A data processing method and device, electronic equipment and readable storage medium

Abstract

This disclosure provides a data processing method, apparatus, electronic device, and readable storage medium applied to a delivery platform. The method includes: periodically acquiring customer feedback information from the delivery platform, the customer feedback information including customer complaint information and / or customer evaluation information; performing semantic analysis on the customer feedback information to determine the corresponding liability assessment scenario; and invoking a liability assessment algorithm corresponding to the liability assessment scenario to process the customer feedback information and determine the responsible party corresponding to the customer feedback information. This disclosure ensures the objectivity and reliability of the liability assessment logic when the delivery platform processes customer feedback information, enabling automatic liability assessment of customer feedback information by the delivery platform and improving the accuracy of liability assessment.

Description

Technical Field

[0001] The embodiments of this disclosure relate to the field of computer processing technology, and more particularly to a data processing method, apparatus, electronic device, and readable storage medium. Background Technology

[0002] In the delivery service industry, delivery personnel may engage in unethical behavior during delivery, leading to customer complaints or negative reviews. Customer service typically handles these complaints and reviews based on user feedback, but delivery platforms often lack the ability to objectively assess these issues. This makes it difficult to accurately determine responsibility for delivery personnel's misconduct or to identify malicious complaints / reviews not attributable to the delivery personnel, resulting in potential misjudgments. Summary of the Invention

[0003] The embodiments of this disclosure provide a data processing method, apparatus, electronic device, and readable storage medium that can improve the accuracy of judgment processing and avoid misjudgments.

[0004] According to a first aspect of the embodiments of this disclosure, a data processing method is provided, applied to a delivery platform, the method comprising:

[0005] The system periodically acquires customer feedback information from the delivery platform, including customer complaint information and / or customer evaluation information.

[0006] Perform semantic analysis on the customer feedback information to determine the corresponding liability assessment scenario;

[0007] The responsibility assessment algorithm corresponding to the aforementioned responsibility assessment scenario is invoked to process the customer feedback information and determine the responsible party corresponding to the customer feedback information.

[0008] According to a second aspect of the embodiments of this disclosure, a data processing apparatus is provided for use in a delivery platform, the apparatus comprising:

[0009] The information acquisition module is used to periodically acquire customer feedback information from the delivery platform, including customer complaint information and / or customer evaluation information.

[0010] The semantic analysis module is used to perform semantic analysis on the customer feedback information to determine the judgment scenario corresponding to the customer feedback information;

[0011] The accountability processing module is used to call the accountability algorithm corresponding to the accountability scenario to process the customer feedback information and determine the responsible party corresponding to the customer feedback information.

[0012] According to a third aspect of the embodiments of this disclosure, an electronic device is provided, comprising:

[0013] A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the aforementioned data processing method.

[0014] According to a fourth aspect of the embodiments of the present disclosure, a readable storage medium is provided, which, when instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to perform the aforementioned data processing method.

[0015] Embodiments of this disclosure provide a data processing method, apparatus, electronic device, and readable storage medium applied to a delivery platform. The method includes: periodically acquiring customer feedback information from the delivery platform, the customer feedback information including customer complaint information and / or customer evaluation information; performing semantic analysis on the customer feedback information to determine the liability assessment scenario corresponding to the customer feedback information; and invoking a liability assessment algorithm corresponding to the liability assessment scenario to process the customer feedback information and determine the responsible party corresponding to the customer feedback information.

[0016] The embodiments of this disclosure perform semantic analysis on the acquired customer feedback information to determine the corresponding liability assessment scenario, and call the corresponding liability assessment algorithm to process the customer feedback information to determine the responsible party. This ensures that each customer feedback information can be matched with a unique liability assessment algorithm based on its corresponding liability assessment scenario, guaranteeing the objectivity and reliability of the liability assessment logic when the delivery platform processes customer feedback information. This enables the delivery platform to automatically process customer feedback information and improves the accuracy of liability assessment. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments of this disclosure will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart of the data processing method steps in one embodiment of this disclosure is shown;

[0019] Figure 2 A structural block diagram of a data processing apparatus according to one embodiment of the present disclosure is shown;

[0020] Figure 3 A structural diagram of an electronic device according to one embodiment of the present disclosure is shown. Detailed Implementation

[0021] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort are within the protection scope of the embodiments of this disclosure.

[0022] Example 1

[0023] Reference Figure 1 The document illustrates a flowchart of the data processing method in one embodiment of this disclosure, as follows:

[0024] Step 101: Periodically obtain customer feedback information from the delivery platform, including customer complaint information and / or customer evaluation information.

[0025] Step 102: Perform semantic analysis on the customer feedback information to determine the corresponding liability assessment scenario.

[0026] Step 103: Call the accountability algorithm corresponding to the accountability scenario to process the customer feedback information and determine the responsible party corresponding to the customer feedback information.

