Retrieval method and system for efficiently collecting accurate global potential customer information

Abstract

The present invention discloses a retrieval method and system for efficiently collecting accurate global potential customer information, the method comprising collecting and preprocessing multi-source heterogeneous data from e-commerce platforms, social media, corporate official websites, exhibition information, industry databases and customs, generating standardized data through spatiotemporal feature extraction and multi-dimensional credibility evaluation; adopting feature decomposition reconstruction and value network construction methods to establish a dynamic topological structure; based on adaptive classification and multi-dimensional trend analysis, realizing customer value prediction and accurate recommendation; through system quality evaluation and feedback optimization, realizing dynamic update and optimized display of results. The present invention can effectively improve the accuracy and timeliness of customer information retrieval, reduce information redundancy, and improve the reliability of recommendation effect.

Description

Technical Field

[0001] The present invention relates to data mining and artificial intelligence technology, and in particular to a retrieval method and system for efficiently collecting accurate global potential customer information. Background Art

[0002] With the acceleration of global economic integration and the booming development of cross-border e-commerce, enterprises have an increasingly urgent need to accurately obtain information about global potential customers. Effective customer information retrieval can not only help enterprises quickly identify market opportunities and reduce customer acquisition costs, but also provide accurate guidance on product positioning and marketing strategies. Especially in the current complex and changing international trade environment, how to quickly and accurately identify and locate potential customers from massive multi-source heterogeneous data has become one of the key factors determining the international competitiveness of enterprises.

[0003] At present, customer information retrieval mainly adopts methods such as keyword matching, behavior analysis and social network analysis. Among them, keyword matching extracts relevant information from the Internet and databases through preset retrieval rules; behavior analysis builds user portraits based on users' historical access records, search trajectories and transaction data; social network analysis identifies potential customers' interests, preferences and social relationships by mining social media data. At the same time, some researchers have also tried to combine the traditional RFM (Recency-Frequency-Monetary) model with machine learning to guide the information retrieval process through customer value assessment. In addition, regional market analysis methods based on geographic information systems are also used to assist in the collection and screening of customer information.

[0004] However, there are some specific technical problems in the existing customer information retrieval methods: First, in terms of data quality assessment, there is a lack of accurate modeling of time decay, which leads to the over-magnification or underestimation of the reference value of historical data; second, the existing feature association analysis methods often separate the time and space dimensions, ignoring the co-evolution characteristics of spatiotemporal features; third, in the process of multi-source data fusion, the traditional linear weighting method is difficult to effectively handle the nonlinear coupling relationship between features, which reduces the accuracy of information extraction; fourth, the current customer value evaluation model is mostly based on static indicators, lacking in-depth consideration of value flow and conduction effects; fifth, in the link of generating recommendation results, the existing methods rarely consider the complementarity and redundancy between recommended items, affecting the overall utility of the recommendation list; finally, there is a lack of dynamic adjustment mechanism for the credibility evaluation of different data sources, which is difficult to adapt to the time-varying characteristics of data quality. These technical problems seriously restrict the accuracy and practicality of customer information retrieval. Summary of the invention

[0005] The purpose of the invention is to provide a retrieval method and system for efficiently collecting accurate global potential customer information, in order to solve at least one technical problem existing in the prior art.

[0006] The technical solution is a retrieval method for efficiently collecting accurate global potential customer information, including the following steps:

[0007] Step S1, taking multi-source heterogeneous data and performing spatiotemporal feature extraction and credibility assessment to generate a spatiotemporal feature data set, a credibility score data set and a cross-validation score data set; then performing timeliness optimization processing to generate a timeliness weighted feature data set; based on the pre-built association relationship between entities, generating an association strength data set and an association credibility data set and performing fusion processing to obtain a fused feature data set and a metadata index; the multi-source heterogeneous data includes e-commerce platform data, social media data, corporate official website data, exhibition information, industry database information and customs data;

[0008] Step S2, obtaining the fusion feature data set and metadata index, performing feature decomposition and reconstruction processing, and constructing a value association network, generating a value association data set and a dynamic topology structure data set and evaluating multi-dimensional value, outputting a value scoring data set and a comprehensive value score data set and optimizing the processing, generating an optimization feedback data set, a feature optimization data set and processing metadata;

[0009] Step S3, obtaining an optimization feedback data group, a feature optimization data group, and processing metadata, and performing dynamic classification processing to generate a classification feature data group and a category attribution probability data group, and predicting value trends to obtain prediction results including a trend prediction data group and a value prediction data group; performing precise recommendation analysis on the prediction results, outputting a recommendation score data group and an optimization recommendation list, and providing feedback optimization to generate a feedback optimization data group, an optimization adjustment data group, and quality control metadata;

[0010] Step S4, obtain quality control metadata, optimization adjustment data group and optimization recommendation list, perform system quality assessment, generate quality assessment data group and comprehensive quality score data group; perform anomaly detection and processing on the assessment results, generate anomaly detection data group and processing strategy data group; establish a timeliness update mechanism, generate update frequency data group and update priority data group; integrate and display all results, generate display optimization data group, final display plan and system metadata.

