Transport capacity service determination method and device

Abstract

The invention relates to the technical field of online car hailing, and discloses a transport capacity service determination method and device, and the method comprises the steps: obtaining the related data of a driver when an order is received; wherein the related data comprises the time when the driver sends a call to the passenger; according to the related data, determining whether the transport capacity service of the driver is a target transport capacity service; wherein the target transport capacity service indicates the transport capacity service of the highest level in all the transport capacity services.

Description

Technical Field

[0001] This invention relates to the field of ride-hailing technology, specifically to a method and apparatus for determining transportation capacity services. Background Technology

[0002] In related technologies, manual screening is used to confirm the quality of a driver's service in order to determine the driver's transport capacity.

[0003] However, manual screening is costly and subjective, which makes it difficult to accurately determine the capacity available for transportation services.

[0004] Therefore, how to improve the accuracy of determining transportation capacity services while reducing labor costs has become a technical problem that needs to be solved. Summary of the Invention

[0005] In view of this, the present invention provides a method and apparatus for determining transportation capacity services.

[0006] In a first aspect, the present invention provides a method for determining a transportation service, the method comprising: acquiring relevant data of a driver when an order is received; wherein the relevant data includes the time when the driver initiates a call with the passenger; and determining, based on the relevant data, whether the driver's transportation service is a target transportation service; wherein the target transportation service indicates the highest level of transportation service among all transportation services.

[0007] The method for determining transportation capacity services provided in this embodiment acquires relevant driver data (including the time when the driver initiates a call with the passenger) when an order is received, and determines whether the driver's transportation capacity service is the target transportation capacity service based on this data. The entire process does not rely on manual screening, thereby reducing labor costs. Furthermore, determining transportation capacity services based on objective driver-related data avoids the subjectivity of manual verification, thus improving the accuracy of determining transportation capacity services.

[0008] In one possible implementation, the relevant data also includes: the driver's voice data; and determining whether the driver's transportation service is the target transportation service based on the relevant data, including: when the driver's voice data matches the preset script data, detecting whether there is target voice data in the driver's voice data that meets the preset specification conditions; and when there is no target voice data in the driver's voice data that meets the preset specification conditions, determining that the driver's transportation service is the target transportation service.

[0009] The method for determining transportation capacity provided in this embodiment, in addition to basic data such as the time when the driver makes a call to the passenger, incorporates driver voice data, enabling a more comprehensive evaluation of driver service. This avoids inaccuracies caused by relying on a single data point and makes the determination of the driver's transportation capacity service level more accurate.

[0010] In one possible implementation, the method further includes: when the driver's voice data does not conform to the preset script data, obtaining the number of times the driver's voice data does not conform to the preset script data in multiple orders; when the number exceeds the threshold, sending an alarm message to the driver.

[0011] The method for determining transportation capacity provided in this embodiment sends an alarm message when the driver's voice data does not conform to the preset script data and the number of occurrences exceeds a threshold. This allows the driver to promptly realize any problems in their communication with passengers. Drivers can reflect on their script usage based on the alarm message, proactively learn and improve, enhance their communication skills and service levels, and avoid negatively impacting service quality due to long-term poor communication habits.

[0012] In one possible implementation, the relevant data also includes: driver image data; determining whether the driver's transportation service is the target transportation service based on the relevant data, including: detecting whether the driver's image data matches preset image data; wherein, the preset image data indicates an image of the driver wearing a work badge and a vest; when the driver's image data matches the preset image data, determining that the driver's transportation service is the target transportation service.

[0013] The method for determining transportation services provided in this embodiment allows passengers to intuitively confirm the driver's identity and platform when they see the driver wearing a name tag and vest, reducing their doubts and anxieties about strangers, thereby enhancing their trust in the driver and the platform and improving the user experience.

[0014] In one possible implementation, the relevant data also includes: the driver's rating rate; wherein the rating rate includes a positive rating rate and a negative rating rate; based on the relevant data, determining whether the driver's transportation service is the target transportation service includes: when the positive rating rate is higher than the positive rating rate threshold and the negative rating rate is lower than the negative rating rate threshold, determining that the driver's transportation service is the target transportation service; when the positive rating rate is lower than the positive rating rate threshold and / or the negative rating rate is higher than the negative rating rate threshold, determining that the driver's transportation service is not the target transportation service.

