Riding code recommendation method and device, equipment, storage medium and program product

Abstract

The invention discloses a riding code recommendation method and device, equipment, a storage medium and a program product, and relates to the technical field of information processing, and the method comprises the steps: obtaining a riding intention when it is detected that a user uses a riding APP in a target range of a target subway station, and sharing the riding intention to a pre-trained AI large model through a preset intention interface; using a pre-trained AI large model to analyze whether the user has the intention of using the riding code at the target subway station based on the riding intention; and if the pre-trained AI large model identifies that the user has the intention of using the riding code, recommending a riding APP to the user to generate or display the riding two-dimensional code. According to the method, the riding intention of the user is analyzed through the AI large model to confirm whether the user has the intention of opening the riding code at the target subway station, and the riding code service is recommended to the user in combination with the user position information, so that the opening efficiency of the riding code and the user experience are improved.

Description

Technical Field

[0001] The present application relates to the field of information processing technology, and in particular to a method, device, equipment, storage medium and program product for recommending a ride code. Background Art

[0002] Currently, when users use the ride code to take a ride, they need to open their mobile phones, find the ride APP, open the ride APP and enter the ride code function page. However, users sometimes cannot quickly find the location of the ride code APP, and the opening efficiency is low. Summary of the invention

[0003] The main purpose of this application is to provide a method, device, equipment, storage medium and program product for recommending a boarding code, aiming to solve the technical problem of how to improve the efficiency of opening the boarding code.

[0004] To achieve the above purpose, the present application proposes a method for recommending a ride code, which is applied to a mobile terminal, on which a ride APP is installed, and includes: When it is detected that the user uses the ride APP within the target range of the target subway station, the ride intention is obtained, and the ride intention is shared with the pre-trained AI big model through a preset intention interface; Utilizing the pre-trained AI big model to analyze whether the user intends to use the boarding code at the target subway station based on the boarding intention; If the pre-trained AI large model recognizes that the user has the intention to use the boarding code, the boarding APP is recommended to the user to generate or display the boarding QR code.

[0005] In one embodiment, before the step of obtaining the boarding intention and sharing the boarding intention with the pre-trained AI big model through the preset intention interface, the following steps are included: Initialize the intent template parameters for calling and viewing the boarding code on the boarding APP, and obtain and maintain the location information of the target subway station; According to the location information of the target subway station, it is identified whether the user uses the ride APP and turns on the ride code function within the target range of the target subway station. If so, the user's intention information is obtained.

[0006] In one embodiment, the step of obtaining the boarding intention and sharing the boarding intention to the pre-trained AI big model through a preset intention interface includes: Analyze the intention information according to the scenario to determine the user's intention to take the vehicle; The riding intention is shared with the pre-trained AI big model through the preset intention interface.

[0007] In one embodiment, the step of using the pre-trained AI big model to analyze whether the user has the intention to use the boarding code at the target subway station based on the boarding intention includes: Utilize the pre-trained AI big model to analyze the riding intention and extract the time information and location information when the user opens the riding APP; Based on the time information and location information when the user opens the ride APP, and combined with the user's ride history data, intention analysis is performed to determine whether the user intends to use the ride code within the target range of the target subway station.

[0008] In one embodiment, the step of performing intention analysis based on the time information and location information when the user opens the ride APP and in combination with the user's ride history data to determine whether the user intends to use the ride code within the target range of the target subway station includes: Determine whether the location information when the user opens the ride APP meets the target range of the target subway station; and According to the current time and historical ride data when the user opens the ride APP, the probability of the user using the ride code in the current scenario is evaluated; If the location information of the user when opening the ride APP meets the target range of the target subway station, and the probability of the user using the ride code in the current scenario is greater than a preset threshold, it is determined that the user has the intention to use the ride code within the target range of the target subway station.