[0011] According to one aspect of the present application, step S1 specifically comprises:

[0012] Step S11, obtaining multi-source heterogeneous data, including e-commerce platform data, social media data, corporate official website data, exhibition information, industry database information and customs data; performing format standardization and noise cleaning to generate a preprocessed data set; extracting time dimension features, space dimension features and entity attribute features from the preprocessed data set; using the time-space projection transformation method to unify the feature space and generate a time-space feature data set; calculating the data consistency value, timeliness value and source reliability value, and generating a credibility score data set through nonlinear combination; performing weighted calculation on the credibility score based on the importance of the data source and the matching degree of the entity information, and generating a cross-validation score data set;

[0013] Step S12, obtaining a spatiotemporal feature data group and a credibility score data group; calculating the timeliness attenuation value of the data, and optimizing it using a polynomial method; generating timeliness weight data in combination with seasonal fluctuation data, wherein seasonal fluctuations are obtained by calculating periodic parameters and phase offset values; multiplying the spatiotemporal feature data group with the timeliness weight data to generate a timeliness weighted feature data group;

[0014] Step S13, obtaining a timeliness weighted feature data set and a cross-validation score data set; calculating the forward association probability value, reverse association probability value and co-occurrence association strength value between entities, and generating an association strength data set by weighted synthesis; performing a product operation on the association strength data set and the cross-validation score of the corresponding entity to generate an association credibility data set;

[0015] Step S14, obtaining a timeliness weighted feature data group and timeliness weight data, as well as an associated credibility data group; processing them using a nonlinear feature conversion method, and then synthesizing them through adaptive weighting to generate a fused feature data group; recording the data source, processing process and credibility information, and generating a metadata index.

[0016] According to one aspect of the present application, step S2 is specifically:

[0017] Step S21, obtaining a fusion feature data set and a metadata index; calculating the mean contribution value, variance influence value and correlation weight value of each feature; weighting and synthesizing the mean contribution value, variance influence value and correlation weight value to generate a feature importance data set; processing the feature importance data set using adaptive weights to generate a feature reconstruction weight data set;

[0018] Step S22, obtaining a feature importance data group and a feature reconstruction weight data group; calculating feature association values, weight mapping values ​​and marginal effect values; performing weighted synthesis on the feature association values, weight mapping values ​​and marginal effect values ​​to generate a value association data group; performing time series analysis on the value association data group, and combining the structure optimization parameters to generate a dynamic topology structure data group;

[0019] Step S23, obtaining a value-related data group and a dynamic topological structure data group; calculating the value component value of each dimension and performing time decay processing to generate an attenuated value component data group; performing weighted synthesis on the attenuated value component data group to generate a value scoring data group; optimizing the value scoring data group in combination with the gradient information of the topological structure to generate a comprehensive value score data group;

[0020] Step S24, obtain the value score data group and the comprehensive value score data group; calculate the value relevance value, score similarity value and topological association value; perform weighted synthesis on the value relevance value, score similarity value and topological association value to generate an optimization feedback data group; use adaptive optimization weights to process the optimization feedback data group to generate a feature optimization data group; record the optimization process information and parameter selection information, and generate processing metadata.

[0021] According to one aspect of the present application, step S3 is specifically:

[0022] Step S31, obtaining an optimization feedback data group, a feature optimization data group and processing metadata; performing mapping transformation on the optimization feedback data to generate feedback mapping data; performing mapping transformation on the feature optimization data to generate feature mapping data; performing mapping transformation on the processing metadata to generate metadata mapping data; performing weighted synthesis on the feedback mapping data, feature mapping data and metadata mapping data to generate a classification feature data group; calculating the probability of each entity belonging to different categories to generate a category belonging probability data group;

[0023] Step S32, obtaining a classification feature data group and a category belonging probability data group; analyzing the change trend of each feature in different time windows to generate feature trend data; analyzing the evolution law of the category belonging probability to generate probability evolution data; analyzing the influence of the time factor to generate time influence data; weighting and synthesizing the feature trend data, probability evolution data and time influence data to generate a trend prediction data group; performing time series mapping processing on the trend prediction data group, and combining the time evolution parameters to generate a value prediction data group;

[0024] Step S33, obtaining a trend prediction data group and a value prediction data group; performing an influence analysis on the trend prediction data to generate trend influence data; performing mapping conversion on the value prediction data to generate value mapping data; calculating the interaction intensity between entities to generate interaction intensity data; weighting and synthesizing the trend influence data, value mapping data and interaction intensity data to generate a recommendation score data group; based on the recommendation score data group and the preset feature weights, screening the best recommendation items from the candidate recommendation set to generate an optimized recommendation list;

[0025] Step S34, obtain the recommendation score data group and the optimized recommendation list; evaluate the recommendation effect and generate effect evaluation data; analyze the impact of the recommendation list and generate list impact data; count the errors that occur and generate error statistics data; weightedly synthesize the effect evaluation data, list impact data and error statistics data to generate a feedback optimization data group; optimize the feedback optimization data group in combination with the error gradient information to generate an optimized adjustment data group; record the optimization effect information and adjustment suggestion information to generate quality control metadata.

[0026] According to one aspect of the present application, step S4 is specifically:

[0027] Step S41, obtaining quality control metadata, optimization adjustment data group and optimization recommendation list; performing segment analysis on the optimization adjustment data to generate time series segment data; calculating the adjustment effect value of each time segment to generate effect evaluation data; constructing an adjustment response model based on the time series segment data and the effect evaluation data to generate response feature data; normalizing the response feature data to generate an adjustment effect data group; constructing a recommendation accuracy evaluation model to generate an accuracy evaluation data group; weighted synthesis of the adjustment effect data group and the accuracy evaluation data group to generate a quality evaluation data group; performing credibility adjustment on the quality evaluation data group to generate a comprehensive quality score data group;

[0028] Step S42, obtaining a quality assessment data group and a comprehensive quality score data group; performing deviation analysis on the quality assessment data to generate quality deviation data; analyzing the abnormal points of the score data to generate score abnormal data; determining the threshold range for abnormal determination to generate threshold adjustment data; weighted synthesis of the quality deviation data, score abnormal data and threshold adjustment data to generate an abnormal detection data group; generating a processing strategy data group according to the abnormal detection results and the quality score;

