Cross-border e-commerce operation simulation system and method based on multi-agent simulation

Abstract

The invention discloses a cross-border e-commerce operation simulation system and method based on multi-agent simulation, and relates to the technical field of data processing, and the method comprises the steps: executing cross-dimension segmentation according to collected transaction content data, interaction content data and culture semantic data, obtaining cross-culture semantic data, applying multi-directional mapping disturbance to the cross-culture semantic data, and obtaining a cross-border e-commerce operation simulation result. Generating behavior driving data; executing cross-structure splitting according to the collected policy semantic data to obtain policy constraint data, and inputting the policy constraint data and behavior-driven data into bidirectional entanglement calculation to generate preference evolution data; implementing culture domain difference on the basis of the culture semantic data and the behavior driving data to obtain difference behavior data, and performing cross stitching calculation on the difference behavior data and the preference evolution data; according to the method, on the premise of not depending on a priori user portrait template and keeping high modeling efficiency, behavior dynamics and preference evolution of consumers under the multi-culture and multi-policy background are simulated more finely.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and specifically to a cross-border e-commerce operation simulation system and method based on multi-agent simulation. Background Technology

[0002] In the process of cross-border e-commerce operation modeling in multicultural environments, there is often a high reliance on user profile templates. The effectiveness of user behavior simulation is limited by the accuracy and applicability of the templates. When cultural semantics become blurred or context drifts, the modeling results often fail to accurately reflect the behavioral changes caused by cultural differences. At the same time, the consumer behavior decision-making process often exhibits implicit evolutionary trends. Traditional modeling methods are unable to effectively respond to such dynamic changes through static labels or predefined features, resulting in insufficient generalization ability of behavior simulation and difficulty in supporting the reconstruction of consumption patterns and the evolution of intelligent agents in heterogeneous cultural backgrounds.

[0003] In summary, the urgent technical challenge to be solved is: how to achieve behavioral simulation and dynamic evolution of preferences of consumer intelligent agents in a multicultural and multi-policy context without relying on prior user profile templates and without sacrificing modeling and computational efficiency. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a cross-border e-commerce operation simulation system and method based on multi-agent simulation.

[0005] A cross-border e-commerce operation simulation system based on multi-agent simulation, the system comprising: Segmentation Generation Module S11: Based on the collected transaction content data, interaction content data, and cultural semantic data, it performs cross-dimensional segmentation to obtain cross-cultural semantic data, applies multi-directional mapping perturbation to the cross-cultural semantic data, and generates behavior-driven data; Entanglement decomposition module S12: Performs cross-structure decomposition based on the collected policy semantic data to obtain policy constraint data, and inputs the policy constraint data and behavior-driven data into bidirectional entanglement calculation to generate preference evolution data; Differential stitching module S13: Based on cultural semantic data and behavior-driven data, cultural domain differential is performed to obtain differential behavior data, and the differential behavior data and preference evolution data are cross-stitched to obtain stitched preference data; Coupled simulation module S14: By using behavior-driven data, preference evolution data, and stitching preference data, it performs behavior coupling to obtain multi-agent update data, and generates cross-border e-commerce operation simulation data based on the multi-agent update data.

[0006] Furthermore, the steps for generating behavior-driven data include: S111, perform text segmentation based on paragraph structure markers in transaction content data and interaction content data, and generate semantic fragment index data; S112, Based on the language tag index in the cultural semantic data, perform classification and aggregation on the semantic fragment index data to generate language dimension fragment data; S113, based on language dimension fragment data, performs semantic density measurement and classification boundary calculation to generate cross-cultural semantic data; S114 applies a semantic perturbation function mapping to cross-cultural semantic data to generate behavior-driven data.

[0007] Furthermore, the steps for generating cross-cultural semantic data include: S113.1, construct language fragment group data by using fragment sets of the same language category in the language dimension fragment data; S113.2, Perform semantic aggregation vector encoding based on the language segment group data to generate preliminary language aggregation vector data; S113.3, based on the preliminary language aggregation vector data, calculate the semantic deviation value and semantic overlap value to generate cross-cultural semantic data.

[0008] Furthermore, the steps for generating preference evolution data include: S121, Perform structural partitioning based on the structural unit labels in the policy semantic data to generate structural block distribution data; S122, extract constraint content segments from high-frequency word fragments in the structure block distribution data to generate policy constraint data; S123, input policy constraint data and behavior-driven data into the bidirectional entanglement calculation structure to generate bidirectional effect result data; S124, based on the semantic response sites and perturbation-sensitive parameters in the bidirectional interaction result data, performs offset value evaluation to generate preference evolution data.

[0009] Furthermore, the steps for generating policy constraint data include: S122.1 Extract the content of segments marked as restricted expressions from the structure block distribution data and construct restricted segment data; S122.2 Perform language segment alignment processing on the restricted segment data, filter out repetitive expressions and semantic redundancy, and generate concise restricted content data; S122.3 Input the simplified constraint content data into the constraint information filtering function to generate policy constraint data.

[0010] Furthermore, the steps for generating stitching preference data include: S131, construct a semantic deviation comparison matrix based on cultural semantic data and behavior-driven data to generate cultural domain difference data; S132, Perform difference combination processing on the cultural domain difference data and preference evolution data to generate difference behavior data; S133, Based on the segment-level change range in the differential behavior data, match the offset segment in the preference evolution data to generate stitching preference data.

[0011] Furthermore, the logic for generating differential behavioral data includes: S132.1 Extract high-offset fragment data from the cultural domain differential data and construct a set of behavioral difference fragments; S132.2, Perform difference fitting analysis on the set of behavioral difference segments and the corresponding segments in the preference evolution data to generate segment-level behavioral difference data; S132.3 performs semantic condensation encoding processing based on fragment-level behavioral difference data to generate differential behavioral data.

[0012] Furthermore, the steps for generating cross-border e-commerce operation simulation data include: S141, construct a behavior preference combination mapping diagram based on behavior-driven data and preference evolution data, and generate combined behavior expression data; S142, Perform agent state mapping based on stitching preference data and combined behavior expression data to generate multi-agent update data; S143, based on the simulation of multi-agent data update execution behavior sequence, generates cross-border e-commerce operation simulation data.

