Intelligent judgment method, device and equipment for responsible party of online car-hailing order cancelling and storage medium

Abstract

The invention discloses a responsibility party intelligent judgment method, device and equipment for online car-hailing order cancelling and a storage medium. The responsibility party intelligent judgment method comprises the following steps of obtaining historical online car-hailing order data; based on historical online car-hailing order data, constructing an order cancelling responsibility judgment model; when a cancel event of the online car-hailing order is obtained, inputting order data of the online car-hailing order into the cancel order responsibility judgment model; and based on the order cancelling responsibility judgment model, outputting a responsibility party judgment result of cancelling the online car-hailing order. According to the invention, an intelligent order cancelling responsibility judgment model is constructed by using a big data technology and a machine learning algorithm. The model can comprehensively consider data of multiple dimensions, such as order time, vehicle real-time position, road condition and the like, thereby more accurately analyzing and judging a responsible party for cancelling an order.

Description

Technical Field

[0001] The present invention relates to the field of information processing technology, and specifically to a method, device, equipment and storage medium for intelligently determining the party responsible for canceling an online car-hailing order. Background Art

[0002] With the rapid development of the online car-hailing industry, the issue of order cancellation has become increasingly prominent and has become a common focus of attention for drivers, passengers and online car-hailing platforms. During the operation of online car-hailing, order cancellations occur from time to time, which may be initiated by drivers or passengers. Accurately determining the responsible party for canceling an order is crucial to protecting the legitimate rights and interests of both drivers and passengers, maintaining the operating order of the platform and improving user experience.

[0003] However, at present, when determining the responsible party for canceling an order, most online ride-hailing platforms use manual judgment or simple rule-based judgment methods. These methods have many problems: on the one hand, manual judgment is easily affected by subjective factors, resulting in unfair and inaccurate judgment results; on the other hand, simple rule-based judgment methods cannot fully consider the complex actual situation, and the judgment efficiency is low and prone to misjudgment. These problems not only affect the satisfaction of both drivers and passengers, but also have a negative impact on the operating efficiency and reputation of the platform.

[0004] Therefore, there is an urgent need for a more scientific, objective and efficient judgment method that can comprehensively consider multiple factors and accurately and quickly determine the party responsible for canceling an order to meet the needs of the rapid development of the online car-hailing industry. Summary of the invention

[0005] In order to overcome the above technical defects, the present invention provides a

[0006] In order to solve the above problems, the present invention is implemented according to the following technical solutions:

[0007] In a first aspect, the present invention provides a method for intelligently determining the responsible party for canceling an online car-hailing order, comprising the following steps:

[0008] Get historical online ride-hailing order data;

[0009] Based on historical online ride-hailing order data, a cancellation order responsibility determination model is constructed;

[0010] When a cancellation event of an online car-hailing order is obtained, the order data of the online car-hailing order is input into the cancellation order responsibility determination model;

[0011] Based on the order cancellation responsibility determination model, the determination result of the responsible party for canceling the online ride-hailing order is output.

[0012] In combination with the first aspect, the present invention also provides a first preferred implementation scheme of the first aspect. Specifically, the historical online car-hailing order data includes the passenger order time, order dispatch time, order acceptance time, estimated pick-up time, cancellation initiator, vehicle trajectory, map data, number of calls between driver and passenger, and order recording data.

[0013] In combination with the first aspect, the present invention further provides a second preferred implementation manner of the first aspect. Specifically, the responsible party intelligent determination method further includes:

[0014] The historical online car-hailing order data is preprocessed, and the preprocessing includes data cleaning and data standardization.

[0015] In combination with the first aspect, the present invention further provides a third preferred implementation of the first aspect, specifically, based on historical online car-hailing order data, a cancellation order responsibility determination model is constructed, specifically including:

[0016] Extract target features related to the determination of responsibility for order cancellation from historical online ride-hailing order data;

[0017] The extracted target features are trained using machine learning algorithms to build a model for determining the responsibility for order cancellation.

[0018] In combination with the first aspect, the present invention further provides a fourth preferred implementation manner of the first aspect, specifically, the target features include:

[0019] Based on the order dispatch time and order cancellation time, get the order cancellation period;

[0020] Calculate the remaining distance for the vehicle to pick up passengers based on the vehicle's location and passenger boarding point when the order was canceled;

[0021] The vehicle's pick-up distance is calculated based on the vehicle's location when the order is canceled and the vehicle's location when the order is accepted;

[0022] Estimated pick-up time.