[0027] It should be noted that the data processing method provided in the embodiments of this disclosure can be applied to a delivery platform to determine the responsible party for customer feedback information, such as customer complaints and customer reviews, obtained by the delivery platform. This helps identify malicious complaints / negative reviews that are not the responsibility of the delivery person, thereby improving the accuracy of the determination. The customer feedback information can be complaint information and / or review information submitted by users through a client. The client can be a webpage, application, or other service that provides delivery services. The client transmits the customer feedback information submitted by the user to the delivery platform, which then determines the responsible party for the customer feedback information. The client can run on an electronic device used by the user, including but not limited to smartphones, personal computers (PCs), personal digital assistants, tablets, laptops, in-vehicle computers, handheld game consoles, smart wearable devices, virtual display devices, or display devices.

[0028] In one possible application scenario of this disclosure, a user interacts with a client, submitting customer feedback information for a completed delivery order. The client then transmits this feedback information to the delivery platform, which performs semantic analysis to determine the corresponding liability scenario. The platform then invokes the appropriate liability algorithm to process the feedback and determine the responsible party. Furthermore, the delivery platform can generate a service evaluation report based on the liability assessment results and send it to the server of the delivery person associated with the feedback. This server is a webpage or application on the delivery person's electronic device that provides delivery services. The delivery person can receive and accept delivery orders through the server, providing delivery services.

[0029] In embodiments of this disclosure, the delivery platform can periodically acquire customer feedback information through a feedback channel, which is used to receive customer feedback information transmitted by clients. As an example, the feedback channel may include the delivery platform's complaint hotline, customer evaluation subsystem, customer service subsystem, etc. The acquisition period for customer feedback information can be determined based on the delivery platform's preset accountability period. For example, if the delivery platform's accountability period is 5 days, the acquisition period for customer feedback information can be set to 5 days. Customer feedback information acquired within 5 days can be centrally processed to determine the responsible party for each piece of customer feedback and generate an accountability result. Alternatively, to further ensure the timeliness of accountability, customer feedback information from the delivery platform can be acquired through a sliding time window. As an example, the sliding interval of the sliding time window can be predetermined based on the accountability period, for example, setting the sliding interval to 5 days. Then, customer feedback information within the sliding interval corresponding to the sliding window is acquired, ensuring that each piece of customer feedback information is processed promptly, thus improving the timeliness of accountability.

[0030] After obtaining customer feedback information, semantic analysis is performed on the feedback to determine the corresponding liability scenario. This liability scenario indicates the service problem reported to the delivery person, such as: the delivery person failing to deliver upstairs, the delivery person failing to notify the user after delivery, the delivery person having poor service attitude, or the user being unable to contact the delivery person. The liability scenario corresponds to the feedback type or customer complaint tag of the customer feedback information. As an example, semantic analysis of the customer feedback information can determine the corresponding feedback type and / or customer complaint tag. Then, based on the correspondence between the feedback type and / or customer complaint tag and the liability scenario, the liability scenario corresponding to the customer feedback information can be determined.

[0031] After determining the liability assessment scenario corresponding to the customer feedback information, the delivery platform uses the corresponding liability assessment algorithm to process the customer feedback information, determine the responsible party, and obtain the assessment result. It should be noted that the judgment conditions and standards for determining whether the delivery person's delivery behavior is problematic differ under different liability assessment scenarios. Therefore, in the embodiments disclosed in this disclosure, the liability assessment algorithms corresponding to different scenarios are also different. For example, for the liability assessment scenario where the delivery person fails to deliver the goods upstairs, it is necessary to analyze the communication records between the delivery person and the user, the delivery person's movement trajectory, and whether the community where the delivery address is located allows the delivery person to go upstairs, etc., to determine whether the responsible party is the delivery person; for the liability assessment scenario where the delivery person does not notify the user after delivering the goods, it is necessary to analyze the delivery person's call records, online communication records, etc., after arriving at the delivery address, to determine whether the delivery person notified the user, and so on.

[0032] As an example, corresponding liability assessment algorithms can be pre-determined based on various liability assessment scenarios appearing on the delivery platform, and the correspondence between liability assessment scenarios and algorithms can be stored. Once the liability assessment scenario corresponding to customer feedback information is determined, based on the correspondence between the liability assessment scenario and the algorithm, the corresponding liability assessment algorithm is invoked to process the customer feedback information and determine the responsible party.

[0033] The embodiments of this disclosure perform semantic analysis on the acquired customer feedback information to determine the corresponding liability assessment scenario, and call the corresponding liability assessment algorithm to process the customer feedback information to determine the responsible party. This ensures that each customer feedback information can be matched with a unique liability assessment algorithm based on its corresponding liability assessment scenario, guaranteeing the objectivity and reliability of the liability assessment logic when the delivery platform processes customer feedback information. This enables the delivery platform to automatically process customer feedback information and improves the accuracy of liability assessment.

