A path fitting method and a geographic information visualization system that loads the method

Abstract

This invention provides a path fitting method and a geographic information visualization system incorporating this method. During online billing, the Dijkstra algorithm combined with a multi-validation model is used to verify and optimize vehicle on-the-go data and nodes in the fitted path, removing gantry systems with incorrect traffic relationships or abnormal traffic conditions to ensure that the final fitted vehicle travel path more closely reflects reality. Simultaneously, by establishing a comprehensive multi-layered data and business management system, the accuracy of complex business operations is guaranteed, while reducing the system's operational load.

Description

Technical Field

[0001] This invention relates to the field of vehicle traffic management technology, and more particularly to a path fitting method and a geographic information visualization system loaded with the method. Background Technology

[0002] Existing vehicle access management systems meet the needs of vehicle toll transactions and traffic management by providing various services. These systems display and provide diverse service information during user interactions. These services are client-side and management-side oriented, with complex and diverse content, massive data volumes, and problems such as high error rates and large operational resource requirements. For example, toll roads primarily use media-based billing. However, when media billing fails, online billing is initiated at the exit. The success rate and accuracy of online billing path fitting are crucial to the accuracy of online billing. Currently, the online billing path fitting algorithm is Dijkstra's algorithm, a classic path fitting algorithm. Dijkstra's algorithm follows a greedy approach, selecting a locally optimal solution at each step in the hope of generating an optimal solution. However, during path fitting, Dijkstra's algorithm may result in discrepancies between the fitted path and the user's actual travel path. Furthermore, in visualization, the number of links (the set of point information that makes up the infrastructure on the map) in the geographic information layer is enormous, consuming significant storage resources and resulting in slow layer loading.

[0003] Therefore, there is an urgent need for a new path fitting scheme to meet the billing requirements of the vehicle access management system, as well as a new vehicle access management system to provide efficient and systematic management and display solutions for complex business operations. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a path fitting method and a geographic information visualization system loaded with the method, so as to eliminate or improve the problems in the prior art where the toll fees do not match the actual costs due to inaccurate path fitting schemes, and the high error rate and high system load due to the complexity and diversity of business content in the vehicle traffic management system.

[0005] On one hand, the present invention provides a path fitting method for a multi-validation model, the method being executed on a trading platform, the method comprising the following steps:

[0006] Acquire the on-the-way data of the target vehicle collected by the gantry and / or the entry / exit toll station, wherein the on-the-way data includes the identification information of the gantry and / or the entry / exit toll station that sensed the target vehicle and the timestamp corresponding to the time of sensing;

[0007] Obtain a traffic directed graph within the target block area. The nodes in the traffic directed graph are used to mark the identification information of the gantries and / or the entry and exit toll stations within the target block area. The edges in the traffic directed graph are used to mark the connection relationship of the gantries and / or the entry and exit toll stations within the target block area. The toll amount between the gantries and / or the entry and exit toll stations is used as the weight of the edge.

[0008] Based on the Dijkstra algorithm, the actual driving path of the target vehicle is fitted to minimize the total cost. During the path fitting process, nodes with empty timestamps or reversed travel order in the on-the-way data are removed by verifying the timestamps. At the same time, nodes corresponding to speeding gantries, nodes corresponding to duplicate gantries, nodes corresponding to gantries with incorrect connectivity, nodes corresponding to gantries with an error rate higher than a first set value, and nodes with fewer than a second set value of adjacent gantries are removed.

[0009] In some embodiments, the path fitting method of the multiple verification model, removing nodes corresponding to overspeed gantry, includes:

[0010] Obtain the in-transit data and the fitted path, including the timestamp and gantry position corresponding to when the target vehicle passes through the gantry;

[0011] The speed of the target vehicle passing through the road segments between each passing gantry is calculated using the following formula:

[0012] v i,i+1 =s i,i+1 / (t i+1 -t i );

[0013] Among them, v i,i+1 Let s be the speed at which the target vehicle passes through the section between the i-th gantry and the (i+1)-th gantry. i,i+1 Let t be the distance between the i-th gantry and the (i+1)-th gantry. i Let t be the timestamp of the target vehicle passing through the i-th gantry. i+1 The timestamp of the target vehicle passing through the (i+1)th gantry;

[0014] Determine whether the speed of the target vehicle passing through the road segment between each passing gantry exceeds the set speed. If the speed of the segment exceeds the set speed, mark the gantry at the end of the corresponding road segment as the speeding gantry and remove it.

[0015] In some embodiments, the path fitting method of the multiple verification model, removing nodes corresponding to duplicate gantry frames, includes:

[0016] Obtain the in-transit data and the fitted path, as well as the identification information of the gantry through which the target vehicle passes;

[0017] Based on the identification information of each passing gantry, determine whether there are duplicate identification information between adjacent gantry. If so, mark the corresponding adjacent gantry as a duplicate gantry and remove it.

[0018] In some embodiments, the path fitting method of the multiple verification model, removing nodes corresponding to gantry nodes with connectivity errors, includes:

[0019] Based on the traffic directed graph, it is determined whether the passing gantries of the target vehicle in the in-transit data and the fitted path are connected. If all the passing gantries are connected, path fitting is performed.

[0020] When there are disconnected erroneous gantry, the erroneous gantry is removed, and it is determined whether the passing gantry within a set number of lines before and after the erroneous gantry is connected. If they are connected, path fitting is performed; if they are not connected, path fitting is indicated as failed.

