A cross-border logistics multi-language term conversion method and system

Abstract

The application discloses a cross-border logistics multilingual term conversion method and system, relates to the cross-border logistics multilingual term conversion field, and is used for improving the final data accuracy and reliability of a multilingual term database and comprises the following steps: when capturing an instruction input in a front end, performing preliminary processing on the instruction, encapsulating the processed instruction as a to-be-processed transaction record, and temporarily storing the to-be-processed transaction record in a transaction buffer; asynchronously reading the to-be-processed transaction record in the transaction buffer, performing semantic consistency verification on the to-be-processed transaction record, and obtaining a verification result; according to the verification result, if the to-be-processed transaction record passes the verification, the to-be-processed transaction record that passes the verification is updated to the multilingual term database in an atomic operation mode; according to the verification result, if the to-be-processed transaction record fails the verification or the multilingual term database fails the update, an exception processing mechanism is triggered.

Description

Technical Field

[0001] This invention relates to the field of cross-border logistics multilingual terminology conversion, and in particular to a method and system for cross-border logistics multilingual terminology conversion. Background Technology

[0002] In the process of multilingual terminology conversion in cross-border logistics, the manual proofreading stage faces multiple challenges. Operators must focus on the screen display for extended periods, verifying the accuracy of the source language terminology and the target language conversion results, and updating the reviewed data to the multilingual terminology database. However, complex operating environments, such as fluctuations in ambient humidity, can cause localized performance degradation of the display screen (e.g., micro-condensation or localized fogging of the screen surface coating), affecting the clarity of character recognition.

[0003] This could cause the system's front-end input module to fail to accurately recognize the proofreading instructions, resulting in occasional inconsistencies or missing data in the multilingual terminology database update. Summary of the Invention

[0004] This invention provides a method for converting multilingual terms in cross-border logistics, which improves the accuracy and reliability of the final data in a multilingual terminology database.

[0005] Firstly, to address the aforementioned technical problems, this invention provides a method for converting multilingual terms in cross-border logistics, comprising: when capturing instructions input from the front end, performing preliminary processing on the instructions and encapsulating the processed instructions into transaction records to be processed, and temporarily storing them in a transaction buffer; the preliminary processing is used to remove physical input anomalies in the instructions; asynchronously reading the transaction records to be processed in the transaction buffer and performing semantic consistency verification on the transaction records to be processed to obtain verification results; based on the verification results, if the transaction records to be processed pass the verification, updating the verified transaction records to the multilingual terminology database in an atomic operation manner; based on the verification results, if the transaction records to be processed fail the verification, or the multilingual terminology database update fails, triggering an exception handling mechanism, the exception handling mechanism including recording exception transactions, generating exception alarms, and maintaining the original data state of the multilingual terminology database.

[0006] Optionally, semantic consistency verification is performed on the transaction record to be processed to obtain the verification result, including: performing semantic consistency verification on the transaction record to be processed based on multiple semantic rules that the terms in the transaction record to be processed can simultaneously satisfy, and obtaining the verification result; or, performing semantic consistency verification on the transaction record to be processed based on the context information of real-time operations related to the terms in the transaction record to be processed, and obtaining the verification result.

[0007] Optionally, based on the multiple semantic rules that the terms in the transaction record to be processed can simultaneously satisfy, a semantic consistency check is performed on the transaction record to be processed to obtain a check result, including: identifying the multiple semantic rules that the terms in the transaction record to be processed can simultaneously satisfy, and obtaining multiple semantic attributes assigned to the terms by the multiple semantic rules; encapsulating the multiple semantic attributes into a semantic attribute file and attaching it to the transaction record to be processed; selecting a semantic attribute from the semantic attribute file as the first processing semantic of the transaction record to be processed according to a preset business priority rule; and determining whether the transaction record to be processed passes the semantic consistency check based on the first processing semantic, and obtaining a check result.

[0008] Optionally, based on the context information of real-time operations related to the terms in the transaction record to be processed, a semantic consistency check is performed on the transaction record to be processed to obtain a check result. This includes: obtaining the context information of real-time operations related to the terms in the transaction record to be processed; based on the context information, selecting context-specific semantic rules associated with the context information from a preset set of semantic rules; evaluating the semantic rules that the terms in the transaction record to be processed can satisfy and their assigned semantic attributes according to the context-specific semantic rules to determine the set of valid semantic attributes of the terms; combining the set of valid semantic attributes with preset business priority rules to determine the second processing semantic of the transaction record to be processed; and determining whether the transaction record to be processed passes the semantic consistency check according to the second processing semantic, thereby obtaining a check result.

[0009] Optionally, the instruction is preliminarily processed, and the processed instruction is encapsulated into a transaction record to be processed and temporarily stored in a transaction buffer. This includes: capturing the original electrical signal of the instruction input from the front end; performing waveform feature analysis on the original electrical signal to identify whether there is an ambiguous signal pattern in the instruction input from the front end; if an ambiguous signal pattern is identified, the operational intent and corresponding confidence level of the instruction input from the front end are preliminarily inferred based on the current operation context; when the confidence level is lower than a preset threshold, a non-intrusive intent confirmation interaction is triggered, which includes providing visual feedback and monitoring the subsequent behavior of the operator; based on the subsequent behavior, it is determined whether an implicit confirmation of the preliminary intent of the instruction has been obtained; combining the results of the preliminary intent and the implicit confirmation, the instruction input from the front end is determined; and the determined instruction input from the front end is encapsulated into a transaction record to be processed and temporarily stored in a transaction buffer.

[0010] Optionally, based on subsequent behavior, determine whether implicit confirmation of the initial intent of the instruction has been obtained, including: continuously collecting interaction signal data between the operator and the input device within a preset time window after visual feedback is triggered; the interaction signal data includes key pressure, contact duration, and micro-motion information of the input device; performing feature analysis on the interaction signal data to extract feature parameters reflecting the operator's operational stability; the feature parameters include signal waveform smoothness, instantaneous pressure fluctuation amplitude, and continuous contact interval time; setting discrimination rules based on feature parameters and operational intent; the discrimination rules are used to distinguish the operation type corresponding to the interaction signal data; the operation type includes: implicit confirmation, explicit rejection, or unintentional action; and determining whether implicit confirmation of the initial intent of the instruction has been obtained based on the execution result of the discrimination rules.

[0011] Optionally, based on feature parameters and operation intent, discrimination rules are set, including: constructing multi-dimensional threshold judgment logic; the multi-dimensional threshold judgment logic uses signal waveform smoothness, instantaneous pressure fluctuation amplitude, and continuous contact interval time as judgment criteria; according to the multi-dimensional threshold judgment logic, the feature intervals of different operation types conforming to the interactive signal data are determined in order to set discrimination rules.

