Intelligent expressway toll collection management system and method based on cloud technology

Abstract

The application provides a cloud technology-based intelligent expressway toll collection management system and method, wherein the system comprises: a toll collection management demand acquisition subsystem, which is used to acquire the toll collection management demand of a target expressway; a cloud resource deployment subsystem, which is used to perform cloud resource deployment based on cloud technology according to the toll collection management demand; and a toll collection management subsystem, which is used to perform intelligent expressway toll collection management of the target expressway after all the cloud resources to be deployed are deployed. The cloud technology-based intelligent expressway toll collection management system and method acquire the toll collection management demand of the target expressway. Cloud technology is introduced, cloud resource deployment is performed through the Internet, required computing resources and services are acquired, and finally intelligent expressway toll collection management is realized. The expansibility of the toll collection management is higher, and the toll collection management is more intelligent.

Description

Technical Field

[0001] This invention relates to the field of automatic fare collection systems and services, and in particular to an intelligent high-speed toll collection management system and method based on cloud technology. Background Technology

[0002] Cloud computing is a technology that provides computing resources and services via the internet. It centralizes computing, storage, and networking resources in data centers and delivers them to users on demand, elastically, and scalably via the internet. An intelligent highway toll management system is a system that uses intelligent technologies and information technology to manage and optimize highway toll collection. Cloud technology can provide the following cloud resources for an intelligent highway toll management system: virtual machine instances, object storage, database services, cloud functions and serverless computing, artificial intelligence and machine learning services, etc.

[0003] The invention patent application with application number CN201810572053.9 discloses a standardized platform and construction method for highway toll stations. This platform has a framework consisting of a station manager or administrator unit, a shift supervisor or operator unit, a toll collection supervisor or toll collector unit, and a ticket clerk unit. Each unit is composed of corresponding functional modules. The aforementioned standardized highway toll station platform has management functions for station managers or administrators, shift supervisors or operators, toll collection supervisors or toll collectors, and ticket clerks. It can be closely integrated with the daily management and performance evaluation of toll stations, achieving standardized management of highway toll stations.

[0004] However, the existing standardized highway toll station platforms are local, and the resources used on the platforms are fixed and limited. When faced with complex toll management situations, they cannot achieve flexible resource expansion and are not intelligent enough.

[0005] In view of this, there is an urgent need for a cloud-based intelligent highway toll management system and method to at least address the above-mentioned shortcomings. Summary of the Invention

[0006] One of the objectives of this invention is to provide an intelligent highway toll management system and method based on cloud technology, which obtains the toll management requirements of a target highway. By introducing cloud technology and deploying cloud resources via the Internet, the necessary computing resources and services are obtained, ultimately realizing intelligent highway toll management. This results in higher scalability and greater intelligence in toll management.

[0007] The cloud-based intelligent highway toll collection management system provided in this embodiment of the invention includes:

[0008] The toll management requirements acquisition subsystem is used to acquire the toll management requirements of the target highway.

[0009] The cloud resource deployment subsystem is used to deploy cloud resources based on cloud technology and according to billing management requirements.

[0010] The toll management subsystem is used to perform intelligent toll management for the target highway after all the cloud resources that need to be deployed have been deployed.

[0011] Preferably, the fee management requirement acquisition subsystem includes:

[0012] The charging management requirement acquisition module is used to obtain the trigger information for charging management requirements, parse the trigger information, and obtain the charging management requirements.

[0013] Preferably, the cloud resource deployment subsystem includes:

[0014] The cloud service platform node set acquisition module is used to acquire cloud service platform node sets based on cloud technology and according to billing management requirements.

[0015] The cloud resource deployment module is used to deploy cloud resources through a set of cloud service platform nodes.

[0016] Preferably, the cloud resource deployment module includes:

[0017] The pre-deployment submodule is used to perform pre-deployment and obtain pre-deployment results before deploying cloud resources through the cloud service platform node set;

[0018] The first cloud resource deployment submodule is used to deploy cloud resources based on pre-deployment results and billing management requirements.

