Cross-border e-commerce business language training resource management system and method

Abstract

The present application belongs to the field of information and communication technology specially applicable to administration, business and management, and particularly relates to the field of cross-border e-commerce business operation compliance and resource management, and relates to a cross-border e-commerce business language training resource management system and method, which comprises a corpus structured modeling module, a real training situation awareness module, a rule drift compensation module and a resource adaptive scheduling module, the modeling module constructs a resource structured tensor containing expression compliance degree, emotional comfort degree and clause accuracy degree dimensions; the situation awareness module analyzes log streams, calculates actual scores and target dimension orthogonal difference to generate a skill gap vector; the compensation module analyzes external new rule abolished vocabulary, determines a decay factor based on a negative penalty value; the scheduling module calculates a matching score based on vector inner product ratio and multiplicative adjustment term, and when the score is lower than a threshold value, slice rendering is blocked, the present application eliminates the risk of resource failure caused by platform rule evolution, and realizes real-time interception and accurate scheduling of risk slices.

Description

Technical Field

[0001] This invention belongs to the field of information and communication technology, which is specifically applicable to administrative, commercial, and management purposes, and particularly relates to a cross-border e-commerce business language training resource management system and method. Background Technology

[0002] Language training resource management systems are commonly used in cross-border e-commerce scenarios to annotate and distribute business training materials such as customer service scripts, marketing terms, and appeal texts. Existing systems typically use static resource catalogs or general language level classifications, dividing training materials into different resources according to fixed knowledge points. In actual business, the transaction rules and compliance requirements of different e-commerce platforms for different target regions are constantly changing. Static resource catalogs usually assume that the resource content and business rules remain unchanged for a long time, making it difficult to identify platform policy changes in a timely manner and to judge the impact of rule adjustments on the validity of historical training materials. When business personnel make mistakes in using compliant vocabulary or quoting business clauses in simulated training, traditional systems can generally only perform rough searches based on static tags, making it difficult to perform searches based on transaction rules. Accurately matching specific issues in the mutual log with corresponding training content can easily lead to a mismatch between the distributed training resources and the actual business scenario. For example, Chinese invention patent application CN118135851A discloses an intelligent language education system and method. However, this solution exposes obvious fundamental limitations when facing complex dynamic interaction scenarios of learners: its path design and feedback adjustment rely heavily on a preset state machine model and static rule engine. When the learner's cognitive load changes, it cannot make semantic compensation for changes in the learning state, resulting in the learning path adjustment lagging behind actual needs. Its memory optimization and resource recommendation are mostly based on offline analysis of historical statistical data, lacking precise coupling with current multidimensional constraints, which can easily lead to a mismatch between resource recommendations and actual needs.

[0003] To address the issues of untimely resource matching and the disconnect between training content and platform rules, existing technologies typically employ manual, periodic updates of resource tags or real-time analysis using large-parameter language models. Manual tag updates have long response cycles, making it difficult to keep up with the rapid pace of rule updates by e-commerce platforms. Large-parameter language models, on the other hand, require significant computing resources, and their processing time is unstable, making it difficult to consistently provide millisecond-level responses when a large number of trainees are simultaneously conducting training. Therefore, existing technologies are based on relatively fixed business rules, while the compliance requirements of cross-border e-commerce platforms are constantly changing, and training resource scheduling has high real-time requirements, resulting in a clear incompatibility between the two.

[0004] Therefore, how to promptly identify the shortcomings of business personnel's expression and changes in external platform rules while controlling computational overhead and ensuring stable response time, accurately allocate cross-border e-commerce business language training resources, and prevent the distribution of invalid or non-compliant training content has become a problem that existing technologies need to solve. Summary of the Invention

[0005] To address the problems in the background art, the technical solution of the present invention is as follows: A cross-border e-commerce business language training resource management system, comprising:

[0006] The corpus structuring module is used to acquire cross-border business corpus materials, cut them to generate resource slices, and construct a resource structure tensor that includes dimensions of expression compliance, emotional comfort, and accuracy of business terms.

[0007] The training situation awareness module is used to analyze the trainees' business language interaction log stream, calculate the orthogonal difference between the trainees' actual scores and target dimensions in the dimensions of expression compliance, emotional comfort, and accuracy of business terms, and generate a skills gap vector.

[0008] The rule drift compensation module is used to communicate with the compliance database of an external e-commerce platform, parse policy update features, generate a rule drift compensation coefficient based on negative penalty values ​​when parsing new rules abolished, and calculate the resource update time interval based on the compliance verification timestamp and the current scheduling time to determine the resource decay factor.

[0009] The resource adaptive scheduling module is communicatively connected to the corpus structured modeling module, the training situation awareness module, and the rule drift compensation module. It is used to calculate the ratio of the dot product of the resource structured tensor and the skill gap vector to the normalized modulus product, and to add a multiplicative adjustment term composed of the rule drift compensation coefficient and the resource attenuation factor to calculate the resource matching score. When the resource matching score is lower than the preset scheduling threshold, an interception command is issued to block the rendering output of resource slices with resource matching scores lower than the preset scheduling threshold.

[0010] Preferably, the training situation awareness module analyzes the dialogue interaction logs of a preset number of consecutive sessions, extracts the actual score components of trainees in the dimensions of expression compliance, emotional comfort, and accuracy of business terms, and performs orthogonal difference calculations on each actual score component with the standard expression compliance component, standard emotional comfort component, and standard business terms accuracy component required by the target scenario to construct a skill gap vector with three orthogonal components.

[0011] Preferably, the rule drift compensation module includes a policy incremental patch parsing unit; the policy incremental patch parsing unit is used to obtain the change log of the compliance database of the external e-commerce platform and parse the added and deleted items of the policy clauses; when a new clause of the external e-commerce platform is parsed and the new clause is positively correlated with the discourse vocabulary of the resource slice, the rule drift compensation coefficient is determined based on the positive gain value.