[0021] In some embodiments, the path fitting method of the multiple verification model, removing nodes with a number of adjacent gantry frames less than a second preset value, includes:

[0022] Calculate the distance between the target vehicle and other gantries on the fitted path through the passing gantries, and mark the gantries with distances less than a set distance as adjacent gantries of the corresponding passing gantries;

[0023] The number of adjacent gantries for each passing gantry is counted. If the number of adjacent gantries for a passing gantry is less than the second set value, the corresponding passing gantry is marked as abnormal and removed.

[0024] On the other hand, the present invention provides a geographic information visualization system for networked toll roads, the system comprising an information perception layer, a resource service layer, a service middleware layer and an application system layer;

[0025] The information perception layer is used to acquire target vehicle on-the-way data, base map data, basic business data, and transaction data. The on-the-way data includes the identification information of the gantries and / or entry / exit toll stations that sensed the target vehicle, as well as the timestamp corresponding to the sensing event. The base map data includes a traffic directed graph, where nodes are used to mark the identification information of the gantries and / or entry / exit toll stations within the target block, and edges are used to mark the connection relationships between the gantries and / or entry / exit toll stations within the target block, with the toll amount between the gantries and / or entry / exit toll stations used as the edge weight. The basic business data includes standard data supporting business operation. The transaction data includes vehicle information used to execute toll transactions.

[0026] The resource service layer includes a resource layer, a data layer, and a service layer, and is used to build basic services based on computing resources, storage resources, network resources, security resources, and encryption management resources. The basic services include: database services, big data services for visualization and visual analysis, storage services, and integration services.

[0027] The service platform integrates the geographic information system and the basic services to provide a path fitting method for executing the multi-verification model as described in any one of claims 1 to 5, so as to provide a driving path fitting service for the target vehicle, as well as multi-scenario navigation service, data analysis service, toll transaction service and geographic information system visualization service.

[0028] The application system layer is used to provide multiple application platforms and to display and provide operation windows for the driving path fitting service, the multi-scenario navigation service, the data analysis service, the toll transaction service, and the geographic information system visualization service to multiple types of users.

[0029] In some embodiments, the geographic information visualization system for networked toll roads is deployed on a network consisting of terminal nodes, edge nodes, and cloud nodes;

[0030] The terminal nodes include, but are not limited to, lane equipment, gantry equipment, parking lot equipment, and user terminals, and are used to acquire the in-transit data, the base map data, the basic business data, and the transaction data;

[0031] The edge node connects to the terminal node, and the edge node is used to load and provide the driving path fitting service, the multi-scenario navigation service, the data analysis service, the toll transaction service, and the geographic information system visualization service to the terminal node;

[0032] Multiple cloud nodes are configured and connected to the edge nodes to provide the basic services. The cloud nodes are configured to call each other through mirroring and serve as disaster recovery for each other.

[0033] In some embodiments, the geographic information visualization system for networked toll roads includes a big data service for visualization and visual analysis comprising: visualizing layers based on a set scaling ratio, wherein the set scaling ratio is calculated as follows:

[0034]

[0035] Where, x i The x-coordinate of the starting point of the target object is displayed. i Let x be the ordinate of the starting point of the target object. j To display the x-coordinate of the target object's endpoint, y j The target object's endpoint is displayed using its ordinate. And θ is the scaling factor;

[0036] In addition, the point information set of the layer is compressed, wherein the point information set only records the position of the starting gantry, and for subsequent gantry, only the deviation from the starting gantry is recorded.

[0037] On the other hand, the present invention also provides a path device for a multi-verification model, including a processor and a memory, wherein the memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the device implements the steps of the above method.

[0038] On the other hand, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0039] Compared with the prior art, the beneficial effects of the present invention are at least as follows:

[0040] The path fitting method and geographic information visualization system based on this invention, during online billing, utilize the Dijkstra algorithm combined with a multi-validation model to verify and optimize vehicle on-the-go data and nodes in the fitted path. This removes gantry systems with incorrect traffic relationships or abnormal traffic conditions, ensuring that the final fitted vehicle travel path more closely reflects reality. Simultaneously, by establishing a comprehensive multi-layered data and business management system, accuracy is guaranteed during complex business operations, while reducing system load.

[0041] Furthermore, during the visualization process, the point information set of the layer is compressed, recording only the position of the starting gantry and only the deviation of subsequent gantry from the starting gantry, to reduce the occupied resource space. The scaling ratio of the layer is configured according to the coordinates of the starting and ending points of the target display object to assist in the accurate positioning and display of infrastructure.

[0042] Additional advantages, objects, and features of the invention will be set forth in part in the description which follows, and will also become apparent in part to those skilled in the art upon studying the description, or may be learned by practice of the invention. The objects and other advantages of the invention can be realized and obtained by means of the structures specifically pointed out in the description and drawings.

[0043] Those skilled in the art will understand that the objectives and advantages achievable with this invention are not limited to those specifically described above, and that the above and other objectives achievable with this invention will become clearer from the following detailed description. Attached Figure Description

[0044] The accompanying drawings, which are provided to further illustrate the invention and form part of this application, are not intended to limit the scope of the invention.

[0045] Figure 1 This is a flowchart illustrating the path fitting method for a multiple verification model according to an embodiment of the present invention.

[0046] Figure 2 This is an optimal path vector graph in one embodiment of the present invention.

[0047] Figure 3 This is a flowchart of the vehicle gantry connectivity determination process in the path fitting method of the multi-verification model according to an embodiment of the present invention.

[0048] Figure 4 (a) is a schematic diagram of the abnormal gantry point of the opposing lane being hijacked to the current lane in the path fitting method of the multi-verification model according to an embodiment of the present invention.

[0049] Figure 4 (b) is a schematic diagram of abnormal gantry points in the path fitting method of the multi-verification model according to an embodiment of the present invention, where adjacent points are abnormal.

