Interactive cash payment robot

Abstract

The invention provides an interactive cash payment robot, and relates to the technical field of robots. Vehicle travel data are acquired through an information acquisition module; a travel interaction degree is generated through a plug-in card identification module, and a dynamic identification node when the interactive cash payment robot performs plug-in card identification is generated according to the travel interaction degree; verification configuration parameters during payment approval are determined through an element processing module, and nesting matching is performed on payment element information during vehicle payment according to the verification configuration parameters; a payment decision label during payment configuration difference is called through a payment support module, then dynamic matching data corresponding to a payment mode is determined, and then the payment mode of the interactive cash payment robot is dispatched and controlled back according to the dynamic matching data; the payment element information is identified and verified through the bill printing module, and the payment accuracy of the interactive cash payment robot can be improved under the influence of identification errors or delay of the interactive cash payment robot.

Description

Technical Field

[0001] The present application relates to the field of robotics, and more specifically, to an interactive cash payment robot. Background Art

[0002] A robot refers to an automated device or system that combines electronic, mechanical and computer technologies, and can perform pre-programmed tasks or make autonomous decisions to a certain extent. These devices usually have the ability to perceive, process information, make decisions and execute actions. Robots can obtain information about the external environment through sensors, and make corresponding operations after internal calculations or algorithm processing.

[0003] Existing interactive cash payment robots operate efficiently through automated processes and are mainly used in scenarios such as highway toll stations. Their operation first relies on sensors and card reading devices. When a user inserts a card (such as a pass card or a payment card), the robot immediately reads the vehicle information and payment information. Then, the robot communicates with the backend server in real time through the internal payment system to check the vehicle's traffic records and calculate the payable fees. After confirming the payment information, the robot completes the payment processing according to the payment method selected by the user (such as cash or electronic payment). In the cash payment mode, the robot is equipped with cash recognition and change functions, which can accurately identify the amount of banknotes and make change according to the payment amount. In the electronic payment mode, the robot completes the payment transaction safely and quickly through the connection with the payment gateway. However, in the process of collecting and paying fees, the payment robot relies on traditional card recognition and cannot dynamically adjust the recognition accuracy, which leads to recognition errors or delays in high traffic or special payment scenarios, thereby reducing the payment accuracy of the interactive cash payment robot. Therefore, how to improve the payment accuracy of the interactive cash payment robot under the influence of recognition errors or delays is a problem faced by the industry. Summary of the invention

[0004] The present application provides an interactive cash payment robot, which can improve the payment accuracy of the interactive cash payment robot when the interactive cash payment robot has recognition errors or delays.

[0005] The present application provides an interactive cash payment robot, the payment robot comprising: An information collection module is used to collect vehicle travel data on the exit lanes of the target highway toll station; A card insertion recognition module, used to extract the vehicle's trip participation information from the vehicle trip data, generate a trip interaction degree according to the trip participation information, and generate a dynamic recognition node when the interactive cash payment robot performs card insertion recognition according to the trip interaction degree; An element processing module is used to obtain the behavior status information of the vehicle when paying the fee, determine the verification configuration parameters when approving the payment according to the behavior status information, and nest and coordinate the payment element information when the vehicle pays the fee according to the verification configuration parameters; The payment support module is used to call the payment decision tag when the payment configuration difference is detected according to the payment authority information updated after the payment verification of the interactive cash payment robot, determine the dynamic matching data corresponding to the payment method according to the payment decision tag and the dynamic identification node, and then schedule the payment method of the interactive cash payment robot according to the dynamic matching data; The receipt printing module is used for the interactive cash payment robot to identify and verify the payment element information according to the payment method after scheduling return control, and print the payment receipt.

[0006] In this embodiment, vehicle travel data on the exit lane of the target highway toll station is collected by high-resolution cameras, geomagnetic sensors and lidar.

[0007] In this embodiment, the trip participation information refers to the interaction data between the vehicle and other vehicles or traffic facilities during the driving process.