[0034] In one optional embodiment of this disclosure, step 102, which involves performing semantic analysis on the customer feedback information to determine the corresponding liability scenario, includes:

[0035] Step S11: Perform semantic analysis on the customer feedback information to determine the feedback keywords corresponding to the customer feedback information;

[0036] Step S12: Match the feedback keywords with each customer complaint tag in the preset database to determine the matching degree between each customer complaint tag and the feedback keywords;

[0037] Step S13: Determine the customer complaint tags that match the feedback keywords with a degree greater than a preset threshold as the target customer complaint tags corresponding to the customer feedback information;

[0038] Step S14: Based on the pre-determined correspondence between customer complaint tags and liability assessment scenarios, determine the liability assessment scenario corresponding to the customer feedback information according to the target customer complaint tag.

[0039] In embodiments of this disclosure, semantic analysis of customer feedback information can be performed to first determine the corresponding feedback keywords. Specifically, semantic analysis of customer feedback information can be performed based on a machine learning model to obtain the corresponding feedback keywords. The machine learning model can be any neural network model that excels at semantic analysis and intent recognition, such as Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), Latent Semantic Analysis (LSA), Neural Network Language Model (NNLM), Generative Pre-Training (GPT), Bidirectional Encoder Representations from Transformers (BERT), etc., and this disclosure does not specifically limit it.

[0040] In the embodiments of this disclosure, customer complaint types of the delivery platform can be summarized and categorized in advance, and corresponding customer complaint tags can be generated and stored in a preset database based on the customer complaint types. After determining the feedback keywords of the customer feedback information, the feedback keywords of the customer feedback information can be matched with each customer complaint tag in the preset database to determine the matching degree between each customer complaint tag and the feedback keyword. It should be noted that the feedback keywords of the customer feedback information can be one or more. If there are multiple feedback keywords, then for each feedback keyword, it is matched with each customer complaint tag in the preset database in turn to determine the matching degree between each feedback keyword and each customer complaint tag.

[0041] Then, customer complaint tags with a matching degree greater than a preset threshold are identified as the target customer complaint tags corresponding to the customer feedback information. Alternatively, customer complaint tags are sorted in descending order of matching degree, and the top N customer complaint tags are identified as the target customer complaint tags corresponding to the customer feedback information. The preset threshold and N can be set according to actual needs; for example, the preset threshold can be set to 0.5, and N can be set to 2, etc.

[0042] Finally, based on the pre-determined correspondence between customer complaint tags and liability assessment scenarios, the liability assessment scenario corresponding to the customer feedback information is determined according to the target customer complaint tags.

[0043] It should be noted that, in the embodiments of this disclosure, for a given liability assessment scenario, one corresponding customer complaint label can be determined, or at least two corresponding customer complaint labels can be determined to improve the accuracy of determining the liability assessment scenario corresponding to customer feedback information. Regardless of whether one or at least two corresponding customer complaint labels are set for a given liability assessment scenario, in the embodiments of this disclosure, each customer complaint label will have a corresponding liability assessment scenario. Therefore, after determining the target customer complaint label corresponding to the customer feedback information, the liability assessment scenario corresponding to that customer feedback information can be determined based on the target customer complaint label. If the customer feedback information corresponds to at least two target customer complaint labels, then the liability assessment scenario determined based on the target customer complaint labels may be one of the following: all target customer complaint labels correspond to the same liability assessment scenario; or there may be multiple liability assessment scenarios determined based on the target customer complaint labels: different target customer complaint labels correspond to different liability assessment scenarios.

[0044] When a liability assessment scenario is determined based on the target customer complaint label, the corresponding liability assessment algorithm is directly invoked to assess and process the customer feedback information.

[0045] When at least two liability scenarios are identified based on the target customer complaint tags, it indicates that the user has simultaneously provided feedback on different liability scenarios. For example, the identified liability scenarios might include the delivery person failing to notify the user after delivery to the merchant and the user being unable to contact the delivery person; or they might include the delivery person failing to deliver upstairs or having poor service attitude, etc. In this case, to simplify the liability handling process and reduce complexity, various liability scenarios can be pre-classified, and the priority or importance of each scenario can be determined. When customer feedback corresponds to multiple liability scenarios, the scenario with the highest priority or most important importance is identified as the primary liability scenario corresponding to the customer feedback. Then, the liability handling algorithm corresponding to this primary liability scenario is called to process the customer feedback. Alternatively, to further improve the accuracy of liability determination, the corresponding liability determination algorithm can be called sequentially for each liability determination scenario to process the customer feedback information, determine the responsible party for the customer feedback information under each liability determination scenario, and generate a reward and punishment scheme for the delivery person corresponding to the customer feedback information based on the management mechanism corresponding to each liability determination scenario, so as to obtain a more granular liability determination result.

[0046] In one optional embodiment of this disclosure, step 103, which involves invoking the accountability algorithm corresponding to the accountability scenario to process the customer feedback information and determine the responsible party for the customer feedback information, includes:

[0047] Step S21: Obtain the delivery order information corresponding to the customer feedback information. The delivery order information includes the delivery address, delivery person's identification, delivery time, and delivery communication records.