[0050] Figure 5 This is a schematic diagram of the three-layer GIS visualization system structure according to an embodiment of the present invention.

[0051] Figure 6 This is a schematic diagram of the four-layer GIS visualization system structure according to an embodiment of the present invention.

[0052] Figure 7This is a schematic diagram of the cloud-edge-device deployment structure in a GIS visualization system according to an embodiment of the present invention.

[0053] Figure 8 This is a schematic diagram of the network topology design of a GIS visualization system according to an embodiment of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the embodiments and accompanying drawings. Here, the illustrative embodiments and descriptions of this invention are used to explain the invention, but are not intended to limit the invention.

[0055] It should also be noted that, in order to avoid obscuring the invention with unnecessary details, only the structures and / or processing steps closely related to the solution according to the invention are shown in the accompanying drawings, while other details that are not closely related to the invention are omitted.

[0056] It should be emphasized that the term "including / comprises" as used herein refers to the presence of a feature, element, step, or component, but does not exclude the presence or addition of one or more other features, elements, steps, or components.

[0057] It should also be noted that, unless otherwise specified, the term "connection" in this article can refer not only to a direct connection, but also to an indirect connection involving an intermediary.

[0058] Existing vehicle access management systems offer a variety of services, but the complexity of these services and the poor execution logic of some services lead to high system load and low efficiency. For example, when calculating toll fees, traditional path fitting schemes sometimes result in discrepancies between the fitted path and the actual path, causing billing errors. During visualization, the link strings (the point information of the layer) record the coordinates of each point, resulting in high resource consumption and a heavy system load.

[0059] This invention provides a path fitting method for a multi-validation model, which is used for execution on a trading platform, such as... Figure 1 As shown, the method includes the following steps S101 to S103:

[0060] Step S101: Obtain the on-the-way data of the target vehicle collected by the gantry and / or the entrance / exit toll station. The on-the-way data includes the identification information of the gantry and / or the entrance / exit toll station that sensed the target vehicle and the corresponding timestamp when the sensing occurred.

[0061] Step S102: Obtain the traffic directed graph within the target block area. The nodes in the traffic directed graph are used to mark the identification information of gantries and / or entry / exit toll stations within the target block area. The edges in the traffic directed graph are used to mark the connection relationship of gantries and / or entry / exit toll stations within the target block area, and the toll amount between gantries and / or entry / exit toll stations is used as the weight of the edge.

[0062] Step S103: Based on the Dijkstra algorithm, fit the actual driving path of the target vehicle to minimize the total cost; during the path fitting process, remove nodes with empty timestamps or reversed travel order in the on-the-way data by verifying the timestamps, and at the same time remove nodes corresponding to speeding gantries, nodes corresponding to duplicate gantries, nodes corresponding to gantries with incorrect connectivity, nodes corresponding to gantries with error rates higher than the first set value, and nodes with fewer than the second set value of adjacent gantries.

[0063] Steps S101 to S103 are for fitting the vehicle's travel path to meet the requirements of vehicle toll calculation. In practical applications, vehicles communicate with onboard units and roadside units while traveling on highways to mark their travel locations. The onboard unit can be an onboard unit (OBU) or a CPC card (composite toll card). The roadside units include gantries and / or toll stations at entrances and exits. When a vehicle passes through a gantry or / or toll station, the onboard unit or CPC card can be read and written via a transaction antenna to record the gantry the vehicle passed through. However, in actual operation, communication or equipment failures may cause problems writing to the onboard unit or CPC card, making media-based toll calculation impossible. In this case, online toll calculation is used to reconstruct the vehicle's travel path and calculate and collect tolls based on the vehicle's on-the-go data recorded by the gantries or toll stations.

[0064] In step S101, the on-route data is the data generated when a target vehicle is detected by a gantry or toll station. The sensing process can include sensing on-board tags or composite toll cards, or identification via computer vision. The identification information of the gantry or toll station, along with the timestamp corresponding to the sensing event, can be used to record the target vehicle's passage through the respective gantry or toll station, forming information that records the target vehicle's route. Of course, the on-route data in online tolling may be incomplete, requiring further path fitting.

[0065] In step S102, a directed traffic graph is obtained to prepare for the subsequent fitting process. The directed traffic graph is generated based on real-world road conditions during the road planning and construction phase and is updated during road construction.

[0066] In step S103, path fitting is performed based on the Dijkstra algorithm. During this process, various constraints are constructed to enhance the fitting accuracy of the actual path. The Dijkstra algorithm is used to find the shortest path from a starting node to other nodes in a weighted graph. This algorithm is widely used in network routing, map navigation, and resource allocation.

[0067] Among them, nodes with empty timestamps or reversed travel order in the data are removed by verifying timestamps. This is mainly implemented for the actual data collected in transit. In the actual data collection process, gantry timestamp errors are caused by various factors such as read / write errors and misreading / writing of opposite lanes. These unreasonable points are removed as dirty points and reasonable points and timestamps are added again in the subsequent fitting.

[0068] Specifically, the path fitting method of the multi-validation model removes the nodes corresponding to the overspeed gantry, including steps S201 to S203:

[0069] Step S201: Obtain the in-transit data and the timestamp and gantry position corresponding to when the target vehicle passes through the gantry in the fitted path.

[0070] Step S202: Calculate the speed of the target vehicle as it passes through the road segments between each passing gantry. The calculation formula is:

[0071] v i,i+1 =s i,i+1 / (t i+1 -t i );

[0072] Among them, v i,i+1 Let s be the speed at which the target vehicle passes through the section between the i-th gantry and the (i+1)-th gantry. i,i+1 Let t be the distance between the i-th gantry and the (i+1)-th gantry. i Let t be the timestamp of the target vehicle passing through the i-th gantry. i+1 The timestamp of the target vehicle passing through the (i+1)th gantry;

[0073] Step S203: Determine whether the speed of the target vehicle passing through the section between each passing gantry exceeds the set speed. If there is a section speed that is greater than the set speed, mark the gantry at the end of the corresponding section as an overspeed gantry and remove it.

