Passenger transport journey early warning method, training method of journey early warning model and related equipment

Abstract

The invention provides a passenger transportation journey early warning method, a journey early warning model training method and related equipment, and the method comprises the steps: obtaining journey data generated by a passenger transportation journey in real time in a passenger transportation journey process; performing text processing on the travel data and order data corresponding to a target order associated with the passenger transport travel to obtain text data; inputting the text data into the trained travel early warning model to obtain early warning information; and sending an early warning to a driver terminal and / or a passenger terminal associated with the passenger transportation travel based on the early warning information. According to the journey early warning method provided by the embodiment of the invention, the order data does not need to be manually sorted, so that a large amount of labor cost does not need to be consumed, and the early warning efficiency is improved through an automatic early warning mode.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and in particular, to a passenger trip warning method, a training method for a trip warning model, and related devices. Background Art

[0002] During a passenger transportation service trip, effectively identifying and dealing with trip risks can ensure the safety and reliability of the service. After identifying possible risks in the trip, timely warnings are sent to the driver side and the passenger side, thereby eliminating potential dangerous situations and providing a safe and controllable passenger transportation service.

[0003] Currently, the trip warning method manually sorts order data, collects order risk characteristics through manually formulated rules, and then issues warnings based on the order risk characteristics. However, the above trip warning method is overly dependent on manual labor, consumes a large amount of human resources, and has low efficiency. Summary of the Invention

[0004] The main objective of this application is to provide a passenger trip warning method, a training method for a trip warning model, and related devices, aiming to solve the technical problem that the existing trip warning method is overly dependent on manual labor, consumes a large amount of human resources, and has low efficiency.

[0005] To achieve the above objective, this application provides a passenger trip warning method, and the method includes:

[0006] During the passenger trip process, real-time obtain the trip data generated by the passenger trip;

[0007] Perform text processing on the trip data and the order data corresponding to the target order associated with the passenger trip to obtain text data;

[0008] Input the text data into the trained trip warning model to obtain warning information;

[0009] Based on the warning information, send a warning to the driver side terminal and / or the passenger side terminal associated with the passenger trip.

[0010] Optionally, the real-time obtaining the trip data generated by the passenger trip includes:

[0011] Real-time obtain the chat record between the driver side terminal and the passenger side terminal during the passenger trip;

[0012] Real-time obtain the call record between the driver side terminal and the passenger side terminal during the passenger trip;

[0013] Real-time obtain the recording file generated during the passenger trip.

[0014] Optionally, performing text processing on the trip data and the order data corresponding to the target order associated with the passenger trip to obtain text data, including:

[0015] Converting the chat record into text data to obtain chat text;

[0016] Converting the call record into text data to obtain call text;

[0017] Converting the recording file into text data to obtain recording text;

[0018] Converting the order data into text data to obtain order text;

[0019] Performing structured processing on the chat text, the call text, the recording text, and the order text according to the text features in the chat text, the call text, the recording text, and the order text to obtain the text data;

[0020] Wherein, the text data includes order features, driver-side features, and passenger-side features.

[0021] Optionally, sending a warning to the driver-side terminal and / or the passenger-side terminal associated with the passenger trip based on the warning information, including at least one of the following:

[0022] When the warning time represented by the warning information is earlier than the start time of the passenger trip, sending a warning to the driver-side terminal and / or the passenger-side terminal associated with the passenger trip before the passenger trip starts;

[0023] When the warning time represented by the warning information is within the trip time period of the passenger trip, sending a warning to the driver-side terminal and / or the passenger-side terminal associated with the passenger trip during the passenger trip;

[0024] When the warning time represented by the warning information is later than the end time of the passenger trip, sending a warning to the driver-side terminal and / or the passenger-side terminal associated with the passenger trip after the passenger trip ends.

[0025] In addition, to achieve the above object, the present application further provides a training method for a trip warning model, including:

[0026] Obtaining training data and warning labels; the training data includes text data, and the warning labels include warning information corresponding to a warning time earlier than the start time of the trip, warning information corresponding to a warning time within the trip time period, and warning information corresponding to a warning time later than the start time of the trip;

[0027] Input the training data and the warning labels into a trip warning model, and perform iterative training on the trip warning model;

[0028] When the value of the loss function corresponding to the trip warning model is lower than a preset threshold, determine the trip warning model as a trained trip warning model.