[0012] Optionally, based on the multi-dimensional threshold judgment logic, the characteristic range of different operation types that the interactive signal data conforms to is determined, including: obtaining the judgment result of the operation intention and the manual correction feedback information; dynamically adjusting the threshold range of signal waveform smoothness, instantaneous pressure fluctuation amplitude and continuous contact interval time based on the judgment result of the operation intention and the manual correction feedback information; and setting the discrimination rules based on the adjusted threshold range.

[0013] Optionally, dynamically adjusting the threshold ranges for signal waveform smoothness, instantaneous pressure fluctuation amplitude, and continuous contact interval time includes: calculating the deviation between the judgment result and the manual correction feedback information based on the judgment result and the manual correction feedback information; and, based on the deviation, fine-tuning the threshold ranges for signal waveform smoothness, instantaneous pressure fluctuation amplitude, and continuous contact interval time with a preset adjustment step size and direction, so as to iteratively optimize the threshold ranges for signal waveform smoothness, instantaneous pressure fluctuation amplitude, and continuous contact interval time.

[0014] Secondly, this invention provides a cross-border logistics multilingual terminology conversion system for cross-border logistics multilingual terminology conversion, the system comprising:

[0015] The instruction capture and temporary storage module is used to perform preliminary processing on the captured instructions input from the front end, and encapsulate the processed instructions into a transaction record to be processed and temporarily store it in the transaction buffer; the preliminary processing is used to remove physical input anomalies in the instructions;

[0016] The asynchronous transaction verification module is used to asynchronously read the transaction records to be processed in the transaction buffer, perform semantic consistency verification on the transaction records to be processed, and obtain the verification result.

[0017] The database atomic update module is used to update the verified transaction record to the multilingual terminology database in an atomic manner based on the verification result.

[0018] The exception handling module is used to trigger the exception handling mechanism if the verification of the transaction record to be processed fails or the update of the multilingual terminology database fails, based on the verification results. The exception handling mechanism includes recording the exception transaction, generating the exception alarm, and maintaining the original data state of the multilingual terminology database.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] This application provides a method and system for converting multilingual terms in cross-border logistics. By performing preliminary processing on the instructions input from the front end to remove physical input anomalies and performing semantic consistency verification on the transaction records, the accuracy and consistency of the multilingual terminology database updates are ensured. This has the advantages of improving the accuracy and consistency of cross-border logistics multilingual terminology updates and effectively avoiding data errors caused by physical input anomalies. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of a cross-border logistics multilingual terminology conversion method provided by an embodiment of the present invention;

[0022] Figure 2 This is a schematic diagram of another cross-border logistics multilingual terminology conversion method provided in this embodiment of the invention;

[0023] Figure 3 This is a schematic diagram of the structure of a cross-border logistics multilingual terminology conversion system provided in an embodiment of the present invention. Detailed Implementation

[0024] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0025] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0026] The following specific embodiments will provide a detailed introduction and explanation of a cross-border logistics multilingual terminology conversion method provided in this application.

[0027] Reference Figure 1 This invention provides a method for converting multilingual terms in cross-border logistics, comprising the following steps:

[0028] S1: When capturing the instruction input from the front end, perform preliminary processing on the instruction, and encapsulate the processed instruction into a transaction record to be processed and temporarily store it in the transaction buffer.

[0029] The initial processing step removes physical input anomalies from the instructions. A pending transaction record is a unified data unit that encapsulates the user's operational intent and related data after initial processing. It can be carried in structured data formats, message queue elements, etc., such as a data packet containing terms, operation types, and timestamps. The transaction buffer is an intermediate storage area used to temporarily store pending transaction records. It can be implemented using memory queues, disk caches, or message queues, such as a first-in-first-out (FIFO) queue.

[0030] As one possible implementation, the system can use techniques such as sampling signal filtering, noise suppression, and pattern recognition to perform preliminary processing on the instructions, and encapsulate the processed instructions into transaction records to be processed and temporarily store them in the transaction buffer; the preliminary processing is used to remove physical input anomalies in the instructions.

[0031] In one example, when a worker inputs instructions via keyboard or touchscreen, such as a calibrated logistics term "Containerized Cargo," the system's front-end input module captures the raw electrical signals. The system performs preliminary processing on these raw signals, such as using digital filters to remove glitches caused by key bounce, or using pattern recognition algorithms to identify and correct signal incompleteness caused by rapid clicks. The processed instructions, such as "Containerized Cargo" and its corresponding operation (e.g., "update"), are encapsulated into a JSON-formatted transaction record and sent to a message queue (e.g., Apache Kafka) as a transaction buffer.

[0032] As one possible implementation, the system can perform preliminary processing on the instructions based on the following steps, and then encapsulate the processed instructions into transaction records to be processed and temporarily store them in the transaction buffer.

[0033] S11. Capture the raw electrical signal of the instruction input from the front end.

[0034] As one possible implementation, when operators perform terminology verification in the cross-border logistics terminology conversion system and attempt to input confirmation commands via keyboard, the system first captures the raw electrical signals of the keyboard keys through a high-speed data acquisition module.

[0035] This module can be a dedicated ADC (analog-to-digital converter) integrated into the keyboard controller, continuously acquiring instantaneous changes in key contact voltage or current at a sampling rate of thousands of times per second.

[0036] S12. Perform waveform feature analysis on the original electrical signal to identify whether there is an ambiguous signal pattern in the instructions input from the front end.

[0037] Among them, the fuzzy signal mode refers to the irregular, incomplete or non-standard waveforms of the input instructions at the front end at the electrical signal level. For example, signal distortion caused by key bounce, unstable contact or instantaneous sensor failure can be identified by features such as signal amplitude fluctuation, frequency abnormality or duration deviation. Its purpose is to provide a basis for subsequent intent inference and confirmation.

[0038] For example, Fourier transform can be used to identify the frequency components of a signal, or wavelet analysis can be used to detect instantaneous anomalies in a signal, thereby identifying whether there are ambiguous signal patterns, such as multiple trigger pulses caused by key bounce or signal amplitude attenuation caused by insufficient pressing.

[0039] S13. If a fuzzy signal pattern is identified, the operational intent of the front-end input instruction and its corresponding confidence level are preliminarily inferred based on the current operation context.

[0040] The confidence score decreases depending on the degree of deviation between the signal and the preset operational intent.

[0041] As one possible implementation, after recognizing a fuzzy signal pattern, the system can use the selection options in the current context as the operational intent of the command input from the front end.

[0042] The system pre-stores signal features of different operational intentions. The system can determine the similarity between the signal features of the inferred operational intention and the original electrical signal, and determine the similarity as the confidence level.

