Cross-border logistics customer service conflict intelligent mediation method and device

Abstract

The application relates to the technical field of artificial intelligence, and discloses a cross-border logistics customer service conflict intelligent mediation method and device, wherein the method comprises the following steps: acquiring multi-modal complaint data of a user, performing cross-language semantic analysis and alignment on the multi-modal complaint data, and generating unified semantic representation; based on the unified semantic representation, automatically associating and fusing logistics event data in multiple heterogeneous data sources, and constructing a visual evidence chain corresponding to the complaint; matching and reasoning the visual evidence chain with a pre-constructed responsibility association graph, and generating a responsibility judgment result; according to the responsibility judgment result, combining the cargo attributes and customer parameters, and performing joint optimization calculation through a compensation strategy model to generate and output a customized mediation execution scheme. The application can improve the analysis adaptability of multi-modal complaint data and the integration efficiency of multi-source heterogeneous logistics data in cross-border logistics customer service conflict mediation, and improve the accuracy of responsibility judgment and the pertinence of compensation schemes.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, specifically to a method and device for intelligent mediation of cross-border logistics customer service conflicts. Background Technology

[0002] With the rapid rise of the global cross-border e-commerce and international logistics industries, the scale of cross-border cargo transportation continues to expand. Customer service conflicts and disputes arising from delays, customs clearance obstacles, and cargo damage in the logistics process are becoming increasingly frequent. Cross-border logistics scenarios are characterized by significant multilingualism, multiple stages, multiple responsible parties, and cross-cultural aspects. The complexity of customer service conflict resolution far exceeds that of domestic logistics scenarios, and the quality and efficiency of its handling directly affect a company's international market competitiveness, customer retention rate, and operational cost control.

[0003] Currently, cross-border logistics customer service conflict resolution mainly relies on manual processing combined with traditional, simple systems. However, this approach has the following significant drawbacks: Manual processing requires retrieving various types of data, such as bills of lading, GPS tracking, and customs declaration records, to organize dispute-related information. This process is not only cumbersome but also prone to problems such as untimely evidence chain construction due to data fragmentation. Furthermore, existing technologies rely heavily on human experience for liability determination, and compensation plans often use fixed standards, failing to fully consider the specific details of the logistics event and the characteristics of the goods. This frequently leads to discrepancies in liability allocation or compensation plans that do not meet actual needs. Therefore, existing cross-border logistics customer service conflict resolution methods suffer from low data integration efficiency and insufficient accuracy in liability determination and compensation plans, failing to meet the need for rapid and reasonable resolution of cross-border logistics disputes.

[0004] The preceding description is intended to provide general background information and does not necessarily constitute prior art. Summary of the Invention

[0005] This application provides a method and apparatus for intelligent mediation of cross-border logistics customer service conflicts, which can improve the parsing and adaptability of multimodal complaint data and the integration efficiency of multi-source heterogeneous logistics data in cross-border logistics customer service conflict mediation, while improving the accuracy of responsibility determination and the pertinence of compensation schemes.

[0006] In a first aspect, embodiments of this application provide a method for intelligent mediation of cross-border logistics customer service conflicts, including:

[0007] Acquire users' multimodal complaint data, perform cross-language semantic parsing and alignment on the multimodal complaint data, and generate a unified semantic representation;

[0008] Based on the unified semantic representation, logistics event data from multiple heterogeneous data sources are automatically associated and merged to construct a visual evidence chain corresponding to this complaint.

[0009] The visualized chain of evidence is matched and reasoned with a pre-constructed responsibility association graph to generate a responsibility determination result;

[0010] Based on the liability determination results, combined with cargo attributes and customer parameters, a joint optimization calculation is performed through a compensation strategy model to generate and output a customized adjustment and execution plan.

[0011] Furthermore, in some embodiments of this application, the step of acquiring the user's multimodal complaint data, performing cross-language semantic parsing and alignment on the multimodal complaint data, and generating a unified semantic representation includes:

[0012] Receive raw complaint data containing multiple natural languages;

[0013] When the original complaint data contains voice information, the voice information is converted into complaint text data through a voice recognition engine;

[0014] By using a cross-language pre-trained model, the complaint text data in different languages ​​are mapped to a shared semantic vector space, generating semantic vectors independent of the source language, which serve as the unified semantic representation.

[0015] Furthermore, in some embodiments of this application, the step of using a cross-language pre-trained model to map the complaint text data in different languages ​​to a shared semantic vector space, generating semantic vectors independent of the source language as the unified semantic representation, includes:

[0016] The cross-linguistic pre-trained model is trained using a contrastive learning algorithm so that semantically similar but linguistically different logistics event phrases have similar vector representations in the shared semantic vector space.

[0017] Furthermore, in some embodiments of this application, after using a cross-language pre-trained model to map the complaint text data in different languages ​​to a shared semantic vector space, generating source language-independent semantic vectors as the unified semantic representation, the method further includes:

[0018] Perform sentiment analysis on the complaint text data to obtain the user's sentiment intensity value;

[0019] Based on the mapping relationship between preset emotion intensity values ​​and user demands, the user's potential real needs are inferred according to the emotion intensity values.

[0020] The unified semantic representation is modified based on the potential real needs.

[0021] Furthermore, in some embodiments of this application, the step of automatically associating and fusing logistics event data from multiple heterogeneous data sources based on the unified semantic representation to construct a visualized evidence chain corresponding to this complaint includes:

[0022] Extract at least one key logistics identifier from the unified semantic representation;

[0023] Using the key logistics identifier as an index, the system automatically extracts time-series event data related to this logistics event from the bill of lading system, GPS positioning system, and customs declaration system.

[0024] The extracted time-series event data is time-aligned and logically correlated to generate a visual transportation timeline containing timestamps and event descriptions, which serves as the visual evidence chain.

[0025] Furthermore, in some embodiments of this application, the step of matching and reasoning with the visualized chain of evidence and a pre-constructed responsibility association graph to generate a responsibility determination result includes:

[0026] Obtain a pre-constructed responsibility association graph, which is used to encode the relationships and weights between logistics nodes, responsible parties, contract terms, and historical precedents;

[0027] By matching the events in the visualized evidence chain with the logistics nodes in the responsibility association graph, all potential responsibility paths are identified;

[0028] Based on feedback data corresponding to historical liability judgment data, the weights of the liability paths are dynamically adjusted, the liability contribution of each responsible party is calculated, and the liability judgment results are generated by summarizing the data.

[0029] Furthermore, in some embodiments of this application, the step of generating and outputting a customized adjustment execution plan by performing joint optimization calculations through a compensation strategy model based on the liability determination result, combined with cargo attributes and customer parameters, includes:

[0030] Construct a cargo value-time sensitivity matrix to quantify the sensitivity of different categories of cargo to transportation timeliness;

[0031] The liability determination result, the value level of the goods, the time sensitivity level, and the customer level are used as joint input parameters and input into the compensation strategy model.

[0032] The compensation strategy model is used to perform multi-objective optimization calculations on the joint input parameters, and a step-by-step compensation scheme is output as the adjustment execution scheme.

[0033] Furthermore, in some embodiments of this application, the step of generating and outputting a customized adjustment execution plan by performing joint optimization calculations through a compensation strategy model based on the liability determination result, combined with cargo attributes and customer parameters, further includes:

[0034] Based on the cultural characteristics of the user's region, the appropriate text template is retrieved from the pre-stored communication template library;

[0035] The execution plan data is populated into the text template to generate customized customer service communication content, which is then sent to the user's client for display.

[0036] Furthermore, in some embodiments of this application, the method further includes:

[0037] Collect the results of the responsibility determination and the application effect data of the adjustment and implementation plan as feedback data;

[0038] The feedback data is used to incrementally learn and optimize the cross-language pre-trained model, the responsibility association graph, and the compensation strategy model.

[0039] Secondly, embodiments of this application provide a cross-border logistics customer service conflict intelligent mediation device, comprising:

[0040] The data acquisition module is used to acquire users' multimodal complaint data, perform cross-language semantic parsing and alignment on the multimodal complaint data, and generate a unified semantic representation.

[0041] The evidence chain module is used to automatically associate and merge logistics event data from multiple heterogeneous data sources based on the unified semantic representation to construct a visual evidence chain corresponding to this complaint.

[0042] The responsibility determination module is used to match and reason with the visualized evidence chain and the pre-constructed responsibility association graph to generate a responsibility determination result;

[0043] The solution generation module is used to generate and output a customized adjustment and execution solution based on the liability determination result, combined with the cargo attributes and customer parameters, through joint optimization calculation using a compensation strategy model.

