Riding information generation method and device, electronic equipment and storage medium

Abstract

Embodiments of the invention provide a riding information generation method and apparatus, an electronic device and a storage medium. The method comprises the steps of obtaining natural language data used for expressing a work schedule of a user; determining travel information of the user based on the natural language data; obtaining current regular bus scheduling information; and generating riding information for the travel information based on the travel information and the current regular bus scheduling information, thereby forming a complete intelligent regular bus scheduling system. By means of the steps, the system can achieve the whole process from natural language understanding to route planning. According to the scheme, a natural language processing technology is combined with a traditional scheduling algorithm, and more intelligent and convenient travel service is provided for the user.

Description

Technical Field

[0001] The present invention relates to the technical field of riding information generation, and in particular to a riding information generation method, a riding information generation device, an electronic device and a computer-readable storage medium. Background Art

[0002] With the acceleration of urbanization, traffic congestion in big cities is becoming increasingly serious, and the traffic pressure during rush hour is even worse. In order to ease traffic pressure and improve the quality of life of residents, efficient public transportation solutions are imminent. As an effective way of commuting, corporate shuttle services are highly favored. However, the traditional shuttle dispatching model can no longer meet the increasingly complex commuting needs. Therefore, developing an intelligent and flexible shuttle dispatching system and optimizing route planning and resource allocation can not only reduce corporate operating costs, but also improve employee satisfaction and thus improve work efficiency. Summary of the invention

[0003] The embodiments of the present invention provide a method, device, electronic device and computer-readable storage medium for generating riding information to overcome the above problems or at least partially solve the above problems.

[0004] The embodiment of the present invention discloses a method for generating riding information, which is characterized by comprising:

[0005] Acquire natural language data for expressing the user's work schedule;

[0006] Determining the user's itinerary information based on the natural language data;

[0007] Get the current shuttle bus schedule information;

[0008] The boarding information for the trip information is generated based on the trip information and the current bus schedule information.

[0009] Optionally, before the step of acquiring natural language data for expressing the user's work schedule, the method further includes:

[0010] Obtain historical emails, and extract target information for expressing the user's work schedule from the historical emails through a semantic big model;

[0011] Obtain employees' historical ride records through the shuttle bus system;

[0012] A pre-trained model is generated based on the target information and the historical riding records.

[0013] Optionally, the step of determining the user's itinerary information based on the natural language data includes:

[0014] Inputting the natural language data into the pre-trained model;

[0015] The pre-trained model is used to output the trip information, where the trip information includes the user's boarding point, alighting point and ride time.

[0016] Optionally, the step of generating the boarding information for the trip information based on the trip information and the current shuttle bus schedule information includes:

[0017] Based on the travel information and the current bus schedule information, determining whether there is at least one recommended bus schedule;

[0018] If it is determined that there is not at least one recommended flight, generating new scheduling application information for the trip information;

[0019] When the newly added shift application information meets the preset conditions, the latest shift information for the travel information is generated;

[0020] A bus service vehicle corresponding to the latest bus service information is determined as a target vehicle, and boarding information for the target vehicle is generated.

[0021] Optionally, it also includes:

[0022] If at least one recommended bus is determined, initial bus information of the recommended bus is obtained; the initial bus information includes waypoints and a travel route of the recommended bus;

[0023] Determining whether the waypoints include the boarding point and the alighting point;

[0024] If it is determined that the route includes the boarding point and the alighting point, the schedule vehicle of the initial schedule information is determined as the target vehicle, and the boarding information for the target vehicle is generated.

[0025] Optionally, it also includes:

[0026] If it is determined that the waypoints do not include the boarding point and the alighting point, determining whether the boarding point and the alighting point are on the driving path;

[0027] Get other users' itinerary information for other users;

[0028] If it is determined that the pick-up point and the alighting point are on the driving path, then when the other user travel information meets the preset conditions, a request to add a stop point is generated through the pick-up point and the alighting point, and based on the request to add a stop point, a new stop point is added on the driving path of the initial schedule information, and the schedule vehicle of the initial schedule information is determined as the target vehicle, and the boarding information for the target vehicle is generated; the newly added stop point is used to control the target vehicle to stop at the pick-up point and the alighting point.

[0029] Optionally, it also includes:

[0030] If it is determined that the pick-up point and the alighting point are not on the driving path, and it is determined that the distances between the pick-up point and the alighting point and the driving path are less than a preset distance threshold, and when the other user travel information meets the preset conditions, a driving path modification request is generated through the pick-up point and the alighting point, and based on the driving path modification request, the driving path of the initial shift information is modified to generate a newly added driving path, a stop is added to the newly added driving path, the shift vehicle of the initial shift information is determined as the target vehicle, and the boarding information for the target vehicle is generated; the newly added driving path is used to control the driving path of the target vehicle, and the newly added stop is used to control the target vehicle to stop at the pick-up point and the alighting point.

[0031] Optionally, before the step of generating the riding information for the target vehicle, the method further includes:

[0032] Obtaining the average occupancy rate of the target vehicle at the departure station;

[0033] When the average occupancy rate is greater than a preset threshold, determining whether there are other users who get off at the boarding point or at a station before the boarding point based on the other users' travel information;

[0034] If it is determined that there are no other users who get off at the boarding point or at a station before the boarding point, the step of generating new scheduling application information for the trip information is executed;

[0035] When it is determined that there are other users who get off at the boarding point or at a station before the boarding point, the step of generating the boarding information for the target vehicle is executed.

[0036] Optionally, before the step of generating the riding information for the target vehicle, the method further includes:

[0037] If it is determined that the route includes the boarding point and the alighting point, the shift vehicle of the initial shift information is marked as a type-one information vehicle;

[0038] If it is determined that the waypoints do not include the boarding point and the alighting point, and it is determined that the boarding point and the alighting point are on the driving path, the shift vehicle of the initial shift information is marked as a second-category information vehicle;

[0039] If it is determined that the boarding point and the alighting point are not on the driving path, and it is determined that the distances between the boarding point and the alighting point and the driving path are less than a preset distance threshold, the shift vehicle of the initial shift information is marked as a Class III information vehicle;

[0040] Determine a recommendation order based on the first type of information vehicles, the second type of information vehicles, and the third type of information vehicles;

[0041] The first-category information vehicle, the second-category information vehicle, and the third-category information vehicle are recommended in sequence based on the recommendation order.