[0043] For example, if you are currently on the "Term Confirmation" screen and the screen displays a term about "Cargo Type", and the options in the current context include "Confirm Current Term" and "Modify Current Term", you can preliminarily infer that the operator's intention is to "confirm the current term" or "modify the current term".

[0044] S14. When the confidence level is lower than the preset threshold, trigger a non-intrusive intent confirmation interaction.

[0045] Non-intrusive intent confirmation interaction includes providing visual feedback and monitoring the operator's subsequent behavior.

[0046] As one possible implementation, the system can briefly display a faint visual feedback at the edge of the current operating area on the screen in a semi-transparent and non-obstructive manner, while the system background continuously monitors the operator's subsequent behavior to trigger a non-intrusive intent confirmation interaction.

[0047] For example, visual feedback could be a blinking "?" icon or a color gradient border.

[0048] For example, subsequent actions could be the mouse movement path, the force of pressing the button again, or the duration of the keystroke.

[0049] S15. Based on subsequent actions, determine whether implicit confirmation of the initial intent of the instruction has been obtained.

[0050] As one possible approach, if the operator performs an action consistent with the initial inferred intention after receiving visual feedback, they are considered to have received implicit confirmation.

[0051] S16. Based on the initial intent and the implicit confirmation, determine the instruction input from the front end.

[0052] As one possible implementation, the system determines that the input command from the front end is normal if the initial intent is implicitly confirmed. Conversely, if the initial intent is not implicitly confirmed, the input command from the front end is determined to be abnormal.

[0053] For example, if the initial intent is "confirm" and implicit confirmation is received, it is ultimately determined to be a "confirmation instruction". If the initial intent is "confirm" but implicit confirmation is not received (e.g., the operator cancels the operation), it may be re-inferred or marked as an anomaly.

[0054] S17. Encapsulate the determined front-end input instructions into a transaction record to be processed and temporarily store it in the transaction buffer.

[0055] Through the above technical solution, this application can effectively address the ambiguous signal patterns in front-end input instructions. By analyzing the waveform characteristics of the original electrical signals and combining them with the operational context for intent inference, the accuracy of operational intent recognition is significantly improved. When the system's confidence in the operational intent is low, implicit confirmation is obtained through non-intrusive intent confirmation interaction without interrupting the operator's normal operation, further enhancing the reliability of instruction judgment. This ensures that even in complex operating environments, the operator's true operational intent can be accurately converted into a pending transaction record, thereby guaranteeing the accuracy of subsequent data processing and the reliability of multilingual terminology database updates from the source, effectively avoiding data inconsistency or missing data caused by inaccurate front-end input.

[0056] S2. Asynchronously read the transaction records to be processed in the transaction buffer and perform semantic consistency verification on the transaction records to be processed to obtain the verification result.

[0057] Semantic consistency verification refers to the process of verifying the terms or operations contained in transaction records according to logic and business rules. It can be achieved by methods such as matching based on preset rules, context association analysis, and machine learning model judgment. For example, it checks whether the terms meet the semantic requirements of industry standards or specific business scenarios. Its purpose is to ensure that the transaction records to be processed meet the requirements at the business logic level.

[0058] As one possible implementation, the system can asynchronously read transaction records to be processed from the transaction buffer. After reading, the transaction records are sent to the semantic validation engine. The semantic validation engine determines the logical validity and business compliance of the terms in the transaction records to be processed according to preset business rules. If both the logical validity and business compliance of the terms in the transaction records to be processed pass the validation, the transaction records to be processed are determined to have passed the validation; otherwise, the transaction records to be processed are determined to have failed the validation.

[0059] For example, if the instruction input at the front end is the logistics term "Containerized Cargo", the system can determine whether it conforms to the list of valid terms under the current transportation mode, or whether it conflicts with existing term definitions, thereby performing semantic consistency verification.

[0060] As another possible implementation, the system can perform semantic consistency verification on the transaction record to be processed based on multiple semantic rules that the terms in the transaction record can simultaneously satisfy, and obtain the verification result.

[0061] Among them, multiple semantic rules refer to a series of preset standards or conventions used to define and constrain the meaning, usage and conversion norms of terms. These can include industry standards, internal enterprise norms, linguistic rules or conventions under specific business scenarios. Their purpose is to provide multi-dimensional judgment criteria for the accuracy of terms.

[0062] As another possible implementation, the system can perform semantic consistency verification on the transaction record to be processed based on the context information of real-time operations related to the terms in the transaction record to be processed, and obtain the verification result.

[0063] The contextual information includes the current mode of transport, the physical status of the cargo, and information on temporary regulatory adjustments for specific destinations or routes.

[0064] The current mode of transport refers to the specific mode of transport used for goods in the logistics process, such as land transport, sea transport, air transport, or multimodal transport. Its purpose is to influence the specific meaning of the term in different transport scenarios.

[0065] The physical condition of goods refers to the actual physical properties or conditions of goods during transportation or storage, such as fragile, liquid, dangerous goods, or refrigerated, with the aim of influencing the specific meaning of the term under different cargo characteristics.

[0066] Temporary regulatory changes for a specific destination or route refer to changes in laws, regulations, or policies that are temporarily effective for a specific destination country, region, or particular transport route and that affect logistics operations or the use of terminology. The purpose is to ensure that the use of terminology complies with the latest compliance requirements.

[0067] In some preferred embodiments, the system can perform verification based on multiple semantic rules that the terms in the transaction record to be processed can simultaneously satisfy. For example, when the system receives a transaction record to be processed containing the term "ETA," the term may simultaneously satisfy multiple preset semantic rules. Rule 1 may define "ETA" as the estimated time of arrival and require its format to be a date and timestamp; Rule 2 may be for maritime scenarios, requiring "ETA" to include the name of the destination port; Rule 3 may be for a specific customer, requiring "ETA" to be accurate to the hour. The system can identify that the term simultaneously satisfies these three rules and comprehensively evaluate the expression of the term based on these rules. If the "ETA" in the transaction record is expressed as "2023-12-31 10:00, Shanghai Port," it can be determined that it simultaneously satisfies Rule 1 and Rule 2, thus considering the term to be semantically consistent under multiple rules. As another specific implementation, verification can be performed based on real-time operational context information related to the terms in the transaction record to be processed. For example, when the system processes a transaction record to be processed containing the term "customs clearance documents," the system can obtain real-time context information related to the term. If the current transport mode is air freight, the goods are perishable, and there are temporary regulatory adjustments for a specific destination (e.g., a particular country) (e.g., the country temporarily requires an additional certificate of origin for air freight of perishable goods), the system can dynamically determine whether the term "customs clearance documents" includes all necessary document types based on this contextual information. If the transaction record only mentions regular commercial invoices and packing lists without mentioning a certificate of origin, the system can determine that the term has a semantic inconsistency in the current real-time context, thus triggering a verification failure. In this way, the verification results of the term can be adapted to dynamically changing business scenarios and regulatory requirements.