[0074] In steps S201-S203, the speeding gantry may be due to a timestamp error during abnormal reading or the existence of a shortcut in the passage path, requiring refitting. In practical applications, the speed setting can be adjusted according to actual application requirements. Furthermore, based on statistical driving patterns, approximately 60% of vehicles speed through the last gantry, so the last gantry can be directly removed.

[0075] Specifically, the path fitting method for the multiple validation model, which removes nodes corresponding to duplicate gantry frames, includes steps S301 to S302:

[0076] Step S301: Obtain in-transit data and the fitted path, as well as the identification information of the gantries through which the target vehicle passes.

[0077] Step S302: Based on the identification information of each passing gantry, determine whether there is a situation where the identification information of adjacent gantry is repeated. If so, mark the corresponding adjacent gantry as a duplicate gantry and remove it.

[0078] Specifically, the path fitting method of the multi-validation model removes nodes corresponding to gantry nodes with connectivity errors, including steps S401 to S402:

[0079] Step S401: Based on the traffic directed graph, determine whether the gantries through which the target vehicle passes in the on-the-way data and the fitted path are connected. If all gantries are connected, then perform path fitting.

[0080] Step S402: When there are disconnected erroneous gantry, remove the erroneous gantry and determine whether there are connected passing gantry within a set number of lines before and after the erroneous gantry. If they are connected, perform path fitting; if they are not connected, indicate that path fitting has failed.

[0081] Specifically, the path fitting method of the multi-validation model removes nodes whose number of adjacent gantry frames is less than a second set value, including steps S501 to S502:

[0082] Step S501: Calculate the distance between the gantries through which the target vehicle passes and other gantries on the fitted path, and mark the gantries with distances less than a set distance as the adjacent gantries of the corresponding gantries.

[0083] Step S502: Count the number of adjacent gantries for each passing gantry. When the number of adjacent gantries of a passing gantry is less than the second set value, mark the corresponding passing gantry as abnormal and remove it.

[0084] The present invention also provides a path device for a multi-verification model, including a processor and a memory. The memory stores computer instructions, and the processor executes the computer instructions stored in the memory. When the computer instructions are executed by the processor, the device implements the steps of the method described in steps S101-S103, S201-S203, S301-S302, S401-S402, and S501-S502.

[0085] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the methods described in steps S101-S103, S201-S203, S301-S302, S401-S402, and S501-S502.

[0086] The present invention also provides a geographic information visualization system for networked toll roads, the system comprising an information perception layer, a resource service layer, a service middleware layer, and an application system layer;

[0087] The information perception layer is used to acquire target vehicle on-the-way data, base map data, basic business data, and transaction data. On-the-way data includes the identification information of the gantries and / or entry / exit toll stations that sensed the target vehicle, as well as the timestamp corresponding to the sensing event. The base map data includes a traffic directed graph, where nodes are used to mark the identification information of gantries and / or entry / exit toll stations within the target block, and edges are used to mark the connection relationships between gantries and / or entry / exit toll stations within the target block, with the toll amount between gantries and / or entry / exit toll stations used as the edge weight. The basic business data includes standard data supporting business operations. The transaction data includes vehicle information used to execute toll transactions.

[0088] Base map data serves as the foundation for building Geographic Information System (GIS) visualizations, providing a geographic reference framework and background environment. Base map data typically includes the following aspects: Geographic features: Base map data can contain various geographic features such as rivers, lakes, mountains, roads, and buildings. These geographic features are represented as vector data and can be used to display geographic characteristics and spatial relationships; Topography: Base map data can contain topographic information, such as elevation models (DEMs) or digital elevation models (DEMs). This data can be used to represent changes in the Earth's surface elevation, such as mountains, valleys, and plains; Imagery: Base map data can also contain remote sensing imagery, such as aerial photographs or satellite images. This imagery can be used to provide a realistic view of the terrain and land cover, and can be used as a background for overlaying other geographic data; Administrative boundaries: Base map data can also include administrative boundaries, such as the boundaries of national, provincial, and county-level administrative divisions. This data can be used to display the division and boundaries of different administrative regions; Place name labeling: Base map data can also contain place name labeling, used to identify place names and landmarks. These labels provide better geographic reference and location.

[0089] The resource service layer comprises a resource layer, a data layer, and a service layer. It is used to build basic services based on computing resources, storage resources, network resources, security resources, and encryption management resources. The basic services include: database services, big data services for visualization and visual analytics, storage services, and integration services.

[0090] The service platform integrates geographic information systems and basic services to provide a path fitting method for executing the multi-verification model described in steps S101 to S103 above, so as to provide a driving path fitting service for the target vehicle, as well as multi-scenario navigation services, data analysis services, toll transaction services, and geographic information system visualization services.

[0091] The application system layer is used to provide multiple application platforms and to display and provide operation windows for the driving path fitting service, multi-scenario navigation service, data analysis service, toll transaction service and geographic information system visualization service to various types of users.

[0092] In some embodiments, the geographic information visualization system for networked toll roads described in this embodiment is deployed on a network consisting of terminal nodes, edge nodes, and cloud nodes.

[0093] Terminal nodes include, but are not limited to, lane equipment, gantry equipment, parking lot equipment, and user terminals, used to acquire the in-transit data, base map data, basic business data, and transaction data;

[0094] Edge nodes connect to the terminal nodes and are used to load and provide the terminal nodes with driving path fitting services, multi-scenario navigation services, data analysis services, toll transaction services, and geographic information system visualization services.