[0042] The embodiment of the present invention further discloses a vehicle riding information generating device, comprising:

[0043] A natural language data acquisition module, used to acquire natural language data for expressing the user's work schedule;

[0044] A travel information determination module, used to determine the user's travel information based on the natural language data;

[0045] The current bus schedule information acquisition module is used to obtain the current bus schedule information;

[0046] The boarding information generating module is used to generate boarding information for the travel information based on the travel information and the current bus schedule information.

[0047] The embodiment of the present invention further discloses an electronic device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0048] The memory is used to store computer programs;

[0049] The processor is used to implement the method described in the embodiment of the present invention when executing the program stored in the memory.

[0050] The embodiment of the present invention further discloses a computer-readable storage medium having instructions stored thereon, which, when executed by one or more processors, enables the processors to execute the method described in the embodiment of the present invention.

[0051] The embodiments of the present invention include the following advantages:

[0052] The embodiment of the present invention forms a complete intelligent shuttle bus scheduling system by acquiring natural language data used to express the user's work schedule; determining the user's itinerary information based on the natural language data; acquiring the current shuttle bus schedule information; and generating the boarding information for the itinerary information based on the itinerary information and the current shuttle bus schedule information. Through the above steps, the system can realize the whole process from natural language understanding to itinerary planning. This solution combines natural language processing technology with traditional scheduling algorithms to provide users with more intelligent and convenient travel services. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a flowchart of a method for generating riding information provided in an embodiment of the present invention;

[0054] Figure 2 It is a schematic diagram of a process of outputting travel information by a pre-trained model provided in an embodiment of the present invention;

[0055] Figure 3 It is a flow chart of a method for generating riding information provided in an embodiment of the present invention;

[0056] Figure 4 is a flow chart of another method for generating riding information provided in an embodiment of the present invention;

[0057] Figure 5 It is a flowchart of another method for generating riding information provided in an embodiment of the present invention;

[0058] Figure 6 is a structural block diagram of a vehicle-riding information generating device provided in an embodiment of the present invention;

[0059] Figure 7 is a hardware structure block diagram of an electronic device provided in an embodiment of the present invention;

[0060] Figure 8 is a schematic diagram of a computer-readable medium provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0061] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0062] With the acceleration of urbanization, traffic congestion in big cities is becoming increasingly serious, and the traffic pressure during rush hour is even worse. In order to ease traffic pressure and improve the quality of life of residents, efficient public transportation solutions are imminent. As an effective commuting method, corporate shuttle services are highly favored. However, the traditional shuttle dispatch model can no longer meet the increasingly complex commuting needs.

[0063] In this context, intelligent real-time dynamic adjustment of bus scheduling methods came into being. It is not only a product of technological development, but also an inevitable result of social development and people's demand for more efficient and convenient travel. Through big data analysis and artificial intelligence technology, we can accurately predict employees' commuting needs and adjust shuttle bus routes and resource allocation in real time, thereby significantly improving the quality and efficiency of shuttle bus services.

[0064] Specifically, the intelligent arranging method can bring the following benefits:

[0065] Relieve urban traffic pressure: By optimizing shuttle bus routes and scheduling, private car travel can be reduced and urban traffic congestion can be alleviated.

[0066] Improve employee satisfaction: Provide more convenient and comfortable commuting services to improve employee work efficiency and satisfaction with the company.

[0067] Reduce enterprise operating costs: Reduce operating costs and improve enterprise economic benefits by optimizing resource allocation.

[0068] Promote sustainable urban development: Promote the development of public transportation, reduce carbon emissions, and contribute to building green and low-carbon cities.

[0069] In summary, the intelligent real-time dynamic adjustment of vehicle arranging is an important means to solve urban traffic problems and improve the level of public transportation services. Its application can not only meet people's yearning for a better life, but also promote the sustainable development of cities.

[0070] The embodiment of the present invention integrates multi-source data through big data technology, and uses semantic big model technology to make accurate predictions and provide personalized flight recommendation services; it acquires and analyzes data in real time, dynamically adjusts the flight schedule, ensures that passengers have seats to sit, improves user satisfaction, and improves the utilization rate of shuttle bus seats.

[0071] Furthermore, through the integration of multiple data sources and comprehensive prediction, personalized recommendations and real-time dynamic adjustments can be achieved to provide highly flexible and efficient shuttle services; through the three options of complete matching, path matching and nearby matching, the possibility of employees finding alternative shifts is increased, and the reliability and flexibility of the system are enhanced; shuttle routes and stops are dynamically adjusted according to employee needs, the flexibility and adaptability of shuttle services are improved, and the needs of more employees are met; through intelligent matching and dynamic adjustment, the utilization rate of shuttle seats is improved, vacant seats are reduced, and resource allocation is optimized.

[0072] Reference Figure 1 , shows a flowchart of a method for generating riding information provided in an embodiment of the present invention, which may specifically include the following steps:

[0073] Step 101, obtaining natural language data for expressing the user's work schedule;

[0074] Step 102, determining the user's itinerary information based on the natural language data;

[0075] Step 103, obtaining the current shuttle bus schedule information;

[0076] Step 104: Generate boarding information for the travel information based on the travel information and the current bus schedule information.

[0077] In practical applications, the embodiments of the present invention can be applied to an intelligent shuttle bus dispatching system for scheduling vehicles, hereinafter referred to as the "system". In addition to arranging vehicle shifts, the system can also set driving routes for vehicles and add stops to control automatic driving of vehicles.

[0078] The embodiment of the present invention can obtain natural language data used to express the user's work schedule, and its purpose is to collect the original information of the user expressing the work schedule. This information usually comes from emails, chat records, and applications used for scheduling.

[0079] By obtaining natural language data used to express the user's work schedule, it can provide raw materials for subsequent natural language processing and information extraction. By obtaining natural language data, the system can understand the user's work schedule, such as meeting time, location, business trip schedule, etc.