[0029] Step S43, obtaining anomaly detection data group and processing strategy data group; analyzing the impact of abnormal data on the system to generate abnormal impact data; evaluating the execution effect of the processing strategy to generate strategy effect data; calculating the time sensitivity of the data to generate time sensitive data; weighting and synthesizing the abnormal impact data, strategy effect data and time sensitive data to generate an update frequency data group; combining the time decay factor to prioritize the update frequency data group to generate an update priority data group;

[0030] Step S44, obtain the update frequency data group, the update priority data group and the optimization recommendation list; analyze the impact of the update frequency on the display effect and generate the update impact data; convert the priority information into the display parameter and generate the priority mapping data; calculate the importance of the recommendation result and generate the importance data; weightedly synthesize the update impact data, the priority mapping data and the importance data to generate the display optimization data group; optimize the display optimization data group according to the time correlation parameter and generate the final display plan; record the whole process indicator information and the update suggestion information and generate the system metadata.

[0031] According to one aspect of the present application, step S11 is specifically:

[0032] Step S111, obtaining e-commerce platform data, social media data, corporate official website data, exhibition information data, industry database data and customs data; performing format consistency check on each type of data to generate format check report data; automatically correcting data that does not meet format requirements based on the format check report data to generate format correction data; performing noise detection on the corrected data to generate noise marker data; cleaning the original data based on the noise marker data to generate a preprocessed data group;

[0033] Step S112, obtaining a preprocessed data group; extracting time dimension information from the preprocessed data group to generate time feature initial data; hierarchically processing the time feature initial data using a multi-scale time segmentation method to generate multi-scale time feature data; calculating the continuity index of the time feature to generate time continuity data; fusing the multi-scale time feature data and the time continuity data to generate a time dimension feature data group;

[0034] Step S113, obtaining a preprocessed data group; extracting geographic location information from the preprocessed data group to generate an initial location data group; performing word segmentation on the address text data in the initial location data group, extracting administrative division information, and generating division standard data; matching and verifying the longitude and latitude data with the division standard data, and correcting inconsistent data using a density weighted method to generate location calibration data; constructing a multi-level geographic code based on the location calibration data to generate a spatial dimension feature data group;

[0035] Step S114, obtaining a preprocessed data group; extracting enterprise scale data, industry code data and transaction frequency data from the preprocessed data group to generate an attribute initial data group; using a recursive feature decomposition method to perform noise reduction on the attribute initial data group to generate attribute noise reduction data; calculating the coupling degree between attributes, using an adaptive threshold method to screen highly correlated attributes to generate attribute related data; integrating the attribute noise reduction data and the attribute related data to generate an entity attribute feature data group;

[0036] Step S115, obtaining a time dimension feature data group, a space dimension feature data group, and an entity attribute feature data group; unifying the three sets of feature data into the same feature space using a time-space projection transformation method to generate unified feature data; calculating mutual information values ​​between features to generate feature association data; constructing feature fusion weights based on the feature association data to generate weight allocation data; performing weighted combination of the unified feature data according to the weight allocation data to generate a time-space feature data group;

[0037] Step S116, obtaining a spatiotemporal feature data group and a preprocessed data group; calculating data consistency indicators between different data sources to generate consistency score data; calculating timeliness indicators using an exponential decay model based on the timestamp information of the data to generate timeliness score data; calculating source reliability indicators using a sliding window method based on the accuracy records of historical data to generate reliability score data; performing a nonlinear combination of consistency score data, timeliness score data, and reliability score data to generate a credibility score data group;

[0038] Step S117, obtain a credibility score data group; calculate the historical contribution of each data source and generate data source weight data; use an improved entity matching algorithm to calculate the similarity of entity information in different data sources and generate entity matching data; based on the data source weight data and entity matching data, use a weighted voting method to calculate the cross-validation coefficient and generate verification coefficient data; multiply the verification coefficient data by the credibility score data group to generate a cross-validation score data group.

[0039] According to one aspect of the present application, step S22 is specifically:

[0040] Step S221, obtaining a feature importance data group and a feature reconstruction weight data group; performing local sensitivity analysis on each feature in the feature importance data group, using a recursive partitioning method to calculate the marginal contribution value of the feature, and generating marginal contribution data; using an improved information gain ratio method to calculate the degree of complementarity between features, and generating feature complementarity data; constructing a feature combination effect matrix based on the marginal contribution data and feature complementarity data, and generating combination effect data; mapping and transforming the combination effect data and the feature reconstruction weight data group, and generating a feature association initial data group;

[0041] Step S222, obtaining a feature association initial data group; grouping the feature association initial data using a multi-level dynamic clustering method to generate association group data; calculating the interaction strength of intra-group features, and using an adaptive weight allocation method to generate intra-group association data; constructing a transfer relationship network of inter-group features, and using an improved path analysis method to generate inter-group association data; fusing the intra-group association data and the inter-group association data to generate a multi-level association data group;

[0042] Step S223, obtaining a multi-level association data group; constructing a feature flow probability matrix, using an improved Markov chain analysis method to calculate the feature migration probability, and generating feature migration data; based on the feature migration data, using a time series association mining method to identify the feature evolution pattern, and generating evolution pattern data; calculating the stability index of the feature evolution, and using an entropy method to generate stability evaluation data; integrating the feature migration data, the evolution pattern data, and the stability evaluation data to generate a value association data group;

[0043] Step S224, obtaining a value-related data group; constructing a characteristic state sequence based on a time window, and generating state transition data by using a state transition probability calculation method; performing time series decomposition on the state transition data, and generating time series characteristic data by using a multi-scale analysis method; constructing a dynamic evolution network based on the time series characteristic data, and generating structural evolution data by using a network growth model; optimizing and combining the state transition data and the structural evolution data to generate a topology preprocessing data group;