[0015] The method for determining transportation capacity services provided in this embodiment links positive review rates and negative review rates with target transportation capacity services, giving drivers a clear direction for their efforts and incentive goals. When a driver's positive review rate is higher than a positive review rate threshold and their negative review rate is lower than a negative review rate threshold, they are identified as a target transportation capacity service, which may result in more order allocations, higher income rewards, or better career development opportunities. This incentive mechanism can stimulate drivers' work enthusiasm and initiative, prompting them to continuously improve service quality.

[0016] In one possible implementation, the method further includes: generating an evaluation result for the driver; wherein the evaluation result indicates whether the driver's capacity service is the target capacity service; and sending the evaluation result to the driver's terminal.

[0017] The method for determining transportation capacity provided in this embodiment clearly informs drivers whether their transportation capacity service meets the target standard, allowing drivers to have an accurate understanding of their service level. Whether at the target transportation capacity service level or below, drivers can clearly understand their position on the platform, thereby enabling them to develop targeted improvement or enhancement plans.

[0018] Secondly, the present invention provides a means for determining a transportation service, the means comprising: an acquisition module for acquiring relevant data of a driver when an order is received; wherein the relevant data includes the time when the driver initiates a call with the passenger; and a determination module for determining, based on the relevant data, whether the driver's transportation service is a target transportation service; wherein the target transportation service indicates the highest level of transportation service among all transportation services.

[0019] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the method for determining capacity services described in the first aspect or any corresponding embodiment thereof.

[0020] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to perform the capacity service determination method of the first aspect or any corresponding embodiment thereof.

[0021] Fifthly, the present invention provides a computer program product, including computer instructions for causing a computer to execute the method for determining capacity services described in the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0022] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating a method for determining transportation capacity services according to an embodiment of the present invention;

[0024] Figure 2 This is a schematic diagram of a method for determining transport capacity services according to an embodiment of the present invention;

[0025] Figure 3This is a structural block diagram of a capacity service determination device according to an embodiment of the present invention;

[0026] Figure 4 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] According to an embodiment of the present invention, a method for determining capacity services is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0029] This embodiment provides a method for determining transportation capacity services, which can be used with computer equipment such as computers and servers. Figure 1 This is a flowchart illustrating a method for determining transport capacity services according to an embodiment of the present invention, as shown below. Figure 1 As shown, the process includes the following steps:

[0030] Step S101: Obtain relevant data about the driver when the order is received; wherein, the relevant data includes the time when the driver made a call to the passenger.

[0031] Driver-related data includes various information about drivers during order acceptance and service processes, used to evaluate driver service performance and transportation capacity quality. This data includes the time when the driver initiates a call to the passenger. The time when the driver initiates a call to the passenger indicates the moment the driver, after receiving an order, proactively contacts the passenger to confirm trip details (such as the specific pick-up location, any special needs, etc.).

[0032] Specifically, it can obtain relevant data about the driver when an order is received.

[0033] As an example, the ride-hailing platform's backend system automatically activates a monitoring mechanism after an order is assigned to a driver. When a driver initiates a call to a passenger using a communication tool specified by the platform (such as the platform's built-in voice call function), the system automatically records the precise time the call was initiated and stores it in the database, linking it to the order and driver information.

[0034] As an example, after assigning an order, the platform sends a notification to the driver's app, reminding the driver to manually enter the time of the call after contacting the passenger. After completing the call, the driver enters the call time in the corresponding input box on the app interface, clicks submit, and the data is then transmitted to the platform's backend for storage.

[0035] As an example, relevant data for the driver in at least one order can be extracted from the platform system's backend.

[0036] Step S102: Based on relevant data, determine whether the driver's capacity service is the target capacity service; wherein, the target capacity service indicates the highest level of capacity service among all capacity services.

[0037] The target capacity service indicates the highest level of service among all capacity services, representing that the driver has reached the optimal standards set by the platform in terms of service quality, professionalism, and passenger satisfaction.