[0080] In the embodiment of the present invention, the user's itinerary information can also be determined based on natural language data, the purpose of which is to extract key itinerary information from the acquired natural language data, and to convert the unstructured natural language text into structured itinerary data so as to facilitate system processing and analysis. For example, from the natural language data "meeting in the company's (XX Road No. XX, XX City District) conference room at 10 am tomorrow, and leave at 12 noon to eat at YY Restaurant (YY Road No. YY, YY City District)" extract "meeting time: tomorrow morning 10 am", "dinner time: tomorrow noon 12 noon"; then the boarding point is "XX Road No. XX, XX City District", the alighting point is "YY Road No. YY, YY City District", and the driving time from "XX Road No. XX, XX City District" to "YY Road No. YY, YY City District" is expected to take 15 minutes, then the ride time can be set between "11:30-11:45 am tomorrow", and other itinerary information.

[0081] The embodiment of the present invention can also obtain the current shuttle bus schedule information, the purpose of which is to obtain the current shuttle bus operation status, including departure time, stop sites, remaining seats, etc., to provide basic data for subsequent itinerary matching. By obtaining the current shuttle bus schedule information, the system can understand which shuttle buses have empty seats and which shuttle buses pass the sites that users need to go to.

[0082] After obtaining the current shuttle bus schedule information, the embodiment of the present invention can generate ride information for the trip information based on the trip information and the current shuttle bus schedule information, and recommend the most suitable shuttle bus to the user according to the user's trip requirements and the current shuttle bus situation, thereby matching the user's trip requirements with the shuttle bus information and generating personalized ride recommendations. For example, the system can recommend the most suitable shuttle bus according to the user's business trip time and destination, and inform the user of the boarding point, the alighting point and the estimated arrival time.

[0083] The embodiment of the present invention forms a complete intelligent shuttle bus scheduling system by acquiring natural language data used to express the user's work schedule; determining the user's itinerary information based on the natural language data; acquiring the current shuttle bus schedule information; and generating the boarding information for the itinerary information based on the itinerary information and the current shuttle bus schedule information. Through the above steps, the system can realize the whole process from natural language understanding to itinerary planning. This solution combines natural language processing technology with traditional scheduling algorithms to provide users with more intelligent and convenient travel services.

[0084] Based on the above embodiment, a variant embodiment of the above embodiment is proposed. It should be noted that in order to make the description concise, only the differences from the above embodiment are described in the variant embodiment.

[0085] In an optional embodiment of the present invention, before the step of acquiring natural language data for expressing the user's work schedule, the method further includes:

[0086] Obtain historical emails, and extract target information for expressing the user's work schedule from the historical emails through a semantic big model;

[0087] Obtain employees' historical ride records through the shuttle bus system;

[0088] A pre-trained model is generated based on the target information and the historical riding records.

[0089] In a specific implementation, the embodiment of the present invention obtains historical emails, and extracts target information for expressing the user's work schedule from the historical emails through a semantic big model, with the purpose of establishing a corpus containing the user's historical work schedule information.

[0090] The specific implementation method is to obtain the user's historical emails from the company's email system. In addition to accessing the company's email system, the data source can also be obtained by accessing the overtime application system, project management system, ZenTao management system, etc. to obtain the target information as the source data for this system analysis.

[0091] In addition to the traditional API interaction mode, you can also choose to use Webhooks to obtain data. In order to avoid interface problems that lead to incompatibility in actual applications, you can also perform the following operations: 1. Choose a stable API version and avoid using unstable test versions to ensure that the code is compatible with different versions of the API and avoid problems caused by API changes; 2. Detailed testing and verification to ensure that each interface call works as expected and that the interaction and data flow between different modules are correct. 3. Use monitoring tools to monitor the system's operating status in real time to detect and solve problems in a timely manner.

[0092] Regarding the issue of obtaining permissions for historical emails, most methods require access to the company's system or server, and these accesses usually involve permission management. Therefore, in actual applications, it is necessary to ensure that you have the permission to access and process the target data and comply with relevant data security and privacy regulations.

[0093] Semantic analysis can use semantic models such as BERT and GPT to analyze email content and extract key information related to work schedules, such as meeting time, location, and business trip destination. The technical key points are:

[0094] Named entity recognition: Identify entities such as time, place, and person in emails.

[0095] Relationship extraction: Extract the relationship between entities, such as "meeting at...", "will go to..."

[0096] Event extraction: Identify events in emails, such as "meeting" and "business trip".

[0097] The embodiment of the present invention can enrich training data by obtaining historical emails and extracting target information for expressing the user's work schedule from the historical emails through a large semantic model. Subsequent model training provides a large amount of labeled data and can also improve model accuracy. By learning a large amount of historical emails, the model can better understand the user's work habits and language expressions.

[0098] The embodiment of the present invention can obtain the historical riding records of employees through the shuttle bus system, with the purpose of collecting the user's previous riding behavior data. For example, the user's riding records, including boarding time, alighting location, shift and other information, are extracted from the database or log file of the shuttle bus system. By extracting the historical riding records, the user's travel habits can be understood. By analyzing the historical riding records, the user's travel rules, preferences, etc. can be understood, thereby providing features for the prediction model. The historical riding records can be used as the input features of the model to help the model more accurately predict the user's future travel behavior.

[0099] In the embodiment of the present invention, a pre-training model may be generated based on the target information and the historical riding record, with the purpose of building a pre-training model that can understand natural language and predict user travel behavior. The training process may include:

[0100] Data preprocessing: Cleaning, denoising, and feature engineering of data extracted from historical emails and ride records.

[0101] Model selection: Choose a suitable pre-trained model, such as BERT, GPT, etc.

[0102] Model training: The model is trained using the preprocessed data so that it can map natural language text to corresponding vector representations and predict users’ travel behavior.

[0103] The embodiment of the present invention can provide a pre-trained basic model for subsequent specific tasks (such as itinerary prediction) by providing a basic model.

[0104] Improve model generalization ability: By pre-training on a large amount of data, the model can better adapt to new data and tasks.

[0105] In another optional embodiment of the present invention, the pre-training model can be generated in the following manner:

[0106] 1. Data preprocessing stage:

[0107] Combined features: Combine multiple original features to form new features

[0108] Polynomial features: The original features are expanded through polynomial operations to form new features.

[0109] These new features are weighted versions of the original features. For example, if the original feature is x, you can create a new feature 2x or x^2 and then use this new feature when training.

[0110] 2. Model architecture design phase:

[0111] Custom network layer: Design a specific network layer to implement feature weighting. For example, you can build an attention mechanism layer to allow the model to automatically learn the importance weights of different features. The attention mechanism performs a weighted summation of input features, allowing the model to focus on important features, thereby improving the performance of the model.