[0012] Preferably, the resource adaptive scheduling module calculates the resource matching score. Follow the formula below: ,in, This is the resource structure tensor of the resource slice to be scheduled. This represents the current skill gap vector for trainees. This is the transpose of the skill gap vector. The modulus of the resource structuring tensor. Let the magnitude of the skill gap vector be denoted as . For resource update intervals, As a resource depletion factor, For regular drift compensation coefficient, For the natural constant An exponential function with base 0.

[0013] Preferably, the resource adaptive scheduling module includes a threshold dynamic comparison unit and a priority sorting unit; the threshold dynamic comparison unit is used to compare the calculated resource matching score with a preset scheduling threshold; the priority sorting unit is used to sort multiple resource slices in gradient descending order according to the resource matching score from high to low when the resource matching score is greater than or equal to the preset scheduling threshold, and generate an adaptive scheduling instruction containing sorting position and rendering priority.

[0014] Preferably, the resource adaptive scheduling module also includes a risk blocking unit and a resource storage pool; the risk blocking unit connects the client's front-end interface and the resource storage pool, and is used to send an interception command to the resource storage pool when the resource matching score is less than the preset scheduling threshold, thereby blocking the rendering output of resource slices with resource matching scores lower than the preset scheduling threshold to the client.

[0015] Preferably, the corpus structured modeling module includes a discourse feature decoupling unit and a timeliness marking unit; the discourse feature decoupling unit is used to parse the input business language material into quantitative feature weights of three orthogonal dimensions: expression compliance dimension, emotional comfort dimension, and business clause accuracy dimension; the timeliness marking unit is used to inject the platform identifier of the corresponding external e-commerce platform and the compliance verification timestamp into the metadata of the resource slice as a benchmark for calculating the resource structured tensor and the resource update time interval.

[0016] Preferably, the rule drift compensation module includes an attenuation factor correction unit; the attenuation factor correction unit is used to monitor the storage duration of resource slices and, in conjunction with the policy update frequency of external e-commerce platforms, dynamically and stepwise correct the resource attenuation factor; when the policy update frequency of external e-commerce platforms is higher than a preset frequency threshold, the resource attenuation factor is increased.

[0017] Preferably, the resource adaptive scheduling module also includes a concurrent latency hedging unit; the concurrent latency hedging unit is used to monitor the data transmission latency of the resource adaptive scheduling module under high concurrency client request conditions; when the data transmission latency exceeds a preset latency threshold, the solution of the resource matching score is reduced to a linear weighted sum based on preset weights, controlling the data transmission latency to be no higher than 50ms. The system also includes a system data bus and a security isolation buffer; the system data bus is communicatively connected to the corpus structured modeling module, the training situation awareness module, the rule drift compensation module, and the resource adaptive scheduling module, respectively, to transmit business language interaction log streams and resource slice data; the security isolation buffer connects the resource adaptive scheduling module and the external client, is used to temporarily store resource slices to be rendered, and clears the temporarily stored data when an interception command is received.

[0018] A cross-border e-commerce business language training resource management system and a cross-border e-commerce business language training resource management method include the following steps:

[0019] Step S1: Obtain cross-border business corpus materials through the corpus structuring modeling module, cut and generate resource slices, and construct a resource structuring tensor that includes the dimensions of expression compliance, emotional comfort, and accuracy of business terms.

[0020] Step S2: The training situation awareness module analyzes the trainees' business language interaction log stream, calculates the orthogonal difference between the trainees' actual scores and target dimensions in the dimensions of expression compliance, emotional comfort, and accuracy of business terms, and generates a skills gap vector.

[0021] Step S3: The rule drift compensation module communicates with the compliance database of the external e-commerce platform, analyzes the policy update characteristics, generates a rule drift compensation coefficient based on the negative penalty value when it analyzes the expression of repeal of new regulations, and calculates the resource update time interval based on the compliance verification timestamp and the current scheduling time to determine the resource decay factor.

[0022] Step S4: The resource matching score is calculated by solving the ratio of the dot product of the resource structure tensor and the skill gap vector and the normalized modulus product through the resource adaptive scheduling module, and then superimposed with a multiplicative adjustment term consisting of the rule drift compensation coefficient and the resource attenuation factor. When the resource matching score is lower than the preset scheduling threshold, an interception command is issued to block the rendering output of resource slices with resource matching scores lower than the preset scheduling threshold.

[0023] Compared with existing technologies, the cross-border e-commerce business language training resource management system and method of the present invention have the following advantages:

[0024] 1. In the management of cross-border e-commerce business language training resources, the corpus structure modeling module describes resource slices from three dimensions: expression compliance, emotional soothingness, and accuracy of business terms. The training situation awareness module generates a skill gap vector based on the business language interaction log flow, which enables training resources to accurately correspond to the specific expression problems currently existing by trainees. The resource adaptive scheduling module calculates the resource matching score based on this, which can reduce the matching lag caused by static directories and fixed tags, improve the scheduling accuracy of cross-border e-commerce business language training resources, and enable resource distribution to keep up with changes in trainees' abilities.

[0025] 2. The rule drift compensation module corrects the resource matching score based on the policy updates of external e-commerce platforms and the storage time of resources. It can promptly reduce the scheduling priority of resource slices that are outdated or contain obsolete expressions. For resource slices that do not comply with the current platform rules, the system can block their rendering output, thereby reducing the situation where historical training materials continue to be used due to changes in platform policies and reducing the risk of non-compliant expressions being sent to the client.