[0008] In this embodiment, generating a dynamic identification node when the interactive cash payment robot performs card insertion identification according to the trip interaction degree specifically includes: Determine the itinerary pattern of the interactive cash payment robot when inserting a card for recognition during targeted advertising delivery according to the itinerary interactivity; Determine the identification anchor point of the card insertion according to the travel rule; Generate an identification information sequence for card insertion identification according to the identification anchor point; The dynamic identification node when the interactive cash payment robot performs card insertion identification is determined according to the identification information sequence.

[0009] In this embodiment, the dynamic identification node represents an identification position adjusted according to real-time traffic conditions during the card insertion identification process.

[0010] In this embodiment, the behavior status information refers to various data of vehicle behavior recorded during the vehicle payment process, including the vehicle's location, operating behavior, time record (such as dwell time), and interaction status.

[0011] In this embodiment, the payment decision tag when calling the payment configuration difference according to the payment authority information updated after the interactive cash payment robot payment verification specifically includes: Obtain payment authority information after payment verification and update by the interactive cash payment robot; Determine the matching freedom when payment configuration differences are made according to the payment authority information; A payment decision label when determining payment configuration differences based on the matching degrees of freedom.

[0012] In this embodiment, the payment decision tag refers to an identifier used to guide the user to select a payment method and configuration.

[0013] In this embodiment, determining the dynamic matching data corresponding to the payment method according to the payment decision tag and the dynamic identification node specifically includes: Determining a payment session set corresponding to the payment method according to the payment decision tag; Determine a dynamic control list corresponding to the payment method according to the dynamic identification node; Dynamic matching data corresponding to the payment method is determined according to the payment session set and the dynamic control list.

[0014] In this embodiment, the payment methods include three payment methods: scanning code payment, swiping ETC card and cash payment for lane payment.

[0015] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects: By collecting the vehicle travel data on the exit lane of the target highway toll station; extracting the vehicle's travel participation information from the vehicle travel data, generating the travel interaction degree according to the travel participation information, and generating the dynamic identification node when the interactive cash payment robot performs card insertion identification according to the travel interaction degree; by obtaining the behavior state information of the vehicle when paying the fee, determining the verification configuration parameters when approving the payment according to the behavior state information, and nesting and matching the payment element information when the vehicle pays the fee according to the verification configuration parameters; by reading the payment authority information updated after the payment verification of the interactive cash payment robot, calling the payment decision tag when the payment configuration difference is based on the payment authority information, determining the dynamic matching data corresponding to the payment method according to the payment decision tag and the dynamic identification node, and then scheduling the payment method of the interactive cash payment robot according to the dynamic matching data; the interactive cash payment robot identifies and verifies the payment element information according to the payment method after scheduling and controlling, and prints the payment receipt.

[0016] It can be seen that in the present application, the payment element information is identified and verified under the influence of recognition errors or delays in the interactive cash payment robot; wherein, through the information collection module, the vehicle travel data on the exit lanes of the highway toll station can be collected in real time, ensuring that the system has efficient data input capabilities, supporting the automation and intelligence of the entire payment process, thereby improving the integrity and accuracy of data collection; through the card insertion recognition module, the travel participation information is extracted from the vehicle travel data, the travel interactivity is generated, and the dynamic recognition node during card insertion recognition is generated, the accuracy of card recognition is dynamically adjusted, and different traffic and payment scenarios are adapted, which effectively reduces recognition errors and delays, significantly improves the accuracy and efficiency of card recognition, and enhances the robot's ability to operate in complex environments. The module can obtain the behavior status information of vehicles during payment through the element processing module, determine the verification configuration parameters, and realize the dynamic nesting and coordination of payment element information. This module ensures the accuracy of payment verification, can flexibly respond to various payment scenarios, reduce the error rate in the payment process, and improve the flexibility and consistency of the payment process. Through the decision tags and dynamic identification nodes of payment configuration differences, the most suitable payment method is dynamically matched and the robot is scheduled to execute back control, which improves the flexibility of payment methods and the level of intelligent decision-making, and ensures that the robot can automatically adjust according to different users and transaction conditions, thereby optimizing the payment experience and success rate. The bill printing module performs identification and verification of payment element information according to the scheduling back control of the payment method, and finally prints the payment bill.