[0044] This application provides a method and apparatus for intelligent mediation of cross-border logistics customer service conflicts. First, by performing cross-language semantic analysis and alignment on multimodal user complaint data, a unified semantic representation is generated, which effectively solves the problem of misunderstanding of multilingual complaint intent in traditional mediation, allowing for accurate and unified interpretation of complaint requests in different language forms. Second, based on this unified semantic representation, logistics event data from multiple heterogeneous data sources are automatically associated and integrated to construct a visualized evidence chain, significantly improving the efficiency of evidence sorting and avoiding the problems of delayed or incomplete evidence chain construction caused by manual processing. Third, the visualized evidence chain is matched and reasoned with a pre-constructed responsibility association graph to generate a responsibility determination result, replacing the subjective responsibility judgment method that relies on human experience, making the responsibility division more objective and reducing responsibility determination bias. Finally, by combining cargo attributes and customer parameters, a customized mediation execution plan is generated through a compensation strategy model, avoiding the limitations of traditional fixed compensation standards and making the compensation plan more suitable for specific logistics events, cargo characteristics, and customer needs. Therefore, this application can improve the parsing and adaptability of multimodal complaint data and the integration efficiency of multi-source heterogeneous logistics data in cross-border logistics customer service conflict mediation, while improving the accuracy of liability determination and the pertinence of compensation schemes, thus solving the problems of low efficiency and insufficient accuracy in existing cross-border logistics customer service conflict mediation. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is an application environment diagram of the intelligent dispute resolution method for cross-border logistics customer service provided in the embodiments of this application;

[0047] Figure 2 This is a flowchart illustrating the intelligent dispute resolution method for cross-border logistics customer service provided in this application embodiment;

[0048] Figure 3 This is a schematic diagram of the structure of the intelligent conflict resolution device for cross-border logistics customer service provided in the embodiments of this application;

[0049] Figure 4 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0050] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of systems and methods consistent with those detailed in the appended claims or with some aspects of this application.

[0051] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover descriptions such as non-exclusive inclusion, so that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, components, features, and elements with the same names in different embodiments of this application may have the same meaning or different meanings, the specific meaning of which must be determined by its interpretation in that specific embodiment or further in conjunction with the context of that specific embodiment.

[0052] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0053] In the following description, the use of suffixes such as "module," "part," or "unit" to denote elements is solely for the purpose of illustrative purposes and has no specific meaning in itself. Therefore, "module," "part," or "unit" may be used interchangeably.

[0054] To address the aforementioned technical problems and overcome the shortcomings of existing technologies, this application provides a method and apparatus for intelligent mediation of cross-border logistics customer service conflicts. This method and apparatus can improve the parsing and adaptability of multimodal complaint data and the integration efficiency of multi-source heterogeneous logistics data in cross-border logistics customer service conflict mediation, while also improving the accuracy of liability determination and the relevance of compensation schemes.

[0055] Figure 1 This is a diagram illustrating the application environment of an intelligent conflict resolution method for cross-border logistics customer service in one embodiment. (Refer to...) Figure 1This intelligent mediation method for cross-border logistics customer service conflicts is applied to a cross-border logistics customer service conflict intelligent mediation system. The system includes a terminal 110 and a server 120. The terminal 110 and server 120 are connected via a network. The terminal 110 can be a desktop terminal or a mobile terminal, specifically a mobile phone, tablet, or laptop. The server 120 can be a standalone server or a server cluster consisting of multiple servers. The server 120 is configured to execute the aforementioned intelligent mediation method for cross-border logistics customer service conflicts, including: acquiring multimodal complaint data from users; performing cross-language semantic parsing and alignment on the multimodal complaint data to generate a unified semantic representation; automatically associating and fusing logistics event data from multiple heterogeneous data sources based on the unified semantic representation to construct a visualized evidence chain corresponding to the complaint; matching and reasoning the visualized evidence chain with a pre-constructed responsibility association graph to generate a responsibility determination result; and, based on the responsibility determination result, combining cargo attributes and customer parameters, performing joint optimization calculations through a compensation strategy model to generate and output a customized mediation execution plan.

[0056] Please see Figure 2 , Figure 2 This is a flowchart illustrating an embodiment of the intelligent conflict resolution method for cross-border logistics customer service provided in this application. This embodiment primarily uses the application of this intelligent conflict resolution method for cross-border logistics customer service to computer equipment as an example. Specifically, the intelligent conflict resolution method for cross-border logistics customer service provided in this application may include the following steps:

[0057] S1. Obtain users' multimodal complaint data, perform cross-language semantic parsing and alignment on the multimodal complaint data, and generate a unified semantic representation;

[0058] Specifically, for step S1, the first step is to obtain multimodal complaint data from different users. Multimodal complaint data refers to complaint information submitted by users in different formats, including text (such as "my goods are stuck at the customs" in an English email or "mis mercancías están atascadas enla aduana" in a Spanish chat message) and audio (such as Arabic users speaking). During processing, the data first undergoes basic transformations for different modalities, such as converting voice complaints into text in the corresponding language. Then, cross-language semantic parsing technology is used to break down the differences in expression between different languages. For example, whether it's the English "customs detention," the Spanish "detención en la aduana," or the Arabic "..." All of these will be parsed into the core semantics of "goods detained by customs". Finally, through semantic alignment processing, these complaint information in different languages ​​are mapped to a unified semantic dimension, generating a unified semantic representation that is independent of the specific source language, such as a semantic vector or structured information with "goods detained by customs - complaint time 2024.XX.XX - order number involved XXX" as the core, to ensure that subsequent steps can be processed based on consistent information.

[0059] S2. Based on a unified semantic representation, automatically associate and merge logistics event data from multiple heterogeneous data sources to construct a visual evidence chain corresponding to this complaint;

[0060] Specifically, for step S2, the unified semantic representation will include key logistics information related to the complaint, such as order number, bill of lading number, and cargo transportation number. Using this as the core index, information from multiple heterogeneous data sources can be automatically connected to and retrieved. These data sources include, but are not limited to, the bill of lading system (which stores bill of lading number, shipping address, receiving address, cargo type, etc.), the GPS positioning system (which records cargo transportation trajectory, location and duration of stay), and the customs declaration system (which stores declaration time, declaration status, and whether there are any customs clearance anomalies, etc.). The retrieved multi-source data will then be integrated. On the one hand, the data will be aligned in chronological order, such as sorting "GPS shows the goods are stuck at customs" and "Customs declaration is pending review" by time. On the other hand, logical relationships between the data will be established, such as the causal relationship between "Customs declaration pending review" and "GPS shows the goods are stuck at customs". Finally, the complete chain of evidence will be presented in a visual form (such as a timeline chart). For example, the timeline will be marked with "2024.XX.XX 08:00 Goods arrive at Vietnam Customs", "09:30 Customs declaration submitted", "09:35 Declaration pending review", and "10:00-14:00 Goods detained in the customs supervision area", which will intuitively show the logistics event process related to the complaint.

[0061] S3. Match and reason with the visualized chain of evidence and the pre-constructed responsibility association graph to generate a responsibility determination result;

[0062] Specifically, for step S3, the pre-constructed responsibility association graph is a network of associations formed by integrating key elements in the cross-border logistics scenario in advance (such as logistics nodes: customs, logistics companies, warehousing centers; responsible parties: customs departments, logistics carriers, cargo owners; association rules: such as "customs declaration pending review leads to cargo detention → customs department is the main responsible party" and "logistics carrier fails to submit declaration materials in time, leading to detention → logistics carrier is the main responsible party"); contract terms: such as "customs detention exceeding 4 hours requires clarification of the responsible party"). During matching and reasoning, the key events in the visualized evidence chain, such as "goods being detained for more than 4 hours due to customs declaration pending review," are first matched with the logistics nodes and association rules in the responsibility association graph. "Customs declaration pending review" corresponds to the "customs node" in the graph, and "detention for more than 4 hours" triggers the responsibility determination rule of "customs detention timeout" in the graph. Then, reasoning is performed through the logical relationships in the graph, such as "the declaration pending review was not caused by the logistics party or the cargo owner → the responsibility belongs to the customs department." Finally, a clear responsibility determination result is generated, such as "the main responsible party for the customs detention of this goods is the Ho Chi Minh City Customs Department of Vietnam, with a responsibility share of 90%; the logistics carrier has a secondary responsibility share of 10% due to failure to follow up on the declaration progress in a timely manner."

[0063] S4. Based on the liability determination results, combined with the cargo attributes and customer parameters, perform joint optimization calculations through the compensation strategy model to generate and output a customized adjustment and execution plan;

[0064] Specifically, for step S4, the goods attributes include the type of goods (such as precision chips with high time-sensitivity requirements and clothing with ordinary time-sensitivity requirements) and the value of goods (such as electronic components with a unit price of 100,000 yuan and daily necessities with a unit price of 500 yuan); the customer parameters include the customer level (such as VIP customers with long-term cooperation and new customers) and the historical cooperation record (such as high-quality customers with no complaint records and customers with multiple complaints).