[0026] 3. The resource matching process adopts a deterministic calculation relationship between resource structure tensors, skill gap vectors, rule drift compensation coefficients and resource decay factors, without relying on large parameter language models for real-time reasoning. This can reduce the computational overhead required for resource matching, reduce response time fluctuations caused by complex model reasoning, enable resource scheduling in high-concurrency training scenarios to maintain a faster and more stable response, and improve the reliability of client resource rendering and control command issuance. Attached Figure Description

[0027] Figure 1 This is a flowchart of the training resource adaptive matching and interception method of the present invention;

[0028] Figure 2 This is the adaptive scheduling control diagram for training resources in this invention. Detailed Implementation

[0029] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0030] A cross-border e-commerce business language training resource management system, comprising:

[0031] The corpus structuring module is used to acquire cross-border business corpus materials, cut them to generate resource slices, and construct a resource structure tensor that includes dimensions of expression compliance, emotional comfort, and accuracy of business terms.

[0032] The training situation awareness module is used to analyze the trainees' business language interaction log stream, calculate the orthogonal difference between the trainees' actual scores and target dimensions in the dimensions of expression compliance, emotional comfort, and accuracy of business terms, and generate a skills gap vector.

[0033] The rule drift compensation module is used to communicate with the compliance database of an external e-commerce platform, parse policy update features, generate a rule drift compensation coefficient based on negative penalty values ​​when parsing new rules abolished, and calculate the resource update time interval based on the compliance verification timestamp and the current scheduling time to determine the resource decay factor.

[0034] The resource adaptive scheduling module is communicatively connected to the corpus structured modeling module, the training situation awareness module, and the rule drift compensation module. It is used to calculate the ratio of the dot product of the resource structured tensor and the skill gap vector to the normalized modulus product, and to add a multiplicative adjustment term composed of the rule drift compensation coefficient and the resource attenuation factor to calculate the resource matching score. When the resource matching score is lower than the preset scheduling threshold, an interception command is issued to block the rendering output of resource slices with resource matching scores lower than the preset scheduling threshold.

[0035] Preferably, the training situation awareness module analyzes the dialogue interaction logs of a preset number of consecutive sessions, extracts the actual score components of trainees in the dimensions of expression compliance, emotional comfort, and accuracy of business terms, and performs orthogonal difference calculations on each actual score component with the standard expression compliance component, standard emotional comfort component, and standard business terms accuracy component required by the target scenario to construct a skill gap vector with three orthogonal components.

[0036] Preferably, the rule drift compensation module includes a policy incremental patch parsing unit; the policy incremental patch parsing unit is used to obtain the change log of the compliance database of the external e-commerce platform and parse the added and deleted items of the policy clauses; when a new clause is parsed from the external e-commerce platform and the new clause is positively correlated with the discourse vocabulary of the resource slice, the rule drift compensation coefficient is based on the positive gain value; when an expression word is parsed that is explicitly abolished by the external e-commerce platform, the rule drift compensation coefficient is based on the negative penalty value.

[0037] Preferably, the resource adaptive scheduling module calculates the resource matching score. Follow the formula below: ,in, This is the resource structure tensor of the resource slice to be scheduled. This represents the current skill gap vector for trainees. This is the transpose of the skill gap vector. The modulus of the resource structuring tensor. Let the magnitude of the skill gap vector be denoted as . For resource update intervals, As a resource depletion factor, For regular drift compensation coefficient, For the natural constant An exponential function with base 0.

[0038] Preferably, the resource adaptive scheduling module includes a threshold dynamic comparison unit and a priority sorting unit; the threshold dynamic comparison unit is used to compare the calculated resource matching score with a preset scheduling threshold; the priority sorting unit is used to sort multiple resource slices in gradient descending order according to the resource matching score from high to low when the resource matching score is greater than or equal to the preset scheduling threshold, and generate an adaptive scheduling instruction containing sorting position and rendering priority.

[0039] Preferably, the resource adaptive scheduling module also includes a risk blocking unit and a resource storage pool; the risk blocking unit connects the client's front-end interface and the resource storage pool, and is used to send an interception command to the resource storage pool when the resource matching score is less than the preset scheduling threshold, thereby blocking the rendering output of resource slices with resource matching scores lower than the preset scheduling threshold to the client.

[0040] Preferably, the corpus structured modeling module includes a discourse feature decoupling unit and a timeliness marking unit; the discourse feature decoupling unit is used to parse the input business language material into quantitative feature weights of three orthogonal dimensions: expression compliance dimension, emotional comfort dimension, and business clause accuracy dimension; the timeliness marking unit is used to inject the platform identifier of the corresponding external e-commerce platform and the compliance verification timestamp into the metadata of the resource slice as a benchmark for calculating the resource structured tensor and the resource update time interval.

[0041] Preferably, the rule drift compensation module includes an attenuation factor correction unit; the attenuation factor correction unit is used to monitor the storage duration of resource slices and, in conjunction with the policy update frequency of external e-commerce platforms, dynamically and stepwise correct the resource attenuation factor; when the policy update frequency of external e-commerce platforms is higher than a preset frequency threshold, the resource attenuation factor is increased.

[0042] Preferably, the resource adaptive scheduling module also includes a concurrent latency hedging unit; the concurrent latency hedging unit is used to monitor the data transmission latency of the resource adaptive scheduling module under high concurrency client request conditions; when the data transmission latency exceeds a preset latency threshold, the solution of the resource matching score is reduced to a linear weighted sum based on preset weights, controlling the data transmission latency to be no higher than 50ms. The system also includes a system data bus and a security isolation buffer; the system data bus is communicatively connected to the corpus structured modeling module, the training situation awareness module, the rule drift compensation module, and the resource adaptive scheduling module, respectively, to transmit business language interaction log streams and resource slice data; the security isolation buffer connects the resource adaptive scheduling module and the external client, is used to temporarily store resource slices to be rendered, and clears the temporarily stored data when an interception command is received.