[0048] Step S22: Extract the deliveryman's movement status information corresponding to the customer feedback information based on the deliveryman's identity and delivery time;

[0049] Step S23: Based on the delivery communication records, the historical delivery data corresponding to the delivery address, and the delivery person's movement status information, determine the responsible party corresponding to the customer feedback information.

[0050] The delivery rider identification identifier is used to uniquely identify the delivery rider. It can be an identifier assigned to each delivery rider by the delivery platform, such as a rider ID; or it can be an identification identifier submitted by the delivery rider, such as the delivery rider's mobile phone number, ID card number, or name. The delivery time can be the time period between the delivery rider's order acceptance time and the delivery time.

[0051] The delivery communication records may include at least one of the following: online communication records between the delivery person and the user, telephone communication records, and text messages. It should be noted that online communication records can be obtained through the instant messaging system provided by the delivery platform. Telephone communication records can be obtained through the communication device used by the delivery person; for example, call records corresponding to delivery orders can be extracted from the delivery person's communication device based on their privacy number.

[0052] The motion status information is used to reflect the delivery person's movement trajectory. For example, it can be used to analyze whether the delivery person has delivered the goods to the designated location or whether the delivery person has gone upstairs. The motion status information can be extracted from electronic devices such as smartphones and smart bracelets used by the delivery person.

[0053] It should be noted that the delivery communication records and the movement status information were obtained by the delivery platform with the knowledge and consent of both the user and the delivery person.

[0054] The historical delivery data corresponding to the delivery address can be extracted from the delivery platform's database, and may include delivery data of historical orders in the community or building where the delivery address is located, customer feedback information and the judgment results of customer feedback information, etc.

[0055] In the embodiments of this disclosure, based on the delivery order information corresponding to the customer feedback information, such as delivery address, delivery person identification, delivery time, delivery communication records, as well as the delivery person's movement status information and historical delivery data corresponding to the delivery address, multiple aspects of evidence can be collected on the issues reflected in the customer feedback information. Based on multiple data, objective analysis can be performed to determine the responsible party corresponding to the customer feedback information, thereby avoiding misjudgment caused by subjectively trusting the customer feedback information submitted by the user.

[0056] In one optional embodiment of this disclosure, step S23, which involves determining the responsible party for the customer feedback information based on the delivery communication record, the historical delivery data corresponding to the delivery address, and the delivery person's movement status information, includes:

[0057] Sub-step S231: Based on the liability assessment scenario corresponding to the customer feedback information, determine the delivery profile, liability exemption information, and liability exemption conditions corresponding to the non-liability exemption type;

[0058] Sub-step S232: Determine the delivery profile corresponding to the delivery address based on the historical delivery data corresponding to the delivery address;

[0059] Sub-step S233: If the delivery profile corresponding to the delivery address matches the delivery profile corresponding to the non-disclaimer type, then perform semantic analysis on the delivery communication record to determine whether there is any disclaimer information in the delivery communication record;

[0060] Sub-step S234: If there is no exemption information in the delivery communication record, determine whether the delivery person's movement status information meets the exemption conditions;

[0061] Sub-step S235: If the deliveryman's movement status information does not meet the exemption conditions, then the responsible party corresponding to the customer feedback information is determined to be the deliveryman.

[0062] In the embodiments disclosed herein, the delivery profile, exemption information, and exemption conditions corresponding to the non-exemption type can be determined first based on the liability assessment scenario corresponding to the customer feedback information.

[0063] The delivery profile mentioned above reflects the behavior of delivery personnel who are not exempt from liability, that is, the behavior of delivery personnel where the responsibility lies with the delivery personnel. The delivery profile corresponding to the non-exemption type differs depending on the liability assessment scenario. For example, in a liability assessment scenario where the delivery personnel fail to deliver to the door, the delivery profile corresponding to the non-exemption type is that the community or building corresponding to the delivery order allows the delivery personnel to go upstairs; in a liability assessment scenario where the delivery personnel do not notify the user after delivering the goods, the delivery profile corresponding to the non-exemption type is that the user who submitted customer feedback information set the default note in the client to "Leave it at the door, do not call," and so on.

[0064] The disclaimer information is used to reflect delivery communication records that warrant disclaimer. In other words, if the delivery communication record contains disclaimer information, or content that matches the disclaimer information, then it is determined that the responsible party in the customer feedback is not the delivery person, and the delivery person is exempted from liability. For example, in a scenario where the delivery person fails to deliver to the door, the disclaimer information could be "no need to deliver to the door." If the delivery communication record contains content related to "no need to deliver to the door," then it is determined that the responsible party in the customer feedback is not the delivery person.

[0065] The exemption criteria are used to reflect the delivery person's movement status information. In other words, if the delivery person's movement status information meets the exemption criteria, then the responsible party for the customer's feedback is not the delivery person; otherwise, the responsible party for the customer's feedback is the delivery person. For example, in a scenario where the delivery person fails to deliver upstairs, the exemption criteria could be that the floor corresponding to the delivery address is greater than or equal to a preset floor, and that the delivery person's movement status information includes information about delivering upstairs.