[0065] During the joint optimization calculation, the liability determination results (e.g., "secondary liability of the logistics carrier"), cargo attributes (e.g., "precision chips - high value - high timeliness requirement"), and customer parameters (e.g., "VIP customer") are jointly input into the compensation strategy model. The model first determines the compensation subject based on the proportion of liability, such as the logistics carrier bearing secondary compensation responsibility. Then, it determines the compensation priority based on the characteristics of the cargo, such as prioritizing timeliness for precision chips and including "expedited customs clearance" service in the compensation plan. Finally, it adjusts the compensation level based on the customer's level, such as VIP customers receiving an additional "15% reduction in freight charges". Ultimately, a customized adjustment and execution plan is generated, such as "1. The logistics carrier coordinates with the customs department to initiate expedited customs clearance service to ensure the release of goods within 24 hours; 2. The logistics carrier reduces the freight charge by 15% for the customer as compensation for delay; 3. The customer can enjoy priority warehousing rights for shipments within the next 3 months", and is directly output to customer service personnel or customers.

[0066] This embodiment utilizes intelligent processing throughout the entire process, from complaint analysis, evidence integration, liability determination to solution generation. It eliminates the need for manual data processing and liability determination, effectively improving the efficiency and accuracy of cross-border logistics customer service conflict mediation. While quickly resolving disputes, it also considers customer needs and corporate cost control, achieving efficient and reasonable mediation of cross-border logistics conflicts.

[0067] Furthermore, in some embodiments, step S1, "acquiring users' multimodal complaint data, performing cross-language semantic parsing and alignment on the multimodal complaint data, and generating a unified semantic representation," may specifically include:

[0068] S11. Receive raw complaint data containing multiple natural languages;

[0069] Specifically, for step S11, the original complaint data refers to complaint information submitted by users through various channels and presented in different languages. The language type of this data is not limited to a single language, and the original form submitted by the user is retained. For example, receiving complaints via English email, Spanish instant message, Arabic voice message, and German text ticket, etc., covers the multilingual complaint forms commonly seen in cross-border logistics scenarios.

[0070] S12. When the original complaint data contains voice information, the voice information is converted into complaint text data through a voice recognition engine;

[0071] Specifically, for step S12, if the received original complaint data contains audio, such as complaints submitted by users via customer service hotline or voicemail, the audio information is first converted into text data corresponding to the audio content using a speech recognition engine. The converted text language remains consistent with the language used in the audio, without cross-language translation; only the audio-to-text format conversion is completed. For example, if an Arabic user submits a voice complaint meaning "Our goods have been detained in customs for 3 days without any updates," the speech recognition engine will process it to generate the corresponding Arabic text. This ensures that voice-based complaint information can be parsed using the same subsequent parsing logic as text-based complaint information.

[0072] S13. Using a cross-linguistic pre-trained model, complaint text data in different languages ​​are mapped to a shared semantic vector space to generate semantic vectors independent of the source language, serving as a unified semantic representation.

[0073] Specifically, for step S13, the cross-lingual pre-trained model has the ability to process multilingual text and mine semantic commonalities between different languages. The shared semantic vector space is a unified mathematical space in which texts expressing the same or similar semantics in different languages ​​will correspond to similar vector positions. During processing, the received multilingual text complaints and the converted speech text are input into the cross-lingual pre-trained model respectively. The model will strip away the source language attributes of the text, retain only the core semantic information, and map this semantic information to the shared semantic vector space to generate corresponding semantic vectors. For example, the semantic vectors generated from the texts "customs detention for 3 days" in three different languages ​​will highly overlap in the space, and the vectors will not carry any source language identifiers related to English, Spanish, or Arabic, thus forming a unified semantic representation independent of the source language.

[0074] This embodiment achieves effective processing of multilingual and multimodal complaint data. It not only converts voice complaints into parsable text, but also eliminates the differences in expression between different languages ​​through cross-language semantic mapping, generating a unified semantic vector. This provides a consistent and accurate information foundation for subsequent accurate understanding of the complaint intent and promotion of conflict mediation.

[0075] Furthermore, in some embodiments, step S13, "using a cross-language pre-trained model to map complaint text data in different languages ​​to a shared semantic vector space, generating semantic vectors independent of the source language as a unified semantic representation," may specifically include:

[0076] By training a cross-linguistic pre-trained model using a contrastive learning algorithm, logistics event phrases that are semantically similar but in different languages ​​can have similar vector representations in a shared semantic vector space.

[0077] Specifically, before using cross-linguistic pre-trained models to process multilingual complaint texts, the core direction of training should be determined first. This ensures that the model can identify semantically similar but linguistically different logistics event phrases and present similar vector representations of these phrases in a shared semantic vector space, thereby eliminating the interference of language differences on semantic understanding. Logistics event phrases specifically refer to core expressions related to complaints in cross-border logistics scenarios, such as "customs delays," "goods damage," "customs clearance obstruction," and "delivery delays." These phrases are key information for parsing complaint intent and require priority in achieving cross-linguistic semantic alignment.

[0078] To achieve the above objectives, two types of training sample pairs need to be constructed around logistics event phrases. These pairs are used to convey to the model the criterion of semantically similar but semantically different phrases. Positive sample pairs are constructed by selecting combinations of logistics event phrases that are semantically completely identical or highly similar, but in different languages. Negative sample pairs are constructed by selecting combinations of logistics event phrases that are semantically completely different and can be in the same or different languages, used to distinguish between semantic and linguistic differences. The constructed positive and negative sample pairs are then input into the cross-linguistic pre-trained model to initiate the comparative learning training process.

[0079] The model first semantically encodes the two phrases in each pair of samples, generating their respective initial semantic vectors. Based on the loss function of the contrastive learning algorithm, the model automatically adjusts its parameters. For positive sample pairs, it reduces the loss value to shorten the vector distance between the two phrases, making them closer in the shared semantic vector space. For negative sample pairs, it increases the loss value to widen the vector distance between the two phrases, making them clearly separated in the space. A large number of positive and negative sample pairs of different types are continuously input, and the model is iteratively trained until it can stably distinguish between semantically similar and semantically different multilingual phrases. That is, regardless of the language of the phrases, as long as the meaning is consistent, the vector distance is within a preset short range; as long as the meaning is different, the vector distance is within a preset long range. After training, the model's effectiveness is verified using actual logistics event phrases.

[0080] This embodiment trains a cross-language pre-trained model using a contrastive learning algorithm, which significantly improves the semantic alignment accuracy of multilingual logistics event phrases and effectively avoids semantic understanding bias caused by language differences. This provides a highly accurate model foundation for mapping complaint texts in different languages ​​into a unified semantic representation.

[0081] Furthermore, in some embodiments, after step S13 "using a cross-language pre-trained model to map complaint text data in different languages ​​to a shared semantic vector space, generating semantic vectors independent of the source language as a unified semantic representation", the method may further include:

[0082] S14. Perform sentiment analysis on the complaint text data to obtain the user's sentiment intensity value;

[0083] Specifically, for step S14, for the complaint data that has been converted into text form, including text complaints submitted directly by users and text converted from voice complaints, a sentiment analysis model is used to extract the sentiment association features in the text. These features cover sentiment keywords (such as "anger", "urgent", "serious impact"), tone intensifiers (such as "always", "multiple times", "completely"), and sentence structure (such as exclamatory sentences "This is such a delay!" and rhetorical questions "Why hasn't it been resolved after 5 days?"). Based on these features, a quantitative sentiment intensity value is calculated, usually set to a rating range of 0-10, where 0 represents no negative emotion and 10 represents extreme anger.

[0084] For example, when receiving a Spanish text complaint, “Mis mercancías están atascadas en laaduana desde 6 días! Llamé 4 veces pero no obtuve ninguna respuesta, esto va ahacer que pierda un gran cliente!” (My goods have been held up at customs for 6 days! I've made 4 calls but haven't received any response, and this will cost me a big client!), the sentiment analysis model calculates an emotion intensity value of 9 based on keywords such as “6 días,” “4 veces,” and “pierda un gran cliente” (losing a big client) and the exclamatory sentence structure. If the received complaint text is “¿Podrías informarme por qué mis mercancías tienen unretraso de 1 día en la aduana?” (Can you tell me why my goods are delayed at customs for 1 day?), the model calculates an emotion intensity value of 2 based on the mild questioning sentence structure and the relatively short delay of “1 día.”