[0043] A cross-border e-commerce business language training resource management system and a cross-border e-commerce business language training resource management method include the following steps:

[0044] Step S1: Obtain cross-border business corpus materials through the corpus structuring modeling module, cut and generate resource slices, and construct a resource structuring tensor that includes the dimensions of expression compliance, emotional comfort, and accuracy of business terms.

[0045] Step S2: The training situation awareness module analyzes the trainees' business language interaction log stream, calculates the orthogonal difference between the trainees' actual scores and target dimensions in the dimensions of expression compliance, emotional comfort, and accuracy of business terms, and generates a skills gap vector.

[0046] Step S3: The rule drift compensation module communicates with the compliance database of the external e-commerce platform, analyzes the policy update characteristics, generates a rule drift compensation coefficient based on the negative penalty value when it analyzes the expression of repeal of new regulations, and calculates the resource update time interval based on the compliance verification timestamp and the current scheduling time to determine the resource decay factor.

[0047] Step S4: The resource matching score is calculated by solving the ratio of the dot product of the resource structure tensor and the skill gap vector and the normalized modulus product through the resource adaptive scheduling module, and then superimposed with a multiplicative adjustment term consisting of the rule drift compensation coefficient and the resource attenuation factor. When the resource matching score is lower than the preset scheduling threshold, an interception command is issued to block the rendering output of resource slices with resource matching scores lower than the preset scheduling threshold.

[0048] Example 1: The current cross-border e-commerce business language training resource management system adopts a distributed deployment approach. It receives the original interaction log data stream authorized by the user. The data desensitization module is deployed on a central computing platform with an operating frequency of no less than 2.5GHz and a memory capacity of no less than 64GB. It performs de-identification processing on the original interaction log data stream, removes sensitive personal identification information, generates a de-identified business language interaction behavior dataset, and stores the dataset in a local cache database that is initially set to be configured.

[0049] To address implicit negative event records arising from regional differences in business terminology during cross-border online transactions, the system injects and parses these records through a distributed data processing unit. During processing, the underlying communication network bandwidth remains constant. Implicit negative event records include high-frequency corrections of business terms by cross-border online customer service representatives when interacting with users in specific regions, conversation interruptions due to misunderstandings of word meanings during order communication, and rapid exits caused by stilted translations when browsing multilingual product detail pages. The distributed data processing unit adjusts the resource allocation weights of each language node based on these records to reduce the allocation latency of localized business language training resources under concurrent multilingual requests.

[0050] The training situational awareness module analyzes the business language interaction log stream in real time and slides to extract the text sequence from the trainee's five most recent consecutive dialogue interactions. This module calculates the proportion of compliant words in the trainee's response text to obtain an actual expression compliance score; it analyzes the density of sentence-ending interjections and polite expressions to obtain an actual emotional comfort score; and it compares the expression of clause entities in the response text with the standard knowledge base of the transaction scenario to obtain an actual business clause accuracy score. Subsequently, the actual scores of the three dimensions are subtracted from the standard expression compliance component, standard emotional comfort component, and standard business clause accuracy component set for the current target scenario to obtain the score difference in three orthogonal directions. Based on this, a skill gap vector containing the three orthogonal components is constructed to represent the trainee's current language skill gap.

[0051] The implicit negative event recording injection module inputs the parsed implicit negative event frequency and context deviation degree into the dynamic resource allocation control module. The dynamic resource allocation control module calculates the resource allocation adjustment index based on the implicit negative event frequency and context deviation degree. The calculation formula is as follows: ,in, An index for adjusting the allocation of dimensionless resources; It is a dimensionless implicit negative event frequency factor, which is obtained by dividing the number of occurrences of implicit negative events in a specific business language within a fixed counting window by the total number of interactions within that window. This is a dimensionless contextual deviation coefficient, determined based on the textual distance between current business terminology and the standard corpus. When the resource allocation adjustment index... When the value exceeds the preset threshold of 0.75, the dynamic resource allocation control module controls the implicit negative event recording injection module to adjust the sampling frequency from 10Hz to 50Hz. When a specific language node is detected to still have remaining allocation capacity, the system increases the resource allocation weight of that node. When a specific language node is detected to be saturated, the system decreases the resource allocation weight of that node. When the context deviation coefficient is missing due to the jitter of the underlying network, the system adopts the preset global equal weight benchmark allocation mode.

[0052] The system allocates the total global resource adjustment amount at the top level according to the product matrix ratio of the current available bandwidth and remaining storage space of each resource node, in order to adjust the collaborative state of each language resource node. The rule drift compensation module obtains the change log of the compliance database of the external e-commerce platform through the policy incremental patch parsing unit and parses the addition and deletion of policy clauses. When the change log indicates that the external e-commerce platform has explicitly abolished a certain expression, and the resource slice contains the expression, the policy incremental patch parsing unit applies a fixed penalty value of -0.5 based on the rule drift compensation coefficient. When the external e-commerce platform adds a new clause, and the compliance constraint words in the new clause are the same as or semantically corresponding to the discourse words in the resource slice, the two are determined to be positively correlated, and a gain value of +0.2 is applied based on the rule drift compensation coefficient.

[0053] The resource decay factor is determined based on the resource update time interval between the compliance verification timestamp and the current scheduling time. When the number of days the resource has not been updated is within 30 days, the resource decay factor is set to 0.05. When the policy update frequency of the external e-commerce platform is more than twice a week, the decay factor correction unit will adjust the resource decay factor to 0.10, so that the resource matching score decreases as the resource update time interval increases. The system also sets a 30-day sliding time window to eliminate interaction records, and uses a historical negative event weight decay factor of 0.95 to reduce the weight of historical negative events within the sliding time window. At the end of each natural day, the language training resource baseline is recalibrated. Cross-border business communication data generated by multicultural differences and context switching are used to generate replacement data for high-frequency error context resources, and the resource adaptive scheduling module adjusts the scheduling order of the corresponding resource slices.