[0038] Specifically, based on the driver-related data obtained in step S101 (mainly the time when the driver made a call to the passenger), specific evaluation rules and standards are used to determine whether the driver's transportation service meets the requirements of the target transportation service defined by the platform.

[0039] As an example, the platform pre-sets a reasonable time range for drivers to make calls to passengers as a threshold. For instance, it stipulates that drivers must make a call to passengers within one minute of receiving an order. After obtaining the call time in step S101, the system compares this time with the preset threshold. If the call time is within the threshold range, it is preliminarily determined that the driver is likely to provide the target transportation service; if it is not within the range, it is determined that the driver is unlikely to provide the target transportation service.

[0040] In one scenario, after assigning an order to driver Li Si, the platform sends a notification to Li Si's mobile app, reminding him to report the call time after contacting the passenger. Li Si contacts the passenger at 9:10 AM. He then finds the call time reporting entry for the corresponding order in the app, enters "9:10 AM," and submits the data, which is then transmitted to the platform's backend. Example of judgment based on preset time thresholds: The platform sets the time threshold for drivers to initiate a call to a passenger to be 3-10 minutes after receiving the order. For driver Zhang San's order, the call time is 9:03 AM, and the order was received at 9:00 AM, which is within the threshold range. However, it is necessary to check Zhang San's other performance in this order, such as whether he arrived on time and passenger reviews. If other aspects are also good, Zhang San's transportation service is ultimately determined to be the target transportation service. For driver Wang Wu, he received the order at 10:00 AM but only contacted the passenger at 10:20 AM, and the call time is outside the threshold range. It is initially determined that he is unlikely to provide the target transportation service. After further confirmation based on other factors, it is determined that his transportation service is not the target transportation service.

[0041] The method for determining transportation capacity services provided in this embodiment acquires relevant driver data (including the time when the driver initiates a call with the passenger) when an order is received, and determines whether the driver's transportation capacity service is the target transportation capacity service based on this data. The entire process does not rely on manual screening, thereby reducing labor costs. Furthermore, determining transportation capacity services based on objective driver-related data avoids the subjectivity of manual verification, thus improving the accuracy of determining transportation capacity services.

[0042] In one possible implementation, the relevant data also includes: the driver's voice data; and step S102 above includes:

[0043] Step S1021: When the driver's voice data matches the preset speech data, detect whether there is target voice data in the driver's voice data that meets the preset specification conditions.

[0044] The driver's voice data can indicate the voice information generated by the driver during communication with passengers (such as contacting passengers after accepting an order, communicating during the trip, etc.).

[0045] Pre-set scripts instruct the platform to pre-define a series of standard phrases and expressions that drivers should use when communicating with passengers, based on the standards and norms of high-quality service. These include greetings, statements confirming trip information, and expressions of assistance, used to standardize drivers' communication skills and improve service quality and passenger experience.

[0046] First, check if the driver's voice data matches the preset script data, that is, whether the driver communicates with passengers according to the standard script prescribed by the platform. If the driver's voice data matches the preset script data, further check whether there is target voice data that meets the preset specifications. This step aims to first ensure that the driver uses the correct script framework, and then conduct a thorough check to see if it contains key content that conforms to specific specifications.

[0047] As an example, speech recognition technology is used to convert the driver's voice data into text. The converted text is then compared word-by-word or sentence-by-sentence with pre-defined dialogue data to determine if it conforms to the pre-defined dialogue. For example, string matching algorithms or semantic similarity calculation methods based on natural language processing can be used. If the text conforms to the pre-defined dialogue, further analysis is performed to check for the presence of keywords, phrases, or sentence structures that meet pre-defined criteria. This detection can be performed using pre-defined keyword lists, regular expressions, etc.

[0048] Step S1022: If there is no target voice data in the driver's voice data that meets the preset specification conditions, determine that the driver's transportation capacity service is the target transportation capacity service.

[0049] Preset normative conditions can indicate rules and requirements set for specific key content or behaviors in a driver's voice data. These conditions can be stipulations regarding the occurrence of specific words in the voice, the structure of sentences, the level of politeness in expression, etc., used to determine whether the driver's communication meets the standards of quality service.