[0029] Optionally, obtaining training data includes:

[0030] Obtain chat text, call text, recording text, and order text; the chat text is generated based on the chat record between the driver's terminal and the passenger's terminal during a passenger transportation trip, the call text is generated based on the call record between the driver's terminal and the passenger's terminal during a passenger transportation trip, the recording text is generated based on the recording file generated during a passenger transportation trip, and the order text is generated based on the order data corresponding to the target order associated with the passenger transportation trip;

[0031] According to the text features in the chat text, the call text, the recording text, and the order text, perform structured processing on the chat text, the call text, the recording text, and the order text to obtain training data;

[0032] Among them, the training data includes order features, driver-side features, and passenger-side features.

[0033] In addition, to achieve the above object, the present application also provides a passenger transportation trip warning device, including:

[0034] An acquisition module, configured to, during a passenger transportation trip, acquire in real time the trip data generated by the passenger transportation trip;

[0035] A first processing module, configured to perform text processing on the trip data and the order data corresponding to the target order associated with the passenger transportation trip to obtain text data;

[0036] A second processing module, configured to input the text data into the trained trip warning model to obtain warning information;

[0037] A sending module, configured to send a warning to the driver's terminal and / or the passenger's terminal associated with the passenger transportation trip based on the warning information.

[0038] In addition, to achieve the above object, the present application also provides a training device for a trip warning model, including:

[0039] An acquisition module for acquiring training data and warning labels; the training data includes text data, and the warning labels include warning information with a warning time prior to the start time of the trip, warning information with a warning time within the trip time period, and warning information with a warning time after the start time of the trip;

[0040] A training module for inputting the training data and the warning labels into a trip warning model and performing iterative training on the trip warning model;

[0041] A determination module for determining the trip warning model as a trained trip warning model when the value of the loss function corresponding to the trip warning model is lower than a preset threshold.

[0042] To solve the above technical problems, an embodiment of the present application also provides a computer device, which adopts the following technical solutions:

[0043] The computer device includes a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, it implements the steps of any one of the passenger trip warning methods proposed in the embodiments of the present application, or implements the steps of any one of the trip warning model training methods proposed in the embodiments of the present application.

[0044] To solve the above technical problems, an embodiment of the present application also provides a computer-readable storage medium, which adopts the following technical solutions:

[0045] A computer program is stored on the computer-readable storage medium, and when the computer program is executed by the processor, it implements the steps of any one of the passenger trip warning methods proposed in the embodiments of the present application, or implements the steps of any one of the trip warning model training methods proposed in the embodiments of the present application.

[0046] Compared with the prior art, the embodiments of the present application mainly have the following beneficial effects:

[0047] The present application provides a passenger trip warning method, a training method for a trip warning model, and related devices. The passenger trip warning method includes: during the passenger trip, real-time obtaining trip data generated by the passenger trip; performing text processing on the trip data and the order data corresponding to the target order associated with the passenger trip to obtain text data; inputting the text data into the trained trip warning model to obtain a warning message; and based on the warning message, sending a warning to the driver terminal and / or the passenger terminal associated with the passenger trip. In the embodiments of the present application, through the trained trip warning model, according to the order data and the trip data generated by the passenger trip, a warning message can be obtained, and then a warning is sent to the driver terminal and / or the passenger terminal. The trip warning method provided by the embodiments of the present application does not require manual sorting of order data, so it does not consume a large amount of labor costs, and through the automatic warning method, the warning efficiency is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the solutions in the present application, the following will briefly introduce the drawings required for the description of the embodiments of the present application. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0049] Figure 1 is an exemplary system architecture diagram to which the present application can be applied;

[0050] Figure 2 is a flowchart of the passenger trip warning method provided by the embodiments of the present application;

[0051] Figure 3 is a flowchart of the training method for the trip warning model provided by the embodiments of the present application;

[0052] Figure 4 is a schematic structural diagram of an embodiment of the passenger trip warning device provided by the embodiments of the present application;

[0053] Figure 5 is a schematic structural diagram of an embodiment of the training device for the trip warning model provided by the embodiments of the present application;

[0054] Figure 6 is a basic structural block diagram of a computer device provided by the embodiments of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] The passenger trip warning method provided by the embodiments of this application is applied to a passenger trip warning device, and the training method of the trip warning model provided by the embodiments of this application is applied to a trip warning model training device. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs; the terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above drawings are intended to cover non-exclusive inclusion. The terms "first", "second", etc. in the specification and claims of this application or the above drawings are used to distinguish different objects and not to describe a specific order.