[0054] Under high-concurrency client requests, the physical channel transmission latency between the client and server is determined by the current link status. The concurrency latency mitigation unit monitors the data transmission latency between receiving request data and outputting rendering instructions from the resource adaptive scheduling module. When the data transmission latency exceeds the preset latency threshold, the concurrency latency mitigation unit reduces the solution method for resource matching scores from the inner product and multiplicative adjustment term in the higher-order tensor space to a linear weighted sum based on preset weights. This reduces the background computation time for a single scheduling to less than 2 milliseconds, decreases processor task queuing and data buffering wait, and controls the data transmission latency to less than 50 milliseconds. The system's cross-border e-commerce business language training resource matching latency is no greater than 250ms, and the accuracy rate of multilingual cross-border business terminology usage is no less than 98%.

[0055] The system data bus is connected to the corpus structured modeling module, the training situation awareness module, the rule drift compensation module, and the resource adaptive scheduling module, respectively. It is used to transmit business language interaction log streams and resource slice data. After standardization, the text complexity and domain matching index are used as feature components to fill the resource structured tensor. The resource allocation adjustment index is used to update the rule drift compensation coefficient and the sampling frequency of implicit negative event records. The parameters output by each preprocessing module are transmitted to the resource adaptive scheduling module via the system data bus.

[0056] The resource adaptive scheduling module shrinks the resource structured tensor into a structured feature vector isomorphic to the skill gap vector, calculates the dot product of the structured feature vector and the skill gap vector, and calculates the ratio of the dot product of the dot product to the normalized modulus product. Then, a multiplicative adjustment term consisting of the rule drift compensation coefficient and the resource attenuation factor is superimposed to obtain the resource matching score. When the resource matching score is lower than the preset scheduling threshold, the risk blocking unit sends an interception command to the resource storage pool to block the rendering output of the corresponding resource slice to the client.

[0057] The security isolation buffer connects the resource adaptive scheduling module and the external client. It is used to temporarily store resource slices to be rendered. When the security isolation buffer receives an interception command, it clears the temporary data of the corresponding resource slice. Resource slices that do not receive an interception command are sent to the client according to the scheduling result generated by the resource adaptive scheduling module. Based on this, the system converts the negative feedback of behavior into dynamic scheduling commands for each resource node, thereby realizing the scheduling and adjustment of the business language training resource data stream.

[0058] Example 2: The current resource management server is configured with an 8-core CPU and 16GB of RAM, and runs a cross-border e-commerce business language training resource management system. After the corpus structure modeling module acquires cross-border business language materials, it generates resource slices by cutting them according to a fixed number of words. Then, the discourse feature decoupling unit calculates the multidimensional semantic weights of each resource slice. In terms of expression compliance, the system matches a preset compliance lexicon with a list of sensitive words for violations, and calculates an expression compliance score between 0 and 1 based on the frequency of compliant words and the penalty items for violating words. In terms of emotional comfort, it extracts a soothing effect based on an emotional lexicon. The system calculates an emotional comfort score between 0 and 1 by weighting the probabilities of modal words, polite expressions, and positive emotion words. On the dimension of business terms accuracy, the system compares resource slices with the standard business terms knowledge graph and calculates a business terms accuracy score between 0 and 1 based on the coverage rate of core business terms concept nodes and the entity association accuracy rate. The corpus structuring modeling module maps the scores of the three orthogonal dimensions to the corresponding numerical components, constructs a resource structure tensor, and stores the platform identifier and compliance verification timestamp injected by the timeliness marking unit together with the resource structure tensor in the resource storage pool.

[0059] This embodiment employs a cross-border business text grading model to quantify the text difficulty of resource slices. This model includes a feature input layer, a weighted parsing layer, and a grading mapping layer. The feature input layer converts cross-border e-commerce transaction communication text into text containing a density of core business terms. and average sentence length The feature vector, where the subscript is... Indicates core business terms, subscript Sentence structure; Business core word density The unit is characters per hundred characters, and the average sentence length is... The unit is characters / sentence, and the weighted parsing layer calculates the actual text complexity according to Equation 1. subscript Indicates rating: Here, the constant 0.50 is the word density weight, the constant 0.20 is the sentence length weight, and the level mapping layer receives the actual text complexity. The corresponding resource slices are then sent to the dynamic distribution queue.

[0060] The learner ability assessment module collected historical interaction data from five test groups, including the accuracy rate of answering questions. and response time subscript Indicates accuracy, subscript Indicates response; accuracy rate of answers The response time is recorded as a pure number representing the corresponding percentage. The unit is milliseconds (ms). The learner ability assessment module calculates the learner's ability value according to Equation 2. subscript Representing numerical values: The constant 100.0 is the calibration coefficient.

[0061] During resource scheduling, the adaptive resource scheduling module first performs tensor shrinking on the resource structured tensor along the time axis and slice depth dimensions, projecting it into a three-dimensional structured feature vector with the same dimension as the skill gap vector. Then, it performs dot product accumulation on the corresponding components of the skill gap vector to obtain a vector inner product. Subsequently, it calculates the ratio of this vector inner product to the normalized modulus product, and adds a multiplicative adjustment term consisting of a rule drift compensation coefficient and a resource attenuation factor to obtain a resource matching score. Resource slices whose resource matching scores reach a preset scheduling threshold enter the dynamic distribution queue. This embodiment uses actual text complexity. Learner ability score and the resource adaptation error between the two The scheduling results are quantitatively reviewed, including the index. The error is represented by the resource adaptation error, which is calculated according to Equation 3: In the first test, the density of business core words in resource slices The average sentence length is 4.20. The actual text complexity is calculated using Equation 1, which gives a value of 15.0. The correct answer rate for sample group 1 was 5.10. The response time is 82.0. The time was 1640ms, and the learner's ability value was calculated using Equation 2. The resource slice was set to 5.00. After the resource slice was distributed to sample group 1, the resource adaptation error was calculated using Equation 3. The error is 0.10; when using the traditional static allocation method to process the same resource slices and sample groups, the traditional adaptation error is... 1.50, subscript This refers to the traditional method.