[0050] If, during the detection in step S1021, it is found that the driver's voice data does not contain target voice data that meets the preset specifications, then the driver's transportation service is determined to be a target transportation service. The logic here may be based on the assumption that when a driver uses preset scripts but lacks key target voice data, it may mean that the driver has some potential shortcomings in communication. However, from a specific evaluation perspective, this situation actually meets the criteria for determining a target transportation service (this logic may need to be understood in conjunction with the platform's specific business scenarios and evaluation strategies, and there may also be reverse settings in the wording; normally, it would be more reasonable for target voice data to be present in order to be considered a target transportation service; here, we analyze it according to the given content).

[0051] As an example, a dedicated table is created in the platform database to store the results of whether the driver's voice data meets the preset specifications in each detection. After step S1021 completes the detection, the voice data detection results for the driver's corresponding order are queried. If the query results show that no target voice data meets the preset specifications, the pre-set rules are retrieved from the database to mark the driver's transportation service as the target transportation service.

[0052] In one scenario, after receiving an order, a driver contacts a passenger, saying, "Hello, I'm the driver for your trip. Where are you now? I'll come pick you up right away." The platform converts this into text using speech recognition and compares it with the preset script: "Hello, I'm a driver from [Platform M]. You booked a trip from [departure point] to [destination]. Where are you now? I'll come pick you up immediately." Although the driver's words are not exactly the same, the meaning matches and conforms to the preset script. Further examination of preset norms, such as requiring drivers to ask passengers if they have any special needs, reveals that the driver's voice did not mention this, indicating that no target voice data matching this preset norm does exist. The platform's database has a "Voice Detection Result Table" containing fields such as order number, driver ID, and whether target voice data exists. For the above driver's order, querying this table shows that the value of the "Does target voice data exist?" field is "No." According to the pre-set rules in the database, when this field value is "No," the corresponding driver's service is marked as the target service.

[0053] The method for determining transportation capacity provided in this embodiment, in addition to basic data such as the time when the driver makes a call to the passenger, incorporates driver voice data, enabling a more comprehensive evaluation of driver service. This avoids inaccuracies caused by relying on a single data point and makes the determination of the driver's transportation capacity service level more accurate.

[0054] In one possible implementation, the above method also includes:

[0055] Step a1: When the driver's voice data does not match the preset script data, obtain the number of times the driver's voice data does not match the preset script data in multiple orders.

[0056] First, the voice data in the driver's current order is examined to determine if it matches the preset script. If it does not, the cumulative number of times the driver's voice data does not match the preset script across multiple orders is further counted.

[0057] As an example, advanced speech recognition technology is used to convert the driver's voice data into text. The converted text is then matched word-by-word or sentence-by-sentence with pre-set scripts. If the match rate is below a certain standard (e.g., 80%), the voice data is deemed not to conform to the pre-set scripts. Each instance of non-compliance is recorded in a database, and the number of non-compliances across multiple orders is tallied.

[0058] As an example, some key keywords or phrases from preset speech data are defined in advance. After the driver's voice data is converted into text, it is checked whether the text contains these keywords or phrases. If a certain number of key elements are missing or do not meet expectations, the voice data is determined to not conform to the preset speech data. Similarly, each instance of non-compliance is recorded in a database and the number of occurrences is counted.

[0059] As an example, a machine learning model is trained using a large amount of pre-set speech data and data that does not conform to the pre-set speech data as training samples, allowing the model to learn the features that distinguish between the two. The driver's voice data is then input into the trained model, and the model outputs a judgment result on whether it conforms to the pre-set speech data. A database is used to record each judgment result and count the number of times it does not conform.

[0060] Step a2: When the number of occurrences exceeds the threshold, send an alarm message to the driver.

[0061] The frequency threshold can be a preset threshold. This threshold can be 5 times, 6 times, etc., without specific limitation. The frequency count obtained in step a1 is compared with the preset frequency threshold. If the count exceeds the threshold, it indicates that the driver's voice data frequently deviates from the preset script in multiple orders. In this case, an alarm message is sent to the driver to remind them to standardize their voice communication and improve service quality.

[0062] As an example, alert information can be sent via SMS to the driver's registered mobile phone number through an interface with an SMS service provider. The SMS content can include a brief explanation of the non-compliance, such as "Your voice communication in several recent orders has not met the standards; please make improvements."