[0017] To sum up, the technical solution adopted in this application can improve the payment accuracy of the interactive cash payment robot when the interactive cash payment robot has recognition errors or delays. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0019] Figure 1 It is a module structure diagram of the interactive cash payment robot provided according to the present application; Figure 2 It is a schematic diagram of a process for generating a dynamic identification node according to the present application; Figure 3 It is a flowchart of determining and verifying configuration parameters provided in this application. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0021] The embodiment of the present application provides an interactive cash payment robot, the core of which is to obtain advertising and marketing data generated during Internet search and stored in a receiving end through an acquisition module; collect vehicle travel data on the exit lane of a target highway toll station; extract the travel participation information of the vehicle from the vehicle travel data, generate a travel interaction degree according to the travel participation information, and generate a dynamic identification node when the interactive cash payment robot performs card insertion identification according to the travel interaction degree; obtain the behavior state information of the vehicle when paying, determine the verification configuration parameters when approving the payment according to the behavior state information, and nest and match the payment element information when the vehicle pays according to the verification configuration parameters; read the payment authority information updated after the payment verification of the interactive cash payment robot, call the payment decision tag when the payment configuration difference is different according to the payment authority information, determine the dynamic matching data corresponding to the payment method according to the payment decision tag and the dynamic identification node, and then schedule the payment method of the interactive cash payment robot according to the dynamic matching data; the interactive cash payment robot identifies and verifies the payment element information according to the payment method after scheduling and returning the control, and prints the payment receipt. The above scheme is adopted to improve the payment accuracy of the interactive cash payment robot under the influence of recognition errors or delays in the interactive cash payment robot.

[0022] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. Figure 1 As shown, this figure is a module structure diagram of an interactive cash payment robot according to this embodiment of the present application. The payment robot includes: an information collection module 100, a card insertion recognition module 200, an element processing module 300, a payment support module 400 and a bill printing module 500, which are described as follows: The information collection module 100 is used to collect vehicle travel data on the exit lane of the target highway toll station.

[0023] In the specific implementation, first, high-resolution cameras, geomagnetic sensors, lidar and other equipment are selected for deployment to capture the information of passing vehicles in real time. These data collection devices are integrated with the central data processing system to ensure real-time transmission. Subsequently, the collected data is processed in real time, the original information is cleaned and sorted, and timestamps and positioning information are attached to ensure the timeliness and accuracy of the data. Next, the processed data is stored in a cloud database or a local database, and a regular backup mechanism is set to ensure data security. Finally, data analysis tools are used to conduct in-depth analysis of the stored data, extract key information such as travel time, vehicle type and traffic statistics, and display the analysis results through data visualization tools. That is, high-resolution cameras, geomagnetic sensors and lidar can be used to collect vehicle travel data on the exit lanes of the target highway toll station, which will not be repeated here.

[0024] It should be noted that in this application, vehicle travel data refers to relevant information recorded during the vehicle's driving process, including the vehicle's driving time, driving distance, route, parking time, vehicle type and traffic status, etc.

[0025] The card insertion recognition module 200 is used to extract the vehicle's trip participation information from the vehicle trip data, generate a trip interactivity based on the trip participation information, and generate a dynamic identification node when the interactive cash payment robot performs card insertion recognition based on the trip interactivity.

[0026] In specific implementation, the extraction of the vehicle's trip participation information from the vehicle trip data can be achieved in the following manner, namely: first, selecting fields related to trip participation from the vehicle trip data, such as vehicle ID, travel time, entry and exit information, driving speed, etc., and then filtering the data to remove abnormal values ​​or incomplete data records (such as data with missing travel time or vehicle ID); then, determining the specific content of the trip participation information, such as the vehicle's driving route, stop time and interaction information (such as crossing time with other vehicles), using a data analysis algorithm (such as cluster analysis) to analyze the vehicle trip and identify the patterns and relationships of trip participation, for example, by analyzing the vehicle's driving trajectory in a specific time period, it is determined whether it interacts with other vehicles; finally, the extracted trip participation information is organized into a report or data table.