[0062] In the second test, the density of core business terms The average sentence length is 6.80. The calculated actual text complexity is 22.5. The correct answer rate for sample group 2 was 7.90. The response time is 76.5. The learner's ability value was calculated over a period of 1020ms. The resource adaptation error is 7.50 after the resource slices in this group are distributed to the corresponding sample group 2. The traditional adaptation error obtained by the traditional static allocation method is 0.40. It is 2.30.

[0063] In the third test, the density of core business terms The average sentence length is 3.10. The calculated actual text complexity is 12.5. The accuracy rate was 4.05; the accuracy rate of answering questions in sample group 3 was... The response time is 91.2. The learner's ability value was calculated over a period of 2280ms. The resource adaptation error is 4.00 after the resource slices in this group are distributed to the corresponding sample group 3. The traditional adaptation error obtained by the traditional static allocation method is 0.05. It is 1.20.

[0064] In the fourth test, the density of core business terms The average sentence length is 8.40. The calculated actual text complexity is 28.0. The correct answer rate for sample group 4 was 9.80. The response time is 65.8. The learner's ability score was calculated over a period of 700ms. The resource adaptation error is 9.40 after the resource slices in this group are distributed to the corresponding sample group 4. The traditional adaptation error obtained by the traditional static allocation method is 0.40. It is 3.10.

[0065] In the fifth test, the density of core business terms The average sentence length is 5.50. The calculated actual text complexity is 18.5. The correct answer rate for sample group 5 was 6.45. The response time was 88.2. The learner's ability value was calculated within 1470ms. The resource adaptation error is 6.00 after the resource slices in this group are distributed to sample group 5. The traditional adaptation error obtained by the traditional static allocation method is 0.45. It is 1.80.

[0066] Resource adaptation error in 5 test combinations The minimum value appears in group 3, and the maximum value appears in group 5; traditional adaptation error The minimum value appears in group 3, and the maximum value appears in group 4. The resource adaptation error of each test combination is... All are lower than the corresponding traditional adaptation error .

[0067] Example 3: The current business language training resource management system includes a corpus structured modeling module, a resource adaptive scheduling module, and a resource storage pool. The resource storage pool contains a training resource database. The corpus structured modeling module obtains business text data streams from cross-border e-commerce platforms through a business text parsing unit, statistically analyzes the distribution of high-frequency professional vocabulary, and matches it with pre-stored industry weight coefficients to obtain a vocabulary complexity index. Matching indicators with the business sector , where subscript Indicate complexity, subscript Indicates the degree of matching.

[0068] The business text parsing unit will use lexical complexity indicators Matching indicators with the business sector The input resource adaptive scheduling module calculates the resource adaptation score according to the following formula. : ,in, Indicates resource compatibility score, subscript Indicates fit; This represents the complexity weighting coefficient. The domain matching weight coefficients are represented by the following constraints: Resource compatibility score Used for preliminary screening of candidate resources, not as the final resource matching score for resource slices, the resource adaptive scheduling module will... With selection threshold Comparison, where subscript Indicates a threshold limit, when Greater than or equal to When the first control condition is met, the resource adaptive scheduling module extracts matching indicators for the business domain from the training resource database of the resource storage pool. The corresponding language-specific training resource slices are added to the candidate set to be scheduled; when Less than When the second control condition is met, the resource adaptive scheduling module will transfer the current business text data stream to the standby low-level buffer queue and will not extract the high-level business language training resource slice.

[0069] For resource slices that enter the candidate set to be scheduled, the lexical complexity index Matching indicators with the business sector After standardization, the corresponding quantitative feature weights in the resource structure tensor are corrected. The resource adaptive scheduling module calculates the ratio of the dot product of the resource structure tensor and the current trainee's skill gap vector to the product of the normalized modulus, and adds a multiplicative adjustment term consisting of the rule drift compensation coefficient and the resource attenuation factor to obtain the resource matching score. When the resource matching score is not lower than the preset scheduling threshold, the resource slice enters the rendering output queue and is sent to the client; when the resource matching score is lower than the preset scheduling threshold, the system issues an interception command to block the rendering output of the resource slice.

[0070] In the first set of business text data streams, the lexical complexity index The score is 0.82, which is the matching index for the business sector. The complexity weighting coefficient is 0.69. The domain matching weight coefficient is 0.60. The threshold value is 0.40. The value is 0.75. Substituting the above value into the calculation relationship of resource adaptation score, we get... The value is 0.768. Since 0.768 is greater than 0.75, the first control condition is met. The resource adaptive scheduling module extracts the business negotiation case template resource slices of the corresponding language from the training resource database, adds them to the candidate set to be scheduled, and the resource slices are entered into the rendering output queue after being calculated by resource matching score and compared with the preset scheduling threshold, and then sent to the client.

[0071] In the second set of business text data streams, the lexical complexity index The score is 0.54, which is the matching index for the business sector. The value is 0.71, using the same complexity weighting coefficient. Domain matching weight coefficient Calculate and obtain the resource adaptation score. The value is 0.608. Since 0.608 is less than the selection threshold of 0.75, the second control condition is met. The resource adaptive scheduling module transfers the second group of business text data streams to the standby low-level buffer queue and does not extract high-level business language training resource slices from the training resource database.