[0095] Multiple cloud nodes are set up and connected to edge nodes to provide basic services. The cloud nodes are connected to each other through mirroring for multi-level calls and mutual disaster recovery.

[0096] Specifically, big data services for visualization and visual analytics include: visualizing layers based on a set scaling ratio, wherein the set scaling ratio is calculated as follows:

[0097]

[0098] Where, x i The x-coordinate of the starting point of the target object is displayed. i Let x be the ordinate of the starting point of the target object. j To display the x-coordinate of the target object's endpoint, y j The target object's endpoint is displayed using its ordinate. θ is a scaling factor.

[0099] The present invention will now be described with reference to a specific embodiment:

[0100] Currently, toll roads primarily use media-based billing for toll collection. However, when media-based billing fails, online billing is initiated at the exit. The success rate and accuracy of online billing path fitting are crucial factors affecting the accuracy of online billing.

[0101] Currently, the online tolling path fitting algorithm is Dijkstra's algorithm, a classic path fitting algorithm. The principle of Dijkstra's algorithm is to greedily select a locally optimal solution at each step in the hope of generating an optimal solution. In the process of constructing the road network, if the toll is used as the weight for finding the optimal path for each path, Dijkstra's algorithm will select the node with the smallest weight at each step to process, thereby generating the optimal path solution.

[0102] For example, let the directed graph G = (V, E, W). Each edge e i,j ={v i ,v j The weights are non-negative real numbers, and the set of weights is denoted as w. i,j (e i,j Let the origin s∈V. The task is to find the optimal path from the origin s to all nodes in V.

[0103] by Figure 2For example, the optimal path vector graph shown is a weighted directed graph with 4 vertices, whose vertex set is V = {v1, v2, v3, v4}. The cost is used as the weight, and the values ​​are as follows:

[0104] w 1,2 (e 1,2 ) = 5w 2,3 (e 2,3 ) = 5w 3,4 (e 3,4 ) = 5w 4,1 (e 4,1 ) = 5

[0105] w 4,3 (e 4,3 ) = 6w 3,2 (e 3,2 ) = 5w 4,2 (e 4,2 ) = 15;

[0106] The weights from v4 to v2 are:

[0107] w 4,2 =15w 4,3,2 =11w 4,1,2 =10

[0108] The optimal path is then v4-v1-v2. In actual path fitting, if path points v1 and v3 are missing, Dijkstra's algorithm will be used to fill in the missing points, fitting point v1 instead.

[0109] Because Dijkstra's algorithm uses toll fees as weights for point adjustments, the fitted result may not match the user's actual travel path. This embodiment provides a path fitting method using a multi-validation model. By constructing a multi-validation model, it helps filter out toll nodes that may not conform to the actual travel trajectory, thus making the fitted route as close as possible to the user's actual travel path. During the fitting process using Dijkstra's algorithm, a multi-validation model is simultaneously constructed to correct the path.

[0110] The multi-validation model includes the following components:

[0111] 1) Verify timestamp:

[0112] Path fitting is performed by acquiring in-transit data of vehicle passage. This data includes information on gantries and toll stations along the route, including location information and timestamps. First, the timestamps of passing gantries are checked for emptiness, and then the order of the timestamps is checked for rationality. If a timestamp is empty or the order of gantries is unreasonable, the point is considered a "dirty point" and temporarily removed. During subsequent fitting, points will be added and reasonable timestamps will be supplemented based on connectivity and the actual physical gantry locations.

[0113] 2) Abnormal filtration gantry:

[0114] 2.1 Filtering overspeed gantry:

[0115] This involves determining whether the vehicle's speed when traveling from the previous gantry to the current gantry is within the maximum permissible range. The interval speed of the target vehicle traversing the sections between each passing gantry is calculated using the following formula:

[0116] v i,i+1 =s i,i+1 / (t i+1 -t i );①

[0117] Among them, v i,i+1 Let s be the speed at which the target vehicle passes through the section between the i-th gantry and the (i+1)-th gantry. i,i+1 Let t be the distance between the i-th gantry and the (i+1)-th gantry. i Let t be the timestamp of the target vehicle passing through the i-th gantry. i+1 The timestamp of the target vehicle passing through the (i+1)th gantry;

[0118] If v i,i+1 >v max If the (i+1)th gantry is identified as an overspeeding gantry, it will be temporarily removed as an anomaly. max The speed limit is set.

[0119] 2.2 Filter Repeating Gantt

[0120] This involves determining whether there is any overlap between two adjacent gantries for vehicle passage. If there is overlap, both overlapping gantries are removed, and the correct gantries are subsequently added based on the connectivity and physical location of the gantries.

[0121] 2.3 Filtering unconnected gantry

[0122] Based on the connectivity between the gantries and the entry / exit toll stations, gantries with disconnected paths are filtered out, and then gantries with reasonable connectivity are added later. For example... Figure 3 As shown, the judgment logic is as follows:

[0123] a. Determine if any point in the request is connected. If yes, go to c; otherwise, go to b.

[0124] b. Determine if there is a connection between the n gantry frames before and after a disconnected point. n is the number of hops. If the connection is satisfied, go to c; otherwise, go to e.

[0125] The determination formula is shown in formula ②, where i and j represent any two points in the link string, M is the point set of the link string, and b ijThis indicates whether the path from i to j is connected; if connected, then b... ij =1. n is the number of jumps, calculated based on 22 gantry passes per hour, with a minimum of 8 jumps. If the pass time is half an hour, the number of jumps is 11. Let t be the time from i to j, then the formula for calculating the number of jumps is shown in formula ③.