[0019] Preferably, the pre-deployed submodule includes:

[0020] The historical toll record set acquisition submodule is used to acquire the historical toll record set of the target highway;

[0021] The historical charging time acquisition submodule is used to parse historical charging records in the historical charging record set and obtain the historical charging time.

[0022] The target timeline marking submodule is used to expand historical charging records on a preset target timeline based on historical charging times.

[0023] The Time Traversal submodule is used to traverse each point in time on the target timeline in chronological order. During each traversal, the historical moment of the point being traversed is extracted.

[0024] The current time acquisition submodule is used to obtain the current time.

[0025] The time matching submodule is used to match historical time with the current time. If the time matching is successful, the time point corresponding to the historical time is used as the target time point.

[0026] The first record feature set extraction submodule is used to extract the first record feature set of historical charging records at the target time point;

[0027] The predictive charging management demand determination submodule is used to determine the predicted charging management demand based on the first record feature set and the preset demand comparison template. The demand comparison template includes: a one-to-one corresponding second record feature set and the target predicted charging management demand.

[0028] The pre-deployment submodule is used to pre-deploy based on predicted billing management needs.

[0029] Preferably, the cloud resource deployment module also includes:

[0030] The first cloud service type acquisition submodule is used to acquire the first cloud service type of the cloud service platform nodes in the cloud service platform node set.

[0031] The first cloud service platform node subset determination submodule is used to split the cloud service platform node set according to the first cloud service type to obtain the first cloud service platform node subset;

[0032] The second cloud service type determination submodule is used to determine the second cloud service type based on billing management requirements;

[0033] The service type matching submodule is used to match the first cloud service type and the second cloud service type to determine the third cloud service type that matches the service type;

[0034] The second cloud service platform node subset acquisition submodule is used to filter the first cloud service platform node subset according to the third cloud service type to obtain the second cloud service platform node subset.

[0035] The node evaluation submodule is used to evaluate the first target cloud service platform node in each subset of second cloud service platform nodes, obtain an evaluation score, and sort the first target cloud service platform nodes according to the evaluation score to obtain a sorted list of the first target cloud service platform nodes.

[0036] The second target cloud service platform node determination submodule is used to determine the first target cloud service platform node that ranks highest in each sorting list and use it as the second target cloud service platform node.

[0037] The second cloud resource deployment submodule is used to deploy cloud resources through the second target cloud service platform node.

[0038] Preferably, the second cloud resource deployment submodule includes:

[0039] The cloud service account creation unit is used to create a cloud service account and obtain access credentials for the cloud service account to the second target cloud service platform node.

[0040] The target configuration resource determination unit is used to access the second target cloud service platform node through a cloud service account and determine the target configuration resources.

[0041] Run the test unit to configure target configuration resources and perform runtime tests;

[0042] The supplementary strategy determination unit is used to determine the supplementary strategy based on the results of the test run;

[0043] The strategy setting unit is used to set supplementary strategies and then complete the deployment.

[0044] The cloud-based intelligent highway toll management system provided in this embodiment of the invention further includes:

[0045] The exception handling subsystem is used to acquire toll-related exception events during the toll management process of the target highway and to handle these exceptions accordingly.

[0046] Preferably, the exception handling subsystem includes:

[0047] The first event feature set acquisition module is used to determine the first event feature set of charging anomaly events based on a preset event feature extraction template.

[0048] The risk level determination module is used to determine the risk level based on the first event feature set and the preset risk level library;

[0049] The second event feature set determination module is used to determine the first event feature set as the second event feature set if the risk level is greater than or equal to the preset risk level threshold.

[0050] The target processing strategy determination module is used to determine the target processing strategy based on a preset processing strategy library and the second event feature set.

[0051] The strategy type acquisition module is used to acquire the strategy type of the target processing strategy, wherein the strategy type includes: immediate strategy and future strategy;

[0052] The real-time processing module is used to process the policy according to the corresponding target processing policy if the policy type is real-time policy.

[0053] The future state verification node generation module is used to generate future state verification nodes according to the corresponding target processing strategy if the strategy type is future strategy.