[0023] In a second aspect, the present invention further provides an intelligent device for determining the responsible party for canceling an online car-hailing order, comprising:

[0024] The acquisition module is used to obtain historical online car-hailing order data;

[0025] A construction module is used to build a cancellation order responsibility determination model based on historical online car-hailing order data;

[0026] An input module, which is used to input the order data of the online car-hailing order into the cancellation order responsibility determination model when a cancellation event of the online car-hailing order is obtained;

[0027] An output module is used to output the determination result of the responsible party for canceling the online car-hailing order based on the cancellation order responsibility determination model.

[0028] In combination with the second aspect, the present invention also provides a first preferred implementation scheme of the second aspect. Specifically, the historical online car-hailing order data includes the passenger order time, order dispatch time, order acceptance time, estimated pick-up time, cancellation initiator, vehicle trajectory, map data, number of calls between driver and passenger, and order recording data.

[0029] In a third aspect, the present invention further provides an electronic device, the electronic device comprising:

[0030] at least one processor; and a memory communicatively coupled to the at least one processor;

[0031] The memory stores a computer program executable by the at least one processor, wherein the computer program is

[0032] The at least one processor executes so that the at least one processor can execute the method for intelligently determining the responsible party for canceling an online car-hailing order as described in the first aspect.

[0033] In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program.

[0034] The computer program is used to enable the processor to implement the method for intelligently determining the responsible party for canceling an online car-hailing order as described in the first aspect when executing the computer program.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The present invention provides a method for intelligently determining the responsible party for canceling an online ride-hailing order, comprising the following steps: obtaining historical online ride-hailing order data; constructing a cancellation order responsibility determination model based on the historical online ride-hailing order data; when a cancellation event of an online ride-hailing order is obtained, inputting the order data of the online ride-hailing order into the cancellation order responsibility determination model; and outputting a determination result of the responsible party for canceling the online ride-hailing order based on the cancellation order responsibility determination model.

[0037] This application uses big data technology and machine learning algorithms to build an intelligent model for determining the responsibility for canceling orders. The model can comprehensively consider data from multiple dimensions, such as order time, real-time vehicle location, road conditions, etc., to more accurately analyze and determine the party responsible for canceling orders. Compared with traditional manual judgment or simple rule judgment methods, the method of this application greatly improves the accuracy of judgment and reduces misjudgments caused by subjective judgment or simple rules.

[0038] The technical solution of this application adopts an automated judgment method. Once an order cancellation event occurs, the system can immediately analyze the relevant data and quickly give a judgment result. This automated judgment process not only saves the time and resources required for manual judgment, but also improves the efficiency of the entire judgment process, allowing the online car-hailing platform to handle order cancellation issues more quickly, thereby improving overall operational efficiency.

[0039] By providing accurate, fast and fair liability determination, the technical solution of this application helps to improve the user experience. Passengers and drivers can get timely and fair processing results when the order is cancelled, reducing disputes and dissatisfaction caused by unfair liability determination, thereby enhancing user satisfaction and loyalty to online car-hailing services. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The specific embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings, wherein:

[0041] Figure 1 It is a technical flow chart of a method for intelligently determining the responsible party for canceling an online car-hailing order according to an embodiment of the present invention;

[0042] Figure 2 It is a module diagram of a device for intelligently determining the responsible party for canceling an online car-hailing order according to an embodiment of the present invention;

[0043] Figure 3 It is a structural schematic diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0045] With the rapid development of the online car-hailing industry, the issue of order cancellation has become increasingly prominent and has become a common focus of attention for drivers, passengers and online car-hailing platforms. During the operation of online car-hailing, order cancellations occur from time to time, which may be initiated by drivers or passengers. Accurately determining the responsible party for canceling an order is crucial to protecting the legitimate rights and interests of both drivers and passengers, maintaining the operating order of the platform and improving user experience.

[0046] However, at present, when determining the responsible party for canceling an order, most online ride-hailing platforms use manual judgment or simple rule-based judgment methods. These methods have many problems: on the one hand, manual judgment is easily affected by subjective factors, resulting in unfair and inaccurate judgment results; on the other hand, simple rule-based judgment methods cannot fully consider the complex actual situation, and the judgment efficiency is low and prone to misjudgment. These problems not only affect the satisfaction of both drivers and passengers, but also have a negative impact on the operating efficiency and reputation of the platform.

