A method for managing online car-hailing service information

Abstract

The present invention discloses a method for managing online car-hailing service information, and belongs to the technical field of service information management. The method monitors, processes and analyzes active conversations during the service process of online car-hailing drivers, and obtains whether various precautions during the service process of the driver are fully reminded. The corresponding local conversation processing array is obtained by processing and combining the conversation voice. The method monitors, processes and analyzes the operating behaviors during the service process of the online car-hailing drivers, and obtains whether various operating behaviors during the service process of the driver are standardized. The abnormal operating behaviors monitored are processed and counted, and digital processing of the abnormal operating behaviors is achieved. The service impact is obtained by integrating the local service monitoring and analysis results of different aspects of the driver service of the online car-hailing service order. The present invention is used to solve the technical problems of the single dimension of driver service process monitoring and inaccurate subsequent service evaluation in the existing solutions.

Description

Technical Field

[0001] The present invention relates to the technical field of service information management, and in particular to a method for managing online car-hailing service information. Background Art

[0002] Online ride-hailing services usually include passenger booking, driver dispatch, vehicle services, payment settlement, customer evaluation, complaint handling and other aspects.

[0003] The existing online ride-hailing service information management solutions basically stay at a single video surveillance statistics, audio surveillance statistics and user evaluation feedback to supervise the online ride-hailing services, but they are unable to conduct targeted active monitoring and analysis of the different impacts generated in different dimensions during the driver's service process, and integrate the analysis results of the different impacts generated in different dimensions to integrate and classify the online ride-hailing services and optimize the driver's ratings more accurately, resulting in a single dimension of driver service process monitoring and inaccurate subsequent service evaluation. Summary of the invention

[0004] The purpose of the present invention is to provide a method for managing online car-hailing service information, which is used to solve the technical problems of the single dimension of driver service process monitoring and inaccurate subsequent service evaluation in existing solutions.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A method for managing online car-hailing service information, comprising:

[0007] After the passenger gets on the bus, the whole process of communication between the driver and the passenger is monitored and counted to obtain communication dialogue data, the communication dialogue data is processed and classified to obtain the driver's communication processing data, and the driver's communication processing data is calculated for the positive influence of the dialogue to obtain the positive influence factor of the dialogue;

[0008] The whole process of driver's driving operation is monitored and counted to obtain operation monitoring data, and the operation monitoring data is processed and the operation risk impact is calculated to obtain the operation risk impact factor;

[0009] When an online car-hailing order is completed, a monitoring integration instruction is generated, and the object positive impact factor and the operation risk impact factor are integrated according to the monitoring integration instruction to obtain the service impact degree corresponding to the order. Based on the service impact degree, the service level corresponding to the order is obtained and the driver's score is dynamically updated.

[0010] Preferably, when a passenger gets on the bus, a communication start monitoring instruction is generated, and the time point when the driver actively engages in conversation and the corresponding conversation voice are counted according to the communication start monitoring instruction, the real-time conversation voice is recognized by a speech recognition algorithm to obtain conversation voice recognition text, and the obtained conversation voice recognition text is traversed and searched with the conversation active text database to obtain a conversation active presence label or a conversation active non-presence label.

[0011] Preferably, if there is a sample conversation-positive text identical to the conversation speech recognition text in the conversation-positive text database, a conversation-positive existence label is generated and a conversation-positive influence weight associated with the corresponding sample conversation-positive text is obtained;

[0012] On the contrary, if the sample dialogue positive text identical to the dialogue speech recognition file does not exist in the dialogue positive text database, a dialogue positive non-existence label is generated and its associated label value is set to 0;

[0013] The time point values ​​of the driver's active dialogue communication and the values ​​associated with the dialogue active presence label or the dialogue active absence label obtained by dialogue recognition analysis are extracted and combined to obtain a local dialogue processing array.

[0014] Preferably, when the passenger gets off the bus, a communication end monitoring instruction is generated, and according to the communication end monitoring instruction, all local dialogue processing arrays are arranged and combined in the order of monitoring time to obtain a dialogue processing array set;

[0015] When implementing the conversation positive influence calculation on the driver communication processing data, all local conversation processing arrays sorted in the conversation processing array are obtained and traversed, the values ​​of the last elements in all local conversation processing arrays are counted and summed, and the obtained value is set as the conversation positive influence factor Dyin.