[0013] Furthermore, the logic for generating multi-agent update data is as follows: S142.1 Extract behavioral consistency segments from the stitching preference data and combined behavioral expression data to construct joint input data; S142.2, input the joint input data into the behavior coupling function, perform agent preference splitting processing, and generate agent bias difference data; S142.3, based on the agent bias difference data, execute update instruction allocation and multi-agent structure reorganization to generate multi-agent update data.

[0014] A cross-border e-commerce operation simulation method based on multi-agent simulation, applied to any of the cross-border e-commerce operation simulation systems based on multi-agent simulation, the method comprising: S21: Based on the collected transaction content data, interaction content data, and cultural semantic data, perform cross-dimensional segmentation to obtain cross-cultural semantic data, apply multi-directional mapping perturbation to the cross-cultural semantic data, and generate behavior-driven data; S22: Perform cross-structure decomposition based on the collected policy semantic data to obtain policy constraint data. Input the policy constraint data and behavior-driven data into bidirectional entanglement calculation to generate preference evolution data. S23: Based on cultural semantic data and behavior-driven data, perform cultural domain differentiation to obtain differential behavior data, and perform cross-stitching calculation on differential behavior data and preference evolution data to obtain stitched preference data; S24: By using behavior-driven data, preference evolution data, and stitching preference data, behavior coupling is performed to obtain multi-agent update data, and cross-border e-commerce operation simulation data is generated based on the multi-agent update data.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention introduces the synergistic effect of transaction content data, interaction content data, and cultural semantic data in the context of cross-border e-commerce business where multiple cultures and policies coexist. Based on the distribution characteristics and perturbation feedback of cross-cultural semantic data in the semantic space, it constructs behavior-driven data and continuously influences the decision-making process of the consumer intelligent agent. This enables the consumer intelligent agent to respond to changes in consumption preferences under different cultural contexts without relying on prior user profile templates. Thus, while maintaining the compactness of the modeling structure, it effectively portrays the evolution trend of consumption behavior against the background of complex cultural differences. Furthermore, this invention dynamically couples behavior-driven data, policy-constrained data, and preference evolution data in a unified evolution space, enabling the stitching of preference data to continuously adjust the generation process of multi-agent update data. Without introducing additional computational burden, it achieves adaptive evolution of consumer agents' preferences in scenarios with multiple policy constraints, allowing cross-border e-commerce operation simulation data to simultaneously reflect the comprehensive impact of cultural differences and policy changes on the group's behavioral structure, thereby improving the adaptability of multi-agent simulation results to actual cross-border e-commerce operation decision-making scenarios. In summary, by introducing a multi-agent behavior coupling mechanism and cross-cultural semantic differential modeling, this invention enables a more refined simulation of consumer behavior dynamics and preference evolution in a multicultural and multi-policy context, without relying on prior user profile templates and while maintaining high modeling efficiency. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0017] Figure 1 This is a block diagram of a cross-border e-commerce operation simulation system based on multi-agent simulation provided in Embodiment 1 of the present invention; Figure 2 This is a flowchart of a cross-border e-commerce operation simulation method based on multi-agent simulation provided in Embodiment 2 of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1

[0020] Please see Figure 1 As shown in the figure, this embodiment discloses a cross-border e-commerce operation simulation system based on multi-agent simulation, the system comprising: Segmentation Generation Module S11: Based on the collected transaction content data, interaction content data, and cultural semantic data, it performs cross-dimensional segmentation to obtain cross-cultural semantic data, applies multi-directional mapping perturbation to the cross-cultural semantic data, and generates behavior-driven data; In one specific embodiment, product descriptions, specifications, and order history information are obtained by calling the application programming interface of the target cross-border e-commerce platform to construct transaction content data; user review texts and customer service online communication records are crawled from the target cross-border e-commerce platform to construct interaction content data; business slang, religiously sensitive words, and regional consumption preference information in specific languages ​​are extracted from a preset regional cultural feature database to construct cultural semantic data; spatial feature stripping and multi-dimensional attribute segmentation are performed on the transaction content data, interaction content data, and cultural semantic data through a segmentation generation module to obtain cross-cultural semantic data; and numerical offset processing based on random probability distribution is applied to the cross-cultural semantic data to generate behavior-driven data.

[0021] It should be noted that the degree of difference in cultural context is positively correlated with the frequency of fluctuations in behavior-driven data.

[0022] Specifically, the steps for generating behavior-driven data include: S111, perform text segmentation based on paragraph structure markers in transaction content data and interaction content data, and generate semantic fragment index data; In one specific embodiment, a text parsing operator identifies preset paragraph structure markers in transaction content data and interaction content data; the paragraph structure markers include line breaks, text segmentation labels, and semantic end identifiers; based on the paragraph structure markers, the continuous text stream is divided into multiple independent text sub-blocks, and a unique numerical sequence identifier is assigned to each text sub-block to generate semantic fragment index data.

[0023] Specifically, the calculation logic for generating semantic fragment index data is as follows: count the index position of paragraph structure markers in the text sequence; divide the text into multiple text sub-blocks according to the index position; associate and encapsulate each text sub-block with its original paragraph position and the generated numerical sequence identifier to generate semantic fragment index data.

[0024] S112, Based on the language tag index in the cultural semantic data, perform classification and aggregation on the semantic fragment index data to generate language dimension fragment data; In one specific embodiment, a predefined language tag index is extracted from the cultural semantic data; each text sub-block in the semantic fragment index data is traversed, and the matching degree between the feature distribution of the text sub-block and the language tag index is calculated; text sub-blocks whose matching degree meets the classification matching threshold requirement are classified into the corresponding language set to generate language dimension fragment data.