[0056] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in connection with the embodiments can be included in at least one embodiment of this application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0057] To enable those skilled in the technical field to better understand the solutions of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0058] As Figure 1 shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0059] Users can use the terminal devices 101, 102, 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication passenger end applications may be installed on the terminal devices 101, 102, 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social online platform software, etc.

[0060] The terminal devices 101, 102, and 103 can be various electronic devices with a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 (Moving Picture Experts Group Audio Layer IV) players, laptop computers, desktop computers, and so on.

[0061] The server 105 can be a server that provides various services, such as a background server that supports the pages displayed on the terminal devices 101, 102, and 103.

[0062] It should be noted that the passenger operation schedule warning method provided by the embodiments of the present application is generally executed by the server / terminal device. Correspondingly, the passenger operation schedule warning device is generally set in the server / terminal device.

[0063] It should be understood that Figure 1 the numbers of the terminal devices, networks, and servers in

[0064] Please refer to Figure 2 , which shows a flowchart of an embodiment of the passenger operation schedule warning method proposed according to the present application. The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology.

[0065] The passenger operation schedule warning method provided by the embodiments of the present application can be applied in a passenger transportation scenario. In the passenger transportation scenario, when a driver picks up a passenger and goes to the destination in a passenger transportation order, the terminal used by the driver side can be called the driver-side terminal, the terminal used by the passenger side can be called the passenger-side terminal, the above-mentioned passenger transportation order involving the driver and the passenger can be called the target order, and the journey of the driver picking up the passenger and going to the destination can be called the passenger operation schedule.

[0066] For the need to clearly elaborate the technical solution, the application scenarios of the subsequent embodiments are all the above-mentioned passenger transportation scenarios, and the technical solutions of each embodiment are described.

[0067] The passenger operation schedule warning method provided by the embodiments of the present application includes the following steps:

[0068] S210, during the passenger operation schedule, real-time acquire the schedule data generated by the passenger operation schedule.

[0069] During the process of the driver picking up the passenger and going to the destination in the passenger transportation order, real-time acquire the schedule data generated by the passenger operation schedule.

[0070] S220. Perform text processing on the itinerary data and the order data corresponding to the target order associated with the passenger itinerary to obtain text data.

[0071] In this step, after obtaining the itinerary data, perform text processing on the itinerary data; extract the order data corresponding to the target order and perform text processing on the order data; aggregate the results of text processing on the itinerary data and the order data to obtain text data.

[0072] S230. Input the text data into the trained itinerary warning model to obtain warning information.

[0073] It should be noted that a trained itinerary warning model is preset. For the specific training method of the itinerary warning model, please refer to the subsequent embodiments. Optionally, the above itinerary warning model is a large language model (LLM).

[0074] In this step, input the text data into the trained itinerary warning model to obtain warning information.

[0075] Optionally, the above warning information can be text information, voice information, video information, etc.

[0076] S240. Based on the warning information, send a warning to the driver terminal and / or passenger terminal associated with the passenger itinerary.

[0077] In this step, parse the warning information and then send a warning to the driver terminal and / or passenger terminal associated with the passenger itinerary.

[0078] An optional implementation is to send a warning to the driver terminal associated with the passenger itinerary.

[0079] Another optional implementation is to send a warning to the passenger terminal associated with the passenger itinerary.

[0080] Another optional implementation is to send a warning to the driver terminal and passenger terminal associated with the passenger itinerary.

[0081] As described above, the warning information can be text information, voice information, video information, etc. Therefore, the specific ways to send a warning to the terminal can be text message warning, voice warning, and video warning.

[0082] In the embodiments of the present application, through the trained itinerary warning model, warning information can be obtained based on the order data and the itinerary data generated by the passenger transportation itinerary, and then the warning is sent to the driver terminal and / or the passenger terminal; the itinerary warning method provided by the embodiments of the present application does not require manual sorting of order data, so it does not consume a large amount of labor costs, and through the method of automatic warning, the warning efficiency is improved.

[0083] Optionally, the real-time acquisition of the itinerary data generated by the passenger transportation itinerary includes:

[0084] Real-time acquisition of the chat record between the driver terminal and the passenger terminal during the passenger transportation itinerary;

[0085] Real-time acquisition of the call record between the driver terminal and the passenger terminal during the passenger transportation itinerary;

[0086] Real-time acquisition of the recorded audio file generated during the passenger transportation itinerary.