[0044] Step S225, obtaining a topology preprocessing data group; using an improved spectral clustering algorithm to perform structural analysis on the topology preprocessing data to generate structural feature data; calculating the centrality index of the network nodes, and using a multi-index fusion method to generate node importance data; constructing a hierarchical topology structure based on the node importance data, and using a self-organizing mapping method to generate hierarchical structure data; dynamically optimizing the structural feature data and the hierarchical structure data to generate a dynamic topology structure data group;

[0045] Step S226, obtain a dynamic topology structure data group; use a time-varying network analysis method to calculate the time evolution characteristics of the topology structure and generate time characteristic data; construct a network stability evaluation index and use a multi-dimensional evaluation method to generate stability data; based on the time characteristic data and stability data, design a structure optimization strategy and generate optimization strategy data; adjust and optimize the dynamic topology structure data group according to the optimization strategy data to generate a final dynamic topology structure data group.

[0046] According to one aspect of the present application, step S31 is specifically:

[0047] Step S311, obtaining an optimization feedback data group, a feature optimization data group and processing metadata; adaptively sampling each data in the optimization feedback data group, and generating sample distribution data by using a stratified sampling method; calculating the statistical characteristics of the sample data, and generating feature statistical data by using a multidimensional analysis method; constructing data quality assessment indicators based on the feature statistical data, and generating quality assessment data by using a comprehensive weighting method; optimizing and screening the sample distribution data according to the quality assessment data, and generating a preprocessing feature data group;

[0048] Step S312, obtaining a preprocessed feature data group; using an improved local linear embedding algorithm to reduce the dimension of high-dimensional features to generate reduced-dimensional feature data; constructing a feature importance evaluation model, using an entropy-based feature selection method to generate importance ranking data; designing a feature combination strategy based on the importance ranking data, using a combination optimization algorithm to generate feature combination data; fusing the reduced-dimensional feature data with the feature combination data to generate an optimized feature data group;

[0049] Step S313, obtaining an optimized feature data group; constructing a multi-level feature mapping network, and using an adaptive weight allocation method to generate feature mapping data; calculating the correlation coefficient between features, and using an improved correlation analysis method to generate feature association data; designing feature conversion rules based on the feature mapping data and feature association data, and generating feature conversion data; performing nonlinear combination on the feature conversion data to generate a conversion feature data group;

[0050] Step S314, obtaining a conversion feature data set; using a dynamic kernel density estimation method to calculate the local density of data distribution and generate density distribution data; constructing a density-based cluster center recognition model and using an adaptive threshold method to generate center point data; calculating the distance between the sample point and the cluster center and using an improved distance measurement method to generate distance matrix data; generating a classification feature data set based on the density distribution data and the distance matrix data;

[0051] Step S315, obtaining a classification feature data group; constructing a probability-based category discrimination model, using a multidimensional Gaussian mixture model to generate initial probability data; calculating the similarity between sample features and category features, using an improved cosine similarity method to generate similarity data; designing a probability adjustment strategy based on the initial probability data and similarity data, generating probability adjustment data; normalizing the probability adjustment data, generating a category attribution probability data group;

[0052] Step S316, obtain the category attribution probability data group; construct a probability consistency test model, and use the cross-validation method to generate verification result data; calculate the probability interference degree between different categories, and use the mutual information analysis method to generate interference assessment data; based on the verification result data and the interference assessment data, design a probability optimization strategy and generate optimization solution data; adjust the category attribution probability data group according to the optimization solution data to generate the final category attribution probability data group.

[0053] According to one aspect of the present application, step S33 is specifically:

[0054] Step S331, obtaining a trend prediction data group and a value prediction data group; performing time series decomposition on the trend prediction data, and using the wavelet transform method to generate trend component data; calculating the rate of change of the trend component, and using the dynamic slope analysis method to generate the rate of change data; constructing a trend impact assessment model based on the trend component data and the rate of change data, and generating trend assessment data; performing weight optimization on the trend assessment data, and generating a trend impact data group;

[0055] Step S332, obtaining a trend impact data group; constructing a mapping model for value prediction data, and using a nonlinear projection method to generate value mapping data; calculating a stability index of the mapping data, and using an entropy value evaluation method to generate stability data; designing a mapping optimization strategy based on the stability data, and using an adaptive adjustment method to generate optimized mapping data; performing feature reorganization on the optimized mapping data to generate a value feature data group;

[0056] Step S333, obtaining a value feature data group; constructing an entity interaction strength calculation model, using an improved association rule mining method to generate initial interaction data; calculating the influence propagation efficiency between entities, using a network flow analysis method to generate propagation efficiency data; designing an interaction strength evaluation rule based on the initial interaction data and propagation efficiency data, and generating interaction evaluation data; normalizing the interaction evaluation data to generate an interaction strength data group;

[0057] Step S334, obtaining a trend impact data group, a value feature data group, and an interaction intensity data group; constructing a multi-dimensional recommendation scoring model, and using a hierarchical weighted method to generate initial scoring data; calculating the synergy between scoring indicators, and using a cross-validation method to generate synergy effect data; designing a scoring optimization strategy based on the initial scoring data and the synergy effect data, and generating optimized scoring data; performing comprehensive processing on the optimized scoring data to generate a recommendation score data group;

[0058] Step S335, obtaining a recommendation score data set; constructing a recommendation candidate set screening model, and using a multi-objective optimization method to generate candidate set data; calculating similarity indicators between candidate items, and using an improved cosine similarity method to generate similarity data; designing a diversity optimization strategy based on the candidate set data and similarity data, and generating diversity evaluation data; performing sorting optimization on the diversity evaluation data, and generating a recommendation sorting data set;

[0059] Step S336, obtain the recommendation ranking data group; construct a recommendation list generation model, and use the sequence optimization method to generate the initial list data; calculate the coverage index of the list items, and use the set coverage analysis method to generate the coverage data; based on the initial list data and the coverage data, design a list optimization strategy, and generate the optimization strategy data; adjust the recommendation list according to the optimization strategy data to generate an optimized recommendation list.