[0047] To this end, the present invention provides a method for intelligently determining the responsible party for canceling an online ride-hailing order. By comprehensively considering data from multiple dimensions and utilizing a machine learning algorithm to construct a model for determining the responsibility for canceling an order, intelligent determination of the responsible party for canceling an online ride-hailing order is achieved.

[0048] refer to Figure 1 , an embodiment of the present invention provides a flowchart of a method for intelligently determining the responsible party for canceling an online car-hailing order. The method can be executed by an intelligent device for determining the responsible party for canceling an online car-hailing order, which can be implemented in the form of hardware and / or software, and can be configured in a computer. Figure 1 As shown, the method includes:

[0049] S100: Obtain historical online car-hailing order data.

[0050] In practice, this step involves collecting historical order data from the online ride-hailing platform’s database. The types of data collected include, but are not limited to, order time, estimated pick-up time, cancellation time, real-time vehicle location, road conditions, number of calls between drivers and passengers, and recorded data. These data will serve as the basis for training the model to help the system understand and learn the responsibility for canceling orders in different situations. The data can be categorized as follows:

[0051] ((1) Data related to online ride-hailing orders: order time, estimated pick-up time, cancellation initiator, etc.

[0052] ((2) Map-related data: real-time vehicle location, road conditions, etc.

[0053] ((3) Other data: number of calls between driver and passenger, recording data, etc.

[0054] In a specific implementation, the historical online ride-hailing order data includes the time when the passenger places the order, the time when the order is dispatched, the time when the order is accepted, the estimated pick-up time, the cancellation initiator, the vehicle trajectory, map data, the number of calls between the driver and the passenger, and the order recording data.

[0055] In a preferred implementation, the historical online car-hailing order data is preprocessed, and the preprocessing includes data cleaning and data standardization. The collected data is cleaned, standardized, and other preprocessing operations are performed to eliminate data noise and improve data quality.

[0056] It is understandable that data cleaning: Data cleaning is mainly to remove outliers and noise points in the data to improve data quality and the accuracy of analysis results. This step includes removing duplicate data, processing missing values, and outlier processing. For example, you can use programming languages ​​(such as Python) with data processing libraries (such as pandas) to clean data, remove duplicate records, fill or delete missing values, and identify and process outliers.

[0057] Data standardization: Data standardization is the process of converting data into a uniform format for easier analysis and comparison. This may include timestamp conversion, geolocation encoding, etc. Data standardization helps eliminate the impact of different dimensions and value ranges on data analysis and improves the accuracy and stability of data analysis. For example, converting all time data into a uniform time format, or converting geolocation data into a uniform coordinate system.

[0058] Through these two steps, we can ensure that the data input into the model is clean, accurate, and consistent, thereby improving the effect of model training and the accuracy of the final judgment.

[0059] S200: Based on historical online ride-hailing order data, a cancellation order responsibility determination model is constructed.

[0060] By using the historical data collected in step S100, data preprocessing is performed, including data cleaning, standardization and other operations, to eliminate data noise and improve data quality. Then, features related to the determination of the responsibility for canceling orders are extracted from the preprocessed data, such as the time interval between the order placement time and the cancellation time, the distance between the real-time location of the vehicle and the passenger boarding point, the degree of traffic congestion, etc. Finally, the extracted features are trained using machine learning algorithms, such as random forests, neural networks, etc., to build a model for determining the responsibility for canceling orders.

[0061] Clean the collected data and remove duplicate, erroneous or incomplete records. Standardize the data, such as timestamp conversion and geolocation encoding. Extract features related to order cancellation responsibility determination, such as order time, cancellation time, vehicle location, etc. Select appropriate machine learning algorithms, such as random forest, train the features, and build a model. Use methods such as cross-validation to evaluate and optimize the model to ensure the generalization ability of the model.

[0062] In a specific implementation, the S200 includes the following steps:

[0063] S210: Extract target features related to the determination of responsibility for canceling orders from historical online ride-hailing order data.