[0016] Preferably, when the driver starts the vehicle and sets off, a behavior start monitoring instruction is generated, and targeted image recognition is performed on the first camera target area and the second camera target area of ​​the driver's entire driving operation process in real time according to the start monitoring instruction;

[0017] If the first target appears in the first camera target area, an abnormal target appearance label is generated, and the driver is prompted to drive safely according to the abnormal target appearance label and the first abnormal start time point when the first target appears is obtained; when the first target that appears in the first camera target area disappears, an abnormal target disappearance label is generated and the corresponding first abnormal disappearance time point is obtained; and the abnormal duration is obtained according to the first abnormal start time point and the first abnormal target disappearance time point;

[0018] The total number of abnormal target appearance labels and the duration of each corresponding abnormality are counted and combined to obtain the first abnormality monitoring and analysis data.

[0019] Preferably, if the driver contacts the second target in the second camera target area, an abnormal target contact tag is generated, and the driver is prompted to drive safely according to the abnormal target contact tag and the second abnormal start time point when the second target is contacted is obtained;

[0020] The total number of abnormal target contact labels that appear is counted to obtain the second abnormal monitoring analysis data.

[0021] Preferably, when calculating the impact of the driver's operation risk, the first abnormal monitoring and analysis data and the second abnormal monitoring and analysis data are obtained and traversed and counted, and the formula is used to calculate the impact of the driver's operation risk. Calculate and obtain the corresponding operation risk influence factor Wyin during the driver's driving process; where α1 and α2 are the proportional coefficients of different abnormal influences and are both greater than zero, Ti is the abnormal duration corresponding to each abnormal target appearance label, i=1, 2, 3,..., n; n is a positive integer, representing the total number of appearances; N' is the total number of abnormal target contact labels.

[0022] Preferably, the conversation positive influence factor Dyin and the operation risk influence factor Wyin corresponding to the completed online car-hailing order are obtained, and the service influence degree Fy corresponding to the order is calculated by the formula Fy=(Dyin-Wyin) / 100; when obtaining the service level corresponding to the order according to the service influence degree, the service influence degree is matched with the preset standard service influence range;

[0023] If the service impact falls within the standard service impact range, the driver's service is classified as normal service level;

[0024] When the service impact does not fall within the standard service impact range, if the service impact is less than the minimum value of the standard service impact range, the driver's service is classified as an abnormal service level;

[0025] If the service impact is greater than the maximum value of the standard service impact range, the driver's service is classified as an excellent service level.

[0026] Preferably, when the driver's rating is dynamically updated, the user's evaluation score for the online ride-hailing service order is obtained, the user's evaluation score and the service influence are summed to obtain the final order service score of the driver's service for the online ride-hailing order, and all the driver's historical order service scores are summed and averaged to achieve the update.

[0027] In order to solve the above problem, the present invention further provides a storage medium, comprising at least one processor; and a memory communicatively connected to the at least one processor;

[0028] Among them, the memory stores a computer program that can be executed by at least one processor, and the computer program is executed by at least one processor so that the at least one processor can execute the above-mentioned online car-hailing service information management method.

[0029] Compared with the existing solutions, the present invention achieves the following beneficial effects:

[0030] The present invention monitors, processes and analyzes the active conversations during the service of online car-hailing drivers to obtain whether the various precautions during the service process are fully reminded. By processing the conversation voice and combining it to obtain the corresponding local conversation processing array, the conversation voice is digitally processed, and reliable local data support can be provided for the driver communication processing data to calculate the positive impact of the overall conversation.

[0031] The present invention monitors, processes and analyzes the operating behaviors of online car-hailing drivers during their services, thereby obtaining information on whether the various operating behaviors of the drivers are standardized. By processing and counting the monitored abnormal operating behaviors, the abnormal operating behaviors can be digitally processed. At the same time, reliable local data support can be provided for calculating the overall operational risk impact of the driver's operating behavior processing data, thereby increasing the diversity of analysis and utilization of the driver's service active abnormal operating behavior monitoring data.

[0032] The present invention integrates the local service monitoring and analysis results of different aspects of the driver's service of the online car-hailing service order to obtain the service impact. By analyzing the service impact, the overall situation of the driver of the corresponding online car-hailing service order can be obtained, and reliable data support can be provided for the driver score update of the online car-hailing service order. Compared with the existing technical solutions in which only a single score and scoring item selection are made by passengers, the present invention can achieve more accurate and efficient local and overall supervision effects and score optimization effects of online car-hailing services. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The present invention will be further described below in conjunction with the accompanying drawings.