[0060] According to one aspect of the present application, a retrieval system for efficiently collecting accurate global potential customer information is provided, comprising:

[0061] at least one processor; and,

[0062] a memory communicatively connected to at least one of the processors; wherein,

[0063] The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the retrieval method for efficiently collecting global accurate potential customer information described in any of the above technical solutions.

[0064] The beneficial effect is that it can effectively improve the accuracy and timeliness of customer information retrieval, reduce information redundancy, and reduce resource usage, making it easier to deploy in edge devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a flow chart of the present invention.

[0066] Figure 2 It is a flow chart of step S1 of the present invention.

[0067] Figure 3 It is a flow chart of step S2 of the present invention.

[0068] Figure 4 It is a flow chart of step S3 of the present invention.

[0069] Figure 5 It is a flow chart of step S4 of the present invention. DETAILED DESCRIPTION

[0070] like Figure 1 As shown, the retrieval method for efficiently collecting accurate global potential customer information includes the following steps:

[0071] Step S1, obtain multi-source heterogeneous data, including e-commerce platform data, social media data, corporate official website data, exhibition information, industry database information and customs data, extract spatiotemporal features and conduct credibility assessment on these data, and generate spatiotemporal feature data sets, credibility score data sets and cross-validation score data sets. Perform timeliness optimization processing on the data to generate a timeliness weighted feature data set. Construct the association relationship between entities, generate an association strength data set and an association credibility data set. Perform fusion processing on all generated data sets, and output a fused feature data set and a metadata index.

[0072] Step S2, obtain the fusion feature data set and metadata index output by step S1, perform feature decomposition and reconstruction on these data, and generate feature importance data set and feature reconstruction weight data set. Build a value association network based on these data, generate value association data set and dynamic topology structure data set. Perform multi-dimensional value assessment on the generated data, output value score data set and comprehensive value score data set. Optimize feature association, generate optimization feedback data set, feature optimization data set and processing metadata.

[0073] Step S3, obtain the optimization feedback data group, feature optimization data group and processing metadata output by step S2, perform dynamic classification processing on these data, and generate a classification feature data group and a category attribution probability data group. Perform value trend prediction based on the classification results to generate a trend prediction data group and a value prediction data group. Perform precise recommendation analysis on the prediction results, output the recommendation score data group and the optimization recommendation list. Perform feedback optimization on the entire process to generate a feedback optimization data group, an optimization adjustment data group and quality control metadata.

[0074] Step S4: Obtain the quality control metadata, optimization adjustment data set, and optimization recommendation list output in step S3, perform system quality assessment on these data, and generate a quality assessment data set and a comprehensive quality score data set. Perform anomaly detection and processing on the assessment results to generate anomaly detection data sets and processing strategy data sets. Establish a timeliness update mechanism to generate an update frequency data set and an update priority data set. Integrate and display all results to generate a display optimization data set, a final display plan, and system metadata.

[0075] Through multi-level data processing methods, intelligent processing of the entire process from data collection to result display is realized. In the data collection and preprocessing stage, a reliable data foundation is established through the standardized processing and quality assessment of multi-source heterogeneous data; in the feature extraction and network construction stage, the dynamic topological structure is constructed to achieve accurate characterization of customer value relationships; in the classification prediction and recommendation generation stage, accurate decision support is provided through adaptive classification and personalized recommendation; in the quality control and result optimization stage, the reliable operation of the system is guaranteed through comprehensive quality monitoring and dynamic optimization. The entire technical solution forms a complete closed-loop system, which not only improves the accuracy and efficiency of customer information retrieval, but also realizes dynamic optimization and personalized recommendation of retrieval results, which is particularly suitable for the demand for accurate acquisition of customer information in the cross-border e-commerce environment. The solution significantly reduces the cost of customer acquisition, improves the efficiency of customer development, and provides strong technical support for the company's precision marketing and market expansion.

[0076] like Figure 2 As shown, the step S1 is specifically as follows:

[0077] Step S11, obtain the original data of the e-commerce platform, social media data, enterprise official website data, exhibition information, industry database information and customs data. Read the time information from these data respectively, including the time when the data first appeared, the most recent active time and the active period; read the spatial information, including the longitude, latitude and geographical level code of the geographical location; read the entity attribute information, including the enterprise scale data, industry classification code and historical transaction frequency data. Integrate the read time information, spatial information and entity attribute information to generate a spatiotemporal feature data set. Subsequently, for each data in the spatiotemporal feature data set, calculate its data consistency value, which is specifically obtained by comparing the information differences of different data sources; calculate the timeliness value, which is specifically obtained by the difference between the data generation time and the current time; calculate the source reliability value, which is specifically obtained by the historical accuracy of the data source. Use a nonlinear calculation method to process the data consistency value, timeliness value and source reliability value respectively, and perform weighted synthesis on the processing results to generate a credibility score data set. Among them, when weighted synthesis, different weights are assigned to the data consistency processing results, timeliness processing results and source reliability processing results, and the sum of all weights is 1. According to the importance of each data source and the matching degree of entity information in different data sources, the credibility score data set is weighted to generate a cross-validation score data set. The spatiotemporal feature data set, credibility score data set and cross-validation score data set are used as the output results of this step.