[0085] S15. Based on the preset mapping relationship between emotion intensity values ​​and user demands, infer the user's potential real needs based on the emotion intensity values;

[0086] Specifically, for step S15, a mapping table between emotional intensity values ​​and potential true needs is established and stored in advance by analyzing the correlation patterns between emotional intensity, stated demands, and ultimate resolution requirements in historical complaint data. For example, when the emotional intensity value is ≥7 (high emotional intensity), users whose stated demand is "delay complaint" often have a potential true need of "expedited release of goods" (rather than just requesting an explanation or basic compensation); when the emotional intensity value is 3-6 (medium emotional intensity), users whose stated demand is "delay complaint" often have a potential true need of "clarifying the reason for the delay + determining the release time"; when the emotional intensity value is <3 (low emotional intensity), users whose stated demand is "delay complaint" often have a potential true need of "obtaining updates on the delay progress". Based on this mapping relationship, combined with the obtained emotional intensity value, the potential true needs that users have not directly stated can be inferred.

[0087] For example, if a French user complains about "customs delays" with an emotional intensity of 8 (high emotional intensity), based on the preset mapping relationship, it can be inferred that their potential real need is "to expedite customs clearance within 24 hours to avoid affecting subsequent order delivery," rather than just "complaining about the delay." If another user complains about "customs delays" with an emotional intensity of 4 (medium emotional intensity), it can be inferred that their potential real need is "to clarify the specific reasons for the customs delay (such as problems with declaration materials / customs inspection queues) and to be informed of the estimated release time."

[0088] S16. Modify the unified semantic representation according to potential real needs;

[0089] Specifically, for step S16, the inferred potential real needs are added to the previously generated unified semantic representation. The revised semantic representation not only includes the core events of the complaint, such as goods delay, order number, delay duration, and goods type, but also adds the user's actual demands, ensuring that subsequent processing steps can accurately match user needs, rather than being limited to superficial descriptions.

[0090] For example, the previously generated unified semantic representation was "Customs delay - Order No. ABC123 - Detention duration 6 days - Goods type: Industrial sensor". Combined with the potential real demand "Expedited clearance within 24 hours", the revised semantic representation is updated to "Customs delay - Order No. ABC123 - Detention duration 6 days - Goods type: Industrial sensor - Potential real demand: Expedited clearance within 24 hours". If the potential real demand is "Clear reason for delay + Determine release time", then the revised semantic representation is "Customs delay - Order No. DEF456 - Detention duration 1 day - Goods type: Household goods - Potential real demand: Clear specific reason for delay and inform of estimated release time".

[0091] This embodiment uses sentiment analysis and latent demand inference to improve the unified semantic representation, which can overcome the limitation of "only capturing surface demands" and more accurately reflect the actual needs of users. This provides accurate information support for the targeted handling of cross-border logistics customer service conflicts and avoids deviations in the direction of mediation due to misjudgment of needs.

[0092] Furthermore, in some embodiments, step S2, "based on a unified semantic representation, automatically associating and fusing logistics event data from multiple heterogeneous data sources to construct a visualized evidence chain corresponding to this complaint," may specifically include:

[0093] S21. Extract at least one key logistics identifier from the unified semantic representation;

[0094] Specifically, for step S21, the unified semantic representation includes core logistics information related to this complaint. Key logistics identifiers are unique identifiers that can be linked to specific logistics events and can be used to accurately locate corresponding data scattered across different systems. Common types include order numbers, bill of lading numbers, waybill numbers, and container numbers. For example, if the unified semantic representation is “Customs Delay - Order Number SY20240608 - Bill of Lading Number BL3100567 - Detention Location: Tokyo Customs, Japan - Goods Type: Auto Parts,” then the key logistics identifiers extracted from it may include “Order Number SY20240608” and “Bill of Lading Number BL3100567”; if the unified semantic representation is “Delivery Timeout - Waybill Number YT897654321 - Delivery Address: Los Angeles, USA - Goods Type: Daily Necessities,” then the extracted key logistics identifier is “Waybill Number YT897654321.”

[0095] S22. Using key logistics identifiers as indexes, automatically extract time-series event data related to this logistics event from the bill of lading system, GPS positioning system, and customs declaration system;

[0096] Specifically, for step S22, using the extracted key logistics identifier as the retrieval key, the system automatically connects to the bill of lading system, GPS positioning system, and customs declaration system via system interfaces to filter and obtain time-series event data directly related to this logistics event, i.e., logistics information with timestamps and occurring in chronological order, without the need for manual login to each system individually. The specific extraction logic and example are as follows:

[0097] The bill of lading system inputs key logistics identifiers (such as bill of lading number BL3100567) and extracts the basic time sequence information corresponding to the bill of lading, such as "2024.06.08 09:00 Goods were dispatched from Shanghai Port, China (bill of lading generated)" and "2024.06.15 14:00 Goods arrived at Tokyo Port, Japan (arrival record)".

[0098] The GPS positioning system inputs the same key logistics identifier and extracts time-series data of cargo transportation trajectory, such as "2024.06.15 16:00 Cargo transport vehicle arrives at Tokyo Customs Control Area (GPS coordinates: XXX.XX, XXX.XX)" and "2024.06.15 16:00-2024.06.17 09:00 Vehicle remains in Tokyo Customs Control Area (no location movement)".

[0099] The customs declaration system inputs key logistics identifiers and extracts time-series data of the customs clearance process, such as "Submitted customs declaration materials at 17:00 on June 15, 2024 (Declaration status: Pending review)", "Customs feedback at 10:00 on June 16, 2024: 'Supplementary certificate of origin is required' (Declaration status: Review failed)", and "Resubmitted supplementary materials at 08:00 on June 17, 2024 (Declaration status: Pending review)".

[0100] S23. The extracted time-series event data is time-aligned and logically correlated to generate a visual transportation timeline containing timestamps and event descriptions, serving as a visual chain of evidence;

[0101] Specifically, for step S23, firstly, the time-series event data extracted from each system is time-aligned, and all data is uniformly sorted according to the timestamp of the event occurrence to eliminate the disorder caused by time record discrepancies between different systems; then, logical associations are performed to identify the causal or sequential relationships between events, such as the fact that a declaration review result will only be available after the declaration materials are submitted, and that there is a causal relationship between GPS showing the customs stoppage and the declaration review failure, ensuring that the event sequence conforms to the actual logistics process; finally, the data is visualized in the form of a timeline to form an intuitive visual evidence chain. For example, after processing the extracted time-series data, the generated visualized transportation timeline is as follows (timeline form):

[0102] 2024.06.08 09:00: Goods were shipped from Shanghai Port (Bill of Lading System)

[0103] 2024.06.15 14:00: Goods arrived at Tokyo Port (Bill of Lading System)

[0104] 2024.06.15 16:00: Goods arrived at Tokyo Customs Control Area (GPS system)

[0105] 2024.06.15 17:00: Submitted customs declaration materials (Customs system, status: pending review)

[0106] 2024.06.16 10:00: Customs feedback: "Supplementary certificate of origin required" (Customs system, status: review failed).

[0107] 2024.06.15 16:00-2024.06.17 09:00: Goods remain in Tokyo Customs Control Area (GPS system)

[0108] 2024.06.17 08:00: Resubmitted supplementary materials (Customs system, status: pending review).

[0109] This timeline clearly shows the complete process of the goods from dispatch to being detained in customs, and marks the source system and status of each event, forming a visual chain of evidence for this "customs delay of goods" complaint.

[0110] This embodiment automatically extracts key identifiers and cross-system time-series data to generate visualized integrated evidence, eliminating the need for tedious manual data retrieval and organization. This improves the efficiency of building evidence chains for cross-border logistics complaints. At the same time, the generated timeline intuitively presents the event logic, providing clear and reliable evidence support for subsequent liability determination.