[0066] In the embodiments of this disclosure, after determining the delivery profile, exemption information, and exemption conditions corresponding to the non-exemption type, the delivery profile corresponding to the delivery address is determined based on the historical delivery data corresponding to the delivery address. This delivery profile is then compared with the delivery profile corresponding to the non-exemption type. If the delivery profile matches, it indicates that the responsible party, based on the current delivery profile, is the delivery person. To ensure the accuracy of the liability determination and avoid misjudgment, semantic analysis can be further performed on the delivery communication records to determine if exemption information exists. If no exemption information exists in the delivery communication records, it indicates that, based on the current analysis, the responsible party is still the delivery person. To ensure the accuracy of the liability determination and fully consider the delivery person's subjective initiative, the delivery person's movement status information can be further analyzed to determine whether the delivery person's movement status information meets the exemption conditions. If the delivery person's movement status information also does not meet the exemption conditions, it means that the delivery profile at the delivery address does not meet the non-exempt delivery profile type. That is, there are no objective conditions at the delivery address that would prevent the delivery person from performing their delivery service duties normally. Furthermore, if the delivery person has not fully communicated with the user and obtained the user's consent, and the delivery person has not performed their delivery service duties normally, then it can be determined that the delivery person has indeed engaged in irregular behavior, and the delivery person is responsible for the customer's feedback.

[0067] In one optional embodiment of this disclosure, the method further includes:

[0068] If the delivery profile corresponding to the delivery address does not match the delivery profile corresponding to the non-disclaimer type, or if there is disclaimer information in the delivery communication record, or if the delivery person's delivery status information meets the disclaimer conditions, then the delivery person corresponding to the customer feedback information will be exempted from liability, and the customer feedback information will be determined as invalid information.

[0069] In the embodiments of this disclosure, as long as at least one of the following data—the delivery profile corresponding to the delivery address, the delivery communication record, and the delivery person's movement status information—can prove that the responsible party corresponding to the customer feedback information is not the delivery person, the delivery person will be exempted from liability, and the customer feedback information will be determined as invalid information.

[0070] The following example, using the scenario of a delivery person failing to deliver to the door, illustrates the specific handling process by which the embodiments of this disclosure determine the responsible party for customer feedback information based on delivery communication records, historical delivery data corresponding to the delivery address, and the delivery person's movement status information.

[0071] When it is determined that the liability scenario corresponding to the customer feedback information is that the delivery person did not deliver the goods upstairs, the delivery profile for non-exemption types is determined to be "accessible", the exemption information is "no need to deliver upstairs", the exemption condition is that the floor corresponding to the delivery address is greater than or equal to the preset floor, and the delivery person's movement status information contains information about delivering the goods upstairs.

[0072] Analyze historical delivery data corresponding to the delivery address to determine the delivery profile. If the delivery profile for that address is "under closed management," it means that the community where the delivery address is located does not allow delivery personnel to enter. Therefore, it can be determined that the responsible party for the customer feedback is not the delivery personnel, and the delivery personnel can be exempted from liability. The customer feedback will then be marked as invalid.

[0073] If the delivery profile for the delivery address is "accessible," meaning the community allows delivery personnel to go upstairs, then the delivery communication records are further analyzed to determine if any disclaimers exist. If the communication records contain contextual information related to "no need to deliver upstairs," it indicates that the delivery person has communicated with the user that delivery is not required and has obtained the user's consent. In this case, the delivery person is not responsible for the customer feedback, and the delivery person is exempt from liability. The customer feedback is then marked as invalid.

[0074] If the delivery communication record lacks contextual information related to "no need to deliver upstairs," it indicates that the delivery person did not communicate with the user, or that the delivery person did communicate with the user, but the user did not agree. In this case, further analysis of the delivery person's movement status information is needed to determine whether the delivery person's movement status information meets the exemption conditions. Specifically, assuming the preset floor is 3, if the delivery address is located on a floor greater than or equal to 3, and the delivery person's movement status information contains information about delivering upstairs, then the delivery person's movement status information meets the exemption conditions, and the responsible party for the customer's feedback is not the delivery person. Therefore, the delivery person is exempted from liability, and the customer's feedback information is marked as invalid. If the delivery address is located on a floor less than 3, or the delivery person's movement status information does not contain information about delivering upstairs, then the delivery person's movement status information does not meet the exemption conditions, and the responsible party for the customer's feedback information is determined to be the delivery person.

[0075] This disclosure embodiment comprehensively analyzes various types of information in a progressive manner, and determines the responsible party for customer feedback information based on the analysis results of multiple situations, ensuring the accuracy of the judgment results. It can identify malicious complaints / negative reviews that are not the responsibility of the delivery person as much as possible and avoid misjudgment.