[0072] Example 4: This example combines Figures 1 to 2 This document explains the resource management system and methods for cross-border e-commerce business language training, such as... Figure 1As shown, step S1 acquires cross-border business language materials, cuts them to generate resource slices, and constructs a resource structured tensor containing dimensions of expression compliance, emotional comfort, and accuracy of business terms; step S2 parses the business language interaction log stream of trainees, calculates the orthogonal difference between the trainees' actual scores on relevant dimensions and the target dimension, and generates a skill gap vector; step S3 connects to the compliance database of an external e-commerce platform to parse policy update features, generates rule drift compensation coefficients for abolished expressions based on negative penalty values, and calculates the resource update time interval to determine the resource decay factor; step S4 solves the ratio of the inner product of the resource structured tensor and the skill gap vector to the product of the normalized modulus, superimposes the multiplicative adjustment term composed of the compensation coefficient and the decay factor to calculate the resource matching score, and issues an interception command when the score is lower than the threshold to block the slice rendering output.

[0073] like Figure 2 As shown, after storing the de-identified dataset, the system is in an initial state of pending configuration. After acquiring cross-border business corpus materials, the system performs corpus structure modeling. On the one hand, it generates a skill gap vector through training situational awareness by calculating the orthogonal difference between the actual score and the target dimension, and transmits it to the resource adaptive scheduling. On the other hand, it generates compensation coefficients and determines attenuation factors through rule drift compensation by parsing policy update features, and transmits them to the resource adaptive scheduling. After calculating the resource matching score, the resource adaptive scheduling temporarily stores the resource slices to be rendered. When the calculated resource matching score is lower than the preset scheduling threshold, it blocks the rendering output of the resource slices and clears the temporarily stored data after receiving the interception command. When the calculated resource matching score is greater than or equal to the preset scheduling threshold, it executes the rendering output to the client.

[0074] Example 5: Before deploying the system to the target e-commerce environment, the central processing unit (CPU) reads a sample set containing 1000 business dialogue texts from the configuration memory and calls the data preprocessing module to count the word frequency of each business dialogue text, constructing an initial business text feature matrix. Subsequently, the CPU multiplies the initial business text feature matrix with the benchmark industry weight vector to calculate the initial calibration coefficient vector used to adjust the resource distribution weights. The calculation formula is as follows: ,in, This represents the initial calibration coefficient vector, which is a dimensionless array of feature weights. The initial business text feature matrix is ​​a real matrix composed of word frequency probabilities. The benchmark industry weight vector is a dimensionless column vector. When the high-frequency words in the initial business text feature matrix are concentrated in a specific category, the corresponding components in the initial calibration coefficient vector monotonically increase. The central processing unit writes the calculated initial calibration coefficient vector into the resource allocation control table in memory as the baseline parameters of the system under the previous working conditions.

[0075] The data preprocessing module receives an input stream containing 50 real-time business consultation texts. The central processing unit reads the initial calibration coefficient vector from the resource allocation control table in memory, performs weighted matching on the input stream based on the vector, and allocates specific language training resources accordingly. The allocation response time for specific language training resources is less than or equal to 125.6ms. During a continuous 48-hour test cycle, the data overflow rate in memory is less than or equal to 0.05%. The central processing unit writes the updated training resource allocation status data to the log recording area of ​​external storage.

[0076] Example 6: Before deploying the cross-border e-commerce business language training resource management system to a specific working environment, the pre-calibration unit collects historical form input datasets and extracts the baseline feature distribution matrix of the form fill box attributes from them. Based on this, an input benchmark reference table that meets the business classification requirements is constructed. The form monitoring unit traverses the document object model structure and obtains the element attribute vectors of each fill box in the current language training resource registration form in real time. , element attribute vector With reference feature distribution matrix By comparison, the statistical deviation is obtained. .

[0077] when and At that time, the parameter correction unit initiates the parameter correction logic, based on the statistical deviation. Adjust the matching weight coefficient of the form fill box The system then corrected the input association matching rules for the fill-in box, and the data verification unit extracted the ID card data of the business language trainers according to the cross-validation rules of data entry completeness and logical relevance. and address data ID card data Administrative division codes and address data in The system compares the regional codes in the data entry data. If a logical conflict is found between the two codes, the data verification unit outputs a blocking signal and feedback prompts to restrict the submission of conflicting data, thereby ensuring the accuracy of data entry and the security of system data management.

[0078] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit of this application and the scope of protection of this invention, and all of these forms are within the protection scope of this application.

Claims

1. A cross-border e-commerce business language training resource management system, characterized in that, include: The corpus structuring module is used to acquire cross-border business corpus materials, cut them to generate resource slices, and construct a resource structure tensor that includes dimensions of expression compliance, emotional comfort, and accuracy of business terms. The training situation awareness module is used to analyze the trainees' business language interaction log stream, calculate the orthogonal difference between the trainees' actual scores and target dimensions in the dimensions of expression compliance, emotional comfort, and accuracy of business terms, and generate a skills gap vector. The rule drift compensation module is used to communicate with the compliance database of an external e-commerce platform, parse policy update features, generate a rule drift compensation coefficient based on negative penalty values ​​when parsing new rules abolished, and calculate the resource update time interval based on the compliance verification timestamp and the current scheduling time to determine the resource decay factor. The resource adaptive scheduling module is connected to the corpus structured modeling module, the training situation awareness module, and the rule drift compensation module. It is used to solve the ratio of the dot product of the resource structured tensor and the skill gap vector to the normalized modulus product, and to add a multiplicative adjustment term composed of the rule drift compensation coefficient and the resource attenuation factor to calculate the resource matching score. When the resource matching score is lower than the preset scheduling threshold, an interception command is issued to block the rendering output of resource slices with resource matching scores lower than the preset scheduling threshold.