[0111] Furthermore, in some embodiments, step S3, "matching and reasoning the visualized chain of evidence with a pre-constructed responsibility association graph to generate a responsibility determination result," may specifically include:

[0112] S31. Obtain a pre-built responsibility association graph, which is used to encode the relationships and weights between logistics nodes, responsible parties, contract terms, and historical precedents;

[0113] Specifically, for step S31, the pre-constructed responsibility association map is a structured network that integrates key elements of the entire cross-border logistics chain and quantifies the relationships. The core coding content includes four categories of key information and their relationships and weights. Logistics nodes cover the core nodes of each link in cross-border logistics, such as customs (specifically geographically, such as Tokyo Customs in Japan and Ho Chi Minh City Customs in Vietnam), logistics carriers (such as a branch of an international logistics company), warehousing centers, and cargo owners. Responsible parties correspond one-to-one with logistics nodes, i.e., the responsible entity behind each logistics node, such as "Tokyo Customs" corresponding to "Tokyo Customs Supervision Department," and "a branch of an international logistics company" corresponding to "the company's transportation division." Contract terms and rules incorporate international conventions related to cross-border logistics (such as the Convention on Contracts for the International Carriage of Goods by Road) and inter-company transportation... The agreement includes clauses such as "Customs review of general cargo shall not exceed 4 hours; failure to do so will result in primary liability for delays" and "Carriers who fail to inform cargo owners of customs clearance requirements in advance as agreed will bear secondary liability for failure to pass the review." Historical precedents and weightings are used to assign initial liability weights to different combinations of "node-responsibility-clause" based on the outcomes of similar past disputes. For example, "In historical precedents, Tokyo Customs bears an average of 60% liability for cargo delays caused by review delays," and "In historical precedents, carriers bear an average of 40% liability for delays caused by failure to submit required customs clearance documents." All information is linked and coded through relationships, such as "Tokyo Customs node → Tokyo Customs supervisory department (responsible party) → 'Customs review exceeding 4 hours incurs liability' clause → initial liability weight 60%," forming a liability relationship network that can be directly used for reasoning.

[0114] S32. Match the events in the visualized evidence chain with the logistics nodes in the responsibility association graph to identify all potential responsibility paths;

[0115] Specifically, for step S32, the key logistics events recorded in the visualized evidence chain are precisely matched with the logistics nodes in the responsibility association graph. Through the correspondence between events and nodes, all paths (i.e., potential responsibility paths) from nodes that may involve responsibility to the responsible party are traced and identified. For example, the visualized evidence chain of a complaint includes the core events of "Submitting Tokyo Customs declaration materials at 17:00 on June 15, 2024 → Customs feedback at 10:00 on June 16, 2024: 'Requires supplementary certificate of origin' (approval failed) → Goods continuously detained in Tokyo Customs supervision area from 16:00 on June 15, 2024 to 09:00 on June 17, 2024":

[0116] First, match the events of "customs review failure" and "goods detained at Tokyo Customs" with the "Tokyo Customs node - review stage" and "Tokyo Customs node - detention processing stage" in the data map; then, identify potential liability paths by following the responsible parties and clauses associated with each node:

[0117] Path 1: Tokyo Customs node → Tokyo Customs Supervision Department (responsible party) → Triggering the "Accountability for Customs Review Exceeding 4 Hours" clause (from submission of materials to feedback of failure to pass review exceeds 17 hours, far exceeding the 4-hour agreement), corresponding to the possible liability for "detention caused by customs review delay";

[0118] Path 2: Logistics carrier node (responsible for assisting in the verification of customs clearance materials) → Tokyo branch of an international logistics company (responsible party) → Triggering the clause "carrier is liable for failure to inform customs clearance material requirements in advance" (the evidence chain shows that the cargo owner did not receive advance notice of "the need to submit a certificate of origin"), corresponding to the possible responsibility of "carrier's failure to remind, resulting in missing materials and failure to pass the review".

[0119] S33. Based on the feedback data corresponding to the historical accountability data, dynamically adjust the weight of the accountability path, calculate the accountability contribution of each responsible party, and summarize and generate the accountability determination results;

[0120] Specifically, for step S33, historical liability feedback data refers to data on the actual implementation effect, customer feedback, and whether subsequent disputes recurred after similar liability path determinations. For example, "In cases of delayed review by Tokyo Customs due to backlog of declaration materials in the past 3 months, the customs liability weight needs to be increased by 10% after feedback verification," and "In cases where the carrier failed to submit required materials, if the cargo owner is a new customer, the carrier's liability weight needs to be increased by 5%." Based on this data, the weights of each identified potential liability path are dynamically adjusted, and the liability contribution of each responsible party is calculated according to the weight ratio, ultimately generating a clear liability determination result.

[0121] For example, historical accountability feedback data shows that "in June 2024, due to the peak of quarterly declarations, the customs liability weight for delayed review cases at Tokyo Customs needs to be increased from 60% to 70%", and "this time the cargo owner is a new customer, and the liability weight for the carrier's failure to submit required materials needs to be increased from 30% to 30% (no additional increase, as the carrier has recently optimized the new customer reminder process and the feedback is good)".

[0122] After adjustment, Route 1 (Customs) has a weight of 70%, and Route 2 (Carrier) has a weight of 30%. The responsibility contribution is calculated as follows: the Tokyo Customs supervision department of Japan bears 70% responsibility, and the Tokyo branch of an international logistics company bears 30% responsibility.

[0123] The final liability determination result is: "The responsibility for the detention of this cargo at Tokyo Customs shall be jointly borne by the Tokyo Customs Supervision Department (primary responsibility, 70%, due to the overdue review) and the Tokyo branch of a certain international logistics company (secondary responsibility, 30%, due to the failure to remind customers of the customs clearance material requirements in advance)."

[0124] This embodiment improves the accuracy and objectivity of liability determination in cross-border logistics disputes by pre-constructing a liability association graph and dynamically adjusting weights based on historical feedback. This frees liability determination from reliance on human experience and derives results based on objective data and logical reasoning, providing a reliable basis for the formulation of subsequent compensation plans.

[0125] Furthermore, in some embodiments, step S4, "based on the liability determination result, combined with cargo attributes and customer parameters, performs joint optimization calculations through a compensation strategy model to generate and output a customized adjustment execution plan," may specifically include:

[0126] S41. Construct a cargo value-time sensitivity matrix to quantify the sensitivity of different categories of cargo to transportation timeliness;

[0127] Specifically, for step S41, the cargo value-time sensitivity matrix uses cargo value level and time sensitivity level as two dimensions. Through historical logistics data, cargo characteristic analysis, and industry experience, it quantifies the correlation between the value and time sensitivity of different categories of cargo. The value range can be set to 1-5, with higher values ​​representing stronger time sensitivity, forming a directly callable structured matrix. Among them, the cargo value level is divided into three levels according to the unit price or total value of the cargo: high (e.g., precision chips and industrial sensors with a unit price ≥ 100,000 yuan), medium (e.g., small machinery and equipment with a unit price of 10,000-100,000 yuan), and low (e.g., clothing and daily necessities with a unit price < 10,000 yuan). The time sensitivity level is divided into three levels according to the degree of loss caused by cargo delay: high (delay of 1-2 hours leads to production line stoppage and order default), medium (delay of 1-3 days results in minor losses), and low (delay of more than 3 days has a relatively small impact).

[0128] S42. Input the liability determination result, cargo value level, time sensitivity level, and customer level as joint input parameters into the compensation strategy model;

[0129] Specifically, for step S42, the specific content of the four types of joint input parameters is clarified, and the parameter format is ensured to meet the model processing requirements. Among them, the responsibility determination result must include the responsible party and the proportion of responsibility, such as "the logistics carrier bears 70% of the main responsibility, and the customs department bears 30% of the secondary responsibility"; the value level of the goods is determined by matching the corresponding goods category from the constructed matrix, such as "the goods are precision chips, and the value level is high"; the time sensitivity level is also matched from the matrix, such as "the time sensitivity level of precision chips is high"; the customer level is divided into three levels according to the length of cooperation, order volume, and historical complaint records: VIP (cooperation ≥ 3 years, annual order volume ≥ 1000 orders, no negative complaints), ordinary (cooperation 1-3 years, annual order volume 100-1000 orders), and new customer (cooperation < 1 year, annual order volume < 100 orders), such as "this customer is a VIP customer with 5 years of cooperation". Example of parameter input: Input the four types of parameters, namely “70% liability of carrier + 30% liability of customs”, “precision chip (high value)”, “high time sensitivity”, and “VIP customer”, into the compensation strategy model in the form of structured data.

[0130] S43. Perform multi-objective optimization calculations on the joint input parameters using a compensation strategy model, and output a step-by-step compensation scheme as an adjustment execution scheme;

[0131] Specifically, for step S43, the core of the multi-objective optimization of the compensation strategy model is to balance three types of objectives: meeting customers' needs for timeliness and loss compensation, matching the responsibility ratio of the responsible parties, and controlling the company's compensation costs. The model will calculate the input parameters based on preset optimization rules, such as "the entity with a high responsibility ratio should bear the main compensation, priority should be given to ensuring timeliness compensation for highly sensitive goods, and the compensation level should be appropriately increased for VIP customers," ultimately generating a tiered scheme containing multi-level compensation measures. An example of the output scheme is as follows:

[0132] Time-saving compensation (prioritizing high-sensitivity needs): The logistics carrier, which assumes 70% responsibility, will coordinate with customs to activate the "precision cargo expedited clearance channel" to ensure that the goods are cleared and transferred within 24 hours;

[0133] Economic compensation (matching liability ratio with customer level): The logistics carrier will bear 70% of the freight reduction (reduction amount = this freight × 70% × 1.2, where 1.2 is the compensation coefficient for VIP customers), and the customs department will bear 30% of the responsibility through "subsequent customs clearance service fee reduction" (reducing the customs clearance fee for the next similar goods by 30%).