[0025] Specifically, the calculation logic for generating language dimension fragment data is as follows: obtain the language feature vector of the α-th text sub-block in the semantic fragment index data; obtain the baseline feature vector of the β-th language tag index in the cultural semantic data; Calculate the dot product of two vectors and the product of their magnitudes; Divide the inner product by the product of the moduli to obtain the cosine similarity; If the cosine similarity is greater than or equal to the classification matching threshold, the text sub-block is assigned to the set of corresponding language tag indices to generate language dimension fragment data; Specifically, the classification matching threshold can be determined experimentally on a labeled cross-cultural corpus. For example, with the goal of achieving a classification accuracy of over 90%, it can be determined in the range of [0.7, 0.95] through grid search, and a preferred experimental result is 0.85.

[0026] It should be noted that in the cross-linguistic semantic similarity evaluation system, 0.85 is the high correlation threshold, which can effectively filter out ambiguous noise in cross-language texts and ensure that only text sub-blocks with high semantic consistency are aggregated into the same language set.

[0027] S113, based on language dimension fragment data, performs semantic density measurement and classification boundary calculation to generate cross-cultural semantic data; In one specific embodiment, spatial distribution density detection is performed on the language dimension fragment data to determine the degree of clustering of different language fragments in the semantic space; the overlapping area between different language sets is calculated to determine the classification boundary; and the language dimension fragment data is subjected to dimensionality reduction and compression processing by combining the degree of clustering and the classification boundary to generate cross-cultural semantic data. Specifically, the steps for generating cross-cultural semantic data include: S113.1, construct language fragment group data by using fragment sets of the same language category in the language dimension fragment data; In one specific embodiment, all subsets of segments labeled as belonging to the same language category are extracted from the language dimension segment data; the subsets of segments belonging to the same language category are structured and encapsulated to generate language segment group data.

[0028] S113.2, Perform semantic aggregation vector encoding based on the language segment group data to generate preliminary language aggregation vector data; In one specific embodiment, a preset encoding operator is used to perform numerical transformation on each text sub-block in the language segment group data; the central feature value of all numerical results in the group is calculated to generate preliminary language aggregation vector data.

[0029] Specifically, the calculation logic for generating preliminary language aggregation vector data is as follows: count the total number of segments contained in the language segment group data, and use the encoding operator to convert each segment into a π-dimensional feature vector; sum the values ​​of all feature vectors in the same dimension and divide by the total number of segments, and determine the vector composed of the average values ​​of each dimension as the preliminary language aggregation vector data; It should be noted that the encoding operator is based on a semantic distribution model pre-trained on a large-scale cross-border e-commerce corpus. By learning the word frequency co-occurrence patterns in product descriptions, user reviews, and policy texts, a high-dimensional semantic coordinate system is constructed. This operator is essentially a feature mapping matrix that can map discrete text symbols into spatially oriented numerical vectors.

[0030] S113.3, based on the preliminary language aggregation vector data, calculate the semantic deviation value and semantic overlap value to generate cross-cultural semantic data.

[0031] In one specific embodiment, spatial distance comparison is performed on the preliminary language aggregation vector data of different languages; the displacement distance between vectors is calculated to obtain semantic deviation value; the similarity ratio between vectors is calculated to obtain semantic overlap value; and the semantic deviation value and semantic overlap value are associated as attribute labels with the preliminary language aggregation vector data to generate cross-cultural semantic data.

[0032] Specifically, the calculation logic for generating semantic deviation values ​​is as follows: obtain preliminary language aggregation vector data of the first language and preliminary language aggregation vector data of the second language; calculate the sum of squared differences between the two preliminary language aggregation vector data in each dimension; perform a square root operation on the sum of squared differences to obtain the Euclidean distance; and determine the Euclidean distance as the semantic deviation value.

[0033] Specifically, the calculation logic for generating semantic overlap values ​​is as follows: calculate the vector inner product of the preliminary language aggregation vector data of the two languages; calculate the product of the modulus of the two preliminary language aggregation vector data; divide the vector inner product by the modulus product to obtain the original similarity coefficient; add 1 to the original similarity coefficient and divide it by 2 to make the result linearly map to the interval [0,1] to generate semantic overlap values.

[0034] S114 applies a semantic perturbation function mapping to cross-cultural semantic data to generate behavior-driven data.

[0035] In a specific embodiment, the mapping logic of the semantic perturbation function is as follows: the preset semantic perturbation logic is invoked to randomize the numerical components in the cross-cultural semantic data; when the semantic deviation value in the cross-cultural semantic data is detected to reach the activation threshold, a normally distributed random gain is applied to the data to generate behavior-driven data. Specifically, the calculation logic for generating behavior-driven data is as follows: extract semantic deviation values ​​from cross-cultural semantic data; determine whether the semantic deviation value is greater than or equal to the activation threshold; if the semantic deviation value is greater than or equal to the activation threshold, obtain a random fluctuation coefficient with an expected value of 0 and a variance of 0.01; multiply the feature values ​​in the cross-cultural semantic data by the sum of the random fluctuation coefficient and the value 1 to obtain the behavior-driven data; if the semantic deviation value is less than the activation threshold, then the cross-cultural semantic data is determined as behavior-driven data. Preferably, the activation threshold can be 0.6. It should be noted that: through sensitivity analysis of cross-cultural semantic feature distribution, when the semantic deviation value reaches 0.6, it indicates that cultural differences have generated substantial behavioral traction. At this point, introducing perturbation can more realistically restore the stress response of culturally sensitive groups.

[0036] Entanglement decomposition module S12: Performs cross-structure decomposition based on the collected policy semantic data to obtain policy constraint data, and inputs the policy constraint data and behavior-driven data into bidirectional entanglement calculation to generate preference evolution data; In one specific embodiment, the raw text is automatically crawled by accessing the official portals of customs administrations of various countries, international trade regulations disclosure platforms, or cross-border e-commerce policy information databases. Specifically, the logic for collecting policy semantic data is as follows: using preset web page crawling instructions, the legal provisions of the target trading country regarding e-commerce access, tax declaration, logistics supervision, and prohibited and restricted categories are obtained in real time, summarized, and cleaned to generate policy semantic data; the policy semantic data is then subjected to hierarchical deconstruction and feature stripping using a cross-structure decomposition algorithm to extract policy constraint data with mandatory binding force; the policy constraint data and the behavior-driven data generated in the aforementioned steps are input into a bidirectional entangled computation structure, and the dynamic coupling feedback quantity between the external control environment and internal behavioral dynamics is calculated by simulating the mutual constraints and feedback mechanism between the two to generate preference evolution data.