[0063] As an example, if a driver uses a specific platform application, alerts can be sent to the driver via push notifications within the application. The alert content is displayed in the application's message center, and the driver will see the notification when they open the application.

[0064] As an example, when a driver accepts an order, a pre-recorded alarm message can be played to the driver via the in-vehicle device or the voice function on the driver's mobile phone, such as "We have detected that there are many non-compliant situations in your recent voice communication. Please adjust them in time."

[0065] The method for determining transportation capacity provided in this embodiment sends an alarm message when the driver's voice data does not conform to the preset script data and the number of occurrences exceeds a threshold. This allows the driver to promptly realize any problems in their communication with passengers. Drivers can reflect on their script usage based on the alarm message, proactively learn and improve, enhance their communication skills and service levels, and avoid negatively impacting service quality due to long-term poor communication habits.

[0066] In one possible implementation, the relevant data also includes: driver image data; and step S102 above includes:

[0067] Step b1: Detect whether the driver's image data matches the preset image data; wherein, the preset image data indicates an image of the driver wearing a name tag and a vest.

[0068] The driver's image data can refer to information containing the driver's image obtained through image acquisition devices such as cameras. It can be a static photograph or a frame from a dynamic video stream. Preset image data can refer to pre-defined image features or patterns used as judgment criteria. In this scenario, it refers to the image status of the driver wearing a name tag and a vest.

[0069] Specifically, the collected driver image data is analyzed and processed to determine whether it matches the preset image data.

[0070] As an example, image segmentation algorithms (such as color- and texture-based segmentation methods) are first used to separate the driver region from the background in the image. Then, features are extracted for the work badge and vest separately. For the work badge, shape features (such as a rectangular outline) and color features (such as specific color regions) can be extracted; for the vest, unique color distribution and texture features can be extracted. The extracted features are then matched with the features of work badges and vests stored in the preset image data. The similarity between features (such as Euclidean distance, cosine similarity, etc.) can be calculated. When the similarity exceeds a certain threshold, it is determined that the corresponding work badge and vest exist in the image, meaning the image data matches the preset image data.

[0071] As an example, a large dataset of images containing drivers wearing name tags and vests, as well as images that do not meet this condition, is collected to construct a training dataset. Object detection models, such as Faster R-CNN and YOLO, are built using deep learning frameworks (e.g., TensorFlow, PyTorch). The model is trained using the training dataset, enabling it to learn the features that accurately identify name tags and vests. In practical applications, the collected driver image data is input into the trained model, and the model outputs detection results, determining whether name tags and vests are present in the image. If both are detected, the image data is considered to match the preset image data.

[0072] Step b2: When the driver's image data matches the preset image data, the driver's transportation service is determined to be the target transportation service.

[0073] When the detection result of step b1 shows that the driver's image data matches the preset image data, it is confirmed that the driver is wearing a work badge and a vest. At this point, it can be determined that the transportation service provided by the driver is the target transportation service.

[0074] The method for determining transportation services provided in this embodiment allows passengers to intuitively confirm the driver's identity and platform when they see the driver wearing a name tag and vest, reducing their doubts and anxieties about strangers, thereby enhancing their trust in the driver and the platform and improving the user experience.

[0075] In one possible implementation, the relevant data also includes: driver ratings; and step S102 above includes:

[0076] Step c1: When the positive review rate is higher than the positive review rate threshold and the negative review rate is lower than the negative review rate threshold, determine the driver's transportation capacity service as the target transportation capacity service.

[0077] Positive review rate indicates the percentage of positive reviews a driver receives out of the total number of reviews for a driver's completed orders within a certain statistical period (e.g., 1 month). Negative review rate indicates the percentage of negative reviews a driver receives out of the total number of reviews for a driver's completed orders within a certain statistical period (e.g., 1 month).

[0078] Specifically, the driver's positive review rate is compared with a preset positive review rate threshold, and the negative review rate is also compared with a preset negative review rate threshold. Only when both conditions are met simultaneously—positive review rate higher than the positive review rate threshold and negative review rate lower than the negative review rate threshold—is the driver's capacity service determined to be the target capacity service.