[0134] Compensation for VIP Customers (Enhancing VIP Customer Retention): This program provides customers with priority warehousing rights for precision chip transportation for the next three months, ensuring the stability of subsequent order delivery times. This tiered design, combining timeliness, cost-effectiveness, and benefits, covers diverse customer needs and optimizes multiple objectives by assigning compensation to different stakeholders based on their liability percentages.

[0135] This embodiment, by constructing a quantitative matrix and performing joint optimization of multiple parameters, allows the compensation scheme to break free from the limitations of traditional fixed standards. It can accurately match responsibility, cargo characteristics, and customer level to generate a tiered compensation scheme, which effectively meets customer needs while reasonably controlling compensation costs, thereby improving the pertinence and rationality of cross-border logistics conflict mediation.

[0136] Furthermore, in some embodiments, step S4, "based on the liability determination result, combined with cargo attributes and customer parameters, performs joint optimization calculations through a compensation strategy model to generate and output a customized adjustment execution plan," may further include:

[0137] S44. Based on the cultural characteristics of the user's region, retrieve the appropriate text template from the pre-stored communication template library;

[0138] Specifically, for step S44, firstly, the user's region is identified through multi-dimensional information, including but not limited to the delivery address in the logistics order associated with the complaint, the contact address reserved by the user during registration, and the IP address or time zone information of the device submitting the complaint; then, based on the preset region-culture feature mapping library, the core cultural preferences of users in that region in customer service communication are extracted, including key elements such as tone of voice (e.g., formal and solemn for the Middle East, concise and efficient for North America), content structure (e.g., detailed apologies and improvement instructions for East Asia, priority listing of solution implementation nodes for some European regions), and etiquette (e.g., whether to use honorifics, whether to add thanks at the end).

[0139] For example, if the order delivery address indicates that the user is located in the UAE (Middle East), the cultural characteristics are: "Communication must demonstrate a high degree of respect for the customer, include a formal written apology paragraph, and avoid overly brief statements"; if the IP address indicates that the user is located in Canada (North America), the cultural characteristics are: "Prioritize presenting the core content of the solution and clear timelines, the tone can be moderately relaxed, and excessive formalities are unnecessary"; if the user's reserved address indicates that the user is located in South Korea (East Asia), the cultural characteristics are: "Polite language must be used, the cause of the problem and subsequent preventive measures must be explained in detail, and the rigor of communication must be ensured."

[0140] S45. Populate the execution plan data into the text template to generate customized customer service communication content and send it to the user's client for display;

[0141] Specifically, for step S45, a structured communication template library categorized by region and culture is built in advance. Each template in the library has a fixed framework designed for the cultural preferences of a specific region, reserving only positions for dynamic variables, such as [customer name], [problem type], [specific measures of the solution], and [execution time], to ensure that the templates can be directly connected to the subsequent data filling stage. From the generated adjustment and execution plan, key data to be filled into the template is extracted, including the customer's full name, the specific problem type corresponding to this complaint (such as "goods detained by customs" or "delivery timeout"), the core execution measures in the plan (such as "initiate expedited customs clearance within 24 hours" or "reduce freight by 15%), the time nodes for the initiation and completion of the measures (such as "initiate before 10:00 on [Date]" or "complete freight refund within 3 working days"), and the user's exclusive contact information (such as exclusive customer service email and telephone). These data are matched one by one and filled into the variable positions reserved in the template to form complete and customized customer service communication content that fits the user's actual situation.

[0142] Before sending the communication content, we first determine the user's commonly used customer service communication client types through the user's historical interaction records or complaint submission channels. These include, but are not limited to, the logistics service APP registered by the user, the SMS client corresponding to the user's reserved mobile phone number, the email client corresponding to the user's reserved email address, and the instant messaging tools used by the user. Then, the generated customized customer service communication content is converted according to the format requirements of the corresponding client, such as adapting the pop-up format for the APP, the plain text format for SMS, and the HTML format for email. It is then sent to the client through the system interface and the display mechanism is triggered to ensure that the user can directly see the communication content when opening the client.

[0143] This embodiment ensures that customer service communication content is both adapted to users' regional cultural habits and can be quickly accessed and displayed through users' commonly used clients, avoiding communication misunderstandings caused by cultural differences. At the same time, it ensures that users obtain mediation solution information in a timely manner, improves users' perception and satisfaction with the mediation process, and enhances customers' trust in cross-border logistics services.

[0144] Furthermore, in some embodiments, the intelligent dispute resolution method for cross-border logistics customer service provided in this embodiment may further include:

[0145] S51. Collect data on the results of responsibility determination and the application effects of adjusting the implementation plan as feedback data;

[0146] Specifically, for step S51, the core components of the feedback data must be defined, which must include two types of key information. The first is the feedback data related to the liability determination result, that is, after the liability determination result is issued, the acceptance of the liability ratio by each responsible party (such as logistics carriers and customs departments) (whether they raised objections and the reasons for the objections), and the degree of matching between the liability determination result and the actual facts (such as subsequent verification that the responsible party was misjudged). The second is the application effect data of the adjustment and implementation plan, that is, after the plan is implemented, the customer's acceptance of the plan (whether they agree to implement it), satisfaction (such as the evaluation of the compensation level and processing efficiency), the plan implementation rate (such as whether "expedited customs clearance" is completed on time), the cost changes after the plan is implemented (such as the deviation between the actual compensation amount and the expected cost), and the subsequent recurrence of disputes (such as whether the same customer complains again for the same problem).

[0147] For example, if the liability determination in a cross-border logistics dispute is "the carrier bears 60% of the responsibility and the customs bears 40% of the responsibility," then the required feedback data on the liability determination includes "whether the carrier agrees with the 60% liability ratio (e.g., raising objections and reasons for 'only 30% responsibility should be borne')" and "whether subsequent verification found any factual discrepancies such as 'the customs' actual responsibility is only 20%'." If the corresponding adjustment and implementation plan is "the carrier reduces freight by 5% + customs expedited clearance," then the required application effect data includes "whether the customer agrees to the plan," "customer satisfaction rating (1-5 points, e.g., a score of 3 with a note 'insufficient compensation')," "whether expedited clearance was completed within 24 hours (implementation rate)," "the difference between the actual cost of this compensation and the budget," and "whether the customer has filed another complaint within 3 months due to 'customs clearance delays' (recurrence)."

[0148] For different types of feedback data, design appropriate collection channels and methods to avoid data omission or distortion.

[0149] Regarding the feedback data on the liability determination results, the opinions of the responsible party are collected through the system's built-in responsible party confirmation module. For example, after the carrier logs into the system, they can click "Approval" or "Objection" on the liability determination results page and fill in the reasons. The matching degree data is collected through the subsequent fact verification module. For example, after customer service personnel retrieve supplementary evidence, they can enter "Discretion in Liability Determination" into the system.

[0150] To assess the effectiveness of the implementation plan, the system collects acceptance and satisfaction data through customer feedback questionnaires. After the plan is pushed out, the system automatically sends short-link questionnaires to customers, including options such as "Do you agree with the plan?", "Satisfaction rating", and "Comments". The system also collects implementation rate data through the plan implementation tracking module. The system connects to GPS and customs systems to automatically monitor the completion time of "expedited customs clearance" and the receipt of "freight reduction" payments. Cost data is directly extracted from the financial system, such as the actual amount of compensation paid in this case. The system also uses a complaint record retrieval module to analyze the recurrence of disputes, allowing the system to search for similar complaint records within the past three months by customer ID.

[0151] S52. Incremental learning and optimization of the cross-language pre-trained model, responsibility association graph, and compensation strategy model are performed using feedback data;

[0152] Specifically, for step S52, the collected raw feedback data is cleaned and classified, invalid data such as blank questionnaires and unfounded objections are removed, and the data is categorized according to the optimization object (cross-language pre-trained model, responsibility association graph, compensation strategy model) to ensure that each type of data can accurately correspond to the model or graph to be optimized.

[0153] Data categorized as cross-language pre-trained model optimization data mainly consists of feedback that leads to incorrect responsibility determination or solution adaptation due to semantic understanding bias, resulting in responsibility determination bias and solution mismatch. Such feedback data should be classified as cross-language model optimization data.