[0034] Figure 1 This is a flowchart of a method for managing online car-hailing service information according to the present invention.

[0035] Figure 2 This is a flowchart of dynamically updating the driver's score according to the service impact in the present invention.

[0036] Figure 3 A schematic diagram of the structure of a computer device for implementing a method for managing online car-hailing service information. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary operation and maintenance personnel in this field without creative work are within the scope of protection of the present invention.

[0038] like Figure 1 As shown, the present invention is a method for managing online car-hailing service information, comprising:

[0039] After the passenger gets on the bus, the whole process of communication between the driver and the passenger is monitored and counted to obtain communication dialogue data, the communication dialogue data is processed and classified to obtain driver communication processing data, and the driver communication processing data is calculated for the positive impact of the dialogue to obtain the positive impact factor of the dialogue; including:

[0040] When a passenger gets on the bus, a communication start monitoring instruction is generated, which can be obtained through monitoring by the pressure sensors set on the front and rear seats, and the time point when the driver actively conducts a conversation and the corresponding conversation voice are counted according to the communication start monitoring instruction. The unit of the time point is accurate to seconds, and the real-time conversation voice is recognized by the speech recognition algorithm to obtain the conversation voice recognition text. The speech recognition algorithm is an existing conventional technical solution, and the specific steps are not repeated here, and the obtained conversation voice recognition text is traversed and retrieved from the conversation positive text database; the conversation positive text database is determined by the online car-hailing platform corresponding to the existing driver service requirements, and the conversation positive text database contains a number of sample conversation positive texts and corresponding conversation positive impact weights, and the values ​​of the conversation positive impact weights can all be 1; the sample conversation positive texts include but are not limited to "fasten your seat belts", "pay attention to the rear vehicles when getting off the car", etc.

[0041] If there is a sample conversation positive text identical to the conversation speech recognition text in the conversation positive text database, a conversation positive existence label is generated and a conversation positive influence weight associated with the corresponding sample conversation positive text is obtained;

[0042] On the contrary, if the sample dialogue positive text identical to the dialogue speech recognition file does not exist in the dialogue positive text database, a dialogue positive non-existence label is generated and its associated label value is set to 0;

[0043] Extract the time point value of the driver's active dialogue communication and the value associated with the dialogue active presence label or dialogue active absence label obtained by dialogue recognition analysis, and combine them to obtain a local dialogue processing array;

[0044] When the passenger gets off the bus, a communication end monitoring instruction is generated. According to the communication end monitoring instruction, all local dialogue processing arrays are arranged and combined in the order of monitoring time to obtain a dialogue processing array set;

[0045] In the embodiment of the present invention, by monitoring and processing the active dialogue of the online car-hailing driver during the service process, it is obtained whether the driver has been reminded of various precautions during the service process. By processing the dialogue voice and combining to obtain the corresponding local dialogue processing array, the dialogue voice is digitally processed, and reliable local data support can be provided for the driver's communication processing data to calculate the overall dialogue positive impact.

[0046] When calculating the positive impact of the conversation on the driver communication processing data, obtain and traverse all the local conversation processing arrays that are centrally sorted in the conversation processing array, count and sum the values ​​of the last elements in all the local conversation processing arrays, and set the obtained value as the conversation positive impact factor Dyin; wherein the conversation positive impact factor is a value used to process and calculate the conversation data of the driver's active reminder during the online car-hailing service to analyze the overall positive impact of the entire service process;

[0047] In the embodiment of the present invention, the conversation positive impact factor obtained by calculation can not only digitally represent the overall conversation positive impact of the driver's service, but also provide reliable data support for the conversation positive impact for subsequent driver service evaluation updates, thereby improving the diversity of driver service active conversation monitoring data analysis and utilization.

[0048] The whole process of driver's driving operation is monitored and counted to obtain operation monitoring data, and the operation monitoring data is processed and the operation risk impact is calculated to obtain the operation risk impact factor; including:

[0049] When the driver starts the vehicle and sets off, a behavior monitoring start instruction is generated, and targeted image recognition is performed on the first camera target area and the second camera target area of ​​the driver's entire driving operation process in real time according to the monitoring start instruction;

[0050] The first camera target area may be the upper body of the driver, and the second camera target area may be the area near the steering wheel where several mobile phones used for navigation or order taking are located;

[0051] If an abnormal target appearance label is generated when the first target appears in the first camera target area, the first target may be a mobile phone, which means that the driver has picked up the mobile phone to make a call or perform voice operation during the online car-hailing service, and the driver is prompted to drive safely based on the abnormal target appearance label and the first abnormal start time point when the first target appears is obtained. When the first target that appears in the first camera target area disappears, an abnormal target disappearance label is generated and the corresponding first abnormal disappearance time point is obtained. The abnormal duration is obtained based on the first abnormal start time point and the first abnormal target disappearance time point, and the unit of the abnormal duration is seconds.