[0087] In this embodiment, during the process of the driver carrying the passenger to the destination in the passenger transportation order, the chat record between the driver terminal and the passenger terminal is acquired in real time. For example, the above chat record can be the record formed by the driver terminal and the passenger terminal through the chat software.

[0088] During the process of the driver carrying the passenger to the destination in the passenger transportation order, the call record between the driver terminal and the passenger terminal is acquired in real time. For example, the above call record can be the call recording and the number of calls between the driver terminal and the passenger terminal.

[0089] During the process of the driver carrying the passenger to the destination in the passenger transportation order, the recorded audio file generated during the passenger transportation itinerary is acquired in real time, wherein the above recorded audio file records the communication content between the driver and the passenger.

[0090] In this embodiment, by acquiring the chat record, call record and the generated recorded audio file between the driver terminal and the passenger terminal during the passenger transportation itinerary, and determining the above information as the itinerary data, and then warning through the above itinerary data, the accuracy of the warning behavior is improved.

[0091] Optionally, the text processing of the itinerary data and the order data corresponding to the target order associated with the passenger transportation itinerary to obtain text data includes:

[0092] Converting the chat record into text data to obtain chat text;

[0093] Converting the call record into text data to obtain call text;

[0094] Converting the recorded audio file into text data to obtain recorded audio text;

[0095] Convert the order data into text data to obtain order text;

[0096] According to the text features in the chat text, the call text, the recording text, and the order text, perform structured processing on the chat text, the call text, the recording text, and the order text to obtain the text data;

[0097] Wherein, the text data includes order features, driver-side features, and passenger-side features.

[0098] In this embodiment, the chat record, the call record, the recording file, and the order data are converted into text data to obtain the chat text, the call text, the recording text, and the order text. And extract the order features, driver-side features, and passenger-side features in the chat text, the call text, the recording text, and the order text, and perform structured processing on the chat text, the call text, the recording text, and the order text to obtain the text data.

[0099] In this embodiment, by extracting features from the chat text, the call text, the recording text, and the order text, text data including order features, driver-side features, and passenger-side features is formed, so as to collect multi-source information in the passenger transportation journey and improve the robustness of the early warning ability.

[0100] Optionally, the sending of an early warning to the driver-side terminal and / or the passenger-side terminal associated with the passenger transportation journey based on the early warning information includes at least one of the following:

[0101] When the early warning time represented by the early warning information is earlier than the start time of the passenger transportation journey, before the passenger transportation journey starts, send an early warning to the driver-side terminal and / or the passenger-side terminal associated with the passenger transportation journey;

[0102] When the early warning time represented by the early warning information is within the journey time period of the passenger transportation journey, during the passenger transportation journey, send an early warning to the driver-side terminal and / or the passenger-side terminal associated with the passenger transportation journey;

[0103] When the early warning time represented by the early warning information is later than the end time of the passenger transportation journey, after the passenger transportation journey ends, send an early warning to the driver-side terminal and / or the passenger-side terminal associated with the passenger transportation journey.

[0104] It should be noted that the passenger transportation journey includes a journey start time, a journey time period, and a journey end time. If the early warning time is earlier than the journey start time, it means that the passenger transportation journey has not started yet; if the early warning time is within the journey time period of the passenger transportation journey, it means that the passenger transportation journey is in progress; if the early warning time is later than the journey end time, it means that the passenger transportation journey has ended.

[0105] In this embodiment, the warning information can be parsed to obtain the warning time when the warning is issued.

[0106] When the warning time is earlier than the start time of the trip, a warning is sent to the driver terminal and / or the passenger terminal before the passenger trip starts.

[0107] When the warning time is within the trip time period, a warning is sent to the driver terminal and / or the passenger terminal during the passenger trip.

[0108] When the warning time is later than the end time of the trip, a warning is sent to the driver terminal and / or the passenger terminal after the passenger trip ends.

[0109] It should be understood that in an optional implementation scenario, warnings can be issued simultaneously in three stages: before the start of the passenger trip, during the passenger trip, and after the end of the passenger trip.

[0110] In this embodiment, according to the warning time in the warning information, warnings are given before the start of the passenger trip, during the passenger trip, and after the end of the passenger trip, realizing pre-trip, in-trip, and post-trip warnings, thereby improving the warning effect.