[0068] S3. Based on the verification results, if the transaction record to be processed passes the verification, the verified transaction record to be processed will be updated to the multilingual terminology database in an atomic operation manner.

[0069] Atomicity refers to a characteristic of database operations, meaning that a single operation or a series of operations either all succeeds or none succeeds; partial success is not possible.

[0070] As one possible implementation, the system can use technologies such as database transaction management and two-phase commit protocols to update the verified pending transaction records to the multilingual terminology database in an atomic manner. For example, when updating the multilingual terminology database, it can ensure that all related data modifications are completed as a complete unit, with the aim of guaranteeing the data integrity and consistency of the multilingual terminology database.

[0071] For example, if the instruction input from the front end is the logistics term "Containerized Cargo," and the system confirms that "Containerized Cargo" is a term consistent with business logic, then the transaction record will be passed to the database service. The database service will then initiate a database transaction, updating "Containerized Cargo" and its multilingual correspondence to the multilingual terminology database in an atomic manner.

[0072] S4. Based on the verification results, if the verification of the transaction record to be processed fails, or the update of the multilingual terminology database fails, the exception handling mechanism will be triggered.

[0073] The exception handling mechanism includes recording exception transactions, generating exception alerts, and maintaining the original data state of the multilingual terminology database.

[0074] As one possible implementation, if the verification of the pending transaction record fails, the system will record the abnormal transaction, generate an abnormal alarm, and refuse to update the pending transaction record to the multilingual terminology database.

[0075] In the event of any error during the update process (such as network interruption or database deadlock), based on this atomic operation method, the entire transaction will be rolled back, and the multilingual terminology database will delete the cached data to maintain the original state before the update.

[0076] Through the above technical solutions, this application solves the problem of data inconsistency or missing information in the multilingual terminology database caused by physical input anomalies, semantic inconsistencies, and database update failures in the front-end input instructions during the cross-border logistics terminology conversion process. The preliminary processing stage can filter and correct input signal anomalies caused by operator hand instability or equipment malfunctions, thereby improving the accuracy of the data source. The asynchronous semantic consistency verification mechanism ensures that the terms entering the database meet the requirements in terms of business logic and semantics, avoiding erroneous data introduced by human misjudgment or cognitive bias. The introduction of atomic operation methods ensures the integrity of multilingual terminology database updates and eliminates the risk of data inconsistency caused by partial update failures. Furthermore, a comprehensive anomaly handling mechanism can detect and respond to various anomalies during system operation. By recording anomalies, generating alarms, and maintaining the original state of the database, it further enhances the robustness of the system and the reliability of the data. These measures work together to improve the data quality of the cross-border logistics multilingual terminology database and the stability of system operation, reducing the risks and correction costs caused by data errors in subsequent business processes.

[0077] In one possible design, such as Figure 2As shown, based on the multiple semantic rules that the terms in the transaction record to be processed can simultaneously satisfy, a semantic consistency check is performed on the transaction record to be processed to obtain the check result. This application may also include the following steps:

[0078] S101. Identify multiple semantic rules that a term in a transaction record to be processed can simultaneously satisfy, and obtain multiple semantic attributes assigned to the term by the multiple semantic rules.

[0079] Among these, multiple semantic rules refer to a predefined set of conditions or standards used to define the effective use, meaning, or context of terms in the cross-border logistics field. These rules can cover linguistic norms, industry standards, etc., and their purpose is to ensure the accuracy and consistency of terminology. Multiple semantic attributes refer to the specific features or values ​​assigned to a term by applying one or more semantic rules. For example, a term "container" can have attributes such as "type: 20-foot dry cargo container," "status: empty," "location: Shanghai Port," and "dangerous goods: no," the purpose of which is to provide detailed semantic information for the term under different rule interpretations.

[0080] In one example, suppose a pending transaction record contains the term "FOB" (Free On Board). First, the system identifies multiple semantic rules that the term "FOB" can simultaneously satisfy. For example, these rules may include "Incoterms 2020," "Regional Customs Regulations (A)," and "Internal Transportation Policy." Next, the system obtains multiple semantic attributes assigned to the term "FOB" by these semantic rules. For example, according to "Incoterms 2020," its semantic attribute could be "Risk transfers on board the ship at the port of shipment, specifying the port of shipment"; according to "Regional Customs Regulations (A)," its semantic attribute could be "The valuation includes all costs to the port of export."

[0081] S102. Encapsulate multiple semantic attributes into a semantic attribute file and attach it to the transaction record to be processed.

[0082] A semantic attribute archive is a structured data container or record used to aggregate all semantic attributes for a specific term, identified from various applicable semantic rules. It can be implemented as a data structure such as a JSON object, an XML document, or a database record, with the aim of centrally managing and organizing all potential semantic interpretations of a term for efficient access and selection.

[0083] One possible implementation is to encapsulate these acquired semantic attributes into a semantic attribute archive of a structured data object. For example, a JSON document containing key-value pairs, where each key represents a semantic rule source and the corresponding value is a semantic description under that rule. This semantic attribute archive is appended to the transaction record to be processed for easy access during subsequent processing.

[0084] S103. Based on the preset business priority rules, select a semantic attribute from the semantic attribute file as the first processing semantic for the transaction record to be processed.

[0085] Among them, business priority rules refer to a set of preset judgment criteria or algorithms used to determine the priority order of multiple semantic attributes when they simultaneously apply to a term. These rules are usually set based on business logic, operational requirements, or compliance requirements. For example, attributes derived from customs regulations may be given priority over general language rules. The purpose is to solve the problem of semantic ambiguity by providing a deterministic method to select the most relevant semantic interpretation based on operational requirements.

[0086] The first processing semantic refers to a single, explicit semantic attribute selected from the semantic attribute file according to business priority rules. It represents the authoritative interpretation of the term in the current processing context, and its purpose is to provide a clear and unambiguous semantic basis for subsequent verification and processing steps, thereby ensuring consistency and accuracy.

[0087] As one possible implementation, the system can select the semantic attribute with the highest semantic priority from the semantic attribute file based on the preset business priority rules, and use it as the first processing semantic for the transaction record to be processed.

[0088] For example, a preset business priority rule can be set as "customs regulations take precedence over international trade terms, and international trade terms take precedence over company internal policies." If the current transaction involves imports from region A, the semantic attribute assigned to "customs regulations of region A" will be selected as the first processing semantic.

[0089] S104. Based on the first processing semantics, determine whether the transaction record to be processed has passed the semantic consistency check, and obtain the check result.

[0090] As one possible implementation, the system can determine that a transaction record to be processed passes the semantic consistency check if it conforms to the first processing semantics. If the transaction record to be processed does not conform to the first processing semantics, it can determine that it fails the semantic consistency check and obtain the check result.