[0054] The reminder module is used to remind target personnel of the node progress and the execution progress of future strategies based on the future status of the check node.

[0055] Preferably, the fee management subsystem includes:

[0056] The first scheduling identifier acquisition module is used to acquire the first scheduling identifier of the locally deployed resources after all cloud resources that need to be deployed have been deployed.

[0057] The second scheduling identifier acquisition module is used to acquire the current charging management requirements and acquire the second scheduling identifier based on the current charging management requirements.

[0058] The toll management module is used to match the first and second scheduling identifiers and perform intelligent highway toll management based on the local deployment resources of the first scheduling identifier that has successfully matched the scheduling identifier.

[0059] The intelligent highway toll management method based on cloud technology provided in this embodiment of the invention includes:

[0060] Step 1: Obtain the toll management requirements of the target highway;

[0061] Step 2: Deploy cloud resources based on cloud technology and according to the requirements of billing management;

[0062] Step 3: Once all the cloud resources that need to be deployed are deployed, implement intelligent highway toll management for the target highway.

[0063] Preferably, step 1: Obtain the toll management requirements of the target highway, including:

[0064] Obtain the trigger information for the fee management request, parse the trigger information, and obtain the fee management request.

[0065] The beneficial effects of this invention are as follows:

[0066] This invention identifies the toll management needs of a target highway. By introducing cloud technology and deploying cloud resources via the internet, it obtains the necessary computing resources and services, ultimately achieving intelligent highway toll management. This results in greater scalability and intelligence in toll management.

[0067] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in this application.

[0068] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0069] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0070] Figure 1 This is a schematic diagram of a cloud-based intelligent highway toll collection management system in an embodiment of the present invention;

[0071] Figure 2 This is a schematic diagram of a cloud-based intelligent highway toll management method in an embodiment of the present invention. Detailed Implementation

[0072] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0073] This invention provides an intelligent highway toll management system based on cloud technology, such as... Figure 1 As shown, it includes:

[0074] Toll Management Requirements Acquisition Subsystem 1 is used to acquire the toll management requirements of the target highway; wherein, the target highway is: the highway for which toll management is carried out; and the toll management requirements are: the configuration requirements and specifications of the cloud resources for the toll management system of the target highway.

[0075] Cloud Resource Deployment Subsystem 2 is used to deploy cloud resources based on cloud technology and according to billing management requirements. Cloud technology refers to the technology that provides computing resources and services through the Internet. Cloud resource deployment is the process of deploying various computing, storage, and network resources required by applications and systems to a cloud platform. Cloud resource deployment includes processes such as virtual machine instances, configuring storage services, setting up network connections, and creating database services.

[0076] The toll management subsystem 3 is used to perform intelligent toll management of the target highway after all the cloud resources that need to be deployed have been deployed.

[0077] The working principle and beneficial effects of the above technical solution are as follows:

[0078] This application addresses the toll management needs of the target highway. By introducing cloud technology and deploying cloud resources via the internet, the necessary computing resources and services are obtained, ultimately enabling intelligent toll management for the highway. This approach offers greater scalability and greater intelligence in toll management.

[0079] In one embodiment, the fee management requirement acquisition subsystem includes:

[0080] The toll management request acquisition module is used to acquire and parse the trigger information for toll management requests. The trigger information is the signal emitted by a vehicle leaving the toll station exit.

[0081] The working principle and beneficial effects of the above technical solution are as follows:

[0082] This application determines the toll information that needs to be processed based on real-time vehicle departure signals from the toll station, and then configures cloud resources according to the toll information, thereby improving the rationality of toll management needs acquisition.

[0083] In one embodiment, the cloud resource deployment subsystem includes:

[0084] The cloud service platform node set acquisition module is used to acquire the cloud service platform node set based on cloud technology and according to the billing management requirements; wherein, the cloud service platform node set is: a collection of cloud service platform nodes, and the cloud service platform nodes are: communication nodes of the cloud service platform;

[0085] The cloud resource deployment module is used to deploy cloud resources through a set of cloud service platform nodes. Deploying cloud resources through a set of cloud service platform nodes involves accessing cloud service platform nodes and deploying the required cloud resources in the cloud.