[0064] In a specific implementation, the target features include:

[0065] Based on the order dispatch time and order cancellation time, get the order cancellation period;

[0066] Calculate the remaining distance for the vehicle to pick up passengers based on the vehicle's location and passenger boarding point when the order was canceled;

[0067] The vehicle's pick-up distance is calculated based on the vehicle's location when the order is canceled and the vehicle's location when the order is accepted;

[0068] Estimated pick-up time.

[0069] In the present invention, the purpose of this step is to extract the most helpful features for determining the responsibility for order cancellation from the historical online ride-hailing order data. These features will be used as input variables for model training to help the model learn and predict the responsible party for order cancellation. Specific implementation method:

[0070] Order cancellation period: Calculate the difference between the order dispatch time and the order cancellation time to get the order cancellation period. This period can reflect the timeliness of order cancellation and is of great significance for determining the responsible party.

[0071] Remaining distance for vehicle pickup: Calculates the remaining distance for the vehicle to reach the pickup point based on the vehicle's location and the passenger's pickup point at the time of order cancellation. This distance can reflect the driver's progress in picking up the passenger and help determine the responsible party for canceling the order.

[0072] Vehicle pick-up distance: The distance driven by the driver from accepting an order to canceling the order is calculated based on the vehicle position when the order is canceled and the vehicle position when the order is accepted. This distance can reflect whether the driver has any substantial pick-up behavior after accepting the order.

[0073] Estimated pick-up time: Based on the vehicle's pick-up distance and remaining distance, the estimated time required for the driver to pick up the vehicle. This time can reflect the driver's efficiency in picking up the vehicle and is also of certain reference value for determining the responsible party.

[0074] S220: Use machine learning algorithms to train the extracted target features and build a model for determining the responsibility for canceling orders.

[0075] In step S220, taking a neural network as an example, a detailed implementation may include the following steps:

[0076] (1) Selecting a neural network model: Select an appropriate neural network model based on the characteristics of the problem and the nature of the data. For example, you can use convolutional neural networks (CNNs) and deep neural networks (DNNs).

[0077] (2) Design network structure: Design the structure of the neural network, including the input layer, hidden layer, and output layer. The number of nodes in the input layer should match the number of features, and the number of nodes in the output layer is usually 1 or 2, representing the driver's responsibility and the passenger's responsibility, respectively.

[0078] (3) Data preprocessing: Normalize or standardize the input data to improve the convergence speed and performance of the model.

[0079] (4) Training the neural network: Use the enhanced traffic data set to train the neural network and obtain the neural network model. During the training process, the model performance can be optimized by adjusting hyperparameters such as learning rate and batch size.

[0080] (5) Model evaluation and optimization: After training is completed, use the test set to evaluate the model and check the model's performance indicators such as accuracy, recall, and F1 score. If the model's performance does not meet the requirements, you can go back to the feature extraction step and try to extract different features, or adjust the structure and parameters of the neural network and retrain the model.

[0081] (6) Model deployment: The trained model is deployed to the production environment to determine the responsible party for the cancellation of online ride-hailing orders in real time.

[0082] S300: When a cancellation event of an online ride-hailing order is obtained, the order data of the online ride-hailing order is input into the cancellation order responsibility determination model.

[0083] In specific implementation, when the online ride-hailing platform receives an order cancellation event, the system will automatically collect relevant data of the order, including basic information of the order, location information of the vehicle and passengers, road condition information, etc. These data will be used as input and sent to the trained order cancellation responsibility determination model in step S200 for analysis.

[0084] S400: Based on the order cancellation responsibility determination model, output the determination result of the responsible party for canceling the online car-hailing order.

[0085] In specific implementation, the model will automatically analyze and determine the responsible party for canceling an order based on the input data and learned feature relationships. The judgment result will clearly indicate whether it is the driver's responsibility or the passenger's responsibility, or in some cases, it may be the platform's responsibility. This result will provide accurate responsibility judgments for drivers, passengers, and platforms, help quickly resolve disputes, and safeguard the rights and interests of all parties.

[0086] like Figure 2 As shown, the present invention also provides a responsible party intelligent determination device for canceling an online car-hailing order, which is used to execute and implement the above-mentioned responsible party intelligent determination method, and the responsible party intelligent determination device includes:

[0087] The acquisition module is used to obtain historical online car-hailing order data;

[0088] A construction module is used to build a cancellation order responsibility determination model based on historical online car-hailing order data;

[0089] An input module, which is used to input the order data of the online car-hailing order into the cancellation order responsibility determination model when a cancellation event of the online car-hailing order is obtained;

[0090] An output module is used to output the determination result of the responsible party for canceling the online car-hailing order based on the cancellation order responsibility determination model.