2. The cross-border e-commerce business language training resource management system according to claim 1, characterized in that, The training situation awareness module analyzes the dialogue interaction logs of a preset number of consecutive sessions, extracts the actual score components of trainees in the dimensions of expression compliance, emotional comfort, and accuracy of business terms, and performs orthogonal difference calculations between each actual score component and the standard expression compliance component, standard emotional comfort component, and standard business terms accuracy component required by the target scenario to construct a skill gap vector with three orthogonal components.

3. The cross-border e-commerce business language training resource management system according to claim 1, characterized in that, The rule drift compensation module includes a policy incremental patch parsing unit. This unit is used to obtain change logs from the compliance database of external e-commerce platforms and parse the additions and deletions of policy clauses. When a new clause is parsed from an external e-commerce platform and this new clause is positively correlated with the discourse vocabulary of the resource slice, the rule drift compensation coefficient is based on a positive gain value. When an expression that has been explicitly abolished by an external e-commerce platform is parsed, the rule drift compensation coefficient is based on a negative penalty value.

4. The cross-border e-commerce business language training resource management system according to claim 1, characterized in that, The resource adaptive scheduling module calculates the resource matching score. Follow the formula below: ,in, This is the resource structure tensor of the resource slice to be scheduled. This represents the current skill gap vector for trainees. This is the transpose of the skill gap vector. The modulus of the resource structuring tensor. Let the magnitude of the skill gap vector be denoted as . For resource update intervals, As a resource depletion factor, For regular drift compensation coefficient, For the natural constant An exponential function with base 0.

5. The cross-border e-commerce business language training resource management system according to claim 4, characterized in that, The resource adaptive scheduling module includes a threshold dynamic comparison unit and a priority sorting unit. The threshold dynamic comparison unit is used to compare the calculated resource matching score with the preset scheduling threshold. The priority sorting unit is used to sort multiple resource slices in gradient descending order according to the resource matching score from high to low when the resource matching score is greater than or equal to the preset scheduling threshold, and generate an adaptive scheduling instruction that includes the sorting position and rendering priority.

6. The cross-border e-commerce business language training resource management system according to claim 5, characterized in that, The resource adaptive scheduling module also includes a risk blocking unit and a resource storage pool. The risk blocking unit connects the client's front-end interface with the resource storage pool. When the resource matching score is less than the preset scheduling threshold, it sends an interception command to the resource storage pool to block the rendering output of resource slices with resource matching scores lower than the preset scheduling threshold to the client.

7. The cross-border e-commerce business language training resource management system according to claim 1, characterized in that, The corpus structured modeling module includes a discourse feature decoupling unit and a timeliness marking unit; the discourse feature decoupling unit is used to parse the input business language material into quantitative feature weights of three orthogonal dimensions: compliance dimension, emotional comfort dimension, and accuracy dimension of business terms. The time stamp unit is used to inject the platform identifier of the corresponding external e-commerce platform and the compliance verification timestamp into the metadata of the resource slice, as a benchmark for calculating the resource structured tensor and the resource update time interval.

8. The cross-border e-commerce business language training resource management system according to claim 1, characterized in that, The regular drift compensation module includes an attenuation factor correction unit; The attenuation factor correction unit is used to monitor the storage duration of resource slices and, in conjunction with the policy update frequency of external e-commerce platforms, dynamically and stepwise corrects the resource attenuation factor; when the policy update frequency of external e-commerce platforms exceeds the preset frequency threshold, the resource attenuation factor is increased.

9. The cross-border e-commerce business language training resource management system according to claim 1, characterized in that, The resource adaptive scheduling module also includes a concurrent latency hedging unit. This unit monitors the data transmission latency of the resource adaptive scheduling module under high-concurrency client request conditions. When the data transmission latency exceeds a preset latency threshold, the solution for the resource matching score is reduced to a linear weighted sum based on preset weights, controlling the data transmission latency to be no higher than 50ms. The system also includes a system data bus and a security isolation buffer. The system data bus is communicatively connected to the corpus structured modeling module, the training situation awareness module, the rule drift compensation module, and the resource adaptive scheduling module, transmitting business language interaction log streams and resource slice data. The secure isolation buffer connects the resource adaptive scheduling module and the external client. It is used to temporarily store resource slices to be rendered and clears the temporary data when an interception command is received.

10. A cross-border e-commerce business language training resource management system and a cross-border e-commerce business language training resource management method, characterized in that, Includes the following steps: Step S1: Obtain cross-border business corpus materials through the corpus structuring modeling module, cut and generate resource slices, and construct a resource structuring tensor that includes the dimensions of expression compliance, emotional comfort, and accuracy of business terms. Step S2: The training situation awareness module analyzes the trainees' business language interaction log stream, calculates the orthogonal difference between the trainees' actual scores and target dimensions in the dimensions of expression compliance, emotional comfort, and accuracy of business terms, and generates a skills gap vector. Step S3: The rule drift compensation module communicates with the compliance database of the external e-commerce platform, analyzes the policy update characteristics, generates a rule drift compensation coefficient based on the negative penalty value when it analyzes the expression of repeal of new regulations, and calculates the resource update time interval based on the compliance verification timestamp and the current scheduling time to determine the resource decay factor. Step S4: The ratio of the dot product of the resource structure tensor and the skill gap vector and the normalized modulus product is solved by the resource adaptive scheduling module. A multiplicative adjustment term consisting of the rule drift compensation coefficient and the resource attenuation factor is added to calculate the resource matching score. When the resource matching score is lower than the preset scheduling threshold, an interception command is issued to block the rendering output of resource slices with resource matching scores lower than the preset scheduling threshold.

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