[0079] Step c2: When the positive review rate is lower than the positive review rate threshold and / or the negative review rate is higher than the negative review rate threshold, determine that the driver's capacity service is not the target capacity service.

[0080] If a driver's positive review rate is lower than the positive review rate threshold, or a negative review rate is higher than the negative review rate threshold, or both occur simultaneously, it is determined that the driver's transportation service is not the target transportation service.

[0081] In one possible implementation, the first condition can be whether the positive review rate is lower than a threshold, and the second condition can be whether the negative review rate is higher than a threshold. When the positive review rate is lower than the threshold, there's no need to further evaluate the second condition; it's sufficient to determine that the driver's service is not the target service, thus reducing the system's computational load and improving processing speed.

[0082] The method for determining transportation capacity services provided in this embodiment links positive review rates and negative review rates with target transportation capacity services, giving drivers a clear direction for their efforts and incentive goals. When a driver's positive review rate is higher than a positive review rate threshold and their negative review rate is lower than a negative review rate threshold, they are identified as a target transportation capacity service, which may result in more order allocations, higher income rewards, or better career development opportunities. This incentive mechanism can stimulate drivers' work enthusiasm and initiative, prompting them to continuously improve service quality.

[0083] In one possible implementation, the above method also includes:

[0084] Step d1 generates the driver's evaluation result; the evaluation result indicates whether the driver's capacity service is the target capacity service.

[0085] Based on previously collected and analyzed driver-related data (such as positive review rate, negative review rate, whether the driver wears a name tag and vest, etc.), and in accordance with established rules and standards, an evaluation result is generated regarding whether the driver's transportation service meets the target transportation service criteria. The evaluation result indicates whether the driver's transportation service meets the target transportation service criteria.

[0086] Step d2: Send the evaluation results to the driver's terminal.

[0087] The evaluation results generated in step d1 are accurately sent to the driver's terminal device. The terminal device can be the driver's mobile terminal (such as a mobile phone, smartwatch, etc.) or the driver's fixed terminal, such as the vehicle's central control screen.

[0088] The method for determining transportation capacity provided in this embodiment clearly informs drivers whether their transportation capacity service meets the target standard, allowing drivers to have an accurate understanding of their service level. Whether at the target transportation capacity service level or below, drivers can clearly understand their position on the platform, thereby enabling them to develop targeted improvement or enhancement plans.

[0089] In one possible implementation, please refer to Figure 2 , Figure 2 This is a schematic diagram of a method for determining transportation capacity services according to embodiments of this disclosure.

[0090] Combination Figure 2As shown, after accepting an order, the driver must contact the passenger within one minute to confirm their location and other information. The system uses big data technology to collect the driver's call records, analyzing whether the driver called promptly as required, excluding special cases. For example, if the driver and passenger are very close, a call is not necessary; or even if the passenger hangs up, the driver must still be considered to have fulfilled the request. During the service, the system will record the entire process to ensure the safety of both driver and passenger. The system also uses big data technology to identify and analyze the driver's pre-trip communication, determining whether the driver followed the prescribed scripts, and whether there were any uncivilized or unreasonable behaviors. If a driver exhibits such non-compliant behavior in multiple orders, the system will alert the driver management system and temporarily revoke their "excellent driver" status. The driver can regain their status after making improvements. To ensure that drivers maintain standardized service throughout the trip and provide a better riding experience for customers, the system requires drivers to take a selfie before the trip, completing image capture for the service. In the selfie, the driver must wear their name tag and vest. After completing the selfie, the driver can proceed with the order as normal. The system uses AI to recognize driver selfies, verify the driver's attire, and provide suggestions for improvement. If a driver's service selfies repeatedly fail to meet standards, they may lose their premium driver status and thus be unable to earn additional high-quality service income. Once the driver improves, they can regain their status.

[0091] After completing an order, passengers can provide comprehensive feedback on the driver. The system will then analyze the feedback and provide metrics such as "negative review rate" and "positive review rate." These metrics affect the driver's overall service performance evaluation. After evaluating the driver's service, the system will promptly provide feedback to the driver via SMS, push notifications, and dedicated notifications. For drivers with subpar performance, we will recommend training and improvement before continuing to provide service. After a period of improvement, the system will conduct a comprehensive evaluation of the driver. Those who pass the evaluation will continue to provide service as high-quality transportation capacity.