[0037] It should be noted that the strength of policy control is positively correlated with the degree of compliance preference of individuals. Specifically, the steps for generating preference evolution data include: S121, Perform structural partitioning based on the structural unit labels in the policy semantic data to generate structural block distribution data; In one specific embodiment, a text structure recognition operator is used to extract pre-defined structural unit labels from policy semantic data. The structural unit labels include the title of the regulation, the chapter number, the clause number, and the item symbol. Based on the structural unit labels, the long policy text is divided into text blocks with independent semantic logic, and the spatial arrangement order of each text block in the original document is recorded to generate structural block distribution data.

[0038] Specifically, the calculation logic for generating structure block distribution data is as follows: traverse the policy semantic data and count the hierarchical depth and frequency of occurrence of structural unit labels; divide the policy text into multiple text blocks in descending order of hierarchical depth; associate and encapsulate the text content of each text block with its hierarchical depth and sequence number to generate structure block distribution data.

[0039] S122, extract constraint content segments from high-frequency word fragments in the structure block distribution data to generate policy constraint data; In one specific embodiment, word segmentation and word frequency statistics are performed on the structure block distribution data to identify word segments that appear more than a preset frequency threshold and define them as constraint content segments. By performing further filtering, alignment and function filtering on the constraint content segments, policy constraint data that can quantify policy influence is finally generated.

[0040] Specifically, the steps for generating policy constraint data include: S122.1 Extract the content of segments marked as restricted expressions from the structure block distribution data and construct restricted segment data; In one specific embodiment, a preset restrictive expression dictionary is retrieved, which contains semantic keywords that represent prohibition, necessity, quota limit, proportion limit, and time cutoff point; the structure block distribution data is matched sentence by sentence with the restrictive expression dictionary to extract sentence fragments containing the above keywords and construct restrictive fragment data.

[0041] Specifically, the logic for constructing restricted fragment data is as follows: split each structure block distribution data into independent semantic rows; determine whether each semantic row contains keywords from the restricted expression dictionary; if it does, mark the semantic row as a restricted expression fragment and store it in a temporary set; summarize all marked fragments to generate restricted fragment data.

[0042] S122.2 Perform language segment alignment processing on the restricted segment data, filter out repetitive expressions and semantic redundancy, and generate concise restricted content data; In one specific embodiment, text similarity comparison is performed on each semantic segment in the restricted fragment data; When the similarity between two semantic segments is greater than or equal to the alignment threshold, they are judged as duplicate expressions, and only one of the segments is retained; By filtering out redundant descriptive words and repetitive rule descriptions, concise constraint content data with highly condensed semantic information is generated.

[0043] Specifically, the calculation logic for generating simplified constraint content data is as follows: Calculate the edit distance between any two semantic segments in the restricted fragment data; Divide the edit distance by the longer character length of the two semantic segments to obtain the normalized difference coefficient; The similarity score is obtained by subtracting the difference coefficient from the numerical value of 1. If the similarity score is greater than or equal to the alignment threshold, then the latter of the pair of fragments is removed from the restricted fragment data; Finally, all remaining fragments are aggregated to generate simplified constraint content data.

[0044] Preferably, the alignment threshold is 0.92; It should be noted that, due to the extremely high rigor of policy and legal texts, subtle differences in characters may represent completely different legal effects. Setting a high threshold of 0.92 ensures that while eliminating semantic redundancy, policy details with independent regulatory significance are rigorously preserved.

[0045] S122.3 Input the simplified constraint content data into the constraint information filtering function to generate policy constraint data.

[0046] In one specific embodiment, a preset constraint information filtering function is invoked to perform a numerical evaluation of the simplified constraint content data; The constraint information filtering function assigns a score to the constraint strength based on the keywords of legal effect level and punishment intensity contained in the fragment, generating quantified policy constraint data.

[0047] Specifically, the calculation logic for generating policy constraint data is as follows: Convert each fragment in the simplified constraint content data into a k-dimensional feature vector; Obtain a preset compliance weight vector, which is pre-set by human experience based on the importance of policies; Calculate the dot product of the feature vector and the compliance weight vector; Define the dot product result as the constraint strength value; Summarize the constraint strength values ​​of all segments to generate policy constraint data.

[0048] S123, input policy constraint data and behavior-driven data into the bidirectional entanglement calculation structure to generate bidirectional effect result data; In one specific embodiment, the control boundary defined by policy constraint data and the behavioral tendency defined by behavior-driven data are mapped to a unified evolution space; the stress response of the agent under policy pressure and the adaptive adjustment of the policy to the behavioral feedback are simulated through a bidirectional entanglement computation structure, and the degree of cross-correlation between the two in various feature dimensions is calculated to generate bidirectional interaction result data.

[0049] Specifically, the computational logic of bidirectional entanglement calculation is as follows: obtain the constraint strength vector of policy constraint data and the behavior tendency vector of behavior driving data; perform matrix multiplication operation on the transpose matrix of the constraint strength vector and the behavior tendency vector; that is, calculate the dot product (also known as the inner product) of the constraint strength vector and the behavior tendency vector, perform trace operation on the result of the matrix multiplication operation to obtain a scalar value; multiply the scalar value with the original constraint strength vector to obtain the bidirectional action result data representing the bidirectional interaction strength.

[0050] S124, based on the semantic response sites and perturbation-sensitive parameters in the bidirectional interaction result data, performs offset value evaluation to generate preference evolution data.

[0051] In one specific embodiment, the coordinate points with the most concentrated feature distribution in the bidirectional interaction result data are identified and defined as semantic response sites; perturbation-sensitive parameters generated by the system simulation environment are obtained to measure the intensity of external random interference; and preference evolution data are generated by evaluating the degree of shift of the semantic response sites relative to the original preference state under the intervention of the perturbation-sensitive parameters.