[0154] Data categorized as responsibility association graph optimization mainly includes feedback where the responsibility weights do not match the actual situation. For example, the responsibility coefficient was originally set at 50%, but a large amount of feedback shows that the actual responsibility should reach 70%. Or there is an error in the responsibility path matching, such as mistakenly matching the responsibility of the warehousing center as the responsibility of the carrier. This type of data is classified as responsibility association graph optimization data.

[0155] Data categorized as compensation strategy model optimization mainly consists of feedback regarding low satisfaction with compensation plans and large cost deviations, such as "VIP customers are only 20% satisfied with the '5% shipping fee reduction'" and "the recurrence rate after the implementation of the compensation plan for precision chip goods reached 30%". This type of data is classified as compensation strategy model optimization data.

[0156] In addition, for different optimization targets, an adaptive incremental learning method is adopted, and the model parameters or graph weights are iteratively adjusted based on feedback data to avoid the inefficiency of full retraining and achieve accurate optimization.

[0157] For cross-language pre-trained models, feedback data on semantic understanding biases (such as mis-parsed multilingual complaint texts and correctly labeled semantic data) are used as incremental training samples and input into the model for small-batch iterative training to adjust the model's semantic mapping parameters and improve the parsing accuracy of similar texts.

[0158] For the responsibility association graph, the responsibility weight of the corresponding node in the graph is dynamically adjusted based on the feedback data of responsibility weight deviation; and the association relationship between logistics nodes and responsible parties in the graph is corrected based on the feedback of responsibility path mismatch.

[0159] For the compensation strategy model, feedback data with low customer satisfaction and large cost deviations (such as VIP customers' evaluation of the compensation level and the difference between actual compensation costs and budgets) are used as the basis for optimization. The compensation coefficients in the model are adjusted, such as the compensation multiplier for VIP customers and the timeliness compensation weight for different types of goods.

[0160] This embodiment continuously collects feedback data and incrementally optimizes the cross-language pre-trained model, responsibility association graph, and compensation strategy model to adapt to changes in cross-border logistics scenarios, continuously improve the accuracy of semantic parsing, responsibility determination, and compensation scheme generation, and achieve long-term iterative upgrades of cross-border logistics customer service conflict mediation capabilities.

[0161] In summary, compared with existing technologies, the intelligent mediation method for cross-border logistics customer service conflicts provided in this embodiment generates a unified semantic representation by performing cross-language semantic parsing and alignment on multi-modal user complaint data. Based on this semantic representation, it automatically associates and integrates multiple heterogeneous data sources to construct a visualized evidence chain. Then, it combines a pre-built responsibility association graph to infer the responsibility determination result. Finally, it combines cargo attributes and customer parameters to generate a customized mediation and execution plan through a compensation strategy model. This method can improve the parsing adaptability of multi-modal complaint data and the integration efficiency of multi-source heterogeneous logistics data in cross-border logistics customer service conflict mediation. At the same time, it can improve the accuracy of responsibility determination and the pertinence of compensation plans, thus solving the problems of low efficiency and insufficient accuracy in existing cross-border logistics customer service conflict mediation methods.

[0162] It should be understood that, although Figure 2 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 2 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0163] To facilitate better implementation of the intelligent conflict resolution method for cross-border logistics customer service in this application, this application also provides an intelligent conflict resolution device for cross-border logistics customer service based on the aforementioned method. The meanings of the terms used are the same as in the aforementioned intelligent conflict resolution method for cross-border logistics customer service, and specific implementation details can be found in the descriptions within the method embodiments.

[0164] Please see Figure 3 , Figure 3 This is a schematic diagram of the intelligent mediation device for cross-border logistics customer service conflicts provided in an embodiment of this application. Specifically, the intelligent mediation device for cross-border logistics customer service conflicts may include a data acquisition module 201, an evidence chain module 202, a responsibility determination module 203, and a solution generation module 204, as follows:

[0165] The data acquisition module 201 is used to acquire users' multimodal complaint data, perform cross-language semantic parsing and alignment on the multimodal complaint data, and generate a unified semantic representation.

[0166] The evidence chain module 202 is used to automatically associate and merge logistics event data from multiple heterogeneous data sources based on a unified semantic representation to construct a visual evidence chain corresponding to this complaint.

[0167] The responsibility determination module 203 is used to match and reason with the visualized evidence chain and the pre-constructed responsibility association graph to generate a responsibility determination result;

[0168] The solution generation module 204 is used to generate and output a customized adjustment and execution plan by combining the responsibility determination results with the cargo attributes and customer parameters through joint optimization calculations using a compensation strategy model.

[0169] Furthermore, in some embodiments, the data acquisition module 201 is specifically used for:

[0170] Receive raw complaint data containing multiple natural languages;

[0171] When the original complaint data contains voice information, the voice information is converted into complaint text data through a voice recognition engine;

[0172] By using a cross-language pre-trained model, complaint text data in different languages ​​are mapped to a shared semantic vector space, generating semantic vectors independent of the source language as a unified semantic representation.

[0173] Furthermore, in some embodiments, the data acquisition module 201 is specifically used for:

[0174] By training a cross-linguistic pre-trained model using a contrastive learning algorithm, logistics event phrases that are semantically similar but in different languages ​​can have similar vector representations in a shared semantic vector space.

[0175] Furthermore, in some embodiments, the data acquisition module 201 is specifically used for:

[0176] Perform sentiment analysis on complaint text data to obtain the intensity value of users' emotions;

[0177] Based on the mapping relationship between preset emotion intensity values ​​and user demands, the potential real needs of users are inferred from the emotion intensity values.

[0178] The unified semantic representation is modified based on potential real needs.

[0179] Furthermore, in some embodiments, the evidence chain module 202 is specifically used for:

[0180] Extract at least one key logistics identifier from a unified semantic representation;

[0181] Using key logistics identifiers as indexes, the system automatically extracts time-series event data related to this logistics event from the bill of lading system, GPS positioning system, and customs declaration system.

[0182] The extracted time-series event data is time-aligned and logically correlated to generate a visual transportation timeline containing timestamps and event descriptions, serving as a visual chain of evidence.

[0183] Furthermore, in some embodiments, the responsibility determination module 203 is specifically used for:

[0184] Obtain a pre-built responsibility association graph, which is used to encode the relationships and weights between logistics nodes, responsible parties, contract terms, and historical precedents;

[0185] By matching events in the visualized evidence chain with logistics nodes in the responsibility association graph, all potential responsibility paths can be identified.

[0186] Based on feedback data corresponding to historical liability assessment data, the weights of liability paths are dynamically adjusted, the liability contribution of each responsible party is calculated, and the liability assessment results are generated by summarizing the data.

[0187] Furthermore, in some embodiments, the scheme generation module 204 is specifically used for:

[0188] Construct a cargo value-time sensitivity matrix to quantify the sensitivity of different categories of cargo to transportation timeliness;

[0189] The liability determination result, the value level of the goods, the time sensitivity level, and the customer level are used as joint input parameters and input into the compensation strategy model;

[0190] The joint input parameters are optimized using a compensation strategy model to output a step-by-step compensation scheme as an adjustment and execution scheme.

[0191] Furthermore, in some embodiments, the scheme generation module 204 is specifically used for:

[0192] Based on the cultural characteristics of the user's region, the appropriate text template is retrieved from the pre-stored communication template library;

[0193] The execution plan data is populated into the text template to generate customized customer service communication content, which is then sent to the user's client for display.

[0194] Furthermore, in some embodiments, the device further includes an adjustment module, specifically used for:

[0195] Collect data on the application effects of liability determination results and adjusted implementation plans as feedback data;

[0196] Incremental learning and optimization of the cross-language pre-trained model, responsibility association graph, and compensation strategy model are carried out using feedback data.

[0197] For specific limitations regarding the intelligent conflict resolution device for cross-border logistics customer service, please refer to the limitations of the intelligent conflict resolution method for cross-border logistics customer service mentioned above, which will not be repeated here. Each module in the aforementioned intelligent conflict resolution device for cross-border logistics customer service can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0198] The intelligent mediation device for cross-border logistics customer service conflicts provided in this embodiment can improve the parsing and adaptation of multimodal complaint data and the integration efficiency of multi-source heterogeneous logistics data in cross-border logistics customer service conflict mediation. At the same time, it can improve the accuracy of responsibility determination and the pertinence of compensation schemes, thus solving the problems of low efficiency and insufficient accuracy in existing cross-border logistics customer service conflict mediation.