[0092] This embodiment also provides a capacity service determination device, which is used to implement the above embodiments and preferred embodiments, and will not be repeated as already described. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0093] This embodiment provides a device for determining transportation capacity services, such as... Figure 3 As shown, it includes: an acquisition module 301, used to acquire relevant data of the driver when an order is received; wherein, the relevant data includes the time when the driver made a call to the passenger; and a determination module 302, used to determine whether the driver's transportation service is a target transportation service based on the relevant data; wherein, the target transportation service indicates the highest level of transportation service among all transportation services.

[0094] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0095] In this embodiment, the capacity service determination device is presented in the form of a functional unit. Here, a functional unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0096] This invention also provides a computer device having the above-described features. Figure 3 The device shown is for determining the capacity service.

[0097] Please see Figure 4 , Figure 4 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 4 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 4 Take a processor 10 as an example.

[0098] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GPA), or any combination thereof.

[0099] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0100] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0101] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0102] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0103] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0104] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0105] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for determining transport capacity services, characterized in that, The method includes: Obtain relevant data about the driver when an order is received; wherein, the relevant data includes the time when the driver initiates a call with the passenger; Based on the relevant data, it is determined whether the driver's transportation service is a target transportation service; wherein, the target transportation service refers to the highest level of transportation service among all transportation services.

2. The method for determining transport capacity services according to claim 1, characterized in that, The relevant data also includes: driver's voice data; based on the relevant data, determining whether the driver's transportation service is the target transportation service includes: When the driver's voice data matches the preset speech data, detect whether there is target voice data in the driver's voice data that meets the preset specification conditions; If the driver's voice data does not contain target voice data that meets the preset specifications, the driver's transportation service is determined to be the target transportation service.

3. The method for determining transport capacity services according to claim 2, characterized in that, The method further includes: When the driver's voice data does not conform to the preset script data, the number of times the driver's voice data does not conform to the preset script data in multiple orders is obtained; When the number of occurrences exceeds the threshold, an alarm message is sent to the driver.

4. The method for determining transport capacity services according to claim 1, characterized in that, The relevant data also includes: driver image data; determining whether the driver's transportation service is the target transportation service based on the relevant data includes: The system detects whether the driver's image data matches preset image data; wherein, the preset image data indicates an image of the driver wearing a name tag and a vest. When the driver's image data matches the preset image data, the driver's transportation service is determined to be the target transportation service.

5. The method for determining transport capacity services according to claim 1, characterized in that, The relevant data also includes: driver rating rates; wherein the rating rates include positive rating rates and negative rating rates; based on the relevant data, determining whether the driver's transportation service is the target transportation service includes: When the positive review rate is higher than the positive review rate threshold and the negative review rate is lower than the negative review rate threshold, the driver's transportation service is determined to be the target transportation service. When the positive review rate is lower than the positive review rate threshold and / or the negative review rate is higher than the negative review rate threshold, it is determined that the driver's transportation service is not the target transportation service.

6. The method for determining transport capacity services according to claim 1, characterized in that, The method further includes: Generate an evaluation result for the driver; wherein the evaluation result indicates whether the driver's capacity service is the target capacity service; The evaluation results are sent to the driver's terminal.

7. A device for determining transport capacity services, characterized in that, The device includes: The acquisition module is used to acquire relevant data about the driver when an order is received; wherein, the relevant data includes the time when the driver initiates a call with the passenger; The determining module is used to determine, based on the relevant data, whether the driver's transportation service is a target transportation service; wherein, the target transportation service indicates the highest-level transportation service among all transportation services.

8. A computer device, characterized in that, include: A memory and a processor are communicatively connected, the memory storing computer instructions, and the processor executing the computer instructions to perform the method for determining capacity services according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a computer to perform the method for determining capacity services according to any one of claims 1 to 6.

10. A computer program product, characterized in that, Includes computer instructions for causing a computer to perform the method for determining capacity services according to any one of claims 1 to 6.

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