[0112] Multimodal fusion: If the data contains multiple modalities (such as images and text), you can use multimodal fusion to assign different weights to the features of different modalities. For example, use a fusion layer to combine the features of different modalities, and adjust the contribution of each modality by learning weight parameters. This can make full use of information from different modalities and improve the generalization ability of the model.

[0113] 3. Post-processing stage:

[0114] Feature importance analysis: After the model training is completed, the feature importance analysis method can be used to evaluate the contribution of each feature. For example, you can use gradient-based methods, random forest feature importance evaluation and other techniques to determine which features play a key role in the model's prediction results. Based on the results of the feature importance analysis, you can further adjust the feature weights or prune unimportant features to improve the efficiency and performance of the model.

[0115] Practical application examples:

[0116] Obtain overtime application / notification, business trip notification, leave application and other features, and combine them to form the "job change" feature. Add an attention layer to the model. During the training process, adjust the initial weight of the feature. Changes in employee work arrangements will greatly affect their travel arrangements (for example, if an employee takes a leave of absence on that day, he will not take the usual commute; if a new shift is added on that day, he will not take the usual commute, and may need to take other trips that depart later). Therefore, a higher initial weight is assigned to the "job change" feature in the input layer of the model.

[0117] In an optional embodiment of the present invention, the step of determining the user's itinerary information based on the natural language data includes:

[0118] Inputting the natural language data into the pre-trained model;

[0119] The pre-trained model is used to output the trip information, where the trip information includes the user's boarding point, alighting point and ride time.

[0120] refer to Figure 2 , Figure 2 It is a schematic diagram of a process of outputting travel information by a pre-trained model provided in an embodiment of the present invention;

[0121] In an embodiment of the present invention, the natural language data may be input into the pre-trained model to convert the itinerary information expressed by the user into a vector representation that can be understood by the model.

[0122] For example, the itinerary-related text expressed by users in emails, chat records and other channels can be input into the pre-trained model to map the natural language text into a high-dimensional vector space, where each vector represents the semantic information of the text. In this way, the model can understand the meaning of the text based on these vectors and extract key information such as time, location, and purpose of travel.

[0123] In an embodiment of the present invention, the pre-trained model can also be used to output travel information, where the travel information includes the user's boarding point, alighting point, and ride time, and the user's specific travel information can be extracted from the vector representation output by the model.

[0124] For example, the model can analyze the input vector to predict the user's boarding point, alighting point, and travel time, thereby converting the model output vector into specific travel information to provide a basis for subsequent shuttle bus scheduling.

[0125] The pre-trained model connects the two links of natural language processing and itinerary prediction. Through pre-training, the model has learned a lot of language knowledge and travel rules, so that it can accurately understand the user's intention. Natural language processing is responsible for converting unstructured text data into vector representations that the model can process. Itinerary information extraction converts the vector output by the model into specific itinerary information to provide a basis for subsequent shuttle bus scheduling. Through the above analysis, it can be seen that applying the pre-trained model to the intelligent shuttle bus scheduling system can significantly improve the intelligence level of the system. The pre-trained model can better understand the user's language expression, thereby improving the accuracy of itinerary prediction and providing users with more personalized and convenient travel services.

[0126] In an optional embodiment of the present invention, the step of generating the boarding information for the trip information based on the trip information and the current bus schedule information includes:

[0127] Based on the travel information and the current bus schedule information, determining whether there is at least one recommended bus schedule;

[0128] If it is determined that there is not at least one recommended flight, generating new scheduling application information for the trip information;

[0129] When the newly added shift application information meets the preset conditions, the latest shift information for the travel information is generated;

[0130] A bus service vehicle corresponding to the latest bus service information is determined as a target vehicle, and boarding information for the target vehicle is generated.

[0131] refer to Figure 3 , Figure 3 It is a flow chart of a method for generating riding information provided in an embodiment of the present invention;

[0132] In the embodiment of the present invention, it can be determined whether there is at least one recommended bus schedule based on the travel information and the current bus schedule information, so as to evaluate whether the existing bus can meet the travel needs of the user.

[0133] For example, by comparing the user's travel information (boarding point, alighting point, time, etc.) with the departure time, stop sites, etc. of each bus, it is determined whether there is a suitable bus that can meet the user's needs.

[0134] The present invention can also generate new scheduling application information for the itinerary information if it is determined that there is not at least one recommended bus, so as to apply for a new bus when the existing bus cannot meet the demand. For example, the system will generate a new bus demand based on the user's itinerary information, and this demand will include information such as the departure place, destination, time, etc., to provide a basis for subsequent scheduling.

[0135] The implementation of the present invention can also generate the latest shift information for the travel information when the newly added shift application information meets the preset conditions to determine whether to add a new shift.

[0136] For example, the system will determine whether a new bus schedule is needed based on preset conditions (such as the number of applicants reaching a certain threshold). If the conditions are met, the system will generate a new bus schedule and add it to the bus scheduling system.

[0137] The present invention can also determine the shift vehicle with the latest shift information as the target vehicle, and generate the ride information for the target vehicle, so as to allocate a vehicle to the newly added shift and generate the ride information for the user. The system will allocate a suitable vehicle to the newly added shift, and generate detailed ride information such as departure time, stopover station, etc. according to the shift information, and send it to the user.

[0138] In the embodiment of the present invention, after the system predicts that the user may take a different bus from the normal bus schedule on the same day, if the existing bus schedule fails to match the recommended bus schedule that can be pushed to the corresponding user, a confirmation message is sent to the passenger-side applet to inquire whether the user needs to get on the bus from the target boarding point and get off at the target alighting point at the predicted target boarding time today. After the user confirms, a new bus schedule request is initiated, achieving the following beneficial effects:

[0139] Flexible and adaptable: The system can dynamically adjust the shuttle bus scheduling plan according to the real-time needs of users, which improves the flexibility of the system.

[0140] Personalized service: The system can provide users with personalized travel plans to meet their diverse needs.

[0141] Improve resource utilization: By arranging shifts reasonably, the utilization rate of vehicles can be improved and operating costs can be reduced.

[0142] Improve user experience: Users can easily query shuttle bus information through the system and obtain personalized travel suggestions, which improves the user's travel experience.