[0009] In one embodiment, the step of recommending the ride APP to the user to generate or display a ride QR code includes: In response to a click operation input by the user according to the recommended ride APP, the recommended ride APP is invoked, and the intent template parameters are passed into the recommended ride APP; When the recommended ride APP is started, analyzing whether the user's intention is to use the ride code function according to the intention template parameters; If so, generate a QR code for boarding the bus and display the QR code to the user.

[0010] In addition, to achieve the above purpose, the present application also proposes a boarding code recommendation device, the boarding code recommendation device comprising: The intention sharing module is used to obtain the riding intention when it is detected that the user uses the riding APP within the target range of the target subway station, and share the riding intention with the pre-trained AI big model through the preset intention interface; An intention analysis module, used to analyze whether the user intends to use the boarding code at the target subway station based on the boarding intention by using the pre-trained AI big model; A recommendation module is used to recommend the ride APP to the user to generate or display the ride QR code if the pre-trained AI large model recognizes that the user has the intention to use the ride code.

[0011] In addition, to achieve the above-mentioned purpose, the present application also proposes a boarding code recommendation device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the boarding code recommendation method as described above.

[0012] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the boarding code recommendation method as described above are implemented.

[0013] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the boarding code recommendation method as described above.

[0014] The present application proposes a method, device, equipment, storage medium and program product for recommending a boarding code. The method includes: when it is detected that a user is using a boarding APP within the target range of a target subway station, obtaining the boarding intention, and sharing the boarding intention to a pre-trained AI big model through a preset intention interface; using the pre-trained AI big model to analyze whether the user has the intention to use the boarding code at the target subway station based on the boarding intention; if the pre-trained AI big model recognizes that the user has the intention to use the boarding code, recommending the boarding APP to the user to generate or display the boarding QR code. The method analyzes the user's boarding intention through the AI ​​big model to confirm whether the user has the intention to open the boarding code function at the target subway station, and recommends the "boarding code" service to the user in combination with the user's location information, thereby improving the efficiency of opening the boarding code and the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0016] 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, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 A flowchart of the first embodiment of the method for applying for a ride code recommendation is provided; Figure 2 A flowchart of the second embodiment of the method for recommending a ride code for this application; Figure 3 A brief flowchart of the method for recommending a ride code provided in Embodiment 1 and Embodiment 2 of the present application; Figure 4 This is a schematic diagram of the module structure of the device for recommending a ride code according to an embodiment of the present application; Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the method for recommending a ride code in an embodiment of the present application.

[0018] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0019] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0020] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0021] The main solution of the embodiment of the present application is: when it is detected that a user is using a ride APP within the target range of a target subway station, the ride intention is obtained, and the ride intention is shared to a pre-trained AI big model through a preset intention interface; the pre-trained AI big model is used to analyze whether the user has the intention to use the ride code at the target subway station based on the ride intention; if the pre-trained AI big model recognizes that the user has the intention to use the ride code, the ride APP is recommended to the user to generate or display the ride QR code.

[0022] In this embodiment, for the convenience of description, the following description is made with the mobile terminal as the execution subject.

[0023] Currently, when using the ride code to take a ride, users need to open their mobile phones, find the ride APP, open the ride APP and enter the ride code function page. However, users sometimes cannot quickly find the location of the ride code APP, and the opening efficiency is low.

[0024] This application provides a solution. When it is detected that a user is using a ride APP within the target range of a target subway station, the ride intention is obtained and shared with the pre-trained AI big model through a preset intention interface; the pre-trained AI big model is used to analyze whether the user has the intention to use the ride code at the target subway station based on the ride intention; if the pre-trained AI big model recognizes that the user has the intention to use the ride code, the ride APP is recommended to the user to generate or display the ride QR code. This solution has the intention analysis and recommendation capabilities of the AI ​​big model. When the user opens the ride APP near the subway station, the ride APP shares the ride intention with the AI ​​big model. After the AI ​​big model learns the intention rules, whenever the user arrives near the subway station, the ride APP is actively recommended to the user. The user can quickly click to enter the ride code function page to display the code to ride, and the recommendation based on the riding habits improves the user's travel experience.