[0111] Please refer to Figure 3 , which shows a flowchart of an embodiment of the training method of the trip warning model proposed according to the present application. The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology.

[0112] The training method of the trip warning model provided by the embodiments of the present application includes the following steps:

[0113] S310, acquiring training data and warning labels.

[0114] It should be noted that the training data includes text data, and the warning labels include warning information corresponding to a warning time earlier than the start time of the trip, warning information corresponding to a warning time within the trip time period, and warning information corresponding to a warning time later than the start time of the trip.

[0115] S320, inputting the training data and the warning labels into the trip warning model, and performing iterative training on the trip warning model.

[0116] In this step, a trip warning model is preset. As described above, the trip warning model can be a large language model.

[0117] In this step, the training data is used as the input of the trip warning model, and the warning label is used as the output of the trip warning model, and iterative training is performed on the trip warning model.

[0118] S330. When the loss function value corresponding to the trip warning model is lower than a preset threshold, determine the trip warning model as the trained trip warning model.

[0119] In this embodiment, by training the trip warning model, automatic warning is realized, and there is no need to manually sort out order data, thus improving the warning efficiency.

[0120] Optionally, obtaining training data includes:

[0121] Obtain chat text, call text, recording text, and order text; the chat text is generated based on the chat record between the driver terminal and the passenger terminal during the passenger trip, the call text is generated based on the call record between the driver terminal and the passenger terminal during the passenger trip, the recording text is generated based on the recording file generated during the passenger trip, and the order text is generated based on the order data corresponding to the target order associated with the passenger trip;

[0122] According to the text features in the chat text, the call text, the recording text, and the order text, perform structured processing on the chat text, the call text, the recording text, and the order text to obtain training data;

[0123] Among them, the training data includes order features, driver - side features, and passenger - side features.

[0124] In this embodiment, chat text is generated based on the chat record between the driver terminal and the passenger terminal during the passenger trip; call text is generated based on the call record between the driver terminal and the passenger terminal during the passenger trip; order text is generated based on the order data corresponding to the target order.

[0125] Extract the text features in the chat text, call text, recording text, and order text, and perform structured processing on the chat text, call text, recording text, and order text to obtain training data including order features, driver - side features, and passenger - side features.

[0126] Please refer to Figure 4 , a passenger trip warning device 400 provided by an embodiment of the present application. The passenger trip warning device 400 includes:

[0127] An acquisition module 410, configured to, during the passenger trip, acquire in real time the trip data generated by the passenger trip;

[0128] A first processing module 420, configured to perform text processing on the trip data and the order data corresponding to the target order associated with the passenger trip to obtain text data;

[0129] The second processing module 430 is configured to input the text data into the trained trip warning model to obtain warning information;

[0130] The sending module 440 is configured to send a warning to the driver-side terminal and / or the passenger-side terminal associated with the passenger trip based on the warning information.

[0131] Optionally, the obtaining module 410 is specifically configured to:

[0132] Obtain the chat record between the driver-side terminal and the passenger-side terminal in the passenger trip in real time;

[0133] Obtain the call record between the driver-side terminal and the passenger-side terminal in the passenger trip in real time;

[0134] Obtain the recording file generated in the passenger trip in real time.

[0135] Optionally, the first processing module 420 is specifically configured to:

[0136] Convert the chat record into text data to obtain chat text;

[0137] Convert the call record into text data to obtain call text;

[0138] Convert the recording file into text data to obtain recording text;

[0139] Convert the order data into text data to obtain order text;

[0140] Perform structured processing on the chat text, the call text, the recording text, and the order text according to the text features in the chat text, the call text, the recording text, and the order text to obtain the text data;

[0141] Wherein, the text data includes order features, driver-side features, and passenger-side features.

[0142] Optionally, the second processing module 430 is specifically configured to perform at least one of the following:

[0143] In the case where the warning time represented by the warning information is earlier than the trip start time of the passenger trip, before the passenger trip starts, send a warning to the driver-side terminal and / or the passenger-side terminal associated with the passenger trip;

[0144] In the case where the warning time represented by the warning information is within the trip time period of the passenger trip, during the passenger trip, send a warning to the driver-side terminal and / or the passenger-side terminal associated with the passenger trip;

[0145] When the warning time represented by the warning information is later than the travel termination time of the passenger transportation trip, after the passenger transportation trip terminates, a warning is sent to the driver terminal and / or the passenger terminal associated with the passenger transportation trip.