[0052] Specifically, the calculation logic for generating preference evolution data is as follows: locate the position of the component with the largest value in the bidirectional action result data and determine the coordinate vector of the semantic response site; sum the values ​​of the coordinate vector of the semantic response site in each dimension to obtain the total response intensity; multiply the total response intensity by the perturbation sensitivity parameter to obtain the evolution offset; add the evolution offset to the initial preference vector to obtain the preference evolution data.

[0053] It should be noted that the larger the perturbation sensitivity parameter, the more obvious the deviation of the preference evolution data; by introducing bidirectional entanglement calculation, the user behavior transformation process caused by policy changes in cross-border e-commerce can be simulated more accurately.

[0054] Differential stitching module S13: Based on cultural semantic data and behavior-driven data, cultural domain differential is performed to obtain differential behavior data, and the differential behavior data and preference evolution data are cross-stitched to obtain stitched preference data; In a specific embodiment, the cultural semantic data collected and stored in the regional cultural feature database in the aforementioned steps, as well as the behavior-driven data generated in step S114, are invoked; the vector displacement of the cultural semantic data and the behavior-driven data in the semantic space is calculated through the cultural domain difference module, thereby extracting feature variables reflecting cultural background differences and generating cultural domain difference data; the generated cultural domain difference data and the preference evolution data output in step S124 are subjected to difference operation and logical combination processing to generate difference behavior data; the behavior change features in the difference behavior data and the preference evolution trajectory in the preference evolution data are spatially aligned and stitched using the cross-stitching algorithm, and finally stitched preference data representing the final decision tendency of multi-agents in complex cultural environments is obtained.

[0055] It should be noted that the degree of cultural domain differentiation determines the agent's sensitivity to heterogeneous cultures during the simulation process. The larger the values ​​contained in the cultural domain differentiation data, the stronger the behavioral volatility exhibited by the generated differential behavioral data. This helps the system to reproduce the real-world cultural conflict feedback in cross-border e-commerce.

[0056] Specifically, the steps for generating stitching preference data include: S131, construct a semantic deviation comparison matrix based on cultural semantic data and behavior-driven data to generate cultural domain difference data; In one specific embodiment, the core feature vectors in the cultural semantic data and the driving feature vectors in the behavior-driven data are extracted; the spatial distance between the two in the multidimensional semantic coordinate system is calculated, and all the calculated distance values ​​are arranged in a preset dimensional order to construct a semantic deviation comparison matrix; the deviation amount in each dimension is extracted from the semantic deviation comparison matrix to generate cultural domain difference data.

[0057] Specifically, the calculation logic for generating cultural domain difference data is as follows: obtain the value of the u-th feature dimension in the cultural semantic data; obtain the values ​​of the corresponding u-th feature dimensions in the behavior-driven data; calculate the absolute difference between the two values ​​to obtain the deviation component of a single dimension; traverse all dimensions and repeat the above calculation process to fill all deviation components into the corresponding positions of the matrix to form a semantic deviation comparison matrix; calculate the arithmetic mean of the values ​​in each column of the semantic deviation comparison matrix, and determine the vector composed of the average values ​​as the cultural domain difference data.

[0058] S132, Perform difference combination processing on the cultural domain difference data and preference evolution data to generate difference behavior data; Specifically, the logic for generating differential behavioral data includes: S132.1 Extract high-offset fragment data from the cultural domain differential data and construct a set of behavioral difference fragments; In one specific embodiment, a preset offset evaluation threshold is retrieved; Traverse each feature segment in the cultural domain differential data and determine whether the value of the feature segment is greater than or equal to the offset evaluation threshold. Extract all feature segments that meet the numerical conditions and mark them as high-offset segment data; All extracted high-offset fragment data are aggregated into a unified data container to construct a set of behavioral difference fragments.

[0059] Specifically, the logic for constructing the set of behavioral difference fragments is as follows: Set the offset evaluation threshold; When the value of a segment in the cultural domain difference data is greater than or equal to the offset evaluation threshold, the segment is stored in the behavioral difference segment set. When the value of a fragment in the cultural domain difference data is less than the offset evaluation threshold, it is discarded. Preferably, the offset evaluation threshold can be selected as 0.75; It should be noted that in the cultural domain differential feature space, 0.75 represents the critical point for judging strong offset signals. By using this threshold, the behavioral segments most significantly impacted by heterogeneous cultures can be accurately identified, thereby improving the accuracy of subsequent simulations.

[0060] S132.2, Perform difference fitting analysis on the set of behavioral difference segments and the corresponding segments in the preference evolution data to generate segment-level behavioral difference data; In one specific embodiment, the location preference evolution data contains text segments that have the same business meaning or time label as the set of behavioral difference segments; Calculate the numerical difference between the feature vectors in the behavioral difference segment set and the feature vectors of the corresponding segments in the preference evolution data; The dynamic changes of the above numerical differences within the simulation period are calculated by fitting a function to generate segment-level behavioral difference data.

[0061] Specifically, the calculation logic for generating fragment-level behavioral difference data is as follows: Extracting the first behavioral difference fragment from the set The feature values ​​of each segment, and the evolutionary feature values ​​of the corresponding positions in the preference evolution data; The mean of the eigenvalues ​​is calculated as the first arithmetic mean, and the mean of the evolutionary eigenvalues ​​is calculated as the second arithmetic mean. Subtract the first arithmetic mean from each eigenvalue in turn to obtain the eigenvalue deviation sequence, and subtract the second arithmetic mean from each evolutionary eigenvalue in turn to obtain the evolutionary deviation sequence. Calculate the product of the corresponding values ​​in the feature deviation sequence and the evolution deviation sequence, and sum all the products to obtain the deviation product sum; Calculate the square of each value in the characteristic deviation sequence, and sum all the squared values ​​to obtain the characteristic deviation sum of squares; Dividing the sum of the deviation products by the sum of the squares of the characteristic deviations yields the fitting coefficients used to characterize the strength of the linear correlation between the two. Multiplying the fitting coefficients by each component in the original feature vector yields fragment-level behavioral difference data.