[0086] The working principle and beneficial effects of the above technical solution are as follows:

[0087] This application introduces cloud technology, determines the cloud service platform node set based on the billing management requirements, and then deploys cloud resources through the cloud service platform node set, making it more intelligent.

[0088] In one embodiment, the cloud resource deployment module includes:

[0089] The pre-deployment submodule is used to pre-deploy cloud resources before deploying them through the cloud service platform node set and obtain the pre-deployment results. Pre-deployment means: configuring cloud resources in advance based on the predicted resource requirements for charge management at each time point; the pre-deployment result is: the pre-configuration plan for cloud resources at each time point.

[0090] The first cloud resource deployment submodule is used to deploy cloud resources based on pre-deployment results and billing management requirements. Specifically, when deploying cloud resources based on pre-deployment results and billing management requirements, only minor adjustments to the pre-deployed resources are needed based on the real-time acquired billing management requirements.

[0091] The working principle and beneficial effects of the above technical solution are as follows:

[0092] Generally, traffic flow is relatively fixed in each time period, and the amount of toll collection tasks is similar. However, configuring the system after each toll collection signal results in a slow system response. Therefore, this application introduces the pre-deployment of cloud resources. Before deploying cloud resources through the cloud service platform node set, pre-deployment is performed to obtain the pre-deployment results. The pre-deployment results are then fine-tuned according to the toll collection management requirements before deploying the cloud resources, which greatly improves the efficiency of resource deployment.

[0093] In one embodiment, the pre-deployment submodule includes:

[0094] The historical toll record set acquisition submodule is used to acquire the historical toll record set of the target expressway; wherein, the historical toll record set is: a collection of historical toll records, and the historical toll records are: the toll records of the toll station in the past;

[0095] The historical charging time acquisition submodule is used to parse historical charging records in the historical charging record set and obtain the historical charging time; where historical charging time is: the time when the charging event recorded in the historical charging record occurred;

[0096] The target time axis marking submodule is used to expand historical charging records on a preset target time axis based on historical charging time; wherein, the preset target time axis is: a linear model representing the time sequence, and the above linear model has a time period of 24 hours;

[0097] The Time Traversal submodule is used to traverse each point in time on the target timeline in chronological order. During each traversal, the historical moment of the time point being traversed is extracted. Here, a time point is a point on the target timeline, and each time point corresponds to the timing of a 24-hour clock, such as 14:00.

[0098] The current time acquisition submodule is used to obtain the current time; where the current time is the time corresponding to the 24-hour clock.

[0099] The time-matching submodule is used to match historical time with the current time. If the time match is successful, the time point corresponding to the historical time is used as the target time point. Successful time matching means that the time is consistent.

[0100] The first record feature set extraction submodule is used to extract the first record feature set of historical charging records at the target time point; wherein, the first record feature set includes multiple first record features, and the record features are: the number and type of charging records, and the system resources required for management;

[0101] The charging management requirement determination submodule is used to determine the predicted charging management requirements based on the first record feature set and the preset requirement comparison template. The requirement comparison template includes: a one-to-one corresponding second record feature set and the target predicted charging management requirements. The predicted charging management requirements are: the predicted charging management requirements at the current moment, such as: the predicted amount of computing resources to be called.

[0102] The pre-deployment submodule is used to pre-deploy based on predicted billing management needs.

[0103] The working principle and beneficial effects of the above technical solution are as follows:

[0104] This application acquires a set of historical billing records, parses these records to obtain the historical billing time, and expands the historical billing records on a target timeline based on the historical billing time. It then iterates through the time points on the target timeline in chronological order to determine the historical moments. The acquired current moment and historical moments are matched to identify the target time point where a match is found. The first set of feature values ​​from the historical billing records at the target time point is extracted, and a requirement comparison template is used for comparison to determine the predicted billing management requirements. Based on these predicted billing management requirements, cloud resources are pre-deployed, improving the efficiency of subsequent resource allocation.