[0143] refer to Figure 4 , Figure 4 is a flow chart of another method for generating riding information provided in an embodiment of the present invention;

[0144] In an optional embodiment of the present invention, it also includes:

[0145] If at least one recommended bus is determined, initial bus information of the recommended bus is obtained; the initial bus information includes waypoints and a travel route of the recommended bus;

[0146] Determining whether the waypoints include the boarding point and the alighting point;

[0147] If it is determined that the route includes the boarding point and the alighting point, the schedule vehicle of the initial schedule information is determined as the target vehicle, and the boarding information for the target vehicle is generated.

[0148] The embodiment of the present invention can obtain the initial schedule information of the recommended schedule when at least one recommended schedule is determined, so as to obtain the basic information of the shuttle bus that best matches the user's needs, thereby providing basic data for subsequent route planning and vehicle allocation.

[0149] In the embodiment of the present invention, by judging whether the waypoint includes the boarding point and the alighting point, it is possible to directly judge whether the current bus schedule fully meets the travel needs of the user. If the waypoint includes the boarding point and the alighting point, the bus schedule can be directly allocated to the user, thereby improving the matching efficiency.

[0150] In an optional embodiment of the present invention, it also includes:

[0151] If it is determined that the waypoints do not include the boarding point and the alighting point, determining whether the boarding point and the alighting point are on the driving path;

[0152] Get other users' itinerary information for other users;

[0153] If it is determined that the pick-up point and the alighting point are on the driving path, then when the other user travel information meets the preset conditions, a request to add a stop point is generated through the pick-up point and the alighting point, and based on the request to add a stop point, a new stop point is added on the driving path of the initial schedule information, and the schedule vehicle of the initial schedule information is determined as the target vehicle, and the boarding information for the target vehicle is generated; the newly added stop point is used to control the target vehicle to stop at the pick-up point and the alighting point.

[0154] In a specific implementation, when determining whether the boarding point and the alighting point are on the driving path, the compatibility of the user's needs with the existing shuttle bus routes may be considered.

[0155] Exemplarily, if the pick-up point and the drop-off point are on the driving route but not in the list of waypoints, the user's needs can be met by adding a stop. For example, other users' travel information can include other users' drop-off points of other users and the latest arrival time obtained by reaching other users' drop-off points; the situation where other users' travel information meets the preset conditions can be that the predicted time for the vehicle to arrive at the drop-off point of other users after adding the stop point is predicted. If the predicted time is not later than the latest arrival time of other users, it is determined that the other users' travel information meets the preset conditions. At the same time, a request to add a stop point can be generated through the pick-up point and the drop-off point. Based on the request to add a stop point, a stop point is added to the driving route of the initial shift information, and the shift vehicle of the initial shift information is determined as the target vehicle, and the ride information for the target vehicle is generated; the newly added stop point is used to control the target vehicle to automatically stop at the pick-up point and the drop-off point to realize the automatic driving of the vehicle.

[0156] The purpose of obtaining other users' itinerary information is to comprehensively consider other users' travel needs and optimize the shuttle bus routes. By integrating the travel needs of multiple users, the shuttle bus routes can be planned more reasonably and the utilization rate of vehicles can be improved.

[0157] By generating requests to add stops, existing shuttle routes can be adjusted to meet the special needs of individual users. By adding stops, the travel needs of more users can be met and the flexibility of the service can be improved.

[0158] In an optional embodiment of the present invention, it also includes:

[0159] If it is determined that the pick-up point and the alighting point are not on the driving path, and it is determined that the distances between the pick-up point and the alighting point and the driving path are less than a preset distance threshold, and when the other user travel information meets the preset conditions, a driving path modification request is generated through the pick-up point and the alighting point, and based on the driving path modification request, the driving path of the initial shift information is modified to generate a newly added driving path, a stop is added to the newly added driving path, the shift vehicle of the initial shift information is determined as the target vehicle, and the boarding information for the target vehicle is generated; the newly added driving path is used to control the driving path of the target vehicle, and the newly added stop is used to control the target vehicle to stop at the pick-up point and the alighting point.

[0160] In actual applications, when there is a certain deviation between user needs and existing shuttle routes, but they can be met through reasonable adjustments, the system will actively optimize the routes to maximize the user's travel needs.

[0161] Determining whether the distances between the boarding point and the alighting point and the driving path are less than a preset distance threshold can be used to determine whether the boarding point and the alighting point of the user are too far from the initial driving path.

[0162] Specifically, when user needs change or new travel needs arise, the system can flexibly adjust the shuttle routes to adapt to changing travel scenarios.

[0163] By adjusting the route, more users can be included in the existing shuttle service, improving the utilization rate of vehicles. By optimizing the route, users' travel time can be reduced and travel efficiency can be improved. Users can more conveniently choose to get on and off at the station near their home or company, improving travel convenience.

[0164] In practical applications, the following conditions should be met when replanning the route:

[0165] Path planning algorithm: The system needs to adopt an efficient path planning algorithm to meet the travel needs of users while ensuring vehicle driving efficiency.

[0166] Distance calculation: The distance between the pick-up and drop-off points and the existing driving route needs to be accurately calculated to determine whether to adjust the route.

[0167] Constraints: When adjusting routes, various constraints need to be considered, such as road traffic conditions, vehicle travel time, passenger capacity, etc.

[0168] Specifically, the driving route of the initial shift information can be modified only when the travel information of other users meets the preset conditions. For example, the travel information of other users may include other users' alighting points and the latest arrival time at other users' alighting points. The situation where the travel information of other users meets the preset conditions may be the predicted time when the vehicle arrives at other users' alighting points after the driving route is modified. If the predicted time is not later than the latest arrival time of other users, it is determined that the travel information of other users meets the preset conditions.

[0169] User feedback mechanism: It is necessary to establish a user feedback mechanism to collect users’ opinions and suggestions on the adjusted routes and continuously optimize the system.

[0170] By dynamically adjusting the existing shuttle bus routes, the system can better meet the personalized needs of users and improve service quality and efficiency. At the same time, it also provides a useful reference for the construction of future intelligent transportation systems.

[0171] In an optional embodiment of the present invention, before the step of generating the riding information for the target vehicle, the method further includes:

[0172] Obtaining the average occupancy rate of the target vehicle at the departure station;

[0173] When the average occupancy rate is greater than a preset threshold, determining whether there are other users who get off at the boarding point or at a station before the boarding point based on the other users' travel information;

[0174] If it is determined that there are no other users who get off at the boarding point or at a station before the boarding point, the step of generating new scheduling application information for the trip information is executed;

[0175] When it is determined that there are other users who get off at the boarding point or at a station before the boarding point, the step of generating the boarding information for the target vehicle is executed.