[0062] S132.3, Perform semantic condensation encoding processing based on fragment-level behavioral difference data to generate differential behavioral data; In one specific embodiment, a preset semantic condensation operator is used to perform feature dimensionality reduction and information extraction on fragment-level behavioral difference data; by retaining the most representative salient components in the fragment-level behavioral difference data and removing redundant low-frequency disturbance information, differential behavioral data with concise semantic expression and clear behavioral orientation is generated.

[0063] Specifically, the calculation logic for generating differential behavioral data is as follows: sort all components in the fragment-level behavioral difference data by absolute value; calculate the sum of the absolute values ​​of all components; extract the key components ranked in the top δ positions; calculate the ratio of the sum of the absolute values ​​of the key components to the total sum to obtain the condensed contribution rate; and perform a weighted summation of the condensed contribution rate and the key components to obtain the differential behavioral data. It should be noted that a higher condensation contribution rate indicates a more comprehensive feature coverage of the original fragment-level behavioral difference data by the differential behavioral data.

[0064] S133, Based on the segment-level change range in the differential behavior data, the offset segment in the preference evolution data is matched to generate stitching preference data; In one specific embodiment, the variation range of each numerical component in the differential behavioral data is analyzed and defined as the segment-level variation range; specific regions in the preference evolution data whose numerical fluctuation characteristics match the segment-level variation range are retrieved and marked as offset segments; the differential behavioral data is mapped to the offset segments of the preference evolution data through the cross-stitching operator, feature stitching and smoothing are performed, and finally stitched preference data is generated.

[0065] Specifically, the calculation logic of cross-stitching is as follows: calculate the difference between the maximum and minimum values ​​in the differential behavior data to obtain the segment-level variation range; obtain the offset segment by identifying the continuous intervals in the search preference evolution data where the absolute value of the first derivative is greater than or equal to a preset offset threshold; accumulate the values ​​in the differential behavior data according to the weight ratio to the feature dimension corresponding to the offset segment; perform normalization processing on the accumulated feature vector to make its value range between [0,1] to obtain the stitching preference data. Preferably, the preset offset threshold can be 0.5. It should be noted that the preset offset threshold is based on the first derivative distribution of the time series. A value of 0.5 can effectively capture the inflection point where the slope changes significantly during the preference evolution process, thereby accurately locating the offset segment affected by policies or culture.

[0066] Coupled simulation module S14: By using behavior-driven data, preference evolution data, and stitching preference data, it performs behavior coupling to obtain multi-agent update data, and generates cross-border e-commerce operation simulation data based on the multi-agent update data; In a specific embodiment, the behavior-driven data, preference evolution data, and stitching preference data generated in the aforementioned steps are retrieved; the behavior coupling module performs feature recombination and vector superposition processing on the above three types of data in the agent decision space; the state parameters of each agent in the simulation environment are extracted and multi-agent update data is generated; the multi-agent update data is input into a preset behavior time series deduction model to generate cross-border e-commerce operation simulation data.

[0067] It should be noted that the contribution of stitching preference data to agent decision-making is negatively correlated with the stability of the simulation environment; through behavioral coupling processing of multi-dimensional data, the complete link process from user psychological evolution to specific purchasing behavior in cross-border e-commerce transactions can be simulated.

[0068] Specifically, the steps for generating cross-border e-commerce operation simulation data include: S141, construct a behavior preference combination mapping diagram based on behavior-driven data and preference evolution data, and generate combined behavior expression data; In one specific embodiment, a two-dimensional correlated coordinate system is established with behavioral features as the horizontal axis and preference features as the vertical axis, serving as a behavioral preference combination mapping diagram; behavioral-driven data and preference evolution data are mapped as coordinate points onto the behavioral preference combination mapping diagram; and combined behavioral expression data is generated by calculating the distribution density and motion trajectory of coordinate points in the two-dimensional correlated coordinate system.

[0069] Specifically, the calculation logic for generating combined behavioral expression data is as follows: obtain the first feature vector corresponding to the behavior-driven data and the preset first weight coefficient; obtain the second feature vector corresponding to the preference evolution data and the preset second weight coefficient; calculate the product of the first feature vector and the first weight coefficient; calculate the product of the second feature vector and the second weight coefficient; perform weighted summation on the two products to obtain the combined behavioral expression data. Preferably, the first weighting coefficient is 0.4, and the second weighting coefficient is 0.6.

[0070] S142, Perform agent state mapping based on stitching preference data and combined behavior expression data to generate multi-agent update data; In one specific embodiment, stitching preference data is used as the internal state driving source of the agent, and combined behavior expression data is used as the external performance constraint of the agent; by establishing a mapping relationship between internal and external states, the attribute parameters of each agent are iteratively corrected; the corrected agent attribute parameter set is extracted to generate multi-agent update data.

[0071] Specifically, the logic for generating multi-agent update data is as follows: S142.1 Extract behavioral consistency segments from the stitching preference data and combined behavioral expression data to construct joint input data; In one specific embodiment, a synchronous sliding window segmentation process is performed on the stitching preference data and the combined behavior expression data; the cosine value of the angle between the feature vectors in each corresponding window is calculated and defined as the consistency score; if the consistency score is greater than or equal to the consistency threshold, the corresponding feature segments are spliced ​​together to construct joint input data; Specifically, the logic for constructing the joint input data is as follows: extract the H-th semantic segment from the stitching preference data; extract the H-th semantic segment from the combined behavior expression data; Calculate the dot product of two semantic segments; Calculate the product of the moduli of two semantic segments; Divide the dot product by the product to get the consistency score; If the consistency score is greater than or equal to the consistency threshold, the Hth semantic segment is determined to be a behaviorally consistent segment and stored in the joint input data. Preferably, the consistency threshold is 0.8. It should be noted that in the multi-agent collaborative mechanism, 0.8 is the upper limit of the stable range of behavioral consistency, which can ensure that the internal preferences of the agents and the external performance data are logically highly synchronized, and avoid numerical inconsistencies during the simulation process.

