnormal nodes, mutually opposite abnormal nodes, and reverse connected abnormal nodes, the mislabeled road network nodes in the target vehicle's driving are determined.

[0214] The missing road network nodes and the missing node filling rules are used to fill in the missing road network nodes in the sequence, and the mislabeled road network nodes and the mislabeling removal rules are used to remove the mislabeled road network nodes in the sequence.

[0215] Obtain the sequence of road network nodes that contains the processed target road network nodes.

[0216] Optionally, the restore path building unit is specifically applied to:

[0217] After detecting that all road network nodes in the road network node sequence have completed node determination, it is determined that the path generation conditions are met;

[0218] Obtain each target road network node included in the processed road network node sequence;

[0219] The driving path of the target vehicle is constructed according to the passing time sequence of each target road network node.

[0220] Optionally, the system also includes a minimum fee billing unit, which, when the path reconstruction of the target vehicle fails, determines the driving fee result of the target vehicle according to the set minimum fee standard and feeds it back to the road network node toll terminal.

[0221] The highway toll processing system provided in this embodiment of the invention can execute the highway toll processing method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0222] Example 4

[0223] Figure 7 A schematic diagram of an electronic device 40 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0224] like Figure 7 As shown, the electronic device 40 includes at least one processor 41 and a memory, such as a read-only memory (ROM) 42 or a random access memory (RAM) 43, communicatively connected to the at least one processor 41. The memory stores computer programs executable by the at least one processor. The processor 41 can perform various appropriate actions and processes based on the computer program stored in the ROM 42 or loaded into the RAM 43 from storage unit 48. The RAM 43 may also store various programs and data required for the operation of the electronic device 40. The processor 41, ROM 42, and RAM 43 are interconnected via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0225] Multiple components in electronic device 40 are connected to I / O interface 45, including: input unit 46, such as keyboard, mouse, etc.; output unit 47, such as various types of monitors, speakers, etc.; storage unit 48, such as disk, optical disk, etc.; and communication unit 49, such as network card, modem, wireless transceiver, etc. Communication unit 49 allows electronic device 40 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0226] Processor 41 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 41 performs the various methods and processes described above, such as highway toll processing methods.

[0227] In some embodiments, the highway toll processing method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program may be loaded into and / or installed on electronic device 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the highway toll processing method described above may be performed. Alternatively, in other embodiments, processor 41 may be configured to perform the highway toll processing method by any other suitable means (e.g., by means of firmware).

[0228] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0229] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0230] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0231] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0232] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0233] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0234] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0235] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for processing highway tolls, characterized in that, include: After receiving the billing request from the toll terminal at the road network node relative to the target vehicle, a request to obtain the driving information of the target vehicle is sent to the data platform. Receive the vehicle positioning information fed back by the data platform and the route road network node information fed back after verification according to the request verification rules; Based on the pre-constructed road network model vector map, the vehicle positioning information, and the information of the road network nodes along the route, the driving path of the target vehicle is reconstructed to obtain the reconstructed driving path. Based on the driving recovery path and the preset rate benchmark, the driving tolling result of the target vehicle is determined and fed back to the tolling terminal of the road network node. The step of reconstructing the driving path of the target vehicle based on the pre-constructed road network model vector map, the vehicle positioning information, and the information of the road network nodes along the route, to obtain the reconstructed driving path, includes: Based on the latitude and longitude coordinates of the road network nodes in the road network model vector map, the driving positioning information is converted into road network node conversion information; By merging the road network node transformation information and the path road network node information, the target road network node information is obtained; Based on the target road network node information and the set path restoration strategy, the driving path of the target vehicle is restored to obtain the driving restoration path. The road network model vector diagram is pre-constructed based on the road network nodes in the highway network, and the driving positioning information is the positioning information obtained by locating the target vehicle based on the Beidou system and stored in the data platform. The step of restoring the driving path of the target vehicle based on the target road network node information and a set path restoration strategy to obtain the restored driving path includes: The road network nodes included in the target road network node information are arranged in order of vehicle passage time to form a road network node sequence; For two adjacent road network nodes in the road network node sequence, the two road network nodes are determined according to the target road network node information of the two road network nodes and the set road network node determination rules. Based on the judgment results, the missing and incorrectly marked road network nodes in the target vehicle's driving process are determined, and the missing nodes are filled and the incorrectly marked nodes are removed. When the path generation conditions are met, the driving restoration path of the target vehicle is constructed based on each target road network node obtained after processing. The step of determining the two road network nodes according to the set road network node determination rules based on the target road network node information includes: Obtain the node codes and vehicle intervals of two road network nodes from the target road network node information; Based on the road network model vector map, the node codes, and the vehicle interval time, the following determination is made: Determine whether the two network nodes are connected; If the two network nodes are connected, it is determined whether they are encoded repeatedly; otherwise, the result is that the two network nodes are disconnected abnormal nodes. If they are not duplicates, then determine whether the two network nodes are reverse nodes; otherwise, the result is that the two network nodes are duplicate abnormal nodes. If it is not a reverse node, then jump to determine the time rationality of the two network nodes; otherwise, determine whether the two network nodes are connected in the opposite direction. If the two network nodes are connected in reverse, then the two network nodes are determined to be abnormal nodes in reverse connection; otherwise, the two network nodes are determined to be abnormal nodes in reverse connection, and the shortest path information of the two network nodes is determined to update the abnormal nodes in reverse connection. Determine the reasonableness of the timing of the two network nodes; If reasonable, the two network nodes are determined to be normal nodes; otherwise, the two network nodes are determined to be unreachable abnormal nodes. The step of determining the missing and incorrectly labeled road network nodes in the target vehicle's journey based on the judgment result, and performing missing node replacement and incorrect node removal processing, includes: Summarize the determination results of each pair of adjacent road network nodes; Based on the disconnected and unreachable abnormal nodes in the judgment results, the missing road network nodes in the target vehicle's journey are determined; Based on the judgment results of duplicate abnormal nodes, mutually opposite abnormal nodes, and reverse connected abnormal nodes, the mislabeled road network nodes in the target vehicle's driving are determined. The missing road network nodes and the missing node filling rules are used to fill in the missing road network nodes in the sequence, and the mislabeled road network nodes and the mislabeling removal rules are used to remove the mislabeled road network nodes in the sequence. Obtain the sequence of road network nodes that contains the processed target road network nodes.