[0078] Step S12, obtain the spatiotemporal feature data group and credibility score data group output by step S11. Read the time difference value of the entity data, calculate the timeliness attenuation value of the data, and optimize the timeliness attenuation value using a polynomial calculation method. According to the optimized attenuation value, combined with seasonal fluctuation data, generate timeliness weight data. Among them, seasonal fluctuation data is obtained by calculating periodic parameters and phase offset values. Multiply the time feature data group with the timeliness weight data to generate a timeliness weighted feature data group, and use the timeliness weighted feature data group and the timeliness weight data as the output results of this step.

[0079] Step S13, obtain the timeliness weighted feature data group output by step S12, and the cross-validation score data group output by step S11. Calculate the forward association probability value and reverse association probability value between entities, and simultaneously calculate the co-occurrence association strength value of the entities. Perform weighted synthesis on the forward association probability value, reverse association probability value, and co-occurrence association strength value to generate an association strength data group. Subsequently, perform a product operation on the data in the association strength data group and the cross-validation score of the corresponding entity to generate an association credibility data group. Use the association strength data group and the association credibility data group as the output results of this step.

[0080] Step S14: Obtain the timeliness weighted feature data set and timeliness weight data outputted from step S12, and the associated credibility data set outputted from step S13. Use a nonlinear feature conversion method to process these three sets of data, and use adaptive weights to weight and synthesize the processing results to generate a fused feature data set. Record the source information, processing process information, and credibility information of the data, and generate a metadata index. Use the fused feature data set and metadata index as the output results of this step.

[0081] The improved entity matching algorithm mainly solves the problem of cross-platform entity information matching. Traditional entity matching mainly relies on string similarity, which is easily affected by different languages ​​and different expressions. The improved algorithm introduces a three-layer matching mechanism: the first layer is attribute feature matching, which considers the similarity of structured data such as enterprise size, registered capital, and number of employees; the second layer is text semantic matching, which calculates the semantic similarity of text information such as company name and product description through the word vector model; the third layer is spatiotemporal feature matching, which compares the consistency of spatiotemporal dimensions such as geographic location and active time. Finally, the three-layer matching results are combined through adaptive weights.

[0082] The specific implementation method is as follows: let the matching degree of entity i and entity j be M(i,j), and the calculation formula is: M(i,j) = α·A(i,j) + β·T(i,j) + γ·S(i,j); where A(i,j): attribute feature matching degree; T(i,j) is text semantic matching degree; S(i,j) is spatiotemporal feature matching degree; α, β, γ are adaptive weight coefficients, and α+β+γ=1;

[0083] A(i,j) = Σ(wk·|aik - ajk| / max(aik, ajk)); where wk is the weight of the kth attribute; aik is the kth attribute value of entity i; ajk is the kth attribute value of entity j;

[0084] T(i,j) = cos(Vi, Vj)·L(i,j); where Vi is the text vector representation of entity i; Vj is the text vector representation of entity j; cos() is the cosine similarity; L(i,j) is the normalized value of the edit distance;

[0085] S(i,j) = θ·G(i,j) + (1-θ)·D(i,j); where G(i,j) is the geographic location similarity; D(i,j) is the time dimension similarity; θ is the balance factor; α(t+1) = α(t) + η·dM / dα; β(t+1) = β(t) + η·dM / dβ; γ(t+1) = γ(t) + η·dM / dγ; where η is the learning rate; t is the number of iterations. It should be noted that d is the sign of the partial derivative.

[0086] In this embodiment, high-quality fusion and preprocessing of multi-source heterogeneous data are achieved through spatiotemporal feature extraction and multi-dimensional credibility assessment mechanism. Specifically, the original data is preprocessed by combining stratified sampling and quality assessment, which can effectively remove noise data while maintaining data representativeness; the time features are hierarchically processed by multi-scale time segmentation method, which not only retains the temporal characteristics of the data, but also can adapt to the analysis needs under different time scales; the location data is calibrated by density weighting method, which significantly improves the accuracy of geographic information; the attribute data is denoised by recursive feature decomposition method, which effectively reduces the redundancy of data; the improved entity matching algorithm is used to calculate the similarity of entity information in different data sources, making cross-platform data integration more accurate. This multi-level and multi-dimensional data preprocessing method not only improves data quality, but also establishes a quantitative evaluation system for data credibility, laying a reliable data foundation for subsequent analysis and processing. Especially in the cross-border e-commerce scenario, this method can effectively handle data differences from different countries and different language environments, significantly improving the availability and reliability of data.

[0087] like Figure 3 As shown, according to one aspect of the present application, step S2 is specifically:

[0088] Step S21, obtain the fused feature data set and metadata index output by step S1. Calculate the mean contribution value of each feature, specifically by analyzing the degree of influence of the feature on the overall data distribution; calculate the variance influence value, specifically by analyzing the influence of the fluctuation of the feature on the data stability; calculate the correlation weight value, specifically by analyzing the strength of association between the features. The mean contribution value, variance influence value and correlation weight value are weighted and synthesized to generate a feature importance data set. Subsequently, the feature importance data set is processed using adaptive weights to generate a feature reconstruction weight data set. The feature importance data set and the feature reconstruction weight data set are used as the output results of this step.

[0089] Step S22, obtain the feature importance data group and feature reconstruction weight data group output by step S21. Calculate the feature association value, specifically by analyzing the logical relationship between the features; calculate the weight mapping value, specifically by converting the feature reconstruction weight; calculate the marginal effect value, specifically by analyzing the impact of feature changes on the whole. Perform weighted synthesis on the feature association value, weight mapping value and marginal effect value to generate a value association data group. Subsequently, perform time series analysis on the value association data group, and combine it with the structural optimization parameters to generate a dynamic topology structure data group. Use the value association data group and the dynamic topology structure data group as the output results of this step.