[0126]

[0127]

[0128] c. Normal fit.

[0129] d. Determine whether the number of plazas (entry and exit toll stations) in the fitting result is greater than or equal to 4. If yes, go to e; otherwise, go to f.

[0130] e. Fitting failed.

[0131] f. Fitting ends.

[0132] 2.4 Error-prone filtration gantry

[0133] Nodes corresponding to gantries with error rates exceeding a first set value are removed. Based on analysis of a large amount of overspeed gantry data, the last gantry has the most speeding instances; therefore, gantries in the last 6 minutes and last 6 kilometers of vehicle passage are initially removed during fitting. A reasonable gantry will be added in subsequent fitting processes.

[0134] 2.5 The filtration system shows obvious abnormalities in the gantry.

[0135] like Figure 4 The orange dot on the left side of the abnormal gantry point diagram indicates an abnormal point, where the gantry of the opposite lane is bound to the gantry of the current lane. If no action is taken, the system will attempt to detour from the toll station shown in the diagram. Figure 3 The flowchart for determining vehicle connectivity via gantry points is shown. The logic for identifying abnormal points is illustrated in formula ④. A gantry point is considered abnormal when the number of adjacent gantry points on the path is less than k, where k is 8. Let the coordinates of any two gantry points be i(x...). i ,y i ), j(x j ,y j ), where d is the general distance between gantry frames, and c is the number of adjacent frames. When the number of adjacent frames is less than k, it is considered an outlier.

[0136]

[0137] This embodiment provides a GIS visualization system and device based on a multi-verification model.

[0138] like Figure 5The diagram shows the structure of a three-layer GIS visualization system. The overall design adopts a collaborative approach of three layers: information perception layer, service platform layer, and application system layer. Based on a cloud-edge-device system architecture, it constructs a full-scenario business support and interactive network for highway toll collection.

[0139] First, it acquires diverse data such as basic data, traffic data, transaction data, card recognition data, and base map data through the information perception layer.

[0140] Secondly, through the comprehensive data processing capabilities of the service platform, intelligent computing is performed and integrated with GIS information to build network-wide path fitting service capabilities, multi-scenario navigation service capabilities, multi-dimensional data analysis capabilities, and multi-scenario service support capabilities.

[0141] Third, based on the service capabilities built at the bottom and middle layers, a top-level application is constructed to provide business tools and leverage for B-end billing, display, matching, customer service, auditing, data splitting, and data governance, while providing C-end users with accurate pre-pass cost estimates and post-pass cost inquiries. System implementation is also provided for standardized configuration of unified network-wide rates.

[0142] GIS visualization system design. For example... Figure 6 This is a schematic diagram of a four-layer GIS visualization system architecture. The overall design adopts a collaborative four-layer architecture: information perception layer, resource service layer (IaaS layer), service platform layer (PaaS layer), and application system layer (SaaS layer), based on... Figure 7 The cloud-edge-device architecture shown constructs a GIS-supported interactive network covering all business scenarios in the highway toll collection field.

[0143] The information perception layer mainly consists of existing ETC devices that acquire various basic data, traffic data, transaction data, license plate recognition data, and base map data. This includes ETC lane control software and hardware, gantry control software and hardware, and parking lot control software and hardware.

[0144] Infrastructure services are built through the IaaS layer. The IaaS layer is further divided into resource, data, and service layers. Basic data services are built based on computing, storage, network, security, and encryption management resources. The data layer includes dynamic data such as provincial-level transaction data, in-transit data, log files, and invoice data, as well as static data such as basic data, issuance data, and list data. Additionally, dynamically collected data such as BeiDou data, license plate recognition data, video data, and multi-channel shared GPS data and base map data are also included. Based on the resource and data layers, diverse basic services are built, including database services, big data services for visualization and visual analytics, diversified storage services, and various integrated services.

[0145] We build a rich, diverse, comprehensive, and personalized middleware service through the PaaS layer. Based on underlying data, we construct diverse data services and provide unified integrated GIS platform support for various scenarios in the online toll collection industry. Through comprehensive data processing capabilities, we perform intelligent computing and integrate with GIS information to build network-wide path fitting services, multi-scenario navigation services, multi-dimensional data analysis capabilities, and multi-scenario service support capabilities. Based on the service capabilities built at the bottom and middle layers, we construct top-level applications, providing business tools and leverage for B-end users and diversified user services for C-end users.

[0146] Multiple application platforms are built through the SaaS layer, providing display and operation windows for various types of users. The GIS visualization system includes two secondary subsystems: the Expressway Toll Network GIS Service Support Platform and the Smart Mobility GIS Visualization Support Platform. The Expressway Toll Network GIS Service Support Platform further includes three tertiary subsystems: the Network-wide Toll Collection Business GIS Service Support Platform, the Dispute Resolution Customer Service GIS Service Support Platform, and the Rate Standardization Configuration Platform. The Smart Mobility GIS Visualization Support Platform further includes three tertiary subsystems: the Extended Services GIS Visualization Support Platform, the Traffic Service Support Platform, and the User Profile Analysis Platform. The system supports deployment on multiple terminal devices, providing one-stop GIS visualization services for ministerial, provincial, and road section-level management departments, traffic management departments, emergency rescue departments, and end-users.

[0147] The GIS visualization system proposed in this invention has a large data volume, a wide user base, and a relatively complex system architecture. Therefore, it proposes a cloud-edge-device hierarchical deployment scheme to provide high-bandwidth, low-latency, and highly reliable services for multi-scenario data analysis and applications.