[0105] In one embodiment, the cloud resource deployment module further includes:

[0106] The first cloud service type acquisition submodule is used to acquire the first cloud service type of the cloud service platform nodes in the cloud service platform node set; wherein, the first cloud service type is: the type of cloud service provided by the cloud service platform node, such as: virtual machine;

[0107] The first cloud service platform node subset determination submodule is used to split the cloud service platform node set according to the first cloud service type to obtain the first cloud service platform node subset;

[0108] The second cloud service type determination submodule is used to determine the second cloud service type based on the billing management requirements; wherein, the second cloud service type is: the type of cloud service that needs to be provided by the cloud service platform node, as determined by the billing management requirements;

[0109] The service type matching submodule is used to match the first cloud service type and the second cloud service type to determine the third cloud service type that matches the service type; wherein, the third cloud service type is the intersection of the first cloud service type and the second cloud service type;

[0110] The second cloud service platform node subset acquisition submodule is used to filter the first cloud service platform node subset according to the third cloud service type to obtain the second cloud service platform node subset; wherein, the second cloud service platform node subset is: the first cloud service platform node subset corresponding to the third cloud service type;

[0111] The node evaluation submodule is used to evaluate the first target cloud service platform nodes in each subset of second cloud service platform nodes, obtain evaluation scores, and sort the first target cloud service platform nodes according to the evaluation scores to obtain a sorted list of the first target cloud service platform nodes. The evaluation of the first target cloud service platform nodes is as follows: the evaluation of the service provided by the first target cloud service platform node is based on the quality, timeliness and cost of the resources provided by the first target cloud service platform node. The higher the quality of the resources, the more timely the resources are provided, and the lower the cost, the higher the corresponding evaluation score.

[0112] The second target cloud service platform node determination submodule is used to determine the first target cloud service platform node that ranks first in each sorting list and use it as the second target cloud service platform node; wherein, the second target cloud service platform node is the result obtained by summing the first target cloud service platform nodes that rank first in each sorting list;

[0113] The second cloud resource deployment submodule is used to deploy cloud resources through the second target cloud service platform node.

[0114] The working principle and beneficial effects of the above technical solution are as follows:

[0115] The types, quality, and costs of cloud service resources provided by cloud service platform nodes vary, necessitating screening. Therefore, this application introduces a first cloud service type for cloud service platform nodes. Based on this first cloud service type, the set of cloud service platform nodes is split to obtain a subset of first cloud service platform nodes. Simultaneously, a second cloud service type corresponding to the charging management requirements is determined. Service types of the first and second cloud service types are matched to identify a third cloud service type that matches the service type, and the subset of second cloud service platform nodes corresponding to the third cloud service type is then selected. The first target cloud service platform nodes in each subset of second cloud service platform nodes are evaluated to obtain an evaluation score. Based on the evaluation score, the first target cloud service platform nodes are ranked to obtain a ranking list. The highest-ranking first target cloud service platform nodes in each ranking list are collectively used as second target cloud service platform nodes. Deploying corresponding cloud resources through these second target cloud service platform nodes improves the screening efficiency of second target cloud service platform nodes.

[0116] In one embodiment, the second cloud resource deployment submodule includes:

[0117] The cloud service account creation unit is used to create a cloud service account and obtain access credentials for that account to the second target cloud service platform node. The cloud service account refers to the account information of the toll station on the second target cloud service platform node. The access credentials are an access key, API key, or token.

[0118] The target configuration resource determination unit is used to access the second target cloud service platform node through a cloud service account and determine the target configuration resource; wherein, the target configuration resource is: the cloud platform resource provided by the second target cloud service platform node;

[0119] The test unit is used to configure target resources and perform runtime tests; the runtime test involves simulating a car passing through a toll booth to check whether the system's toll management behavior is reasonable.

[0120] The supplementary strategy determination unit is used to determine the supplementary strategy based on the results of the running test; wherein, the running test result is the result of the running test, such as: storage data overflow; the supplementary strategy is: resource adjustment strategy;

[0121] The strategy setting unit is used to set supplementary strategies and then complete the deployment.