[0090] Step S23, obtain the value association data group and dynamic topology data group outputted in step S22. Calculate the value component values ​​of each dimension, and perform time decay processing on these values ​​to generate the decayed value component data group. Perform weighted synthesis on the decayed value component data group to generate the value scoring data group. Subsequently, optimize the value scoring data group in combination with the gradient information of the topological structure to generate the comprehensive value scoring data group. The value scoring data group and the comprehensive value scoring data group are used as the output results of this step.

[0091] Step S24, obtain the value score data group and the comprehensive value score data group output by step S23. Calculate the value relevance value, specifically by analyzing the correlation of the comprehensive value score; calculate the score similarity value, specifically by comparing the value scores of different entities; calculate the topological association value, specifically by analyzing the connection characteristics of the network structure. Perform weighted synthesis on the value relevance value, score similarity value and topological association value to generate an optimized feedback data group. Subsequently, the optimized feedback data group is processed using adaptive optimization weights to generate a feature optimization data group. Record the optimization process information and parameter selection information, and generate processing metadata. The optimized feedback data group, feature optimization data group and processing metadata are used as the output results of this step.

[0092] The traditional information gain ratio tends to favor features with more values, affecting the accuracy of feature selection. The improved algorithm introduces an adaptive penalty factor to dynamically adjust the information gain ratio according to the distribution of feature values, while taking into account the mutual information between features to reduce the selection of redundant features. This method improves the accuracy and robustness of feature selection while maintaining the advantages of the original information gain ratio. Suppose the calculation formula of the improved information gain ratio IGR'(F) of feature F is: IGR'(F) = IG(F) / (H(F)·P(F)); where IG(F) is the information gain of feature F; H(F) is the entropy of feature F; P(F) is the adaptive penalty factor; IG(F) = H(C) - H(C|F); where H(C) is the entropy of the category; H(C|F) is the conditional entropy of the category under the given feature F; P(F) = (1 + λ·V(F))·(1 + μ·MI(F)); where V(F) is the variance of feature F; MI(F) is the average mutual information between feature F and other features; λ,μ are adjustment coefficients.

[0093] When traditional spectral clustering processes multi-source heterogeneous data, the similarity matrix is ​​not constructed reasonably and the computational complexity is high. The improved algorithm introduces multi-view similarity calculation and sparse processing to achieve efficient clustering of large-scale heterogeneous data.

[0094] The specific implementation method is as follows: suppose the calculation formula of the multi-view similarity matrix S of the sample set X is: S = Σ(ωk·Sk); where Sk is the similarity matrix under the k-th view; ωk is the view weight, and Σωk=1; calculation of the single-view similarity matrix Sk: Sk = exp(-||xi - xj||²k / (2σk²)); where ||xi - xj||k is the distance between samples i and j under the k-th view; Σk is the scale parameter of the k-th view; update of the view weight ωk: ωk(t+1) = ωk(t)·exp(-η·dJ / dωk); where J is the clustering objective function; η is the learning rate; calculation of the Laplace matrix L: L = D – S; D =diag(Σj sij); where D is the degree matrix; sij is the comprehensive similarity of samples i and j.

[0095] In this embodiment, through the feature decomposition reconstruction and value network construction method, the deep mining of customer features and the dynamic expression of value relationships are realized. Specifically, the recursive segmentation method is used to calculate the marginal contribution value of the feature, and the degree of influence of each feature on the target is accurately quantified; the improved information gain ratio method is used to evaluate the degree of complementarity between features, which effectively avoids information redundancy in feature selection; the feature associations are grouped by a multi-level dynamic clustering method, which not only maintains the natural association between features, but also improves the efficiency of analysis; the improved Markov chain analysis method is used to calculate the feature migration probability, and the feature evolution law is accurately grasped; the multi-scale analysis method is used to generate time series feature data, and the dynamic analysis of features at different time scales is realized; the topological structure is analyzed by an improved spectral clustering algorithm, which not only ensures the integrity of the structure, but also improves the calculation efficiency. This analysis method based on dynamic topology can not only accurately capture the evolution law of customer features, but also effectively identify potential value associations, which is particularly suitable for the complex and changeable customer feature analysis needs in the cross-border e-commerce environment.

[0096] like Figure 4 As shown, according to one aspect of the present application, step S3 is specifically:

[0097] Step S31, obtain the optimized feedback data group, feature optimization data group and processing metadata output by step S2. Perform mapping transformation on the data in the optimized feedback data group to generate feedback mapping data; perform mapping transformation on the data in the feature optimization data group to generate feature mapping data; perform mapping transformation on the data in the processing metadata to generate metadata mapping data. Perform weighted synthesis on the feedback mapping data, feature mapping data and metadata mapping data to generate a classification feature data group. Subsequently, calculate the probability of each entity belonging to different categories, which is specifically calculated by the distance between the entity feature and the category center, and consider the weight influence of each feature to generate a category belonging probability data group. Use the classification feature data group and the category belonging probability data group as the output results of this step.

[0098] Step S32, obtain the classification feature data group and category attribution probability data group output by step S31. Analyze the change trend of each feature in different time windows to generate feature trend data; analyze the evolution law of category attribution probability to generate probability evolution data; analyze the influence of time factors to generate time influence data. Perform weighted synthesis on the feature trend data, probability evolution data and time influence data to generate a trend prediction data group. Subsequently, perform time series mapping processing on the trend prediction data group, and combine the time evolution parameters to generate a value prediction data group. Use the trend prediction data group and the value prediction data group as the output results of this step.