[0176] refer to Figure 5 , Figure 5 It is a flowchart of another method for generating riding information provided in an embodiment of the present invention;

[0177] Before generating the passenger information for the target vehicle, the average occupancy rate of the target vehicle at the departure station has two main purposes:

[0178] Ensure passenger riding experience: By monitoring the vehicle's full load, we can avoid overcrowding and improve passenger riding comfort.

[0179] Optimize resource allocation: Dynamically adjust the frequency of flights according to the vehicle's passenger load, improve vehicle utilization and reduce operating costs.

[0180] The beneficial effects are:

[0181] Improve passenger satisfaction:

[0182] Avoid congestion: When the vehicle's occupancy rate is too high, the system will adjust the frequency or add more vehicles to prevent passengers from riding in crowded carriages for a long time.

[0183] Improve punctuality: By properly controlling the passenger capacity, delays caused by vehicle overloading can be reduced and the punctuality of shuttle buses can be improved.

[0184] Optimize resource allocation:

[0185] Dynamically adjust shifts: According to the vehicle's full load rate, dynamically adjust the frequency of shifts to improve vehicle utilization.

[0186] Avoid idle vehicles: For vehicles with low load factors, consider reducing or merging shifts to avoid idle vehicles.

[0187] Improve system dispatch efficiency: By real-time monitoring of vehicle load factors, passenger demand can be predicted more accurately and dispatch efficiency can be improved.

[0188] Reduce operating costs: By optimizing resource allocation, operating costs can be reduced.

[0189] If it is determined that there are no other users who get off at the boarding point or at the station before the boarding point, it means that no other passengers get off before the user gets on the bus. At this time, new scheduling application information can be generated for the itinerary information, thereby attempting to add a new shift.

[0190] When it is determined that there are other users who get off at the boarding point or at a station before the boarding point, it means that other passengers get off before the user gets on. At this time, the boarding information for the target vehicle can be directly generated to allow the user to board the current vehicle.

[0191] Through the above methods, personalized services are supported:

[0192] Provide waiting suggestions: When the vehicle is full, the system can provide passengers with waiting suggestions or recommend other buses.

[0193] Early warning: When the vehicle is about to be fully loaded, the system can send a reminder to passengers in advance so that passengers can make corresponding adjustments.

[0194] Through real-time monitoring and dynamic adjustment of vehicle load conditions, passengers' travel needs can be better met and the system's operational efficiency can be improved.

[0195] In an optional embodiment of the present invention, before the step of generating the riding information for the target vehicle, the method further includes:

[0196] If it is determined that the route includes the boarding point and the alighting point, the shift vehicle of the initial shift information is marked as a type-one information vehicle;

[0197] If it is determined that the waypoints do not include the boarding point and the alighting point, and it is determined that the boarding point and the alighting point are on the driving path, the shift vehicle of the initial shift information is marked as a second-category information vehicle;

[0198] If it is determined that the boarding point and the alighting point are not on the driving path, and it is determined that the distances between the boarding point and the alighting point and the driving path are less than a preset distance threshold, the shift vehicle of the initial shift information is marked as a Class III information vehicle;

[0199] Determine a recommendation order based on the first type of information vehicles, the second type of information vehicles, and the third type of information vehicles;

[0200] The first-category information vehicle, the second-category information vehicle, and the third-category information vehicle are recommended in sequence based on the recommendation order.

[0201] The purpose of classification is to classify and sort the different types of shuttles matched by users so as to recommend shuttles to users more accurately. By classifying shuttles, the system can provide users with more personalized travel suggestions based on the degree of match between the shuttles and user needs.

[0202] The shuttle buses are divided into Class I, Class II and Class III, and the degree of match between each class of shuttle buses and user needs is clearly defined.

[0203] Sort by matching degree to ensure that the most matching shifts are recommended to users first.

[0204] It reduces the difficulty of user selection. Through sorting, users can quickly find the most suitable shift for themselves, improving user experience. It can also increase user trust. The accurate recommendation of the system can increase user trust in the system.

[0205] Optimize system resources:

[0206] Improve vehicle utilization: By giving priority to recommending shifts with high matching degrees, the vehicle's passenger load factor can be increased.

[0207] Reduce system load: Reduce the time users spend on multiple queries and reduce system load.

[0208] Class 1 vehicles directly pass by the user's boarding and alighting points, have the highest matching degree, and should be recommended first.

[0209] Category II information vehicles pass through user boarding and alighting points on their routes but require additional stops. They have a higher degree of matching and can be used as the second choice.

[0210] The third type of information vehicle requires route adjustment to pass through the user's boarding and alighting points, and has a lower matching degree, so it is the third choice.

[0211] Through this classification and sorting, the system can meet the needs of different users. For passengers who urgently need a direct route, Class I buses are recommended; for passengers who can accept a slight adjustment to the route, Class II buses are recommended; and for passengers with higher requirements for the route, multiple options are provided.

[0212] Through the above recommendation method, the flexibility of the system is improved. When the first-class shuttle bus is full, the system can automatically recommend the second or third-class shuttle bus to improve the adaptability of the system. Optimize resource allocation, improve vehicle utilization and reduce operating costs by reasonably allocating passengers.

[0213] The embodiment of the present invention provides users with more accurate and personalized travel suggestions by classifying and sorting shuttle buses, improves the intelligence level of the system, and brings users a better travel experience. At the same time, it also provides a data basis for the optimization and development of the system.

[0214] In another optional embodiment of the present invention, after the system determines the user's itinerary information, it will provide the user with multiple recommended flights, and display them in descending order of flight matching. The user can choose a suitable flight according to his or her needs.

[0215] Highly matched bus schedule: fully meets the user's boarding and alighting point requirements, with sufficient seats. Users can board directly after confirmation.

[0216] Medium-matching flights: There are enough seats, but some stops may be missing. After the user confirms, the system will add the corresponding stops to the flight and update the vehicle route simultaneously.

[0217] Low-matching bus: There are enough seats, but the route is quite different from the user's needs. After the user confirms, the system will re-plan the bus route, add the stops required by the user, and update the vehicle route simultaneously.