[0148] To address the issue of slow layer loading due to the massive amount of link strings (a series of connections between geographic features), this embodiment employs the following method to improve layer processing efficiency: link string compression technology. A link string consists of a series of gantry points with latitude and longitude coordinates. The more precise the location of a layer element, the longer the latitude and longitude string, and the larger the resource space it occupies. Therefore, in the proposed solution, only the starting position of the link string is recorded, and subsequent gantry points are only recorded based on their deviation from the initial position, thereby saving memory and accelerating layer processing efficiency.

[0149] like Figure 7 The diagram shown illustrates the cloud-edge-device deployment structure. Figure 8The network topology design diagram shows that the cloud nodes include five regional clouds, namely, cloud services in North China, South China, East China, Northeast China, and the Ministry Center, enabling multi-level calls and mutual disaster recovery to alleviate system resource pressure and improve network security protection capabilities. Edge nodes include the various business systems of the Ministry Center, provincial centers of major regions, road section companies, and related third-party edge applications on B-end nodes, which can be used for data storage and secondary development of services. C-end node edge applications are for public users but only for service use. Terminal nodes include toll network gantries, lanes, parking lots, roadside unit hardware sensing devices, and other IoT devices. Various information and data are uploaded from terminal nodes to edge nodes. After processing by edge applications, the information is uploaded to the cloud configuration platform, mirrored through the mirror service, and then distributed to edge applications through the IEF platform, finally taking effect at the terminal nodes, achieving closed-loop transmission and processing of information.

[0150] like Figure 7 As shown, because the system provides both ToB and ToC service models, with a wide variety of users and a huge volume of access, a multi-location disaster recovery and backup mechanism should be set up to avoid problems such as network congestion and paralysis, insufficient resource support, and network security risks. Therefore, a partitioned network architecture design is required. The system should be deployed in five regions: North China, South China, East China, Northeast China, and the central hub. Each region should deploy full and consistent toll collection network data and various GIS-related services. Users in different regions should access the cloud services of their respective regions.

[0151] like Figure 8 The diagram shows the network topology design, using the North China region cloud and the Ministry's central cloud as examples.

[0152] 1) The system architecture design involves both intranet GIS service requirements and public network GIS service requirements. It is necessary to deploy map data and map services privately on the intranet, enable public network GIS service request permissions, and ensure that the private map and public network GIS services are unified and the data is unified.

[0153] 2) The cloud production intranets in each region should be interconnected via dedicated lines to complement each other. Dedicated lines should be used to connect to dual-core switches, integrated dual-core switches, and firewalls to ensure bandwidth and security. Based on geographical proximity, if a user in the Northeast region fails to access cloud services in the Northeast region, they should be redirected to cloud services in the North China region, and so on. If cloud services in all four major regions fail, a user should request cloud services from the department center.

[0154] 3) Public GIS services require access to the Internet. Each region should be connected to the Internet through access switches, core switches, core routers, firewalls, and Internet egress switches, and relevant network policies should be configured to ensure high availability and network security.

[0155] In summary, the path fitting method and the geographic information visualization system incorporating this method, during online billing, utilize the Dijkstra algorithm combined with a multi-validation model to verify and optimize vehicle on-the-go data and nodes in the fitted path. This removes gantry systems with incorrect traffic relationships or abnormal traffic conditions, ensuring that the final fitted vehicle travel path more closely reflects reality. Simultaneously, by establishing a comprehensive multi-layered data and business management system, accuracy is guaranteed during complex business operations, while reducing system load.

[0156] Furthermore, during the visualization process, the point information set of the layer is compressed, recording only the position of the starting gantry and only the deviation of subsequent gantry from the starting gantry, to reduce the occupied resource space. The scaling ratio of the layer is configured according to the coordinates of the starting and ending points of the target display object to assist in the accurate positioning and display of infrastructure.

[0157] Corresponding to the above method, the present invention also provides an apparatus / system, the system including a computer device, the computer device including a processor and a memory, the memory storing computer instructions, the processor being used to execute the computer instructions stored in the memory, and when the computer instructions are executed by the processor, the system performs the steps of the method as described above.

[0158] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the aforementioned edge computing server deployment method. The computer-readable storage medium can be a tangible storage medium, such as random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, floppy disks, hard disks, removable storage disks, CD-ROMs, or any other form of storage medium known in the art.

[0159] Those skilled in the art will understand that the exemplary components, systems, and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software, or a combination of both. Whether implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention. When implemented in hardware, it can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this invention are programs or code segments used to perform the desired tasks. The programs or code segments can be stored in a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried in a carrier wave.

[0160] It should be clarified that the present invention is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of the present invention.

[0161] In this invention, features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, and / or combined with or in place of features of other embodiments.

[0162] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. For those skilled in the art, various modifications and variations can be made to the embodiments of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A path fitting method of a multiple check model, characterized by, The method is used for executing on a transaction platform, and comprises the following steps: Obtaining in-route data of a target vehicle collected by gantries and / or entrance and exit toll stations, wherein the in-route data comprises identification information of the gantries and / or the entrance and exit toll stations sensed to the target vehicle and corresponding time stamps when the sensing occurs; Obtaining a traffic directed graph in a target block range, wherein nodes in the traffic directed graph are used to mark identification information of the gantries and / or the entrance and exit toll stations in the target block range, edges in the traffic directed graph are used to mark connection relationships of the gantries and / or the entrance and exit toll stations in the target block range, and a same-trip fee amount between the gantries and / or the entrance and exit toll stations is used as a weight of the edge; Based on a Dijkstra algorithm, fitting an actual driving path of the target vehicle to minimize a total fee amount; in the path fitting process, by checking time stamps, removing nodes with empty time stamps or in reverse order of travel, removing nodes corresponding to overspeed gantries, removing nodes corresponding to repeated gantries, removing nodes corresponding to gantries with incorrect connection relationships, and removing nodes corresponding to gantries with an error rate higher than a first set value, and removing nodes corresponding to gantries with a number of adjacent gantries less than a second set value. Wherein, removing nodes corresponding to overspeed gantries comprises: Obtaining time stamps and gantry positions corresponding to the target vehicle passing through each gantry in the in-route data and the fitted path; Calculating interval speeds of the target vehicle passing through road segments between each gantry, and the calculation formula is: ）； wherein, is a speed of the target vehicle through a road segment between the ith gantry and the (i+1)th gantry, is a distance of the road segment between the ith gantry and the (i+1)th gantry, is a timestamp of the target vehicle passing the ith gantry, is a timestamp of the target vehicle passing the (i+1)th gantry; Judging whether the interval speeds of the target vehicle passing through road segments between each gantry exceed a set speed, and when there is an interval speed greater than the set speed, marking a gantry at the end of the corresponding road segment as the overspeed gantry and removing it.