[0025] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, etc. The following takes a mobile phone as an example to illustrate this embodiment and the following embodiments.

[0026] Based on this, the embodiment of the present application provides a method for recommending a ride code, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the method for recommending a boarding code in this application.

[0027] In this embodiment, the method for recommending a boarding code includes steps S10 to S30: Step S10, when it is detected that the user uses the ride APP within the target range of the target subway station, the ride intention is obtained, and the ride intention is shared with the pre-trained AI big model through a preset intention interface; It should be noted that this embodiment is described by taking a mobile phone as an example, and a ride APP is installed on the mobile phone.

[0028] In addition, it should be noted that the preset intent interface refers to a predefined communication protocol or data format used to transmit specific information between different software systems or modules, such as the user's intention to take a ride. In this embodiment, the ride APP calls a specific interface of the Hongmeng system to share the user's intention data between the ride APP and the AI ​​big model.

[0029] In a feasible embodiment, step S10 may include steps S11-S12: Step S11, analyzing the intention information according to the scenario to determine the user's riding intention; Specifically, when the user enters the target range of the target subway station and opens the ride APP on the mobile phone, the ride APP combines the positioning service of the Hongmeng system to identify the user's location information to ensure that the ride APP can accurately determine whether the user is in a functional scenario that requires the use of the ride code. At the same time, the ride APP obtains the current time information when the user opens the ride APP, so that the AI ​​big model can combine the user's ride history data for more accurate intention analysis.

[0030] Combined with the user's historical riding data, further analyze the user's travel patterns and preferences.

[0031] A comprehensive analysis is performed based on the above collected information to determine the user's riding intention. For example, if the user usually departs from the current location during this time period on weekdays, the system can infer that the user may be taking the subway to a destination that he or she often goes to.

[0032] Step S12, sharing the boarding intention with the pre-trained AI big model through the preset intention interface.

[0033] Specifically, once the user's intention to take a ride is determined, this information is formatted according to the requirements of the Hongmeng system's interface. This includes but is not limited to key information such as the user's departure location, destination, and expected ride time, and is sent to the pre-trained AI model through the Hongmeng system's interface.

[0034] Through the above steps, by utilizing the device's location services, time information, and the user's historical riding data, the system can more accurately analyze and predict the user's riding intention. This not only improves the user experience, but also ensures that the right service is provided at the right time. In addition, with the help of the preset intent interface, the ride APP can quickly and securely share the user's riding intention with the pre-trained AI large model. The rapid realization of data exchange capabilities makes it possible to respond to user needs in real time, greatly shortening the time interval from identifying needs to providing services.

[0035] Step S20, using the pre-trained AI big model to analyze whether the user intends to use the boarding code at the target subway station based on the boarding intention; It should be noted that large AI models are deep learning models with a large number of parameters and complex structures. After being trained with large-scale data sets, these models can perform complex tasks such as natural language processing (NLP), image recognition, speech recognition, and other tasks involving pattern recognition.

[0036] In a feasible embodiment, step S20 may include steps S21-S22: Step S21, using the pre-trained AI big model to analyze the riding intention, and extract the time information and location information when the user opens the riding APP; Specifically, we use a large AI model that has been trained with a large amount of historical data, which can understand and predict user behavior patterns in different scenarios. We analyze the shared user's riding intention based on the pre-trained AI model and extract feature information, such as the time and location information when the user opens the ride APP on his mobile phone.

[0037] In addition to direct time and location information, the pre-trained AI model also considers other relevant factors, such as weather conditions, traffic information, users' personal preferences and historical behavior patterns, to make more accurate judgments.

[0038] Step S22, performing intention analysis based on the time information and location information when the user opens the ride APP, and combined with the user's ride history data, to determine whether the user intends to use the ride code within the target range of the target subway station.