[0218] The embodiments of the present invention achieve accurate response to users' travel needs through intelligent matching, flexible adjustment and personalized services, improve the efficiency and quality of shuttle services, and provide users with a more convenient and comfortable travel experience.

[0219] It should be noted that, for the sake of simplicity, the method embodiments are described as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0220] Reference Figure 6 , shows a structural block diagram of a riding information generating device provided in an embodiment of the present invention, which may specifically include the following modules:

[0221] The natural language data acquisition module 601 is used to acquire natural language data for expressing the user's work schedule;

[0222] A trip information determination module 602, configured to determine the user's trip information based on the natural language data;

[0223] The current bus schedule information acquisition module 603 is used to acquire the current bus schedule information;

[0224] The boarding information generating module 604 is used to generate boarding information for the travel information based on the travel information and the current bus schedule information.

[0225] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0226] In addition, an embodiment of the present invention further provides an electronic device, comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, each process of the above-mentioned embodiment of the method for generating ride information is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0227] The embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, each process of the above-mentioned method for generating ride information is implemented, and the same technical effect can be achieved. To avoid repetition, it is not repeated here. The computer-readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0228] Figure 7 A schematic diagram of the hardware structure of an electronic device for implementing various embodiments of the present invention.

[0229] The electronic device 700 includes but is not limited to: a radio frequency unit 701, a network module 702, an audio output unit 703, an input unit 704, a sensor 705, a display unit 706, a user input unit 707, an interface unit 708, a memory 709, a processor 710, and a power supply 711. Those skilled in the art will appreciate that Figure 7 The electronic device structure shown in the figure does not constitute a limitation on the electronic device, and the electronic device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. In the embodiments of the present invention, the electronic device includes but is not limited to a mobile phone, a tablet computer, a laptop computer, a PDA, a vehicle-mounted terminal, a wearable device, and a pedometer.

[0230] It should be understood that in the embodiment of the present invention, the radio frequency unit 701 can be used for receiving and sending signals during information transmission or communication. Specifically, after receiving downlink data from the base station, it is sent to the processor 710 for processing; in addition, uplink data is sent to the base station. Generally, the radio frequency unit 701 includes but is not limited to an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, etc. In addition, the radio frequency unit 701 can also communicate with the network and other devices through a wireless communication system.

[0231] The electronic device provides users with wireless broadband Internet access through the network module 702, such as helping users to send and receive emails, browse web pages, and access streaming media.

[0232] The audio output unit 703 can convert the audio data received by the RF unit 701 or the network module 702 or stored in the memory 709 into an audio signal and output it as sound. Moreover, the audio output unit 703 can also provide audio output related to a specific function performed by the electronic device 700 (for example, a call signal reception sound, a message reception sound, etc.). The audio output unit 703 includes a speaker, a buzzer, a receiver, etc.

[0233] The input unit 704 is used to receive audio or video signals. The input unit 704 may include a graphics processor (GPU) 7041 and a microphone 7042, and the graphics processor 7041 processes the image data of a static picture or video obtained by an image capture device (such as a camera) in a video capture mode or an image capture mode. The processed image frame can be displayed on the display unit 706. The image frame processed by the graphics processor 7041 can be stored in the memory 709 (or other storage medium) or sent via the radio frequency unit 701 or the network module 702. The microphone 7042 can receive sound and can process such sound into audio data. The processed audio data can be converted into a format output that can be sent to a mobile communication base station via the radio frequency unit 701 in the case of a telephone call mode.

[0234] The electronic device 700 also includes at least one sensor 705, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor includes an ambient light sensor and a proximity sensor, wherein the ambient light sensor can adjust the brightness of the display panel 7061 according to the brightness of the ambient light, and the proximity sensor can turn off the display panel 7061 and / or the backlight when the electronic device 700 is moved to the ear. As a kind of motion sensor, the accelerometer sensor can detect the magnitude of acceleration in each direction (generally three axes), and can detect the magnitude and direction of gravity when stationary, which can be used to identify the posture of the electronic device (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc.; the sensor 705 can also include a fingerprint sensor, a pressure sensor, an iris sensor, a molecular sensor, a gyroscope, a barometer, a hygrometer, a thermometer, an infrared sensor, etc., which will not be repeated here.

[0235] The display unit 706 is used to display information input by the user or information provided to the user. The display unit 706 may include a display panel 7061, which may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), or the like.

[0236] The user input unit 707 can be used to receive input digital or character information, and to generate key signal input related to user settings and function control of the electronic device. Specifically, the user input unit 707 includes a touch panel 7071 and other input devices 7072. The touch panel 7071, also known as a touch screen, can collect the user's touch operation on or near it (such as the user's operation on the touch panel 7071 or near the touch panel 7071 using any suitable object or accessory such as a finger, stylus, etc.). The touch panel 7071 may include two parts: a touch detection device and a touch controller. Among them, the touch detection device detects the user's touch orientation, detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into the contact point coordinates, and then sends it to the processor 710, receives the command sent by the processor 710 and executes it. In addition, the touch panel 7071 can be implemented using multiple types such as resistive, capacitive, infrared and surface acoustic waves. In addition to the touch panel 7071, the user input unit 707 may also include other input devices 7072. Specifically, other input devices 7072 may include but are not limited to a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick, which are not described in detail here.

[0237] Furthermore, the touch panel 7071 may be overlaid on the display panel 7061. When the touch panel 7071 detects a touch operation on or near it, it transmits the information to the processor 710 to determine the type of the touch event. Then, the processor 710 provides a corresponding visual output on the display panel 7061 according to the type of the touch event. Figure 7 In the figure, the touch panel 7071 and the display panel 7061 are used as two independent components to realize the input and output functions of the electronic device. However, in some embodiments, the touch panel 7071 and the display panel 7061 can be integrated to realize the input and output functions of the electronic device, which is not limited here.

[0238] The interface unit 708 is an interface for connecting an external device to the electronic device 700. For example, the external device may include a wired or wireless headset port, an external power supply (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting a device with an identification module, an audio input / output (I / O) port, a video I / O port, a headphone port, etc. The interface unit 708 may be used to receive input (e.g., data information, power, etc.) from an external device and transmit the received input to one or more elements within the electronic device 700 or may be used to transmit data between the electronic device 700 and an external device.