[0072] S142.2, input the joint input data into the behavior coupling function, perform agent preference splitting processing, and generate agent bias difference data; In one specific embodiment, the joint input data is subjected to nonlinear transformation processing through a behavior coupling function, and the uniform preference features are split into multiple differentiated individual preference components using a splitting operator, thus performing agent preference splitting processing; the deviation of each individual preference component from the group average preference is calculated to generate agent bias difference data; Specifically, the logic of the behavior coupling function performing nonlinear transformation processing is as follows: the hyperbolic tangent function is used to map each component in the joint input data, compressing the values ​​to the range of [-1,1], in order to simulate the saturation effect of the behavior subject's response to the policy, that is, after the policy intensity reaches a certain threshold, the rate of increase in behavioral tendency slows down.

[0073] The construction principle of the splitting operator is derived from the heterogeneity decomposition theory in multi-agent dynamics; The specific computational logic of the splitting operator is as follows: determine the individual feature axes of multiple intelligent agents based on the system simulation scale; The first-order deviation calculation formula in statistics is introduced as the basic operator structure; the unified policy represented by joint input data is used as the input variable, and the basic operator structure is projected onto the individual feature axes of each agent according to the normal distribution law; the parameterization configuration from one-dimensional macro logic to multi-dimensional micro logic is completed, thereby generating and obtaining the splitting operator.

[0074] It should be noted that the construction principle of the splitting operator is derived from the heterogeneity decomposition theory in multi-agent dynamics; its source is intended to simulate the decision differentiation of individual consumers in the cross-border e-commerce market when faced with unified policies, and to realize the solution from macro data to micro data. Specifically, the computational logic for performing agent preference splitting is as follows: obtain the total number of features in the joint input data; calculate the arithmetic mean V of all agent preference components; obtain the individual preference component Xp of the Pth agent, calculate the difference between the individual preference component Xp and the arithmetic mean V, square the difference and take the square root to obtain the bias displacement of the agent; summarize the bias displacements of all agents to generate agent bias difference data. It should be noted that: agent bias difference data reflects the degree of individualization within the simulated group; the larger the value in the agent bias difference data, the more diverse the behavior of the agents in the simulated environment; by deconstructing the joint input data through the splitting operator, it can be ensured that the system can still reproduce the decision-making diversity caused by individual deviation distance in the real market through agent bias difference data under the unified cross-border policy constraints.

[0075] S142.3, based on the agent bias difference data, execute update instruction allocation and multi-agent structure reorganization to generate multi-agent update data.

[0076] In one specific embodiment, each agent is assigned a corresponding behavior update step size and update direction instruction based on agent bias difference data; the topological connection relationship and interaction weight in the multi-agent system are adjusted, and multi-agent structure reorganization is performed; the updated agent instructions, position parameters and topological relationships are encapsulated to generate multi-agent update data.

[0077] Specifically, the logic for executing the update instruction allocation is as follows: Set the base update step size; multiply the value in the agent's bias difference data with the base update step size to obtain the agent's actual execution step size; if the actual execution step size is greater than or equal to the preset maximum step size limit, then reset the actual execution step size to the maximum step size limit; It should be noted that the maximum step size limit is preferably 0.1. In order to maintain the update stability of the multi-agent system, the maximum step size should be limited to within 2 times the basic update step size to prevent the agent from having a state jump that is too large in a single iteration, thereby ensuring the continuity of the simulated trajectory.

[0078] The logic for determining the basic update step size is as follows: calculate the value span of each feature dimension in the cross-border e-commerce operation simulation data; divide the value span by the preset total number of simulation iterations to obtain the initial reference value of the basic update step size.

[0079] It should be noted that when the basic update step size is set too large, the behavioral evolution trajectory of the agent may exhibit numerical divergence; when the basic update step size is set too small, the state update frequency of the multi-agent system will slow down, leading to a decrease in simulation efficiency. Preferably, the basic update step size can be 0.05; It should be noted that the basic update step size can be 0.05. In the iterative simulation system, 0.05 can maintain high simulation efficiency while ensuring the system's convergence stability, and prevent evolution trajectory oscillation caused by an excessively large step size or computational redundancy caused by an excessively small step size.

[0080] S143, based on the simulation of multi-agent data update execution behavior sequence, generates cross-border e-commerce operation simulation data.

[0081] In one specific embodiment, the multi-agent update data is loaded into each logical entity of the simulation engine; continuous behavioral sequence iterative calculations are performed according to the preset simulation duration and step size; information such as commodity transaction volume, logistics order status, user complaint rate, and market share fluctuations generated during the simulation process is recorded; the above information is summarized and formatted to generate cross-border e-commerce operation simulation data.

[0082] Specifically, the calculation logic for generating cross-border e-commerce operation simulation data is as follows: Calculate the initial total number of orders within the simulation period, obtain the order growth increment generated by the multi-agent update data, and calculate the sum of the initial total number of orders and the order growth increment; map the calculated sum to an interval for normalization processing to obtain a standardized operational performance value; associate the standardized operational performance value with the simulation spatiotemporal label to generate cross-border e-commerce operation simulation data.

[0083] It should be noted that the simulation duration can be set to 365 time steps; cross-border e-commerce operation simulation data can be used to assess the impact trends of specific policies or cultural strategies on the operational effectiveness of e-commerce platforms.

[0084] Example 2

[0085] Please see Figure 2 As shown, based on a unified inventive concept, this embodiment discloses a cross-border e-commerce operation simulation method based on multi-agent simulation, the method comprising: S21: Based on the collected transaction content data, interaction content data, and cultural semantic data, perform cross-dimensional segmentation to obtain cross-cultural semantic data, apply multi-directional mapping perturbation to the cross-cultural semantic data, and generate behavior-driven data; S22: Perform cross-structure decomposition based on the collected policy semantic data to obtain policy constraint data. Input the policy constraint data and behavior-driven data into bidirectional entanglement calculation to generate preference evolution data. S23: Based on cultural semantic data and behavior-driven data, perform cultural domain differentiation to obtain differential behavior data, and perform cross-stitching calculation on differential behavior data and preference evolution data to obtain stitched preference data; S24: By using behavior-driven data, preference evolution data, and stitching preference data, behavior coupling is performed to obtain multi-agent update data, and cross-border e-commerce operation simulation data is generated based on the multi-agent update data.