[0039] It should be noted that the ride history data includes but is not limited to the specific time each time the user uses the ride code, subway distance information, etc.

[0040] Specifically, first, determine whether the user's location when using the ride APP is within the preset range of the target subway station (for example, a 500-meter radius centered on the subway station). The "target range" is set according to actual needs. For example, considering factors such as walking distance or transfer convenience, the range can be set to an area within a certain distance from the subway station entrance.

[0041] Also, check whether the current time when the user is using the ride-hailing app is consistent with the user’s regular travel pattern. For example, if a user is used to arriving at a subway station around 8 a.m. every weekday, and today is also a weekday and the time is 7:55, this factor supports the assumption that the user is about to take the subway.

[0042] In addition, by analyzing the user's historical riding data, we can obtain information such as the user's past trips to the target subway station, travel frequency, preferred time period, etc. For example, if the user has opened the APP at similar times and locations more than 20 times in the past month and finally used the ride code, it means that there is a high possibility that the ride code will be used this time.

[0043] Combining the above two points and other relevant factors (such as weather conditions and traffic conditions), the AI ​​model calculates the probability of the user using the ride code in the current scenario. For example, by setting a probability threshold (such as 60%) in advance, only when the calculated probability of using the ride code exceeds this threshold will it be confirmed that the user has a strong intention to use the ride code.

[0044] If the user's location when opening the ride APP is indeed within the target range of the target subway station, and the probability of using the ride code evaluated based on time and historical data is higher than the preset threshold, it can be determined that the user is more likely to use the ride code at that subway station.

[0045] Through the above steps, the pre-trained AI model can be used to more accurately predict the user's intention to ride based on the user's historical behavior and current situation. This not only improves the convenience and efficiency of using the ride APP, but also provides users with a more personalized service experience through in-depth analysis of user behavior patterns, preferences and needs in specific scenarios.

[0046] Step S30: If the pre-trained AI large model recognizes that the user has the intention to use the boarding code, the boarding APP is recommended to the user to generate or display the boarding QR code.

[0047] If the pre-trained AI model recognizes that the user intends to use the ride code function within the target range of the target subway station, the ride APP is recommended to the user through the mobile phone screen. When the user clicks on the recommended ride APP on the mobile phone, the recommended ride APP is called up and the intention template parameters are passed to the recommended ride APP. Among them, the intention template parameters are used to enter the ride code function.

[0048] When the ride APP is started, it is determined whether to enter the ride code function based on the intent template parameters. If so, the function page of the ride code APP is jumped to the ride code function page, and the ride QR code is displayed to the user.

[0049] Through the above steps, the seamless connection from identifying the user's possible needs (i.e. using the ride code) to proactively providing services (recommending and directly displaying the ride QR code) has greatly improved the convenience and efficiency of the user experience. It not only saves users time in finding and starting specific functions, but also improves travel efficiency.

[0050] Through the above-mentioned embodiment method, when it is detected that the user uses the ride APP within the target range of the target subway station, the ride intention is obtained, and the ride intention is shared with the pre-trained AI big model through the preset intention interface; the pre-trained AI big model is used to analyze whether the user has the intention to use the ride code at the target subway station based on the ride intention; if the pre-trained AI big model recognizes that the user has the intention to use the ride code, the ride APP is recommended to the user to generate or display the ride QR code. This method analyzes the user's ride intention through the AI ​​big model to confirm whether the user has the intention to open the ride code function at the target subway station, and recommends the "ride code" service to the user in combination with the user's location information, thereby improving the efficiency of opening the ride code and the user experience.

[0051] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 2 Before step S10, the method for recommending a ride code further includes steps S01 to S02: Step S01, initializing the intention template parameters for calling and viewing the boarding code on the boarding APP, and obtaining and maintaining the location information of the target subway station; It should be noted that the intent template parameters are used to trigger specific functions in the ride APP (such as displaying the ride QR code). During initialization, the system will set these parameters to ensure that when certain conditions are met (such as the user enters the target range of a subway station), the corresponding function can be called quickly and accurately.