[0052] Count the total number of abnormal target appearance labels and the duration of each corresponding abnormality and combine them to obtain the first abnormality monitoring and analysis data;

[0053] If the driver comes into contact with the second target in the second camera target area, an abnormal target contact tag is generated. The second target may be a number of mobile phones near the steering wheel for navigation or order taking. The driver is prompted to drive safely based on the abnormal target contact tag and the second abnormal start time point when the second target is contacted is obtained. This indicates that the driver is not concentrating on driving during the online car-hailing service, and there are abnormal driving behaviors such as clicking on a fixed mobile phone for group chatting or checking messages.

[0054] Counting the total number of abnormal target contact labels to obtain second abnormal monitoring analysis data;

[0055] In the embodiment of the present invention, by monitoring and processing and analyzing the operating behaviors of the online car-hailing driver during the service process, it is obtained whether the various operating behaviors of the driver during the service process are standardized. By processing and counting the monitored abnormal operating behaviors, the abnormal operating behaviors are digitally processed, and reliable local data support can be provided for the driver's operating behavior processing data to calculate the overall operating risk impact;

[0056] When calculating the impact of driver operation risk, the first abnormal monitoring and analysis data and the second abnormal monitoring and analysis data are obtained and traversed and counted, and the formula is used to calculate the impact of driver operation risk. Calculate and obtain the corresponding operation risk impact factor Wyin during the driver's driving process; where α1 and α2 are the proportional coefficients of different abnormal impacts and are both greater than zero, Ti is the abnormal duration corresponding to each abnormal target appearance label, i=1, 2, 3, ..., n; n is a positive integer, representing the total number of appearances; N' is the total number of abnormal target contact labels; Among them, the operation risk impact factor is a value used to process and calculate the abnormal operation behavior data of the driver during the online car-hailing service to analyze the overall risk impact of the entire service process;

[0057] In the embodiment of the present invention, the operation hazard impact factor obtained by calculation can not only digitally represent the overall abnormal operation hazard impact of the driver's service, but also provide reliable data support for the operation hazard impact for subsequent driver service evaluation updates, thereby improving the diversity of driver service active abnormal operation behavior monitoring data analysis and utilization.

[0058] When an online car-hailing order is completed, a monitoring integration instruction is generated, and the object positive impact factor and the operation risk impact factor are integrated according to the monitoring integration instruction to obtain the service impact corresponding to the order, and the service level corresponding to the order is obtained according to the service impact and the driver's score is dynamically updated; including:

[0059] like Figure 2 As shown, the conversation positive influence factor Dyin and the operation risk influence factor Wyin corresponding to the completed online car-hailing order are obtained, and the service influence degree Fy corresponding to the order is calculated by the formula Fy=(Dyin-Wyin) / 100; the service influence degree is a value used to integrate the service analysis results of different aspects of the driver during the online car-hailing service process to analyze the overall service impact of the entire service process;

[0060] When obtaining the service level corresponding to the order based on the service impact, the service impact is matched with the preset standard service impact range; the standard service impact range is determined based on the existing online car-hailing driver service requirement data, or based on the test data of the previous driver service, for example: the minimum value of the standard service impact range is: the historical test conversation positive impact factor is zero and the historical operation risk impact factor is the maximum value; the maximum value of the standard service impact range is: the historical test conversation positive impact factor is the maximum value and the historical operation risk impact factor is zero;

[0061] If the service impact falls within the standard service impact range, the driver's service is classified as normal service level;

[0062] When the service impact does not fall within the standard service impact range, if the service impact is less than the minimum value of the standard service impact range, the driver's service is classified as an abnormal service level;

[0063] If the service impact is greater than the maximum value of the standard service impact range, the driver's service is classified as an excellent service level;

[0064] When dynamically updating the driver's rating, obtain the user's evaluation score for the online ride-hailing service order, sum the user's evaluation score with the service influence to get the final order service score of the driver service for the online ride-hailing order, and sum and average all the driver's historical order service scores to achieve the update.