[0027] It should be noted that in this application, trip participation information refers to the interaction data between the vehicle and other vehicles or transportation facilities during driving, including the vehicle's driving route, travel time, stop time, intersection time, and mutual influence between vehicles.

[0028] In specific implementation, the generation of trip interaction according to the trip participation information can be achieved in the following manner, namely: first, clarify the key parameters used to calculate the trip interaction, such as time interaction, spatial interaction (i.e., distance), and dwell time between vehicles, etc., and set an interaction model. For example, vehicles are considered to interact with each other within a certain time and distance range; then, weights are assigned to different interaction parameters to reflect their importance in the total interaction. For example, the time interaction weight can be set to 0.6 and the spatial interaction weight can be set to 0.4 according to actual traffic conditions, and then the trip participation information is sorted to ensure that it has a format that can be used to calculate the interaction, including the vehicle's travel time, location coordinates, etc.; finally, a matching algorithm (such as matching based on a time window) is applied to identify interaction events within a set time range and calculate the corresponding time and space interactions. For example, for two vehicles whose distance is less than a certain threshold in the same time period, they are recorded as interacting with each other. The interaction between vehicles can be calculated using the set interaction model and parameters. For example, the trip interaction can be calculated by the following formula, namely: Trip interaction = α Time Interaction + β Spatial interaction, where α and β are parameter weights determined based on expert experience or experimental data, which will not be described here.

[0029] It should be noted that, in the present application, the trip interaction degree refers to the degree of mutual influence between vehicles and between vehicle tolls within a specific time.

[0030] Preferably, in this embodiment, a dynamic identification node when the interactive cash payment robot performs card insertion identification is generated according to the trip interaction degree, referring to Figure 2 As described above, the figure is a schematic diagram of the process of generating a dynamic identification node in some embodiments of the present application. In this embodiment, the generation of a dynamic identification node can be implemented by the following steps: In step S21, the itinerary pattern of the interactive cash payment robot when performing card insertion recognition during targeted advertising delivery is determined according to the itinerary interactivity; In step S22, the identification anchor point of the card insertion is determined according to the travel rule; In step S23, an identification information sequence for card insertion identification is generated according to the identification anchor point; In step S24, a dynamic identification node when the interactive cash payment robot performs card insertion identification is determined according to the identification information sequence.

[0031] In the specific implementation, firstly, the data of the trip interaction degree is used to statistically analyze the travel time and position of the vehicle, and the travel mode of the vehicle when passing through the toll station (such as average stay time, travel speed, etc.) is identified, that is, the travel pattern is obtained; then, based on the identified travel pattern, the best card insertion recognition anchor point (such as the specific position where the vehicle passes through the toll station) is determined. These positions should meet the characteristics of high interaction and traffic. Among them, the accuracy of these anchor points is confirmed by sensor data and video monitoring to ensure that the card insertion behavior of the passing vehicle can be captured in time; then, according to the determined recognition anchor point, the key information that needs to be captured during the card insertion recognition is set, including vehicle ID, card insertion time, card insertion position, etc., to form a series of recognition information, among which sequence coding is to encode this information to form a structured data sequence for subsequent processing and transmission; finally, according to the recognition information sequence, the dynamic recognition node of the card insertion recognition is set to ensure that the robot can flexibly adapt to different traffic conditions, and then use the real-time data feedback mechanism to adjust the dynamic recognition node to ensure that when the vehicle mobility changes, it can still maintain efficient recognition capabilities. It will not be repeated here.

[0032] It should be noted that, in the present application, travel pattern refers to the traffic pattern of a vehicle at a specific time and place, including dwell time, traffic speed, etc., reflecting the behavioral characteristics of the vehicle in the traffic flow; identification anchor point refers to a specific position selected during the card insertion and recognition process, where the robot performs recognition to improve the accuracy and efficiency of recognition; recognition information sequence refers to a set of structured data required during the card insertion and recognition process, including vehicle information, card insertion time and location, etc.; dynamic identification node represents the recognition position adjusted according to real-time traffic conditions during the card insertion and recognition process, which can improve the accuracy and adaptability of recognition.