[0086] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A cross-border e-commerce operation simulation system based on multi-agent simulation, characterized in that, The system includes: Segmentation Generation Module S11: Based on the collected transaction content data, interaction content data, and cultural semantic data, it performs cross-dimensional segmentation to obtain cross-cultural semantic data, applies multi-directional mapping perturbation to the cross-cultural semantic data, and generates behavior-driven data; S12: Deconstruction and Entanglement Module: Performs cross-structure deconstruction based on the collected policy semantic data to obtain policy constraint data. The policy constraint data and behavior-driven data are then input into bidirectional entanglement calculation to generate preference evolution data. Differential stitching module S13: Based on cultural semantic data and behavior-driven data, cultural domain differential is performed to obtain differential behavior data, and the differential behavior data and preference evolution data are cross-stitched to obtain stitched preference data; Coupled simulation module S14: By using behavior-driven data, preference evolution data, and stitching preference data, it performs behavior coupling to obtain multi-agent update data, and generates cross-border e-commerce operation simulation data based on the multi-agent update data.

2. The cross-border e-commerce operation simulation method based on multi-agent simulation according to claim 1, characterized in that, The steps to generate behavior-driven data include: S111, perform text segmentation based on paragraph structure markers in transaction content data and interaction content data, and generate semantic fragment index data; S112, Based on the language tag index in the cultural semantic data, perform classification and aggregation on the semantic fragment index data to generate language dimension fragment data; S113, based on language dimension fragment data, performs semantic density measurement and classification boundary calculation to generate cross-cultural semantic data; S114 applies a semantic perturbation function mapping to cross-cultural semantic data to generate behavior-driven data.

3. The cross-border e-commerce operation simulation method based on multi-agent simulation according to claim 2, characterized in that, The steps to generate cross-cultural semantic data include: S113.1, construct language fragment group data by using fragment sets of the same language category in the language dimension fragment data; S113.2, Perform semantic aggregation vector encoding based on the language segment group data to generate preliminary language aggregation vector data; S113.3, based on the preliminary language aggregation vector data, calculate the semantic deviation value and semantic overlap value to generate cross-cultural semantic data.

4. The cross-border e-commerce operation simulation method based on multi-agent simulation according to claim 3, characterized in that, The steps to generate preference evolution data include: S121, Perform structural partitioning based on the structural unit labels in the policy semantic data to generate structural block distribution data; S122, extract constraint content segments from high-frequency word fragments in the structure block distribution data to generate policy constraint data; S123, input policy constraint data and behavior-driven data into the bidirectional entanglement calculation structure to generate bidirectional effect result data; S124, based on the semantic response sites and perturbation-sensitive parameters in the bidirectional interaction result data, performs offset value evaluation to generate preference evolution data.

5. The cross-border e-commerce operation simulation method based on multi-agent simulation according to claim 4, characterized in that, The steps to generate policy constraint data include: S122.1 Extract the content of segments marked as restricted expressions from the structure block distribution data and construct restricted segment data; S122.2 Perform language segment alignment processing on the restricted segment data, filter out repetitive expressions and semantic redundancy, and generate concise restricted content data; S122.3 Input the simplified constraint content data into the constraint information filtering function to generate policy constraint data.

6. The cross-border e-commerce operation simulation method based on multi-agent simulation according to claim 5, characterized in that, The steps to generate suture preference data include: S131, construct a semantic deviation comparison matrix based on cultural semantic data and behavior-driven data to generate cultural domain difference data; S132, Perform difference combination processing on the cultural domain difference data and preference evolution data to generate difference behavior data; S133, Based on the segment-level change range in the differential behavior data, match the offset segment in the preference evolution data to generate stitching preference data.

7. The cross-border e-commerce operation simulation method based on multi-agent simulation according to claim 6, characterized in that, The logic for generating differential behavioral data includes: S132.1 Extract high-offset fragment data from the cultural domain differential data and construct a set of behavioral difference fragments; S132.2, Perform difference fitting analysis on the set of behavioral difference segments and the corresponding segments in the preference evolution data to generate segment-level behavioral difference data; S132.3 performs semantic condensation encoding processing based on fragment-level behavioral difference data to generate differential behavioral data.

8. The cross-border e-commerce operation simulation method based on multi-agent simulation according to claim 7, characterized in that, The steps to generate cross-border e-commerce operation simulation data include: S141, construct a behavior preference combination mapping diagram based on behavior-driven data and preference evolution data, and generate combined behavior expression data; S142, Perform agent state mapping based on stitching preference data and combined behavior expression data to generate multi-agent update data; S143, based on the simulation of multi-agent data update execution behavior sequence, generates cross-border e-commerce operation simulation data.

9. The cross-border e-commerce operation simulation method based on multi-agent simulation according to claim 8, characterized in that, The logic for generating multi-agent update data is as follows: S142.1 Extract behavioral consistency segments from the stitching preference data and combined behavioral expression data to construct joint input data; S142.2, input the joint input data into the behavior coupling function, perform agent preference splitting processing, and generate agent bias difference data; S142.3, based on the agent bias difference data, execute update instruction allocation and multi-agent structure reorganization to generate multi-agent update data.

10. A cross-border e-commerce operation simulation method based on multi-agent simulation, applied to the cross-border e-commerce operation simulation system based on multi-agent simulation as described in any one of claims 1-9, characterized in that, The method includes: S21: Based on the collected transaction content data, interaction content data, and cultural semantic data, perform cross-dimensional segmentation to obtain cross-cultural semantic data, apply multi-directional mapping perturbation to the cross-cultural semantic data, and generate behavior-driven data; S22: Perform cross-structure decomposition based on the collected policy semantic data to obtain policy constraint data. Input the policy constraint data and behavior-driven data into bidirectional entanglement calculation to generate preference evolution data. S23: Based on cultural semantic data and behavior-driven data, perform cultural domain differentiation to obtain differential behavior data, and perform cross-stitching calculation on differential behavior data and preference evolution data to obtain stitched preference data; S24: By using behavior-driven data, preference evolution data, and stitching preference data, behavior coupling is performed to obtain multi-agent update data, and cross-border e-commerce operation simulation data is generated based on the multi-agent update data.

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