[0065] In the embodiment of the present invention, the service impact is obtained by integrating the local service monitoring and analysis results of different aspects of the driver service of the online car-hailing service order. By analyzing the service impact, the overall situation of the driver of the corresponding online car-hailing service order can be obtained, and reliable data support can be provided for the update of the driver score of the online car-hailing service order. Compared with the existing technical solution in which only a single score and scoring item selection are made by passengers, the embodiment of the present invention can achieve more accurate and efficient local and overall supervision effects and scoring optimization effects of online car-hailing services.

[0066] In addition, the formulas involved in the above are all dimensionless and numerical calculations. They are a formula that is closest to the actual situation obtained by collecting a large amount of data and simulating it with simulation software.

[0067] like Figure 3 The figure is a schematic diagram of the structure of a computer device for implementing a method for managing online car-hailing service information provided by an embodiment of the present invention.

[0068] The computer device may include a processor, a memory, and a bus, and may also include a computer program stored in the memory and executable on the processor, such as a program for a method for managing online car-hailing service information.

[0069] Among them, the memory includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory can be an internal storage unit of a computer device, such as a mobile hard disk of the computer device. In other embodiments, the memory can also be an external storage device of a computer device, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device. Further, the memory can also include both an internal storage unit of the computer device and an external storage device. The memory can not only be used to store application software and various types of data installed on the computer device, such as the code of a program of a method for managing information of an online car-hailing service, but can also be used to temporarily store data that has been output or is to be output.

[0070] In some embodiments, the processor may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of integrated circuits packaged in the same position or in different positions, including one or more central processing units (CPU), microprocessors, digital processing chips, graphics processors, and a combination of various control chips. The processor is the control core (Control Unit) of the computer device, which uses various interfaces and lines to connect the various components of the entire computer device, and executes or executes programs or modules stored in the memory (such as a program of a method for managing information of online car-hailing services, etc.), and calls data stored in the memory to execute various positions of the computer device and process data.

[0071] The bus may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory and at least one processor, etc.

[0072] Figure 3 Only a computer device with components is shown, and those skilled in the art will understand that Figure 3 The illustrated structure does not constitute a limitation on the computer device, and may include fewer or more components than illustrated, or combine certain components, or arrange the components differently.

[0073] For example, although not shown, the computer device may also include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to at least one processor through a power management device, so that charging management, discharging management, and power consumption management are implemented through the power management device. The power source may also include any components such as one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, etc. The computer device may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0074] Furthermore, the computer device may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is usually used to establish a communication connection between the computer device and other computer devices.

[0075] Optionally, the computer device may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the computer device and to display a visual user interface.

[0076] It should be understood that the above embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0077] The program of a method for managing online car-hailing service information stored in the memory of a computer device is a combination of multiple instructions.

[0078] Specifically, the specific implementation method of the processor for the above instructions can refer to Figure 1 and Figure 2 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0079] Furthermore, if the module / unit integrated into the computer device is implemented in the form of a software location unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0080] The present invention also provides a computer-readable storage medium, which stores a computer program. The computer program is executed by a processor of a computer device.

[0081] In the several embodiments provided by the present invention, it should be understood that the disclosed method can be implemented in other ways. For example, the above-described embodiments of the invention are only illustrative, for example, the division of modules is only a logical position division, and there may be other division methods in actual implementation.

[0082] The modules described as separate components may or may not be physically separated, and the components shown as modules 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 modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0083] In addition, each position module 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. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software position module.