[0033] The element processing module 300 is used to obtain the behavior status information of the vehicle when paying the fee, determine the verification configuration parameters when approving the payment according to the behavior status information, and nest and coordinate the payment element information when the vehicle pays the fee according to the verification configuration parameters.

[0034] In specific implementation, the following methods can be used to obtain the behavioral status information of the vehicle when paying the fee, namely: first, by deploying a variety of sensors (such as video surveillance, infrared sensors and geomagnetic sensors) at toll booths and around interactive cash payment robots to collect data in real time, it is ensured that all behaviors of the vehicle during the payment process can be fully monitored; secondly, machine learning or computer vision algorithms are used to analyze the collected data to identify the behavioral status of the vehicle, such as approaching the payment machine, card insertion action and payment status; next, a reasonable data structure is established to store the identified behavioral status information in the database, and ensure that the information is updated in real time to maintain the accuracy and timeliness of the data; finally, data analysis tools are used to conduct in-depth analysis of the stored behavioral status information, identify high-frequency behavioral patterns and abnormal states, and provide real-time feedback based on the analysis results to adjust the working strategy of the payment robot, that is, the behavioral status information of the vehicle when paying the fee is obtained, which will not be repeated here.

[0035] It should be noted that in this application, behavioral status information refers to the various data recorded on the vehicle's behavior during the vehicle's payment process, including the vehicle's location, operating behavior (such as card insertion, payment), time records (such as stay time), interaction conditions (such as the frequency and order of interaction with the payment robot), etc.

[0036] Preferably, in this embodiment, the verification configuration parameters for payment approval are determined according to the behavior status information, referring to Figure 3 As described above, the figure is a schematic diagram of a process for determining verification configuration parameters in some embodiments of the present application. In this embodiment, determining the verification configuration parameters can be implemented by the following steps: In step S31, payment matching information of a charging target is determined according to the behavior status information; In step S32, the payment matching information is subjected to feature screening to obtain the payment fusion amount of the charging target; In step S33, the approval deviation interval for payment approval is determined; In step S34, the verification configuration parameters for payment approval are determined according to the approval deviation interval and the payment fusion amount.

[0037] In the specific implementation, first, the collected behavior status information is integrated with the basic information of the vehicle (such as license plate number, vehicle model, account information, etc.) to form comprehensive payment matching information. The matching algorithm (such as rule matching or machine learning) can be used to analyze the behavior status information and identify eligible payment targets, including the payment history and time period characteristics of each vehicle; then, key features (such as payment amount, payment method, historical payment behavior, etc.) are extracted from the payment matching information, and classified and weighted. Feature selection algorithms (such as recursive feature elimination or principal component analysis) can be used to screen out those that are relevant to payment approval. The most relevant features are used to form the payment fusion amount; then, based on historical data and behavioral status information, the normal range and deviation of various payment behaviors are analyzed to determine a reasonable deviation interval. Statistical analysis methods (such as mean and standard deviation calculation) can be used to set the approval deviation interval to adapt to demand fluctuations in different payment scenarios; finally, based on the approval deviation interval and the payment fusion amount, specific verification configuration parameters (such as the amount range required to be verified, the required information verification method, etc.) are calculated. In other embodiments, other methods can also be used to determine the verification configuration parameters for payment approval, which are not limited here.

[0038] It should be noted that in this application, payment matching information represents a data set for judging payment needs and status; payment fusion volume refers to the data indicator used for the subsequent approval process formed by screening and integrating key features in the payment matching information; approval deviation interval refers to the allowable payment amount fluctuation range during the approval process, so as to flexibly handle various payment situations; verification configuration parameters refer to the verification conditions and parameter settings required during the payment approval process to ensure the accuracy and validity of the payment information.