[0091] The above technical solution moves beyond simply applying a single semantic rule or isolated contextual information when performing semantic consistency checks on transaction records. By validating based on multiple semantic rules that a term can simultaneously satisfy, the meaning of the term can be comprehensively evaluated, avoiding validation biases caused by the one-sidedness of rules. Simultaneously, validation based on real-time operational contextual information related to the term can dynamically adapt to changes in transportation modes, cargo physical states, and temporary regulatory adjustments, ensuring the accuracy of term meanings in dynamic business scenarios. The introduction of these two validation methods improves the accuracy and reliability of semantic consistency validation results, effectively addressing the problem of insufficient accuracy in validation results, improving the update quality of the multilingual terminology database, and ensuring the accuracy and compliance of cross-border logistics information.

[0092] However, in its implementation, relying solely on the inherent semantic rules of the terms themselves for verification, without considering real-time operational context, may result in verification results that fail to accurately reflect the complex business environment. For example, in air freight, certain maritime-specific terms, even if they meet their inherent semantic rules, should be considered invalid or of low priority, thus affecting the accuracy of verification and the smoothness of business processes. Therefore, in actual cross-border logistics scenarios, considering only the inherent semantic rules of the terms may be insufficient to cope with complex and ever-changing business environments. For instance, the same term may have different meanings and applicability under different transportation modes, cargo statuses, or regulatory environments. How to combine real-time operational context information to more accurately perform semantic consistency verification has become a problem that needs to be solved.

[0093] To this end, in order to perform semantic consistency verification on the transaction record to be processed based on the contextual information of real-time operations related to the terms in the transaction record to be processed, and to obtain the verification result, this application further includes the following steps:

[0094] S201. Obtain context information about real-time operations related to terms in the transaction record to be processed.

[0095] Among them, the context information of real-time operation refers to the set of data that is closely related to the terms in the records of transactions to be processed during the cross-border logistics business processing, is dynamically changing, and reflects the current actual business environment.

[0096] As one possible implementation, the system can use sensor data, system status parameters, and external interfaces to obtain contextual information about real-time operations related to terms in the transaction records to be processed.

[0097] In one example, the system can obtain information through the data interface of the integrated logistics management system that the current transportation mode is "air transport" and the physical status of the goods is "packaged". It can also obtain information through the external regulatory database interface that there is a temporary regulatory adjustment information such as "liquid battery air transport ban" for a specific destination or route.

[0098] S202. Based on context information, select context-specific semantic rules that are associated with the context information from the preset set of semantic rules.

[0099] The pre-defined semantic rule set refers to a structured knowledge base containing all known and potential semantic rules. These rules define the meaning, constraints, and applicability of different terms in various business scenarios. They can be stored and managed in the form of relational databases, graph databases, or ontology-based knowledge graphs.

[0100] As one possible implementation, the system can filter out context-specific semantic rules associated with the context information from a preset set of semantic rules based on the keywords in the context information.

[0101] In one example, the system can filter out context-specific semantic rules related to "air transport," "packaged," and "the ban on air transport of lithium batteries" from a pre-defined set of semantic rules that includes rules for dangerous goods transport under various modes such as sea, air, and land transport. For example, it can filter out rules regarding the declaration process for dangerous goods transported by air, packaging requirements, and restrictions on the transport of lithium batteries.

[0102] S203. Based on context-specific semantic rules, evaluate the semantic rules that the terms in the transaction record to be processed can satisfy and the semantic attributes they are assigned to, so as to determine the set of valid semantic attributes of the terms.

[0103] As one possible implementation, the system can combine the semantic rules that the terms in the transaction record to be processed can satisfy and the semantic attributes they are assigned, and use this combination as the effective set of semantic attributes of the terms.

[0104] In one example, the system evaluates the semantic rules and assigned semantic attributes that the term "Dangerous Goods" in the transaction record to be processed can satisfy. For example, it evaluates whether the term meets the air transport dangerous goods classification rules (such as UN number, category 9), whether it conforms to packaging specifications, and whether it involves lithium batteries and is prohibited from embargo. Through evaluation, the system determines the set of valid semantic attributes for the term. For example, if "Dangerous Goods" specifically refers to non-lithium battery dangerous goods that meet air transport standards, its valid semantic attributes may include "air transport dangerous goods category" and "UN number"; if it refers to lithium batteries, its valid semantic attributes may be empty.

[0105] S204. Combining the effective set of semantic attributes with the preset business priority rules, determine the second processing semantics of the transaction record to be processed.

[0106] As one possible implementation, the system can select the semantic attribute with the highest business priority from the set of valid semantic attributes to determine the second processing semantic of the transaction record to be processed.

[0107] In one example, if both "airborne dangerous goods category" and "customs declaration requirements" exist in the set of valid semantic attributes, and the current business priority rule sets "customs declaration requirements" to have higher priority in the air transport scenario, then "customs declaration requirements" will be treated as the second semantic attribute.

[0108] S205. Based on the second processing semantics, determine whether the transaction record to be processed has passed the semantic consistency check, and obtain the check result.

[0109] As one possible implementation, the system can determine that a transaction record to be processed passes the semantic consistency check if it conforms to the second processing semantics. If the transaction record to be processed does not conform to the second processing semantics, it can determine that it fails the semantic consistency check and obtain the check result.

[0110] In one example, if the second processing semantic is "customs declaration requirements," the system checks whether the pending transaction record contains all the necessary customs declaration information. If the information is complete and conforms to the specifications, the verification passes; otherwise, the verification fails. This approach ensures that, in real-time business scenarios, the semantic verification results of the terms are accurate and meet the current operational requirements.

[0111] Through the aforementioned technical solution, this application can perform semantic consistency verification on multilingual terms in cross-border logistics by combining real-time operational context information. This allows the verification process to dynamically adapt to complex and ever-changing business environments such as different transportation modes, cargo physical states, and temporary regulatory adjustments. This solves the problem that relying solely on the inherent semantic rules of terms is insufficient to accurately address the ambiguity and applicability changes of terms in actual business scenarios. Therefore, this application can improve the accuracy and reliability of semantic verification, ensuring the correct understanding and application of terms in specific business contexts, thereby enhancing the quality of cross-border logistics data processing and the efficiency of business processes.

[0112] In some embodiments, in order to determine whether implicit confirmation of the initial intent of the instruction has been obtained based on subsequent actions, this application further includes the following steps:

[0113] S301. Continuously collect the interaction signal data between the operator and the input device within a preset time window after the visual feedback is triggered.

[0114] The interactive signal data includes key pressure, contact duration, and micro-motion information of the input device.

[0115] S302. Perform feature analysis on the interactive signal data and extract feature parameters that reflect the operational stability of the operators.