[0146] Please refer to Figure 5 , a training device 500 for a travel warning model provided by an embodiment of the present application. The training device 500 for the travel warning model includes:

[0147] An acquisition module 510, configured to acquire, in real time during a passenger transportation trip, travel data generated by the passenger transportation trip;

[0148] A first processing module 520, configured to perform text processing on the travel data and order data corresponding to a target order associated with the passenger transportation trip to obtain text data;

[0149] A second processing module 530, configured to input the text data into a trained travel warning model to obtain warning information;

[0150] A sending module 540, configured to send a warning to the driver terminal and / or the passenger terminal associated with the passenger transportation trip based on the warning information.

[0151] Optionally, the acquisition module 510 is specifically configured to:

[0152] Acquire chat text, call text, recording text, and order text; the chat text is generated based on the chat record between the driver terminal and the passenger terminal during the passenger transportation trip, the call text is generated based on the call record between the driver terminal and the passenger terminal during the passenger transportation trip, the recording text is generated based on the recording file generated during the passenger transportation trip, and the order text is generated based on the order data corresponding to the target order associated with the passenger transportation trip;

[0153] Perform structured processing on the chat text, the call text, the recording text, and the order text according to the text features in the chat text, the call text, the recording text, and the order text to obtain training data;

[0154] Wherein, the training data includes order features, driver-side features, and passenger-side features.

[0155] To solve the above technical problems, an embodiment of the present application also provides a computer device. Specifically, please refer to Figure 6 , Figure 6 which is the basic structural block diagram of the computer device in this embodiment.

[0156] The computer device 6 includes a memory 61, a processor 62, and a network interface 63 that are communicatively connected to each other via a system bus. It should be noted that only the computer device 6 with components 61 - 63 is shown in the figure, but it should be understood that it is not required to implement all the shown components, and more or fewer components can be alternatively implemented. Among them, those skilled in the art of this technology can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0157] The computer device can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The computer device can perform human-computer interaction with users through means such as a keyboard, a mouse, a remote control, a touchpad, or a voice control device.

[0158] The memory 61 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, a hard disk, a multimedia card, a card-type memory (such as an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 61 can be an internal storage unit of the computer device 6, such as the hard disk or memory of the computer device 6. In other embodiments, the memory 61 can also be an external storage device of the computer device 6, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device 6. Of course, the memory 61 can also include both the internal storage unit of the computer device 6 and its external storage device. In this embodiment, the memory 61 is generally used to store the operating system and various application software installed on the computer device 6, such as the program code of the passenger operation warning method, the program code of the training method of the trip warning model, etc. In addition, the memory 61 can also be used to temporarily store various types of data that have been output or will be output.

[0159] In some embodiments, the processor 62 may be a Central Processing Unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips. The processor 62 is generally used to control the overall operation of the computer device 6. In this embodiment, the processor 62 is used to run the program code stored in the memory 61 or process data, such as running the program code of the passenger trip warning method and the program code of the training method of the trip warning model.

[0160] The network interface 63 may include a wireless network interface or a wired network interface, and this network interface 63 is generally used to establish a communication connection between the computer device 6 and other electronic devices.

[0161] The present application also provides another implementation manner, that is, to provide a computer-readable storage medium, which stores the passenger trip warning program and the program of the training method of the trip warning model. The passenger trip warning program can be executed by at least one processor, so that the at least one processor executes the steps of the passenger trip warning method as described above. The training program of the trip warning model can be executed by at least one processor, so that the at least one processor executes the steps of the training method of the trip warning model as described above.

[0162] Through the description of the above embodiments, those skilled in the art can clearly understand that the above embodiment methods can be implemented by means of software plus a necessary general hardware online platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation manner. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disc), and includes several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in various embodiments of the present application.

[0163] This application can be used in numerous general or specific computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and so on. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This application can also be practiced in a distributed computing environment where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0164] Obviously, the embodiments described above are only a part of the embodiments of this application, rather than all of them. The preferred embodiments of this application are given in the drawings, but they do not limit the patent scope of this application. This application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosed content of this application more thorough and comprehensive. Although this application has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions recorded in the foregoing specific embodiments, or perform equivalent replacements for some of the technical features. Any equivalent structure directly or indirectly using the content of this application's specification and drawings in other related technical fields is equally within the scope of this application's patent protection.