[0084] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0085] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for managing online car-hailing service information, characterized in that: include: After the passenger gets on the bus, the whole process of communication between the driver and the passenger is monitored and counted to obtain communication dialogue data, the communication dialogue data is processed and classified to obtain the driver's communication processing data, and the driver's communication processing data is calculated for the positive influence of the dialogue to obtain the positive influence factor of the dialogue; When a passenger gets on the bus, a communication start monitoring instruction is generated, and the time point when the driver actively conducts a conversation and the corresponding conversation voice are counted according to the communication start monitoring instruction. The real-time conversation voice is recognized by a speech recognition algorithm to obtain a conversation voice recognition text, and the obtained conversation voice recognition text is traversed and searched with a conversation active text database to obtain a conversation active presence label or a conversation active non-presence label; If there is a sample conversation positive text identical to the conversation speech recognition text in the conversation positive text database, a conversation positive existence label is generated and a conversation positive influence weight associated with the corresponding sample conversation positive text is obtained; On the contrary, if the sample dialogue positive text identical to the dialogue speech recognition file does not exist in the dialogue positive text database, a dialogue positive non-existence label is generated and its associated label value is set to 0; Extract the time point value of the driver's active dialogue communication and the value associated with the dialogue active presence label or dialogue active absence label obtained by dialogue recognition analysis, and combine them to obtain a local dialogue processing array; When the passenger gets off the bus, a communication end monitoring instruction is generated. According to the communication end monitoring instruction, all local dialogue processing arrays are arranged and combined in the order of monitoring time to obtain a dialogue processing array set; When calculating the positive influence of the conversation on the driver communication processing data, obtain and traverse all the local conversation processing arrays sorted in the conversation processing array set, count and sum the values ​​of the last elements in all the local conversation processing arrays, and set the obtained value as the positive influence factor Dyin; The whole process of driver's driving operation is monitored and counted to obtain operation monitoring data, and the operation monitoring data is processed and the operation risk impact is calculated to obtain the operation risk impact factor; When the driver starts the vehicle, a behavior monitoring start instruction is generated, and targeted image recognition is performed on the first camera target area and the second camera target area of ​​the driver's driving operation process in real time according to the monitoring start instruction; If the first target appears in the first camera target area, an abnormal target appearance label is generated, and the driver is prompted to drive safely according to the abnormal target appearance label and the first abnormal start time point when the first target appears is obtained; when the first target that appears in the first camera target area disappears, an abnormal target disappearance label is generated and the corresponding first abnormal disappearance time point is obtained; and the abnormal duration is obtained according to the first abnormal start time point and the first abnormal target disappearance time point; Count the total number of abnormal target appearance labels and the duration of each corresponding abnormality and combine them to obtain the first abnormality monitoring and analysis data; If the driver comes into contact with the second target in the second camera target area, an abnormal target contact tag is generated, and the driver is prompted to drive safely according to the abnormal target contact tag and the second abnormal start time point when the second target is contacted is obtained; Counting the total number of abnormal target contact labels to obtain second abnormal monitoring analysis data; When calculating the impact of driver operation risk, the first abnormal monitoring and analysis data and the second abnormal monitoring and analysis data are obtained and traversed and counted, and the formula is used to calculate the impact of driver operation risk. Calculate and obtain the corresponding operation risk influence factor Wyin during the driver's driving process; where α1 and α2 are the proportional coefficients of different abnormal influences and are both greater than zero, Ti is the abnormal duration corresponding to each abnormal target appearance label, i=1, 2, 3, ..., n; n is a positive integer, representing the total number of appearances; N' is the total number of abnormal target contact labels; When an online car-hailing order is completed, a monitoring integration instruction is generated, and the object positive impact factor and the operation risk impact factor are integrated according to the monitoring integration instruction to obtain the service impact corresponding to the order. According to the service impact, the service level corresponding to the order is obtained and the driver's score is dynamically updated; Among them, the conversation positive influence factor Dyin and the operation risk influence factor Wyin corresponding to the completed online car-hailing order are obtained, and the service influence degree Fy corresponding to the order is calculated by the formula Fy=(Dyin-Wyin) / 100; when obtaining the service level corresponding to the order according to the service influence degree, the service influence degree is matched with the preset standard service influence range; If the service impact falls within the standard service impact range, the driver's service is classified as normal service level; When the service impact does not fall within the standard service impact range, if the service impact is less than the minimum value of the standard service impact range, the driver's service is classified as an abnormal service level; If the service impact is greater than the maximum value of the standard service impact range, the driver's service is classified as an excellent service level.

2. A method for managing online car-hailing service information according to claim 1, characterized in that: When dynamically updating the driver's rating, obtain the user's evaluation score for the online ride-hailing service order, sum the user's evaluation score with the service influence to get the final order service score of the driver service for the online ride-hailing order, and sum and average all the driver's historical order service scores to achieve the update.

3. A storage medium, characterized in that: comprising at least one processor; And, a memory communicatively connected to at least one processor; wherein the memory stores a computer program executable by at least one processor, and the computer program is executed by at least one processor so that at least one processor can execute a method for managing online car-hailing service information as described in any one of claims 1 to 2.

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