[0199] Furthermore, embodiments of this application also provide an electronic device, such as... Figure 4 As shown, it illustrates a structural schematic diagram of the electronic device involved in the embodiments of this application, specifically:

[0200] The electronic device may include components such as a processor 301 with one or more processing cores, a memory 302 with one or more computer-readable storage media, a power supply 303, and an input unit 304. Those skilled in the art will understand that... Figure 4The electronic device structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein:

[0201] The processor 301 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines, and performs various functions and processes data by running or executing software programs and / or modules stored in the memory 302, and by calling data stored in the memory 302, thereby providing overall monitoring of the electronic device. Optionally, the processor 301 may include one or more processing cores; preferably, the processor 301 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 301.

[0202] The memory 302 can be used to store software programs and modules. The processor 301 executes various functional applications and intelligent dispute resolution methods for cross-border logistics customer service by running the software programs and modules stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the electronic device, etc. In addition, the memory 302 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 302 may also include a memory controller to provide the processor 301 with access to the memory 302.

[0203] The electronic device also includes a power supply 303 that supplies power to various components. Preferably, the power supply 303 can be logically connected to the processor 301 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 303 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0204] The electronic device may also include an input unit 304, which can be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0205] Although not shown, the electronic device may also include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 301 in the electronic device loads the executable files corresponding to the processes of one or more applications into the memory 302 according to the following instructions, and the processor 301 runs the applications stored in the memory 302 to realize various functions, as follows:

[0206] Acquire multimodal complaint data from users, perform cross-language semantic parsing and alignment on the multimodal complaint data to generate a unified semantic representation; based on the unified semantic representation, automatically associate and merge logistics event data from multiple heterogeneous data sources to construct a visualized evidence chain corresponding to this complaint; match and reason with the visualized evidence chain and a pre-built responsibility association graph to generate a responsibility determination result; based on the responsibility determination result, combined with cargo attributes and customer parameters, perform joint optimization calculations through a compensation strategy model to generate and output a customized adjustment and implementation plan.

[0207] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0208] The embodiments of this application can improve the parsing and adaptability of multimodal complaint data and the integration efficiency of multi-source heterogeneous logistics data in cross-border logistics customer service conflict mediation, while improving the accuracy of liability determination and the pertinence of compensation schemes, thus solving the problems of low efficiency and insufficient accuracy in existing cross-border logistics customer service conflict mediation.

[0209] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0210] To this end, embodiments of this application provide a storage medium storing multiple instructions that can be loaded by a processor to execute steps in any of the intelligent conflict resolution methods for cross-border logistics customer service provided in embodiments of this application. For example, the instructions can execute the following steps:

[0211] Acquire multimodal complaint data from users, perform cross-language semantic parsing and alignment on the multimodal complaint data to generate a unified semantic representation; based on the unified semantic representation, automatically associate and merge logistics event data from multiple heterogeneous data sources to construct a visualized evidence chain corresponding to this complaint; match and reason with the visualized evidence chain and a pre-built responsibility association graph to generate a responsibility determination result; based on the responsibility determination result, combined with cargo attributes and customer parameters, perform joint optimization calculations through a compensation strategy model to generate and output a customized adjustment and implementation plan.

[0212] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0213] The storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0214] Since the instructions stored in the storage medium can execute the steps in any of the intelligent mediation methods for cross-border logistics customer service conflicts provided in the embodiments of this application, the beneficial effects that any of the intelligent mediation methods for cross-border logistics customer service conflicts provided in the embodiments of this application can achieve can be realized. For details, please refer to the previous embodiments, which will not be repeated here.

[0215] The above provides a detailed description of a cross-border logistics customer service conflict intelligent mediation method and apparatus provided in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for intelligent conflict resolution in cross-border logistics customer service, characterized in that, include: Acquire users' multimodal complaint data, perform cross-language semantic parsing and alignment on the multimodal complaint data, and generate a unified semantic representation; Extract at least one key logistics identifier from the unified semantic representation; Using the key logistics identifier as an index, the system automatically extracts time-series event data related to this logistics event from the bill of lading system, GPS positioning system, and customs declaration system. The extracted time-series event data is time-aligned and logically correlated to generate a visual transportation timeline containing timestamps and event descriptions, serving as a visual chain of evidence. The visualized chain of evidence is matched and reasoned with a pre-constructed responsibility association graph to generate a responsibility determination result; Based on the liability determination results, combined with cargo attributes and customer parameters, a joint optimization calculation is performed through a compensation strategy model to generate and output a customized adjustment and execution plan.

2. The intelligent dispute resolution method for cross-border logistics customer service as described in claim 1, characterized in that, The process of acquiring users' multimodal complaint data, performing cross-language semantic parsing and alignment on the multimodal complaint data, and generating a unified semantic representation includes: Receive raw complaint data containing multiple natural languages; When the original complaint data contains voice information, the voice information is converted into complaint text data through a voice recognition engine; By using a cross-language pre-trained model, the complaint text data in different languages ​​are mapped to a shared semantic vector space, generating semantic vectors independent of the source language, which serve as the unified semantic representation.

3. The intelligent dispute resolution method for cross-border logistics customer service according to claim 2, characterized in that, The method of using a cross-language pre-trained model to map complaint text data in different languages ​​to a shared semantic vector space, generating semantic vectors independent of the source language as the unified semantic representation, includes: The cross-linguistic pre-trained model is trained using a contrastive learning algorithm so that semantically similar but linguistically different logistics event phrases have similar vector representations in a shared semantic vector space.

4. The intelligent dispute resolution method for cross-border logistics customer service as described in claim 2, characterized in that, After utilizing a cross-lingual pre-trained model to map the complaint text data from different languages ​​to a shared semantic vector space, generating source language-independent semantic vectors as the unified semantic representation, the method further includes: Perform sentiment analysis on the complaint text data to obtain the user's sentiment intensity value; Based on the mapping relationship between preset emotion intensity values ​​and user demands, the user's potential real needs are inferred according to the emotion intensity values. The unified semantic representation is modified based on the potential real needs.

5. The intelligent dispute resolution method for cross-border logistics customer service as described in claim 1, characterized in that, The step of matching and reasoning with the visualized evidence chain and the pre-constructed responsibility association graph to generate a responsibility determination result includes: Obtain a pre-constructed responsibility association graph, which is used to encode the relationships and weights between logistics nodes, responsible parties, contract terms, and historical precedents; By matching the events in the visualized evidence chain with the logistics nodes in the responsibility association graph, all potential responsibility paths are identified; Based on feedback data corresponding to historical liability judgment data, the weights of the liability paths are dynamically adjusted, the liability contribution of each responsible party is calculated, and the liability judgment results are generated by summarizing the data.

6. The intelligent dispute resolution method for cross-border logistics customer service as described in claim 1, characterized in that, Based on the liability determination result, combined with cargo attributes and customer parameters, a customized adjustment and execution plan is generated and output through joint optimization calculation using a compensation strategy model, including: Construct a cargo value-time sensitivity matrix to quantify the sensitivity of different categories of cargo to transportation timeliness; The liability determination result, the value level of the goods, the time sensitivity level, and the customer level are used as joint input parameters and input into the compensation strategy model. The compensation strategy model is used to perform multi-objective optimization calculations on the joint input parameters, and a step-by-step compensation scheme is output as the adjustment execution scheme.

7. The intelligent dispute resolution method for cross-border logistics customer service as described in claim 1, characterized in that, The step of generating and outputting a customized adjustment and execution plan by performing joint optimization calculations through a compensation strategy model based on the liability determination result, combined with cargo attributes and customer parameters, also includes: Based on the cultural characteristics of the user's region, the appropriate text template is retrieved from the pre-stored communication template library; The execution plan data is populated into the text template to generate customized customer service communication content, which is then sent to the user's client for display.

8. The intelligent dispute resolution method for cross-border logistics customer service according to claim 1, characterized in that, The method further includes: Collect the results of the responsibility determination and the application effect data of the adjustment and implementation plan as feedback data; The feedback data is used to incrementally learn and optimize the cross-language pre-trained model, the responsibility association graph, and the compensation strategy model.

9. A cross-border logistics customer service conflict intelligent mediation device, characterized in that, include: The data acquisition module is used to acquire users' multimodal complaint data, perform cross-language semantic parsing and alignment on the multimodal complaint data, and generate a unified semantic representation. The evidence chain module is used to extract at least one key logistics identifier from the unified semantic representation; Using the key logistics identifier as an index, the system automatically extracts time-series event data related to this logistics event from the bill of lading system, GPS positioning system, and customs declaration system. The extracted time-series event data is time-aligned and logically correlated to generate a visual transportation timeline containing timestamps and event descriptions, which serves as the visual evidence chain. The responsibility determination module is used to match and reason with the visualized evidence chain and the pre-constructed responsibility association graph to generate a responsibility determination result; The solution generation module is used to generate and output a customized adjustment and execution plan based on the liability determination result, combined with cargo attributes and customer parameters, through joint optimization calculation using a compensation strategy model.

Images