[0052] In addition, the geographic location data of all target subway stations are collected, including but not limited to latitude and longitude coordinates, entrance and exit locations and other detailed information. This data can be obtained through public map service APIs or provided directly by subway operators. Since subway stations may have new exits opened or closed, or the layout may be temporarily changed due to construction, etc., this location information needs to be regularly updated and maintained to ensure its accuracy.

[0053] Step S02, based on the location information of the target subway station, identify whether the user uses the ride APP and turns on the ride code function within the target range of the target subway station. If so, obtain the user's intention information.

[0054] The user's current exact location is determined by using positioning technologies such as GPS or Wi-Fi positioning. Then, by calculating the distance between the user's current location and the target subway station, it is determined whether the user is within the preset range of the target subway station.

[0055] Once it is confirmed that the user is within the target subway station, the user's behavior patterns will be further analyzed, such as whether they have opened the ride APP and tried to access the ride code function to obtain the user's intention information.

[0056] Through the above-mentioned embodiment method, the "View the boarding code" intention template information for the intention call is initialized to ensure that when the user approaches the target subway station or expresses the intention to use the boarding code, the system can respond quickly and directly guide the user to the boarding code page. Acquiring and maintaining the location information of subway stations helps the AI ​​big model to more accurately predict the user's boarding intention, so that the boarding APP can achieve fast and accurate service recommendations.

[0057] For example, in order to help understand the implementation process of the method for recommending a ride code obtained by combining this embodiment with the above-mentioned embodiment 1, please refer to Figure 3 , Figure 3 A brief flowchart of a method for recommending a ride code is provided, specifically: The system architecture of this embodiment involves the Hongmeng operating system, AI big model, intent framework cloud service, and ride-hailing APP; the modules involved include real-time location service, subway station location maintenance module, and APP ride-hailing code module.

[0058] Based on the above system framework, the specific implementation plan of this embodiment includes: First, when the ride APP is started, a specific intent template parameter is initialized, which defines how to call the function of viewing the ride code. This template is designed to be compatible with the intent framework of the Hongmeng system so that the ride code function can be quickly entered when certain conditions are met (such as the user is approaching a subway station). In addition, the ride APP obtains and regularly updates the location information of all target subway stations by connecting to the cloud service. This includes but is not limited to the exact coordinates of each subway station and its surrounding environment characteristics.

[0059] Then, the ride-hailing app is integrated with the intent sharing function of the Hongmeng system, allowing it to exchange intent information with other applications and services. When the user turns on the ride-hailing function, the ride-hailing app will call the interface provided by the Hongmeng system to share the user's intention (such as wanting to use the ride code) with the pre-trained AI large model for analysis.

[0060] After the pre-trained AI big model obtains the user's intention, based on information such as the time and location when the user opens the ride APP, as well as the user's ride history, the AI ​​big model performs pattern learning and intent analysis to assess whether the user has a tendency to use the ride code near the subway station.

[0061] When the pre-trained AI model calculates that the user's riding intention is greater than the preset threshold, it is determined that the user is very likely to use the riding code function. Furthermore, the intent framework of the Hongmeng system will actively push notifications or recommended content to the user, suggesting that the user use the riding APP. If the user clicks on the recommended riding APP, the intent framework of the Hongmeng system will wake up the recommended riding APP according to the previously set intent template parameters, and pass the necessary parameters (such as the target subway station ID) to the riding APP.

[0062] Finally, after receiving the incoming intent template parameters, the ride APP determines that the user's need is to view the ride QR code based on this information. Therefore, the ride APP directly jumps to the ride code function page and displays the user's ride QR code, ready for scanning.