[0039] In specific implementation, the nesting and matching of the payment element information when the vehicle pays the fee can be determined in the following manner according to the verification configuration parameters, namely: first, the system obtains relevant payment element information from the vehicle identification module, including vehicle identity information, amount payable, payment method, etc., and integrates this information according to the verification configuration parameters. Through the defined verification conditions, the system uses an algorithm to accurately match the integrated payment element information to ensure the accuracy and consistency of the information. For example, by comparing the vehicle information with its account records to confirm its payment eligibility and status, secondly, the system uses real-time monitoring data to dynamically adjust the payment element information to deal with possible abnormal situations. If it is found during the payment process that the payment amount exceeds the approved deviation range or the payment method does not match, the system will immediately trigger the exception handling mechanism, and adjust the verification strategy through feedback to complete the nesting and matching of the payment element information when the vehicle pays the fee.

[0040] It should be noted that in this application, payment element information refers to all data directly related to the payment operation during the vehicle payment process, including the vehicle's identification information (such as license plate number and model), the amount to be paid, the payment method used (such as credit card, cash or e-wallet), the specific time of the payment behavior, and the payment status (such as success, failure or pending).

[0041] The payment support module 400 calls the payment decision tag when the payment configuration difference is detected according to the payment authority information updated after the interactive cash payment robot is verified, determines the dynamic matching data corresponding to the payment method according to the payment decision tag and the dynamic identification node, and then schedules the payment method of the interactive cash payment robot according to the dynamic matching data. In this embodiment, the payment decision tag when calling the payment configuration difference according to the payment authority information updated after the interactive cash payment robot payment verification can be implemented by the following steps, namely: Obtain payment authority information after payment verification and update by the interactive cash payment robot; Determine the matching freedom when payment configuration differences are made according to the payment authority information; A payment decision label when determining payment configuration differences based on the matching degrees of freedom.

[0042] In the specific implementation, first, set a timing or real-time update mechanism to ensure that the interactive cash payment robot can obtain the latest payment authority information, which may include user account status, available payment methods, limits, etc.; then, based on the obtained payment authority information, analyze the availability of different payment methods and their corresponding payment configuration differences. For example, some payment methods may be disabled under certain conditions or have different limits. Among them, an algorithm is used to calculate the matching freedom, that is, the flexibility of the payment methods and configurations that users can choose under the current payment authority; finally, according to the analyzed matching freedom, set corresponding payment decision tags. These tags can indicate specific payment configurations, preferred payment methods, etc. Use a rule engine or machine learning algorithm to automatically generate and update payment decision tags to adapt to real-time changes in payment authority information and matching freedom. I will not go into details here.

[0043] It should be noted that, in this application, payment authority information refers to payment capability data related to a user account, including information such as available payment methods, payment limits and account status; matching freedom refers to the flexibility of the payment methods and configurations that a user can choose based on given payment authority information; and payment decision labels refer to identifiers used to guide users in selecting payment methods and configurations, which are generated based on payment authority information and matching freedom, and are intended to optimize the payment process.

[0044] In this embodiment, determining the dynamic matching data corresponding to the payment method according to the payment decision tag and the dynamic identification node can be implemented by the following steps: Determining a payment session set corresponding to the payment method according to the payment decision tag; Determine a dynamic control list corresponding to the payment method according to the dynamic identification node; Dynamic matching data corresponding to the payment method is determined according to the payment session set and the dynamic control list.

[0045] In specific implementation, first, payment session information related to a specific payment method is extracted from a system database through payment decision tags. Such session information may include the user's historical transaction records, current transaction status, and the frequency of use of the payment method, etc. The extracted session information is then classified according to the payment method to form a payment session set for each payment method; then, control parameters related to the payment method are extracted from the system based on the dynamic identification node. These parameters may include payment limits, available payment channels, user verification requirements, etc.; finally, the payment session set is integrated with the dynamic control list, and the relationship between the two is analyzed to determine the best matching data for a specific payment method in the current context. A matching algorithm (such as priority sorting, conditional judgment) may be used to identify the most suitable payment configuration and process to generate dynamic matching data. In other embodiments, other methods may also be used to determine the dynamic matching data corresponding to the payment method, which is not limited here.