[0122] The working principle and beneficial effects of the above technical solution are as follows:

[0123] This application establishes a unified cloud service account across multiple secondary target cloud service platform nodes, which is more convenient. It obtains access credentials for the cloud service account to the secondary target cloud service platform nodes, and then uses the cloud account to access and determine the target configuration resources. After determination, the corresponding target configuration resources are configured and tested. Based on the test results, a supplementary strategy is determined. Based on the target configuration resources, supplementary strategies are then implemented to complete the deployment, making the resource deployment process more efficient.

[0124] This invention provides an intelligent highway toll collection management system based on cloud technology, which also includes:

[0125] The anomaly handling subsystem is used to acquire toll-related anomalies during the toll management process of the target highway and to handle these anomalies accordingly. The toll management process refers to the use of information technology to manage and optimize the highway toll system; toll-related anomalies are abnormal situations or events occurring on the highway related to the toll process, such as toll system malfunctions, incorrect billing, payment problems, or other discrepancies with the toll process; and anomaly handling involves correcting these anomalies.

[0126] The working principle and beneficial effects of the above technical solution are as follows:

[0127] During the toll collection process, abnormal situations may occur, such as failed deductions and billing errors, which affect toll collection efficiency and driver experience. Therefore, this application monitors and acquires abnormal toll collection events in real time and handles them accordingly, thereby improving the timeliness of abnormal handling.

[0128] In one embodiment, the exception handling subsystem includes:

[0129] The first event feature set acquisition module is used to determine the first event feature set of charging anomaly events based on a preset event feature extraction template. The preset event feature extraction template is a pre-set template for charging anomaly events to compare and extract event features. The event features are: the characteristic representation of the event type and the specific content of the event.

[0130] The risk level determination module is used to determine the risk level based on the first event feature set and the preset risk level library; wherein, the preset risk level library includes: multiple one-to-one corresponding event feature sets and risk levels;

[0131] The second event feature set determination module is used to determine the corresponding first event feature set as the second event feature set if the risk level is greater than or equal to a preset risk level threshold; wherein, the preset risk level threshold is manually set in advance.

[0132] The target processing strategy determination module is used to determine the target processing strategy based on a preset processing strategy library and according to the second event feature set; wherein, the preset processing strategy library includes: multiple one-to-one corresponding event feature sets and target processing strategies;

[0133] The strategy type acquisition module is used to acquire the strategy type of the target processing strategy. The strategy types include: immediate strategy and future strategy. Immediate strategy is a processing strategy that can be implemented immediately, such as re-executing the deduction operation if the charging anomaly event is a charging failure. Future strategy is a processing strategy that requires subsequent processing, such as automatically generating a fee appeal and displaying a fee appeal list to the car owner if the charging anomaly event is a billing error and the deduction has already occurred. The system will also remind the car owner to complete the appeal verification within the appeal period to avoid missing the fee refund deadline.

[0134] The real-time processing module is used to process the policy according to the corresponding target processing policy if the policy type is real-time policy; for example, updating the abnormal status of a highway card.

[0135] The Future State Verification Node Generation Module is used to generate future state verification nodes based on the corresponding target processing strategy if the strategy type is a future strategy. The future state verification node is the time point for checking the execution status of the future strategy; the future strategy is, for example, a fine within 15 days.

[0136] The reminder module is used to provide corresponding reminders to target personnel based on the progress of future state verification nodes and the execution progress of future strategies. The progress of future state verification nodes is manually set; the target personnel are the subjects of abnormal charging events, such as vehicle owners experiencing charging irregularities.

[0137] The working principle and beneficial effects of the above technical solution are as follows:

[0138] This application introduces an event feature extraction template to determine the first event feature set of charging anomalies. Based on the first event feature set and a pre-defined risk level library, the risk level is determined. The first event feature set where the risk level is greater than or equal to the risk level threshold is then identified and used as the second event feature set. A processing strategy library is introduced, and the target processing strategy is determined based on the second event feature set. Strategy types are introduced; when the strategy type is an immediate strategy, direct processing is implemented; when the strategy type is a future strategy, a future state verification node is introduced to monitor the execution progress in real time and alert the target personnel, making anomaly event management more suitable.