[0116] The characteristic parameters include signal waveform smoothness, instantaneous pressure fluctuation amplitude, and continuous contact interval time.

[0117] Among these, signal waveform smoothness refers to the stability of the curve of the interactive signal changing over time, reflecting the continuity and stability of the operator's operation, and its purpose is to identify jitter or irregularity in the operation process. Instantaneous pressure fluctuation amplitude refers to the drastic change in key pressure or contact pressure within a short period of time, reflecting the operator's force control and stability, and its purpose is to distinguish between smoothness, hesitation, or abruptness in operation. Continuous contact interval time refers to the time interval between different contact events in continuous operation, reflecting the rhythm and continuity of the operator's operation, and its purpose is to judge the smoothness or pauses in operation.

[0118] In one example, for key pressure data, a moving average filter or wavelet transform can be applied to calculate the smoothness of the signal waveform; the instantaneous pressure fluctuation amplitude can be obtained by calculating the maximum change of the pressure signal in a very short time; and the interval between consecutive touches can be obtained by recording the timestamps of adjacent touch events and calculating their difference.

[0119] S303. Based on feature parameters and operational intent, set discrimination rules.

[0120] The discrimination rules are used to distinguish the operation type corresponding to the interaction signal data. Operation types include: implicit confirmation, explicit rejection, or unintentional action.

[0121] As one possible implementation, the system can construct a classification model based on decision trees or support vector machines (SVM) as the discrimination rule. During the training phase, this model learns from a large amount of historical interaction data and corresponding true intent labels, thereby establishing a mapping relationship between feature parameters and operation types (implicit confirmation, explicit rejection, or unintentional action).

[0122] For example, if the signal waveform has high smoothness, small instantaneous pressure fluctuation amplitude, and continuous contact interval time is within a stable range, it may be judged as implicit confirmation; if the signal waveform has low smoothness, large instantaneous pressure fluctuation amplitude, and short continuous contact interval time, it may be judged as explicit rejection; if the signal strength is extremely low and the duration is extremely short, it may be judged as unintentional action.

[0123] As another possible implementation, the system can construct a multi-dimensional threshold judgment logic, and based on the multi-dimensional threshold judgment logic, determine the feature range of different operation types that the interactive signal data conforms to, so as to set the discrimination rules.

[0124] It should be noted that the multi-dimensional threshold judgment logic uses signal waveform smoothness, instantaneous pressure fluctuation amplitude, and continuous contact interval time as the judgment basis.

[0125] S304. Based on the execution result of the discrimination rule, determine whether an implicit confirmation of the initial intent of the instruction has been obtained.

[0126] As one possible implementation, the system can determine that implicit confirmation of the initial intent to the instruction has been obtained if the execution result of the discrimination rule is "implicit confirmation." If the execution result of the discrimination rule is explicit rejection or unintentional action, it can determine that implicit confirmation of the initial intent to the instruction has not been obtained.

[0127] Through the above technical solution, this application can continuously collect interaction signal data between operators and input devices, and perform feature analysis on this data to extract feature parameters reflecting operational stability, thereby overcoming the randomness and ambiguity of interaction signals caused by factors such as operator habits and physiological states. Based on these feature parameters and operational intentions, discrimination rules are set to distinguish between operation types such as implicit confirmation, explicit rejection, or unintentional actions, thereby improving the recognition of the operator's true intentions. This enables the system to determine the operator's intentions when faced with fuzzy input signals, thus ensuring instruction judgment and avoiding data inconsistencies or missing data due to misjudgment.

[0128] To further propose discrimination rules based on feature parameters and operational intent, this application also includes:

[0129] S401. Construct multi-dimensional threshold judgment logic.

[0130] To construct multi-dimensional threshold judgment logic, the system can employ a rule-based expert system or a pre-trained classification model. For example, a set of logical rules can be defined: if the signal waveform smoothness is below a certain threshold A, the instantaneous pressure fluctuation amplitude is below a certain threshold B, and the consecutive contact interval is within a certain interval [C1, C2], then it is judged as an "implicit confirmation" operation. These thresholds and intervals can be statistically analyzed and optimized based on a large amount of historical operation data, or trained using machine learning algorithms (such as support vector machines or decision trees) to learn the boundaries of different operation types in the multi-dimensional feature space. Specifically, the system will predefine feature intervals for the three operation types: "implicit confirmation," "explicit rejection," and "unintentional action." For example, the feature interval for "implicit confirmation" might be defined as: signal waveform smoothness between [0.05, 0.2], instantaneous pressure fluctuation amplitude between [0.1N, 0.5N], and consecutive contact interval between [100ms, 300ms]. After feature extraction, the collected interactive signal data is compared with three parameters—signal waveform smoothness, instantaneous pressure fluctuation amplitude, and continuous contact interval—using predefined feature ranges. If all parameters fall within the "implicit confirmation" feature range, the system determines that the interactive signal data conforms to the "implicit confirmation" operation type. Based on this judgment, a discrimination rule is set. For example, if the judgment result is "implicit confirmation," the system outputs a confirmation signal, indicating that the operator has implicitly confirmed the initial intention of the instruction. If the judgment result is "explicit rejection," a rejection signal is output. If the judgment result is "unintentional action," the system may ignore the operation or request further explicit input. In this way, the discrimination rule can accurately identify the operator's true operational intention based on the comprehensive analysis of multi-dimensional features, thereby improving the reliability of implicit confirmation.

[0131] S402. Based on the multi-dimensional threshold judgment logic, determine the feature range of different operation types that the interactive signal data conforms to, so as to set the discrimination rules.

[0132] In one example, feature intervals are defined for three operation types: "implicit confirmation," "explicit rejection," and "unintentional action." For instance, the feature interval for "implicit confirmation" might be defined as: signal waveform smoothness between [0.05, 0.2], instantaneous pressure fluctuation amplitude between [0.1N, 0.5N], and continuous contact interval between [100ms, 300ms]. After feature extraction, the corresponding signal waveform smoothness, instantaneous pressure fluctuation amplitude, and continuous contact interval are compared with these predefined feature intervals.

[0133] If all parameters fall within the characteristic range of "implicit confirmation," the system will determine that the interaction signal data conforms to the "implicit confirmation" operation type. Based on this judgment result, the discrimination rules can be set. For example, if the judgment result is "implicit confirmation," the system will output a confirmation signal, indicating that the operator has implicitly confirmed the initial intention of the instruction. If the judgment result is "explicit rejection," a rejection signal will be output. If the judgment result is "unintentional action," the system may ignore the operation or request further explicit input.

[0134] In this way, the discrimination rules can accurately identify the true operational intentions of operators based on the comprehensive analysis of multi-dimensional features, thereby improving the reliability of implicit confirmation.