Claims

1. A passenger travel early warning method, characterized in that: The method comprises: During the passenger travel, the travel data generated by the passenger travel is obtained in real time; Performing text processing on the itinerary data and the order data corresponding to the target order associated with the passenger itinerary to obtain text data; Inputting the text data into the trained travel warning model to obtain warning information; Based on the warning information, a warning is sent to the driver terminal and / or the passenger terminal associated with the passenger journey.

2. The method according to claim 1, characterized in that The real-time acquisition of the travel data generated by the passenger trip includes: Real-time acquisition of chat records between the driver terminal and the passenger terminal during the passenger journey; Real-time acquisition of call records between the driver's terminal and the passenger's terminal during the passenger journey; The recording files generated during the passenger journey are obtained in real time.

3. The method according to claim 2, characterized in that The text processing of the itinerary data and the order data corresponding to the target order associated with the passenger itinerary to obtain text data includes: Convert the chat record into text data to obtain chat text; Convert the call record into text data to obtain a call text; Convert the recording file into text data to obtain a recording text; Convert the order data into text data to obtain an order text; According to the text features in the chat text, the call text, the recording text and the order text, structural processing is performed on the chat text, the call text, the recording text and the order text to obtain the text data; The text data includes order characteristics, driver characteristics and passenger characteristics.

4. The method according to claim 1, characterized in that: The sending of an early warning to a driver terminal and / or a passenger terminal associated with the passenger trip based on the early warning information includes at least one of the following: In the case where the warning time represented by the warning information is earlier than the start time of the passenger trip, before the passenger trip starts, sending a warning to the driver terminal and / or the passenger terminal associated with the passenger trip; In the case where the warning time represented by the warning information is within the travel time period of the passenger trip, during the passenger trip, sending a warning to the driver terminal and / or the passenger terminal associated with the passenger trip; In the case where the warning time represented by the warning information is later than the trip termination time of the passenger trip, after the passenger trip is terminated, a warning is sent to the driver terminal and / or the passenger terminal associated with the passenger trip.

5. A method for training a travel warning model, characterized in that: include: Obtain training data and warning labels; The training data includes text data, and the warning labels include warning information corresponding to a warning time before the start time of the trip, warning information corresponding to a warning time in the travel time period, and warning information corresponding to a warning time later than the start time of the trip; Inputting the training data and the warning label into a travel warning model, and iteratively training the travel warning model; When the loss function value corresponding to the travel warning model is lower than a preset threshold, the travel warning model is determined as a trained travel warning model.

6. The method according to claim 5, characterized in that Get training data, including: Obtaining chat text, call text, recording text and order text; the chat text is generated based on the chat record between the driver's terminal and the passenger's terminal during the bus trip, the call text is generated based on the call record between the driver's terminal and the passenger's terminal during the bus trip, the recording text is generated based on the recording file generated during the bus trip, and the order text is generated based on the order data corresponding to the target order associated with the bus trip; According to text features in the chat text, the call text, the recording text and the order text, structural processing is performed on the chat text, the call text, the recording text and the order text to obtain training data; The training data includes order features, driver features and passenger features.

7. A passenger travel warning device, characterized in that: include: An acquisition module, used for acquiring the travel data generated by the passenger journey in real time during the passenger journey; A first processing module is used to perform text processing on the itinerary data and order data corresponding to the target order associated with the passenger itinerary to obtain text data; A second processing module is used to input the text data into the trained travel warning model to obtain warning information; A sending module is used to send an early warning to the driver terminal and / or the passenger terminal associated with the passenger journey based on the early warning information.

8. A training device for a travel warning model, characterized in that: include: Acquisition module, used to obtain training data and warning labels; The training data includes text data, and the warning labels include warning information corresponding to a warning time before the start time of the trip, warning information corresponding to a warning time in the travel time period, and warning information corresponding to a warning time later than the start time of the trip; A training module, used for inputting the training data and the warning label into the travel warning model, and performing iterative training on the travel warning model; A determination module is used to determine the travel warning model as a trained travel warning model when the loss function value corresponding to the travel warning model is lower than a preset threshold.

9. A computer device, characterized in that: It comprises a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps of the passenger travel itinerary warning method as described in any one of claims 1 to 4 are implemented, or the steps of the training method of the itinerary warning model as described in claim 5 or 6 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the passenger trip warning method as described in any one of claims 1 to 4, or implements the steps of the trip warning model training method as described in claim 5 or 6.

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