[0063] Through the above-mentioned embodiment method, the habit recommendation capability of the HarmonyOS intent framework is utilized. After the user opens the ride APP near the subway station, the ride APP shares the ride intention with the HarmonyOS system. After the HarmonyOS system learns the intention pattern, whenever the user arrives near the subway station, the ride APP is actively recommended to the user. The user can quickly click to enter the ride code function page to display the code to ride, and the user's travel experience is improved based on the ride habit recommendations.

[0064] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the method of recommending boarding codes in the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0065] This application also provides a device for recommending a ride code. Please refer to Figure 4 , the boarding code recommendation device comprises: The intention sharing module 10 is used to obtain the riding intention when it is detected that the user uses the riding APP within the target range of the target subway station, and share the riding intention with the pre-trained AI big model through a preset intention interface; The intention analysis module 20 is used to analyze whether the user intends to use the boarding code at the target subway station based on the boarding intention by using the pre-trained AI big model; The recommendation module 30 is used to recommend the ride APP to the user to generate or display the ride QR code if the pre-trained AI large model recognizes that the user has the intention to use the ride code.

[0066] The boarding code recommendation device provided by the present application adopts the boarding code recommendation method in the above embodiment, which can solve the technical problem of how to improve the efficiency of opening the boarding code. Compared with the prior art, the beneficial effects of the boarding code recommendation device provided by the present application are the same as the beneficial effects of the boarding code recommendation method provided by the above embodiment, and the other technical features of the boarding code recommendation device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0067] The present application provides a device for recommending a boarding code, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the boarding code recommendation method in the above-mentioned embodiment one.

[0068] Reference below Figure 5 , which shows a schematic diagram of the structure of a ride code recommendation device suitable for implementing the embodiment of the present application. The ride code recommendation device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The ride code recommendation device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0069] like Figure 5As shown, the ride code recommendation device may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory 1002 or the program loaded from the storage device 1003 to the random access memory 1004. In the random access memory 1004, various programs and data required for the operation of the ride code recommendation device are also stored. The processing device 1001, the read-only memory 1002 and the random access memory 1004 are connected to each other through a bus 1005. The input / output interface 1006 is also connected to the bus. Generally, the following systems can be connected to the input / output interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the ride code recommendation device to communicate wirelessly or wired with other devices to exchange data. Although the figure shows a ride code recommendation device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or provided instead.

[0070] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a read-only memory 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0071] The boarding code recommendation device provided by the present application adopts the boarding code recommendation method in the above embodiment, which can solve the technical problem of how to improve the efficiency of opening the boarding code. Compared with the prior art, the beneficial effects of the boarding code recommendation device provided by the present application are the same as the beneficial effects of the boarding code recommendation method provided by the above embodiment, and the other technical features in the boarding code recommendation device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0072] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0073] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0074] The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the method for recommending a ride code in the above-mentioned embodiment.

[0075] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system or device. The program code contained on the computer-readable storage medium may be shared with any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0076] The above-mentioned computer-readable storage medium may be included in the boarding code recommendation device; or it may exist independently without being assembled into the boarding code recommendation device.

[0077] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the boarding code recommendation device, the boarding code recommendation device: when it is detected that the user uses the boarding APP within the target range of the target subway station, obtains the boarding intention, and shares the boarding intention to the pre-trained AI big model through the preset intention interface; uses the pre-trained AI big model to analyze whether the user has the intention to use the boarding code at the target subway station based on the boarding intention; if the pre-trained AI big model recognizes that the user has the intention to use the boarding code, recommends the boarding APP to the user to generate or display the boarding QR code.

[0078] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0079] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0080] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0081] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned boarding code recommendation method, and can solve the technical problem of boarding code recommendation. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the boarding code recommendation method provided in the above-mentioned embodiment, and will not be repeated here.

[0082] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for recommending a ride code.