[0046] It should be noted that, in this application, a payment session set refers to a collection of transaction information related to a specific payment method, including historical transaction records, current transaction status, etc., which is used to guide subsequent payment decisions; a dynamic control list refers to a collection of control parameters related to a specific payment method, including limits, channel selection, verification requirements, etc., which is used to optimize the payment process; dynamic matching data refers to data generated by integrating payment session sets and dynamic control lists under specific payment methods and scenarios, which is used to guide the optimization and personalization of payment processes.

[0047] In specific implementation, the scheduling and back control of the payment method of the interactive cash payment robot by the dynamic matching data can be achieved in the following manner, namely: first, the system identifies the current optimal payment method and its related configuration by parsing the dynamic matching data. This process includes a comprehensive analysis of factors such as the user's payment history, real-time payment authority, and market changes, so as to determine the payment option that best meets the user's needs and payment conditions; then, using decision-making algorithms, such as rule-based reasoning or machine learning models, the system evaluates the identified payment methods to ensure that the selected payment method can operate efficiently in the current environment, reduce payment risks and increase success rates; finally, combined with the real-time parameters in the dynamic control list, the system transmits the scheduling information of the selected payment method to the interactive cash payment robot, executes the adjustment and configuration of the payment method, and completes the scheduling and back control of the payment method of the interactive cash payment robot.

[0048] It should be noted that in this application, the payment methods include three payment methods: scanning code payment, swiping ETC card and cash payment for lane payment.

[0049] The receipt printing module 500, the interactive cash payment robot identifies and verifies the payment element information according to the payment method after scheduling return control, and prints the payment receipt.

[0050] In specific implementation, the interactive cash payment robot identifies and verifies the payment element information according to the payment method after scheduling feedback, and prints the payment receipt. This can be achieved in the following way: first, the robot obtains the payment element information provided by the user when inserting the card through the built-in sensors and reading devices. This information includes vehicle identification information, amount payable, payment method and related user identity verification information. The robot uses advanced image recognition technology and barcode / QR code scanning functions to ensure that the information read is accurate. Next, the system uses a preset algorithm to verify the data based on the payment method determined after scheduling feedback. The process first checks the integrity and consistency of the payment element information. For example, by comparing with the historical transaction data in the payment session set, the accuracy of the vehicle identity and the amount payable is ensured. At the same time, the system also needs to verify the applicability of the current payment method, including payment limits and whether there are specific usage conditions to ensure compliance with the requirements in the dynamic control list. Once the payment element information is verified, the robot will process the payment through a secure payment interface. This process involves real-time communication with the bank or payment gateway to ensure accurate transfer of funds. After the payment is completed, the system will generate a payment receipt based on the user's payment information and transaction status. The receipt content includes important information such as transaction time, vehicle information, payment amount, payment method and transaction status. Finally, the robot uses the built-in printer to print out the payment receipt and hand it to the user.

[0051] It can be seen that in the present application, the payment element information is identified and verified under the influence of recognition errors or delays in the interactive cash payment robot; wherein, through the information collection module, the vehicle travel data on the exit lanes of the highway toll station can be collected in real time, ensuring that the system has efficient data input capabilities, supporting the automation and intelligence of the entire payment process, thereby improving the integrity and accuracy of data collection; through the card insertion recognition module, the travel participation information is extracted from the vehicle travel data, the travel interactivity is generated, and the dynamic recognition node during card insertion recognition is generated, the accuracy of card recognition is dynamically adjusted, and different traffic and payment scenarios are adapted, which effectively reduces recognition errors and delays, significantly improves the accuracy and efficiency of card recognition, and enhances the robot's ability to operate in complex environments. The module can obtain the behavior status information of vehicles during payment through the element processing module, determine the verification configuration parameters, and realize the dynamic nesting and coordination of payment element information. This module ensures the accuracy of payment verification, can flexibly respond to various payment scenarios, reduce the error rate in the payment process, and improve the flexibility and consistency of the payment process. Through the decision tags and dynamic identification nodes of payment configuration differences, the most suitable payment method is dynamically matched and the robot is scheduled to execute back control, which improves the flexibility of payment methods and the level of intelligent decision-making, and ensures that the robot can automatically adjust according to different users and transaction conditions, thereby optimizing the payment experience and success rate. The bill printing module performs identification and verification of payment element information according to the scheduling back control of the payment method, and finally prints the payment bill.