[0076] In one optional embodiment of this disclosure, the method further includes:

[0077] Step S31: For each deliveryman on the delivery platform, collect customer feedback information corresponding to the deliveryman according to a preset period;

[0078] Step S32: Generate a service evaluation report for the delivery person based on the customer feedback information within the preset period and the processing procedure for each piece of customer feedback information;

[0079] Step S33: Send the service evaluation report to the client corresponding to the delivery person.

[0080] In the embodiments disclosed herein, customer feedback information for each delivery person on the delivery platform can be collected at preset intervals. Based on this customer feedback information and the processing procedure for each piece of feedback, a service evaluation report can be generated and sent to the delivery person's client for review. Furthermore, the delivery platform can generate reward and punishment results for delivery persons within the service evaluation report based on management mechanisms corresponding to various liability assessment scenarios, thereby encouraging delivery persons to improve the quality of their delivery services.

[0081] In summary, the embodiments of this disclosure provide a data processing method that performs semantic analysis on the acquired customer feedback information to determine the corresponding liability assessment scenario. Then, it calls the corresponding liability assessment algorithm to process the customer feedback information, thereby determining the responsible party. This ensures that each customer feedback message can be matched with a specific liability assessment algorithm based on its corresponding scenario, guaranteeing the objectivity and reliability of the liability assessment logic when the delivery platform processes customer feedback information. This achieves automatic liability assessment by the delivery platform, improving the accuracy of liability assessment.

[0082] Example 2

[0083] Reference Figure 2 The diagram illustrates a structural diagram of a data processing apparatus in one embodiment of this disclosure, which is applied to a delivery platform, as follows:

[0084] The information acquisition module 201 is used to periodically acquire customer feedback information from the delivery platform, including customer complaint information and / or customer evaluation information.

[0085] The semantic analysis module 202 is used to perform semantic analysis on the customer feedback information to determine the judgment scenario corresponding to the customer feedback information;

[0086] The accountability processing module 203 is used to call the accountability algorithm corresponding to the accountability scenario to process the customer feedback information and determine the responsible party corresponding to the customer feedback information.

[0087] In one optional embodiment of this disclosure, the accountability processing module includes:

[0088] The delivery order information acquisition submodule is used to acquire the delivery order information corresponding to the customer feedback information. The delivery order information includes the delivery address, delivery person identification, delivery time, and delivery communication records.

[0089] The motion status information extraction submodule is used to extract the motion status information of the deliveryman corresponding to the customer feedback information based on the deliveryman's identity and delivery time.

[0090] The responsibility determination submodule is used to determine the responsible party corresponding to the customer feedback information based on the delivery communication records, the historical delivery data corresponding to the delivery address, and the delivery person's movement status information.

[0091] In one optional embodiment of this disclosure, the accountability processing submodule includes:

[0092] The liability determination data unit is used to determine the delivery profile, liability exemption information and liability exemption conditions corresponding to the non-liability exemption type based on the liability determination scenario corresponding to the customer feedback information;

[0093] The delivery profile determination unit is used to determine the delivery profile corresponding to the delivery address based on the historical delivery data corresponding to the delivery address.

[0094] The first analysis unit is used to perform semantic analysis on the delivery communication record if the delivery profile corresponding to the delivery address matches the delivery profile corresponding to the non-disclaimer type, and to determine whether there is disclaimer information in the delivery communication record.

[0095] The second analysis unit is used to determine whether the deliveryman's movement status information meets the exemption conditions if there is no exemption information in the delivery communication record.

[0096] The third analysis unit is used to determine the responsible party corresponding to the customer feedback information as the delivery person if the delivery person's movement status information does not meet the exemption conditions.

[0097] In one optional embodiment of this disclosure, the accountability processing submodule further includes:

[0098] The fourth analysis unit is used to exempt the delivery person corresponding to the customer feedback information from liability if the delivery profile corresponding to the delivery address does not match the delivery profile corresponding to the non-disclaimer type, or if there is disclaimer information in the delivery communication record, or if the delivery person's delivery status information meets the disclaimer conditions, and to determine the customer feedback information as invalid information.

[0099] In one optional embodiment of this disclosure, the semantic analysis module includes:

[0100] The semantic analysis submodule is used to perform semantic analysis on the customer feedback information and determine the feedback keywords corresponding to the customer feedback information.

[0101] The customer complaint tag matching submodule is used to match the feedback keywords with each customer complaint tag in a preset database to determine the matching degree between each customer complaint tag and the feedback keywords;

[0102] The target customer complaint tag determination submodule is used to determine customer complaint tags that match the feedback keywords with a degree greater than a preset threshold as the target customer complaint tags corresponding to the customer feedback information;

[0103] The liability determination scenario submodule is used to determine the liability determination scenario corresponding to the customer feedback information based on the pre-determined correspondence between customer complaint tags and liability determination scenarios, according to the target customer complaint tag.

[0104] In one optional embodiment of this disclosure, the apparatus further includes:

[0105] The feedback information statistics module is used to collect customer feedback information corresponding to each deliveryman under the delivery platform according to a preset period.