[0239] The memory 709 can be used to store software programs and various data. The memory 709 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory 709 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0240] The processor 710 is the control center of the electronic device. It uses various interfaces and lines to connect various parts of the entire electronic device. It executes various functions of the electronic device and processes data by running or executing software programs and / or modules stored in the memory 709, and calling data stored in the memory 709, so as to monitor the electronic device as a whole. The processor 710 may include one or more processing units; preferably, the processor 710 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 710.

[0241] The electronic device 700 may also include a power supply 711 (such as a battery) for supplying power to each component. Preferably, the power supply 711 may be logically connected to the processor 710 through a power management system, thereby implementing functions such as charging, discharging, and power consumption management through the power management system.

[0242] In addition, the electronic device 700 includes some functional modules not shown, which will not be described in detail here.

[0243] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article 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, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0244] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0245] like Figure 8 As shown, in another embodiment provided by the present invention, a computer-readable storage medium 801 is also provided, in which instructions are stored. When the computer-readable storage medium is run on a computer, the computer executes the method for generating riding information described in the above embodiment.

[0246] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

[0247] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the embodiments of the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0248] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0249] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0250] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0251] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0252] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical disks.

[0253] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for generating riding information, characterized in that: include: Acquire natural language data for expressing the user's work schedule; Determining the user's itinerary information based on the natural language data; Get the current shuttle bus schedule information; The boarding information for the trip information is generated based on the trip information and the current bus schedule information.

2. The method according to claim 1, characterized in that Before the step of acquiring the natural language data for expressing the user's work schedule, the method further includes: Obtain historical emails, and extract target information for expressing the user's work schedule from the historical emails through a semantic big model; Obtain employees' historical ride records through the intelligent shuttle bus dispatch system; A pre-trained model is generated based on the target information and the historical riding records.

3. The method according to claim 2, characterized in that The step of determining the user's itinerary information based on the natural language data comprises: Inputting the natural language data into the pre-trained model; The pre-trained model is used to output the trip information, where the trip information includes the user's boarding point, alighting point and ride time.

4. The method according to claim 3, characterized in that The step of generating the boarding information for the trip information based on the trip information and the current shuttle bus schedule information comprises: Based on the travel information and the current bus schedule information, determining whether there is at least one recommended bus schedule; If it is determined that there is not at least one recommended flight, generating new scheduling application information for the trip information; When the newly added shift application information meets the preset conditions, the latest shift information for the travel information is generated; A bus service vehicle corresponding to the latest bus service information is determined as a target vehicle, and boarding information for the target vehicle is generated.

5. The method according to claim 4, characterized in that Also includes: If at least one recommended bus is determined, initial bus information of the recommended bus is obtained; The initial bus information includes the waypoints and travel routes of the recommended bus; Determining whether the waypoints include the boarding point and the alighting point; If it is determined that the route includes the boarding point and the alighting point, the schedule vehicle of the initial schedule information is determined as the target vehicle, and the boarding information for the target vehicle is generated.

6. The method according to claim 5, characterized in that Also includes: If it is determined that the waypoints do not include the boarding point and the alighting point, determining whether the boarding point and the alighting point are on the driving path; Get other users' itinerary information for other users; If it is determined that the pick-up point and the alighting point are on the driving path, then when the other user travel information meets the preset conditions, a request to add a stop point is generated through the pick-up point and the alighting point, and based on the request to add a stop point, a new stop point is added on the driving path of the initial schedule information, and the schedule vehicle of the initial schedule information is determined as the target vehicle, and the boarding information for the target vehicle is generated; the newly added stop point is used to control the target vehicle to stop at the pick-up point and the alighting point.

7. The method according to claim 6, characterized in that Also includes: If it is determined that the pick-up point and the alighting point are not on the driving path, and it is determined that the distances between the pick-up point and the alighting point and the driving path are less than a preset distance threshold, and when the other user travel information meets the preset conditions, a driving path modification request is generated through the pick-up point and the alighting point, and based on the driving path modification request, the driving path of the initial shift information is modified to generate a newly added driving path, a stop is added to the newly added driving path, and the shift vehicle of the initial shift information is determined as the target vehicle, and the boarding information for the target vehicle is generated; the newly added driving path is used to control the driving path of the target vehicle.

8. The method according to claim 6, characterized in that Before the step of generating the boarding information for the target vehicle, the method further includes: Obtaining the average occupancy rate of the target vehicle at the departure station; When the average occupancy rate is greater than a preset threshold, determining whether there are other users who get off at the boarding point or at a station before the boarding point based on the other users' travel information; If it is determined that there are no other users who get off at the boarding point or at a station before the boarding point, the step of generating new scheduling application information for the trip information is executed; When it is determined that there are other users who get off at the boarding point or at a station before the boarding point, the step of generating the boarding information for the target vehicle is executed.

9. The method according to claim 6, characterized in that Before the step of generating the boarding information for the target vehicle, the method further includes: If it is determined that the route includes the boarding point and the alighting point, the shift vehicle of the initial shift information is marked as a type-one information vehicle; If it is determined that the waypoints do not include the boarding point and the alighting point, and it is determined that the boarding point and the alighting point are on the driving path, the shift vehicle of the initial shift information is marked as a second-category information vehicle; If it is determined that the boarding point and the alighting point are not on the driving path, and it is determined that the distances between the boarding point and the alighting point and the driving path are less than a preset distance threshold, the shift vehicle of the initial shift information is marked as a Class III information vehicle; Determine a recommendation order based on the first type of information vehicles, the second type of information vehicles, and the third type of information vehicles; The first-category information vehicle, the second-category information vehicle, and the third-category information vehicle are recommended in sequence based on the recommendation order.

10. A vehicle riding information generating device, characterized in that: include: A natural language data acquisition module, used to acquire natural language data for expressing the user's work schedule; A travel information determination module, used to determine the user's travel information based on the natural language data; The current bus schedule information acquisition module is used to obtain the current bus schedule information; The boarding information generating module is used to generate boarding information for the travel information based on the travel information and the current bus schedule information.

11. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; The memory is used to store computer programs; The processor is used to implement the method according to any one of claims 1 to 9 when executing the program stored in the memory.

12. A computer-readable storage medium having instructions stored thereon, which, when executed by one or more processors, cause the processors to perform the method according to any one of claims 1 to 9.

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