2. The method according to claim 1, characterized in that, The request verification rules include: verification of the vehicle's passage time sequence through road network nodes, verification of a vehicle's single passage at the toll station entrance, and verification of the information items contained in the information of the road network nodes. The information version of the road network node information fed back by the data platform meets the set feedback conditions. The road network node information includes the transit time of the target vehicle through the road network nodes and the node information of the road network nodes; the road network nodes include highway gantries and highway toll stations.

3. The method according to claim 1, characterized in that, When the path generation conditions are met, the driving restoration path of the target vehicle is constructed based on the processed target road network nodes, including: After detecting that all road network nodes in the road network node sequence have completed node determination, it is determined that the path generation conditions are met; Obtain each target road network node included in the processed road network node sequence; The driving path of the target vehicle is constructed according to the passing time sequence of each target road network node.

4. The method according to any one of claims 1-3, characterized in that, Also includes: When the path reconstruction of the target vehicle fails, the toll calculation result of the target vehicle is determined according to the set minimum fee standard and fed back to the toll terminal of the road network node.

5. A highway toll processing system, characterized in that, The system includes: The request sending module is used to send a request to the data platform to obtain the driving information of the target vehicle after receiving the billing request from the toll terminal of the road network node relative to the target vehicle. The feedback information receiving module is used to receive the vehicle positioning information fed back by the data platform and the route road network node information fed back after verification according to the request verification rules; The path restoration module is used to restore the driving path of the target vehicle based on the pre-constructed road network model vector map, the vehicle positioning information, and the information of the road network nodes along the route, and obtain the restored driving path. The billing result determination module is used to determine the driving billing result of the target vehicle based on the driving restoration path and the preset rate benchmark, and feed it back to the road network node toll terminal. The restoration path acquisition module includes: The information conversion submodule is used to convert the driving positioning information into road network node conversion information based on the latitude and longitude coordinates of the road network nodes in the road network model vector map. The information merging submodule is used to merge the road network node conversion information and the information of the road network nodes along the route to obtain the target road network node information; The route restoration submodule is used to restore the driving path of the target vehicle based on the target road network node information and the set route restoration strategy, so as to obtain the driving restoration path. The road network model vector diagram is pre-constructed based on the road network nodes in the highway network; The path restoration submodule includes: A node sequence forming unit is used to arrange each road network node included in the target road network node information according to the vehicle passage time order to form a road network node sequence; The node information determination unit is used to determine the two adjacent road network nodes in the road network node sequence according to the target road network node information of the two road network nodes and the set road network node determination rules. The missing and mislabeled processing unit is used to determine the missing and mislabeled road network nodes in the target vehicle's journey based on the judgment result, and to perform missing point supplementation processing and mislabeled point removal processing. The path reconstruction unit is used to construct the driving reconstruction path of the target vehicle based on each target road network node obtained after processing, when the path generation conditions are met. Specifically, the node information determination unit is applied to: Obtain the node codes and vehicle intervals of two road network nodes from the target road network node information; Based on the road network model vector map, the node codes, and the vehicle interval time, the following determination is made: Determine whether the two network nodes are connected; If the two network nodes are connected, it is determined whether they are encoded repeatedly; otherwise, the result is that the two network nodes are disconnected abnormal nodes. If they are not duplicates, then determine whether the two network nodes are reverse nodes; otherwise, the result is that the two network nodes are duplicate abnormal nodes. If it is not a reverse node, then jump to determine the time rationality of the two network nodes; otherwise, determine whether the two network nodes are connected in the opposite direction. If the two network nodes are connected in reverse, then the two network nodes are determined to be abnormal nodes in reverse connection; otherwise, the two network nodes are determined to be abnormal nodes in reverse connection, and the shortest path information of the two network nodes is determined to update the abnormal nodes in reverse connection. Determine the reasonableness of the timing of the two network nodes; If reasonable, the two network nodes are determined to be normal nodes; otherwise, the two network nodes are determined to be unreachable abnormal nodes. Specifically, the omission and mislabeling processing unit is used for: Summarize the determination results of each pair of adjacent road network nodes; Based on the disconnected and unreachable abnormal nodes in the judgment results, the missing road network nodes in the target vehicle's journey are determined; Based on the judgment results of duplicate abnormal nodes, mutually opposite abnormal nodes, and reverse connected abnormal nodes, the mislabeled road network nodes in the target vehicle's driving are determined. The missing road network nodes and the missing node filling rules are used to fill in the missing road network nodes in the sequence, and the mislabeled road network nodes and the mislabeling removal rules are used to remove the mislabeled road network nodes in the sequence. Obtain the sequence of road network nodes that contains the processed target road network nodes.

6. An electronic device, characterized in that, As an execution device in the system of claim 5, it includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform a highway toll processing method according to any one of claims 1-4.

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