[0106] The service evaluation report generation module is used to generate a service evaluation report for the deliveryman based on customer feedback information within the preset period and the processing procedure for each piece of customer feedback information.

[0107] The service evaluation report sending module is used to send the service evaluation report to the client corresponding to the deliveryman.

[0108] In summary, the embodiments of this disclosure provide a data processing device that performs semantic analysis on the acquired customer feedback information to determine the corresponding accountability scenario, and calls the accountability algorithm corresponding to the accountability scenario to process the customer feedback information, thereby determining the responsible party for the customer feedback information. This ensures that each customer feedback information can be matched with a unique accountability algorithm based on its corresponding accountability scenario, guaranteeing the objectivity and reliability of the accountability processing logic when the delivery platform processes customer feedback information. This enables the delivery platform to automatically process customer feedback information and improves the accuracy of accountability.

[0109] Example 2 is a device embodiment corresponding to Example 1. For detailed description, please refer to Example 1, which will not be repeated here.

[0110] Embodiments of this disclosure also provide an electronic device, with reference to Figure 3The system includes: a processor 301, a memory 302, and a computer program 3021 stored in the memory 302 and executable on the processor. When the processor 301 executes the program, it implements the data processing method of the foregoing embodiments.

[0111] Embodiments of this disclosure also provide a readable storage medium that, when instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to perform the data processing method of the foregoing embodiments.

[0112] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0113] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, the embodiments of this disclosure are not directed to any particular programming language. It should be understood that the embodiments of this disclosure described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the embodiments of this disclosure.

[0114] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this disclosure may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0115] Similarly, it should be understood that, in order to streamline this disclosure and aid in understanding one or more of the various inventive aspects, in the foregoing description of exemplary embodiments of the present disclosure, various features of the embodiments of the present disclosure are sometimes grouped together in a single embodiment, figure, or description thereof. However, this approach to disclosure should not be construed as reflecting an intention that the claimed embodiments of the present disclosure require more features than expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of the present disclosure.

[0116] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0117] The various component embodiments of this disclosure can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the file processing device according to embodiments of this disclosure. Embodiments of this disclosure can also be implemented as device or apparatus programs for performing some or all of the methods described herein. Such programs implementing embodiments of this disclosure can be stored on a computer-readable medium or can take the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0118] It should be noted that the above embodiments are illustrative of embodiments of this disclosure and not restrictive of embodiments of this disclosure, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. Embodiments of this disclosure can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0119] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0120] The above description is only a preferred embodiment of the present disclosure and is not intended to limit the embodiments of the present disclosure. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the embodiments of the present disclosure should be included within the protection scope of the embodiments of the present disclosure.

[0121] The above description is merely a specific implementation of the embodiments of this disclosure, but the protection scope of the embodiments of this disclosure is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the embodiments of this disclosure should be included within the protection scope of the embodiments of this disclosure. Therefore, the protection scope of the embodiments of this disclosure should be determined by the protection scope of the claims.

Claims

1. A data processing method, characterized in that, Applied to a delivery platform, the method includes: The system periodically acquires customer feedback information from the delivery platform, including customer complaint information and / or customer evaluation information. Perform semantic analysis on the customer feedback information to determine the corresponding liability assessment scenario; The responsibility assessment algorithm corresponding to the aforementioned responsibility assessment scenario is invoked to process the customer feedback information and determine the responsible party corresponding to the customer feedback information. The step of determining the responsible party corresponding to the customer feedback information includes: determining the responsible party corresponding to the customer feedback information based on the delivery communication records corresponding to the customer feedback information, the historical delivery data corresponding to the delivery address, and the delivery person's movement status information, specifically including: Based on the liability assessment scenario corresponding to the customer feedback information, determine the delivery profile, exemption information, and exemption conditions corresponding to the non-exemption type; Determine the delivery profile corresponding to the delivery address based on the historical delivery data corresponding to the delivery address; If the delivery profile corresponding to the delivery address matches the delivery profile corresponding to the non-disclaimer type, then semantic analysis is performed on the delivery communication record to determine whether there is any disclaimer information in the delivery communication record; If there is no liability exemption information in the delivery communication record, then determine whether the delivery person's movement status information meets the liability exemption conditions; If the deliveryman's movement status information does not meet the exemption conditions, then the responsible party corresponding to the customer feedback information is determined to be the deliveryman; If the delivery profile corresponding to the delivery address does not match the delivery profile corresponding to the non-disclaimer type, or if there is disclaimer information in the delivery communication record, or if the delivery person's delivery status information meets the disclaimer conditions, then the delivery person corresponding to the customer feedback information will be exempted from liability, and the customer feedback information will be determined as invalid information.

2. The method according to claim 1, characterized in that, The step of invoking the accountability algorithm corresponding to the accountability scenario to process the customer feedback information and determine the responsible party for the customer feedback information includes: Obtain the delivery order information corresponding to the customer feedback information, including the delivery address, delivery person identification, delivery time, and delivery communication records; Based on the deliveryman's identification and delivery time, extract the deliveryman's movement status information corresponding to the customer feedback information; Based on the delivery communication records, the historical delivery data corresponding to the delivery address, and the delivery person's movement status information, the responsible party corresponding to the customer feedback information is determined.