2. The path fitting method of a multiple check model according to claim 1, wherein, Removing nodes corresponding to repeated gantries comprises: Obtaining identification information of the gantries through which the target vehicle passes in the in-route data and the fitted path; According to the identification information of each gantry, judging whether there is a case of repeated identification information of adjacent gantries, and if there is, marking the corresponding adjacent gantries as repeated gantries and removing them.

3. The path fitting method of a multiple check model according to claim 1, wherein, Removing nodes corresponding to gantries with incorrect connection relationships comprises: According to the traffic directed graph, judging whether the gantries through which the target vehicle passes in the in-route data and the fitted path are connected, and when all the gantries are connected, performing path fitting; When there is an incorrect gantry that is not connected, removing the incorrect gantry, and judging whether the gantries within a set number of gantries before and after the incorrect gantry are connected, and if they are connected, performing path fitting, and if they are not connected, prompting that path fitting fails.

4. The path fitting method of a multiple check model according to claim 1, wherein, Removing nodes corresponding to gantries with a number of adjacent gantries less than a second set value comprises: Calculating distances of the gantries through which the target vehicle passes from other gantries on the fitted path, and marking gantries with a distance less than a set distance as adjacent gantries of the corresponding gantries; Counting a number of adjacent gantries of each gantry, and when there is a gantry with a number of adjacent gantries less than the second set value, marking the corresponding gantry as abnormal and removing it.

5. A geographic information visualization system for a networked toll road, characterized by, The system comprises an information perception layer, a resource service layer, a service middle platform layer and an application system layer; The information perception layer is configured to acquire in-route data of a target vehicle, base map data, business basic data and transaction data; the in-route data comprises identification information of the gantry and / or the entrance and exit toll station sensed to the target vehicle and a corresponding timestamp when the sensing occurs; the base map data comprises a traffic directed graph, wherein nodes in the traffic directed graph are used to mark identification information of the gantry and / or the entrance and exit toll station within the target block range, edges in the traffic directed graph are used to mark connection relationships of the gantry and / or the entrance and exit toll station within the target block range, and a same-trip toll amount between the gantry and / or the entrance and exit toll station is used as a weight of the edge; the business basic data comprises standard data supporting business operation; and the transaction data comprises vehicle information used to execute toll transaction; The resource service layer comprises a resource layer, a data layer and a service layer, and is configured to construct basic services based on computing resources, storage resources, network resources, security resources and encryption management resources, wherein the basic services comprise a database service, a visual and visual analysis big data service, a storage service and an integration service; The service middle platform is configured to provide a path fitting method for executing the multi-verification model according to any one of claims 1 to 4 by fusing a geographic information system and the basic services, to provide a driving path fitting service for the target vehicle, and to provide a multi-scenario navigation service, a data analysis service, a toll transaction service and a geographic information system visualization service; The application system layer is configured to provide a plurality of application platforms, and to display and provide the driving path fitting service, the multi-scenario navigation service, the data analysis service, the toll transaction service and an operation window of the geographic information system visualization service to a plurality of users.

6. The geographic information visualization system for networked toll roads of claim 5, wherein, The system is deployed in a network comprising terminal nodes, edge nodes and cloud nodes; The terminal nodes comprise, but are not limited to, lane devices, gantry devices, parking lot devices and user terminals, and are configured to acquire the in-route data, the base map data, the business basic data and the transaction data; The edge nodes are connected to the terminal nodes, and are configured to load and provide the driving path fitting service, the multi-scenario navigation service, the data analysis service, the toll transaction service and the geographic information system visualization service to the terminal nodes; A plurality of cloud nodes are provided, and are connected to the edge nodes to provide the basic services, and the cloud nodes are configured to be called by each other through mirroring and to be disaster recovery for each other.

7. The geographic information visualization system for networked toll roads of claim 5, wherein, The visual and visual analysis big data service comprises: visualizing a layer based on a set zoom ratio, wherein a calculation formula of the set zoom ratio is: ； wherein, is a horizontal coordinate of a starting point of the target presentation object, is a vertical coordinate of the starting point of the target presentation object, is a horizontal coordinate of an ending point of the target presentation object, is a vertical coordinate of the ending point of the target presentation object; and is a scaling factor; and compressing a point information set of the layer, wherein the point information set only records a position of a starting gantry, and only records a deviation from the starting gantry for a subsequent gantry.

8. A path device of a multiple check model, comprising a processor and a memory, characterized in that, The memory has stored therein computer instructions, the processor being configured to execute the computer instructions stored in the memory, the computer instructions, when executed by the processor, causing the apparatus to implement the steps of the method of any one of claims 1 to 4.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The program, when executed by the processor, implements the steps of the method of any one of claims 1 to 4.

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