[0139] In one embodiment, the charging management subsystem includes:

[0140] The first scheduling identifier acquisition module is used to acquire the first scheduling identifier of the locally deployed resources after all cloud resources that need to be deployed have been deployed; wherein, the locally deployed resources are: cloud resources successfully deployed by the intelligent highway toll management platform; the first scheduling identifier is: the tag that triggers the scheduling of various locally deployed resources;

[0141] The second scheduling identifier acquisition module is used to acquire the current toll management requirements and acquire the second scheduling identifier based on the current toll management requirements. The current toll management requirements are: toll management tasks acquired in real time by the intelligent highway toll management platform. The rules for acquiring the second scheduling identifier based on the current toll management requirements are preset manually. The second scheduling identifier is: a tag of the locally deployed resources required to meet the current toll management requirements.

[0142] The toll management module is used to match the first and second scheduling identifiers, and to perform intelligent highway toll management based on the local deployment resources of the first scheduling identifier that has successfully matched. Specifically, scheduling identifier matching involves determining whether the first and second scheduling identifiers are identical.

[0143] The working principle and beneficial effects of the above technical solution are as follows:

[0144] This application introduces a first scheduling identifier for locally deployed resources, and simultaneously obtains a second scheduling identifier based on current toll management needs. The first and second scheduling identifiers are matched, and the locally deployed resources with the successfully matched first scheduling identifier are used for intelligent highway toll management, improving the accuracy of resource scheduling.

[0145] This invention provides a cloud-based intelligent highway toll management method, such as... Figure 2 As shown, it includes:

[0146] Step 1: Obtain the toll management requirements of the target highway;

[0147] Step 2: Deploy cloud resources based on cloud technology and according to the requirements of billing management;

[0148] Step 3: Once all the cloud resources that need to be deployed are deployed, implement intelligent highway toll management for the target highway.

[0149] In one embodiment, step 1: obtaining the toll management requirements of the target highway includes:

[0150] Obtain the trigger information for the fee management request, parse the trigger information, and obtain the fee management request.

[0151] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A cloud-based intelligent highway toll collection management system, characterized in that: include: The toll management requirements acquisition subsystem is used to acquire the toll management requirements of the target highway. The cloud resource deployment subsystem is used to deploy cloud resources based on cloud technology and according to billing management requirements. The toll collection management subsystem is used to perform intelligent toll collection management for the target highway after all the cloud resources that need to be deployed have been deployed. The cloud resource deployment subsystem includes: The cloud service platform node set acquisition module is used to acquire cloud service platform node sets based on cloud technology and according to billing management requirements. The cloud resource deployment module is used to deploy cloud resources through a set of cloud service platform nodes; The cloud resource deployment module includes: The pre-deployment submodule is used to perform pre-deployment and obtain pre-deployment results before deploying cloud resources through the cloud service platform node set; The first cloud resource deployment submodule is used to deploy cloud resources based on pre-deployment results and billing management requirements; Pre-deployment submodules include: The historical toll record acquisition unit is used to acquire the historical toll record set of the target expressway. The historical charging time acquisition unit is used to parse historical charging records in the historical charging record set and obtain historical charging times. The target timeline marking unit is used to expand historical charging records on a preset target timeline according to the historical charging time. The time traversal unit is used to traverse each point in time on the target timeline in chronological order. During each traversal, the historical moment of the point being traversed is extracted. The current time acquisition unit is used to acquire the current time. The time matching unit is used to match historical time with the current time. If the time matching is successful, the time point corresponding to the historical time is used as the target time point. The first record feature set extraction unit is used to extract the first record feature set of historical charging records at the target time point; The predictive charging management demand determination unit is used to determine the predicted charging management demand based on the first record feature set and the preset demand comparison template, wherein the demand comparison template includes: a one-to-one corresponding second record feature set and the target predicted charging management demand; The pre-deployment unit is used for pre-deployment based on predicted billing management needs; The exception handling subsystem is used to acquire toll-related exception events during the toll management process of the target highway and to perform exception handling based on these events. The exception handling subsystem includes: The first event feature set acquisition module is used to determine the first event feature set of charging anomaly events based on a preset event feature extraction template. The risk level determination module is used to determine the risk level based on the first event feature set and the preset risk level library; The second event feature set determination module is used to determine the first event feature set as the second event feature set if the risk level is greater than or equal to the preset risk level threshold. The target processing strategy determination module is used to determine the target processing strategy based on a preset processing strategy library and the second event feature set. The strategy type acquisition module is used to acquire the strategy type of the target processing strategy, wherein the strategy type includes: immediate strategy and future strategy; The real-time processing module is used to process the policy according to the corresponding target processing policy if the policy type is real-time policy. The future state verification node generation module is used to generate future state verification nodes according to the corresponding target processing strategy if the strategy type is future strategy. The reminder module is used to remind target personnel of the node progress and the execution progress of future strategies based on the future status of the check node.