[0091] Figure 3 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0092] like Figure 3 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0093] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0094] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as solving a method for intelligently determining the responsible party for canceling an online car-hailing order.

[0095] In some embodiments, a method for intelligently determining the responsible party for canceling an online ride-hailing order may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for intelligently determining the responsible party for canceling an online ride-hailing order described above may be executed. Alternatively, in other embodiments, the processor 11 may be configured to execute a method for intelligently determining the responsible party for canceling an online ride-hailing order in any other appropriate manner (e.g., by means of firmware).

[0096] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0097] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0098] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0099] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0100] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0101] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0102] An embodiment of the present invention also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements a method for intelligently determining the responsible party for canceling an online car-hailing order as provided in an embodiment of the present invention.

[0103] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0104] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0105] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for intelligently determining the responsible party for canceling an online car-hailing order, characterized in that: The steps include: Get historical online ride-hailing order data; Based on historical online ride-hailing order data, a cancellation order responsibility determination model is constructed; When a cancellation event of an online car-hailing order is obtained, the order data of the online car-hailing order is input into the cancellation order responsibility determination model; Based on the order cancellation responsibility determination model, the determination result of the responsible party for canceling the online ride-hailing order is output.

2. According to claim 1, the method for intelligently determining the responsible party for canceling an online car-hailing order is characterized by: The historical online ride-hailing order data includes the time when the passenger placed the order, the time when the order was dispatched, the time when the order was accepted, the estimated pick-up time, the cancellation initiator, the vehicle trajectory, map data, the number of calls between the driver and the passenger, and the order recording data.

3. According to claim 1, the method for intelligently determining the responsible party for canceling an online car-hailing order is characterized in that: The responsible party intelligent determination method further includes: The historical online car-hailing order data is preprocessed, and the preprocessing includes data cleaning and data standardization.

4. According to claim 2, the method for intelligently determining the responsible party for canceling an online car-hailing order is characterized in that: Based on historical online ride-hailing order data, a cancellation order responsibility determination model is constructed, including: Extract target features related to the determination of responsibility for order cancellation from historical online ride-hailing order data; The extracted target features are trained using machine learning algorithms to build a model for determining the responsibility for order cancellation.

5. According to claim 4, a method for intelligently determining the responsible party for canceling an online car-hailing order, characterized in that: The target features include: Based on the order dispatch time and order cancellation time, get the order cancellation period; Calculate the remaining distance for the vehicle to pick up passengers based on the vehicle's location and passenger boarding point when the order was canceled; The vehicle's pick-up distance is calculated based on the vehicle's location when the order is canceled and the vehicle's location when the order is accepted; Estimated pick-up time.

6. An intelligent device for determining the responsible party for canceling an online car-hailing order, characterized in that: include: The acquisition module is used to obtain historical online car-hailing order data; A construction module is used to build a cancellation order responsibility determination model based on historical online car-hailing order data; An input module, which is used to input the order data of the online car-hailing order into the cancellation order responsibility determination model when a cancellation event of the online car-hailing order is obtained; An output module is used to output the determination result of the responsible party for canceling the online car-hailing order based on the cancellation order responsibility determination model.

7. The intelligent determination device for the responsible party for canceling an online car-hailing order according to claim 6, characterized in that: The historical online ride-hailing order data includes the time when the passenger placed the order, the time when the order was dispatched, the time when the order was accepted, the estimated pick-up time, the cancellation initiator, the vehicle trajectory, map data, the number of calls between the driver and the passenger, and the order recording data.

8. The intelligent determination device for the responsible party for canceling an online car-hailing order according to claim 6 is characterized in that: The responsible party intelligent determination method further includes: The historical online car-hailing order data is preprocessed, and the preprocessing includes data cleaning and data standardization.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; In which, the memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for intelligently determining the responsible party for canceling an online car-hailing order as described in any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that: The computer readable storage medium stores a computer program, The computer program is used to enable the processor to implement the method for intelligently determining the responsible party for canceling an online car-hailing order as described in any one of claims 1 to 5 when executed.

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