[0135] In some embodiments, the system can obtain the determination result of the operation intention and the manual correction feedback information; based on the determination result of the operation intention and the manual correction feedback information, dynamically adjust the threshold range of signal waveform smoothness, instantaneous pressure fluctuation amplitude and continuous contact interval time, and set the discrimination rules based on the adjusted threshold range.

[0136] For example, the system can calculate the deviation between the judgment result and the manual correction feedback information based on the judgment result and the manual correction feedback information. Based on the deviation, the system can fine-tune the threshold range of signal waveform smoothness, instantaneous pressure fluctuation amplitude and continuous contact interval time with a preset adjustment step size and direction, so as to iteratively optimize the threshold range of signal waveform smoothness, instantaneous pressure fluctuation amplitude and continuous contact interval time.

[0137] In some preferred embodiments, when the system determines interactive signal data as an "unintentional action" based on multi-dimensional threshold judgment logic, and the operator implicitly corrects it to "implicit confirmation" through subsequent explicit operation (e.g., re-entering the same command and receiving a correct response from the system), the system records the deviation between this judgment result and the manual correction feedback. For example, an error score can be calculated; if the system determines it as type A, but the manual correction is type B, a deviation value is assigned according to a preset type conversion matrix. Based on this deviation, the system can fine-tune the threshold ranges for signal waveform smoothness, instantaneous pressure fluctuation amplitude, and continuous contact interval time with a preset adjustment step size, such as 0.5% or 1 unit of the threshold range each time, and according to the direction of the deviation (e.g., adjusting towards a more lenient direction if the system judgment is too conservative; adjusting towards a more stringent direction if it is too aggressive), based on the direction of the deviation. For example, if the system tends to misjudge valid confirmations as unintentional actions, the lower limit of signal waveform smoothness or the upper limit of instantaneous pressure fluctuation amplitude corresponding to "implicit confirmation" can be appropriately relaxed. This fine-tuning process can be performed periodically, for example, once after processing every 100 data points with correction feedback, or triggered when the cumulative deviation exceeds a preset threshold, thereby achieving iterative optimization of the threshold range. In this way, the system can continuously learn and adapt to the operator's operating habits. For example, for operators who are accustomed to light touches or heavy presses, their threshold range can be dynamically adjusted to more accurately identify their intentions, thus ensuring the accuracy and adaptability of the discrimination rules.

[0138] like Figure 3 As shown in the figure, this embodiment of the invention also provides a cross-border logistics multilingual terminology conversion system. The system includes:

[0139] The instruction capture and temporary storage module is used to perform preliminary processing on the captured instructions input from the front end, and encapsulate the processed instructions into a transaction record to be processed and temporarily store it in the transaction buffer; the preliminary processing is used to remove physical input anomalies in the instructions;

[0140] The asynchronous transaction verification module is used to asynchronously read the transaction records to be processed in the transaction buffer, perform semantic consistency verification on the transaction records to be processed, and obtain the verification result.

[0141] The database atomic update module is used to update the verified transaction record to the multilingual terminology database in an atomic manner based on the verification result.

[0142] The exception handling module is used to trigger the exception handling mechanism if the verification of the transaction record to be processed fails or the update of the multilingual terminology database fails, based on the verification results. The exception handling mechanism includes recording the exception transaction, generating the exception alarm, and maintaining the original data state of the multilingual terminology database.

[0143] This invention also provides a terminal device. The terminal device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a cross-border logistics multilingual terminology conversion program. When the processor executes the computer program, it implements the steps in the various cross-border logistics multilingual terminology conversion method embodiments described above. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the various system embodiments described above.

[0144] For example, a computer program can be divided into one or more modules / units, one or more of which are stored in memory and executed by a processor to complete the present invention. One or more modules / units can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in a terminal device.

[0145] Terminal devices can be computing devices such as desktop computers, laptops, PDAs, and smart tablets. Terminal devices may include, but are not limited to, processors and memory. Those skilled in the art will understand that the above-described components are merely examples of terminal devices and do not constitute a limitation on the terminal device. The device may include more or fewer components than described above, or a combination of certain components, or different components. For example, a terminal device may also include input / output devices, network access devices, buses, etc.

[0146] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device through various interfaces and lines.

[0147] Memory can be used to store computer programs and / or modules. The processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory. Memory can mainly include a program storage area and a data storage area. The program storage area can 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 can store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). In addition, memory can include high-speed random access memory, and can also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, SmartMedia Card (SMC), Secure Digital (SD) card, Flash Card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0148] If the modules / units integrated into the terminal device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or system capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0149] It should be noted that the system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the system embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0150] The above specific embodiments further illustrate the purpose, technical solution, and beneficial effects of the present invention in detail. It should be understood that the above are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for converting multilingual terms in cross-border logistics, characterized in that, include: When a command input from the front end is captured, the command is initially processed, and the processed command is encapsulated as a transaction record to be processed and temporarily stored in the transaction buffer. The preliminary processing is used to remove physical input anomalies in the instructions; Asynchronously read the pending transaction records in the transaction buffer, and perform semantic consistency verification on the pending transaction records to obtain the verification result; Based on the verification result, if the transaction record to be processed passes the verification, the verified transaction record to be processed is updated to the multilingual terminology database in an atomic operation manner. According to the verification result, if the verification of the transaction record to be processed fails, or the update of the multilingual terminology database fails, an exception handling mechanism is triggered. The exception handling mechanism includes recording the exception transaction, generating an exception alarm, and maintaining the original data state of the multilingual terminology database. The preliminary processing of the instruction, and the encapsulation of the processed instruction into a transaction record to be processed, and its temporary storage in the transaction buffer, includes: Capture the raw electrical signals of the instructions input from the front end; Waveform feature analysis is performed on the original electrical signal to identify whether there is an ambiguous signal pattern in the instructions input from the front end; If the ambiguous signal pattern is identified, the operational intent and corresponding confidence level of the instruction input from the front end are preliminarily inferred based on the current operation context. When the confidence level is lower than a preset threshold, a non-intrusive intent confirmation interaction is triggered, which includes providing visual feedback and monitoring the subsequent behavior of the operator. Based on the subsequent actions, determine whether an implicit confirmation of the initial intent of the instruction has been obtained; Based on the initial intent and the result of implicit confirmation, the instruction input from the front end is determined; The determined front-end input instructions are encapsulated into transaction records to be processed and temporarily stored in the transaction buffer. The step of determining whether implicit confirmation of the initial intent of the instruction has been obtained based on the subsequent actions includes: The interaction signal data between the operator and the input device is continuously collected within a preset time window after the visual feedback is triggered. The interaction signal data includes key pressure, contact duration, and micro-motion information of the input device. The interactive signal data is subjected to feature analysis to extract feature parameters that reflect the operational stability of the operator. The feature parameters include signal waveform smoothness, instantaneous pressure fluctuation amplitude, and continuous contact interval time. Based on the feature parameters and the operation intention, a discrimination rule is set; the discrimination rule is used to distinguish the operation type corresponding to the interaction signal data; the operation type includes: implicit confirmation, explicit rejection, or unintentional action; Based on the execution result of the discrimination rule, it is determined whether an implicit confirmation of the initial intent of the instruction has been obtained.