2. The intelligent highway toll collection management system based on cloud technology as described in claim 1, characterized in that, The fee management requirements acquisition subsystem includes: The charging management requirement acquisition module is used to obtain the trigger information for charging management requirements, parse the trigger information, and obtain the charging management requirements.

3. The intelligent highway toll management system based on cloud technology as described in claim 1, characterized in that, The cloud resource deployment module also includes: The first cloud service type acquisition submodule is used to acquire the first cloud service type of the cloud service platform nodes in the cloud service platform node set. The first cloud service platform node subset determination submodule is used to split the cloud service platform node set according to the first cloud service type to obtain the first cloud service platform node subset; The second cloud service type determination submodule is used to determine the second cloud service type based on billing management requirements; The service type matching submodule is used to match the first cloud service type and the second cloud service type to determine the third cloud service type that matches the service type; The second cloud service platform node subset acquisition submodule is used to filter the first cloud service platform node subset according to the third cloud service type to obtain the second cloud service platform node subset. The node evaluation submodule is used to evaluate the first target cloud service platform node in each subset of second cloud service platform nodes, obtain an evaluation score, and sort the first target cloud service platform nodes according to the evaluation score to obtain a sorted list of the first target cloud service platform nodes. The second target cloud service platform node determination submodule is used to determine the first target cloud service platform node that ranks highest in each sorting list and use it as the second target cloud service platform node. The second cloud resource deployment submodule is used to deploy cloud resources through the second target cloud service platform node.

4. The intelligent highway toll collection management system based on cloud technology as described in claim 3, characterized in that, The second cloud resource deployment submodule includes: The cloud service account creation unit is used to create a cloud service account and obtain access credentials for the cloud service account to the second target cloud service platform node. The target configuration resource determination unit is used to access the second target cloud service platform node through a cloud service account and determine the target configuration resources. Run the test unit to configure target configuration resources and perform runtime tests; The supplementary strategy determination unit is used to determine the supplementary strategy based on the results of the test run; The strategy setting unit is used to set supplementary strategies and then complete the deployment.

5. The intelligent highway toll management system based on cloud technology as described in claim 1, characterized in that, The fee management subsystem includes: The first scheduling identifier acquisition module is used to acquire the first scheduling identifier of the locally deployed resources after all cloud resources that need to be deployed have been deployed. The second scheduling identifier acquisition module is used to acquire the current charging management requirements and acquire the second scheduling identifier based on the current charging management requirements. The toll management module is used to match the first and second scheduling identifiers and perform intelligent highway toll management based on the local deployment resources of the first scheduling identifier that has successfully matched the scheduling identifier.

6. A cloud-based intelligent highway toll management method, applied to the toll management system as described in any one of claims 1-5, characterized in that, include: Step 1: Obtain the toll management requirements of the target highway; Step 2: Deploy cloud resources based on cloud technology and according to the requirements of billing management; Step 3: Once all the cloud resources that need to be deployed are deployed, implement intelligent highway toll management for the target highway.

7. The intelligent highway toll management method based on cloud technology as described in claim 6, characterized in that, Step 1: Obtain the toll management requirements of the target highway, including: Obtain the trigger information for the fee management request, parse the trigger information, and obtain the fee management request.

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