[0083] The computer program product provided by this application can solve the technical problem of how to improve the efficiency of opening the boarding code. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the boarding code recommendation method provided by the above embodiment, which will not be repeated here.

[0084] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for recommending a ride code, characterized in that: The method for recommending a boarding code is applied to a mobile terminal, on which a boarding APP is installed. The method for recommending a boarding code includes: When it is detected that the user uses the ride APP within the target range of the target subway station, the ride intention is obtained, and the ride intention is shared with the pre-trained AI big model through a preset intention interface; Utilizing the pre-trained AI big model to analyze whether the user intends to use the boarding code at the target subway station based on the boarding intention; If the pre-trained AI large model recognizes that the user has the intention to use the boarding code, the boarding APP is recommended to the user to generate or display the boarding QR code.

2. The method for recommending a boarding code according to claim 1, wherein: Before the step of obtaining the boarding intention and sharing the boarding intention with the pre-trained AI big model through the preset intention interface, the step includes: Initialize the intent template parameters for calling and viewing the boarding code on the boarding APP, and obtain and maintain the location information of the target subway station; According to the location information of the target subway station, it is identified whether the user uses the ride APP and turns on the ride code function within the target range of the target subway station. If so, the user's intention information is obtained.

3. The method for recommending a boarding code according to claim 2, wherein: The step of obtaining the boarding intention and sharing the boarding intention to the pre-trained AI big model through a preset intention interface includes: Analyze the intention information according to the scenario to determine the user's intention to take the vehicle; The riding intention is shared with the pre-trained AI big model through the preset intention interface.

4. The method for recommending a boarding code according to claim 1, wherein: The step of using the pre-trained AI big model to analyze whether the user has the intention to use the boarding code at the target subway station based on the boarding intention includes: Utilize the pre-trained AI big model to analyze the riding intention and extract the time information and location information when the user opens the riding APP; Based on the time information and location information when the user opens the ride APP, and combined with the user's ride history data, intention analysis is performed to determine whether the user intends to use the ride code within the target range of the target subway station.

5. The method for recommending a boarding code according to claim 4, characterized in that: The step of performing intention analysis based on the time information and location information when the user opens the ride APP and in combination with the user's ride history data to determine whether the user intends to use the ride code within the target range of the target subway station includes: Determine whether the location information when the user opens the ride APP meets the target range of the target subway station; and According to the current time and historical ride data when the user opens the ride APP, the probability of the user using the ride code in the current scenario is evaluated; If the location information of the user when opening the ride APP meets the target range of the target subway station, and the probability of the user using the ride code in the current scenario is greater than a preset threshold, it is determined that the user has the intention to use the ride code within the target range of the target subway station.

6. The method for recommending a boarding code according to claim 2, wherein: The step of recommending the ride APP to the user to generate or display the ride QR code includes: In response to a click operation input by the user according to the recommended ride APP, the recommended ride APP is invoked, and the intent template parameters are passed into the recommended ride APP; When the recommended ride APP is started, analyzing whether the user's intention is to use the ride code function according to the intention template parameters; If so, generate a QR code for boarding the bus and display the QR code to the user.

7. A device for recommending a boarding code, characterized in that: The boarding code recommendation device comprises: The intention sharing module is used to obtain the riding intention when it is detected that the user uses the riding APP within the target range of the target subway station, and share the riding intention with the pre-trained AI big model through the preset intention interface; An intention analysis module, used to analyze whether the user intends to use the boarding code at the target subway station based on the boarding intention by using the pre-trained AI big model; A recommendation module is used to recommend the ride APP to the user to generate or display the ride QR code if the pre-trained AI large model recognizes that the user has the intention to use the ride code.

8. A device for recommending a ride code, characterized in that: The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the method for recommending a ride code as described in any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for recommending a boarding code as described in any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product includes a computer program, which, when executed by a processor, implements the steps of the method for recommending a ride code as described in any one of claims 1 to 6.

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