2. The method for cross-border logistics multilingual terminology conversion according to claim 1, characterized in that, The step of performing semantic consistency verification on the transaction records to be processed, and obtaining the verification result, includes: Based on the multiple semantic rules that the terms in the transaction record to be processed can simultaneously satisfy, the semantic consistency of the transaction record to be processed is checked, and the check result is obtained. Alternatively, based on the contextual information of real-time operations related to the terms in the transaction record to be processed, a semantic consistency check is performed on the transaction record to be processed to obtain the check result.

3. The method for cross-border logistics multilingual terminology conversion according to claim 2, characterized in that, The semantic consistency check of the transaction record to be processed is performed based on multiple semantic rules that the terms in the transaction record to be processed can simultaneously satisfy, and the check result is obtained, including: Identify multiple semantic rules that a term in the transaction record to be processed can simultaneously satisfy, and obtain multiple semantic attributes assigned to the term by the multiple semantic rules; The multiple semantic attributes are encapsulated into a semantic attribute file and attached to the transaction record to be processed; Based on the preset business priority rules, a semantic attribute is selected from the semantic attribute file as the first processing semantic of the transaction record to be processed; Based on the first processing semantics, determine whether the transaction record to be processed has passed the semantic consistency check, and obtain the check result.

4. The method for cross-border logistics multilingual terminology conversion according to claim 2, characterized in that, The context information of real-time operations related to the terms in the transaction record to be processed is used to process the transaction record to be processed. Semantic consistency verification yields the following results: Obtain context information about real-time operations related to the terms in the transaction record to be processed; Based on the context information, context-specific semantic rules associated with the context information are selected from a preset set of semantic rules; Based on the context-specific semantic rules, the semantic rules that the terms in the transaction record to be processed can satisfy and the semantic attributes they are assigned are evaluated to determine the set of valid semantic attributes of the terms. By combining the set of effective semantic attributes with the preset business priority rules, the second processing semantics of the transaction record to be processed are determined; Based on the second processing semantics, determine whether the transaction record to be processed has passed the semantic consistency check, and obtain the check result.

5. The method for cross-border logistics multilingual terminology conversion according to claim 1, characterized in that, The step of setting discrimination rules based on the feature parameters and the operation intention includes: A multi-dimensional threshold judgment logic is constructed; the multi-dimensional threshold judgment logic uses the smoothness of the signal waveform, the amplitude of the instantaneous pressure fluctuation, and the interval time between consecutive touch points as the judgment criteria; Based on the multi-dimensional threshold judgment logic, the feature ranges of different operation types that the interactive signal data conforms to are determined in order to set the discrimination rules.

6. The method for cross-border logistics multilingual terminology conversion according to claim 5, characterized in that, The step of determining the feature range of different operation types that the interaction signal data conforms to based on the multi-dimensional threshold judgment logic includes: Obtain the results of the determination of the operation intention and the feedback information of manual correction; Based on the determination result of the operation intention and the feedback information of manual correction, the threshold range of the signal waveform smoothness, the instantaneous pressure fluctuation amplitude and the continuous contact interval time are dynamically adjusted. The discrimination rule is set based on the adjusted threshold range.

7. The method for cross-border logistics multilingual terminology conversion according to claim 6, characterized in that, The threshold range for dynamically adjusting the signal waveform smoothness, the instantaneous pressure fluctuation amplitude, and the continuous contact interval includes: Based on the judgment result and the manual correction feedback information, calculate the deviation between the judgment result and the manual correction feedback information; Based on the deviation, the threshold ranges of the signal waveform smoothness, the instantaneous pressure fluctuation amplitude, and the continuous contact interval time are fine-tuned with a preset adjustment step size and direction, so as to iteratively optimize the threshold ranges of the signal waveform smoothness, the instantaneous pressure fluctuation amplitude, and the continuous contact interval time.

8. A cross-border logistics multilingual terminology conversion system, used to implement a method for converting cross-border logistics multilingual terms, characterized in that, The system includes: The instruction capture and temporary storage module is used to perform preliminary processing on the captured instructions input from the front end, and encapsulate the processed instructions into a transaction record to be processed and temporarily store it in the transaction buffer; the preliminary processing is used to remove physical input anomalies in the instructions; It is also used to capture the raw electrical signals of instructions input from the front end; Waveform feature analysis is performed on the original electrical signal to identify whether there is an ambiguous signal pattern in the instructions input from the front end; If the ambiguous signal pattern is identified, the operational intent and corresponding confidence level of the instruction input from the front end are preliminarily inferred based on the current operation context. When the confidence level is lower than a preset threshold, a non-intrusive intent confirmation interaction is triggered, which includes providing visual feedback and monitoring the subsequent behavior of the operator. Based on the subsequent actions, determine whether an implicit confirmation of the initial intent of the instruction has been obtained; Based on the initial intent and the result of implicit confirmation, the instruction input from the front end is determined; The determined front-end input instructions are encapsulated into transaction records to be processed and temporarily stored in the transaction buffer. It is also used to continuously collect the interaction signal data between the operator and the input device within a preset time window after the visual feedback is triggered. The interaction signal data includes key pressure, contact duration and micro-motion information of the input device. The interactive signal data is subjected to feature analysis to extract feature parameters that reflect the operational stability of the operator. The feature parameters include signal waveform smoothness, instantaneous pressure fluctuation amplitude, and continuous contact interval time. Based on the feature parameters and the operation intention, a discrimination rule is set; the discrimination rule is used to distinguish the operation type corresponding to the interaction signal data; the operation type includes: implicit confirmation, explicit rejection, or unintentional action; Based on the execution result of the discrimination rule, determine whether an implicit confirmation of the initial intent of the instruction has been obtained; The asynchronous transaction verification module is used to asynchronously read the pending transaction records in the transaction buffer, perform semantic consistency verification on the pending transaction records, and obtain the verification result. The database atomic update module is used to update the verified transaction record to the multilingual terminology database in an atomic manner if the verification of the transaction record to be processed passes the verification. An exception handling module is used to trigger an exception handling mechanism if the verification of the pending transaction record fails or the update of the multilingual terminology database fails, based on the verification result. The exception handling mechanism includes recording the exception transaction, generating an exception alarm, and maintaining the original data state of the multilingual terminology database.

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