[0052] To sum up, the technical solution adopted in this application can improve the payment accuracy of the interactive cash payment robot when the interactive cash payment robot has recognition errors or delays.

[0053] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0054] A person skilled in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, the storage medium including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically-erasable programmable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0055] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

Claims

1. An interactive cash payment robot, characterized in that: The payment robot includes: An information collection module is used to collect vehicle travel data on the exit lanes of the target highway toll station; A card insertion recognition module, used to extract the vehicle's trip participation information from the vehicle trip data, generate a trip interaction degree according to the trip participation information, and generate a dynamic recognition node when the interactive cash payment robot performs card insertion recognition according to the trip interaction degree; An element processing module is used to obtain the behavior status information of the vehicle when paying the fee, determine the verification configuration parameters when approving the payment according to the behavior status information, and nest and coordinate the payment element information when the vehicle pays the fee according to the verification configuration parameters; The payment support module is used to call the payment decision tag when the payment configuration difference is detected according to the payment authority information updated after the payment verification of the interactive cash payment robot, determine the dynamic matching data corresponding to the payment method according to the payment decision tag and the dynamic identification node, and then schedule the payment method of the interactive cash payment robot according to the dynamic matching data; The receipt printing module is used for the interactive cash payment robot to identify and verify the payment element information according to the payment method after scheduling return control, and print the payment receipt.

2. An interactive cash payment robot as claimed in claim 1, characterized in that: Vehicle travel data on the exit lanes of the target highway toll station is collected through high-resolution cameras, geomagnetic sensors and lidar.

3. An interactive cash payment robot as claimed in claim 1, characterized in that: Trip participation information refers to the interaction data between a vehicle and other vehicles or transportation facilities during driving.

4. An interactive cash payment robot as claimed in claim 1, characterized in that: Generating a dynamic identification node when the interactive cash payment robot performs card insertion identification according to the trip interaction degree specifically includes: Determine the itinerary pattern of the interactive cash payment robot when inserting a card for recognition during targeted advertising delivery according to the itinerary interactivity; Determine the identification anchor point of the card insertion according to the travel rule; Generate an identification information sequence for card insertion identification according to the identification anchor point; The dynamic identification node when the interactive cash payment robot performs card insertion identification is determined according to the identification information sequence.

5. An interactive cash payment robot as claimed in claim 1, characterized in that: The dynamic identification node represents the identification position adjusted according to the real-time traffic conditions during the card insertion identification process.

6. An interactive cash payment robot as claimed in claim 1, characterized in that: Behavior status information refers to the various data on vehicle behavior recorded during the vehicle payment process, including the vehicle's location, operating behavior, time records (such as dwell time), and interaction status.

7. An interactive cash payment robot as claimed in claim 1, characterized in that: The payment decision tags when calling the payment configuration difference according to the payment authority information updated after the interactive cash payment robot payment verification specifically include: Obtain payment authority information after payment verification and update by the interactive cash payment robot; Determine the matching freedom when payment configuration differences are made according to the payment authority information; A payment decision label when determining payment configuration differences based on the matching degrees of freedom.

8. An interactive cash payment robot as claimed in claim 1, characterized in that: Payment decision labels are labels used to guide users in selecting payment methods and configurations.

9. An interactive cash payment robot as claimed in claim 1, characterized in that: Determining the dynamic matching data corresponding to the payment method according to the payment decision tag and the dynamic identification node specifically includes: Determining a payment session set corresponding to the payment method according to the payment decision tag; Determine a dynamic control list corresponding to the payment method according to the dynamic identification node; Dynamic matching data corresponding to the payment method is determined according to the payment session set and the dynamic control list.

10. An interactive cash payment robot as claimed in claim 1, characterized in that: The payment methods include scanning code to pay for lane fees, swiping ETC cards and paying in cash.

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