3. The method according to claim 1, characterized in that, The step of performing semantic analysis on the customer feedback information to determine the corresponding liability assessment scenario includes: Perform semantic analysis on the customer feedback information to determine the corresponding feedback keywords; The feedback keywords are matched with each customer complaint tag in the preset database to determine the matching degree between each customer complaint tag and the feedback keywords; Customer complaint tags with a matching degree greater than a preset threshold with the feedback keywords are identified as the target customer complaint tags corresponding to the customer feedback information; Based on the pre-determined correspondence between customer complaint tags and liability assessment scenarios, the liability assessment scenario corresponding to the customer feedback information is determined according to the target customer complaint tag.

4. The method according to any one of claims 1 to 3, characterized in that, The method further includes: For each delivery person on the delivery platform, customer feedback information corresponding to the delivery person is collected according to a preset period. Based on customer feedback information within a preset period and the processing procedure for each piece of customer feedback, a service evaluation report for the delivery person is generated. The service evaluation report is sent to the client corresponding to the delivery person.

5. A data processing apparatus, characterized in that, The device, used in a delivery platform, includes: The information acquisition module is used to periodically acquire customer feedback information from the delivery platform, including customer complaint information and / or customer evaluation information. The semantic analysis module is used to perform semantic analysis on the customer feedback information to determine the judgment scenario corresponding to the customer feedback information; The accountability processing module is used to call the accountability algorithm corresponding to the accountability scenario to process the customer feedback information and determine the responsible party corresponding to the customer feedback information. The liability determination processing module includes a liability determination processing submodule, which is used to determine the responsible party corresponding to the customer feedback information based on the delivery communication records corresponding to the delivery address, historical delivery data corresponding to the delivery address, and the delivery person's movement status information; the liability determination processing submodule includes: The liability determination data unit is used to determine the delivery profile, liability exemption information and liability exemption conditions corresponding to the non-liability exemption type based on the liability determination scenario corresponding to the customer feedback information; The delivery profile determination unit is used to determine the delivery profile corresponding to the delivery address based on the historical delivery data corresponding to the delivery address. The first analysis unit is used to perform semantic analysis on the delivery communication record if the delivery profile corresponding to the delivery address matches the delivery profile corresponding to the non-disclaimer type, and to determine whether there is disclaimer information in the delivery communication record. The second analysis unit is used to determine whether the deliveryman's movement status information meets the exemption conditions if there is no exemption information in the delivery communication record. The third analysis unit is used to determine the responsible party corresponding to the customer feedback information as the delivery person if the delivery person's movement status information does not meet the exemption conditions. The fourth analysis unit is used to exempt the delivery person corresponding to the customer feedback information from liability if the delivery profile corresponding to the delivery address does not match the delivery profile corresponding to the non-disclaimer type, or if there is disclaimer information in the delivery communication record, or if the delivery person's delivery status information meets the disclaimer conditions, and to determine the customer feedback information as invalid information.

6. The apparatus according to claim 5, characterized in that, The accountability processing module also includes: The delivery order information acquisition submodule is used to acquire the delivery order information corresponding to the customer feedback information. The delivery order information includes the delivery address, delivery person identification, delivery time, and delivery communication records. The motion status information extraction submodule is used to extract the motion status information of the deliveryman corresponding to the customer feedback information based on the deliveryman's identity and delivery time.

7. The apparatus according to claim 5, characterized in that, The semantic analysis module includes: The semantic analysis submodule is used to perform semantic analysis on the customer feedback information and determine the feedback keywords corresponding to the customer feedback information. The customer complaint tag matching submodule is used to match the feedback keywords with each customer complaint tag in a preset database to determine the matching degree between each customer complaint tag and the feedback keywords; The target customer complaint tag determination submodule is used to determine customer complaint tags that match the feedback keywords with a degree greater than a preset threshold as the target customer complaint tags corresponding to the customer feedback information; The liability determination scenario submodule is used to determine the liability determination scenario corresponding to the customer feedback information based on the pre-determined correspondence between customer complaint tags and liability determination scenarios, according to the target customer complaint tag.

8. The apparatus according to any one of claims 5 to 7, characterized in that, The device further includes: The feedback information statistics module is used to collect customer feedback information corresponding to each deliveryman under the delivery platform according to a preset period. The service evaluation report generation module is used to generate a service evaluation report for the deliveryman based on customer feedback information within a preset period and the processing procedure for each piece of customer feedback information. The service evaluation report sending module is used to send the service evaluation report to the client corresponding to the deliveryman.

9. An electronic device, characterized in that, include: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the data processing method as described in any one of claims 1 to 4.

10. A readable storage medium, characterized in that, When the instructions in the storage medium are executed by the processor of the electronic device, the electronic device is able to perform the data processing method as described in any one of claims 1 to 4.

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