[0099] Step S33, obtain the trend prediction data group and the value prediction data group output by step S32. Perform impact analysis on the trend prediction data to generate trend impact data; perform mapping conversion on the value prediction data to generate value mapping data; calculate the interaction strength between entities to generate interaction strength data. Perform weighted synthesis on the trend impact data, value mapping data, and interaction strength data to generate a recommendation score data group. Subsequently, based on the recommendation score data group and the preset feature weights, select the best recommendation items from the candidate recommendation set to generate an optimized recommendation list. The optimized recommendation list is used as the output result of this step.

[0100] Step S34, obtain the recommendation score data group and optimized recommendation list output by step S33. Evaluate the recommendation effect and generate effect evaluation data; analyze the impact of the recommendation list and generate list impact data; count the errors that occur and generate error statistics. Weightedly synthesize the effect evaluation data, list impact data and error statistics to generate a feedback optimization data group. Subsequently, optimize the feedback optimization data group in combination with the error gradient information to generate an optimized adjustment data group. Record the optimization effect information and adjustment suggestion information to generate quality control metadata. Use the feedback optimization data group, the optimized adjustment data group and the quality control metadata as the output results of this step.

[0101] The improved local linear embedding algorithm mainly solves the problem of nonlinear structure preservation in high-dimensional data dimensionality reduction. The improved algorithm introduces adaptive neighborhood selection and robust reconstruction weight calculation, which improves the quality of dimensionality reduction results.

[0102] The specific implementation method is as follows. Assume that the calculation formula of the reconstruction weight matrix W of the high-dimensional sample xi is: W = argmin Σ||xi - Σ(wij·xj)||²; where wij is the reconstruction weight of sample j to sample i; Xj is the neighborhood sample of sample i; adaptive neighborhood selection: Ni = {xj | d(xi,xj) < ri}; ri = r0·(1 + β·vi); where ri is the adaptive neighborhood radius of sample i; vi is the local variance of sample i; β is the adjustment parameter; robust reconstruction weight calculation: wij = (zi - zj) T C -1 (zi - zk) / Σ(zi - zj) T C -1 (zi - zk); where Zi is the local coordinate of the sample; C is the local covariance matrix; the solution of the low-dimensional coordinate Y: Y = argmin Σ||yi - Σ(wij·yj)|| 2 ;st YY T =I.

[0103] In this embodiment, accurate prediction and personalized recommendation of customer value are achieved through dynamic classification and multidimensional trend analysis methods. Specifically, a stratified sampling method is used to generate a representative sample distribution, which effectively reduces the complexity of data processing; the high-dimensional features are reduced by an improved local linear embedding algorithm, which not only maintains the essential attributes of the features, but also improves the computational efficiency; the feature mapping data is generated by an adaptive weight allocation method, which realizes the dynamic adjustment of feature importance; the local density of data distribution is calculated by a dynamic kernel density estimation method, and the clustering characteristics of the data are accurately identified; the initial probability data is generated by a multidimensional Gaussian mixture model, which improves the accuracy of classification; the trend prediction data is decomposed into time series by a wavelet transform method, and the trend characteristics are effectively extracted; the improved association rule mining method is used to generate interactive initial data, and the association pattern between customers is accurately identified. This multi-dimensional and dynamic analysis method not only improves the accuracy of customer value prediction, but also realizes the personalized customization of recommendation results, which is particularly suitable for the accurate identification and dynamic tracking of customer value in the cross-border e-commerce environment.

[0104] like Figure 5 As shown, according to one aspect of the present application, step S4 is specifically:

[0105] Step S41, obtain the quality control metadata, optimization adjustment data group and optimization recommendation list output in step S3. Perform effect analysis on the optimization adjustment results to generate adjustment effect data; evaluate the accuracy of the recommendation results to generate accuracy evaluation data; analyze the time stability of the system operation to generate stability data. Perform weighted synthesis on the adjustment effect data, accuracy evaluation data and stability data to generate a quality evaluation data group. Subsequently, perform a comprehensive calculation on the quality evaluation data group based on the weight coefficients and credibility adjustment parameters of different dimensions to generate a comprehensive quality score data group. Use the quality evaluation data group and the comprehensive quality score data group as the output results of this step.

[0106] Step S42, obtain the quality assessment data group and the comprehensive quality score data group output in step S41. Calculate the deviation value of the quality data to generate quality deviation data; analyze the abnormal points of the score data to generate score abnormal data; determine the threshold range for abnormal judgment to generate threshold adjustment data. Weighted synthesis of the quality deviation data, score abnormal data and threshold adjustment data to generate an abnormal detection data group. Subsequently, according to the abnormal detection results and quality scores, formulate corresponding processing plans to generate a processing strategy data group. The abnormal detection data group and the processing strategy data group are used as the output results of this step.

[0107] Step S43, obtain the anomaly detection data group and processing strategy data group output by step S42. Analyze the impact of the anomaly data on the system and generate anomaly impact data; evaluate the execution effect of the processing strategy and generate strategy effect data; calculate the time sensitivity of the data and generate time sensitive data. Perform weighted synthesis on the anomaly impact data, strategy effect data and time sensitive data to generate an update frequency data group. Subsequently, prioritize the update frequency data group in combination with the time decay factor to generate an update priority data group. Use the update frequency data group and the update priority data group as the output results of this step.

[0108] Step S44, obtain the update frequency data group and update priority data group output by step S43, and the optimization recommendation list output by step S3. Analyze the impact of update frequency on display effect and generate update impact data; convert priority information into display parameters and generate priority mapping data; calculate the importance of recommendation results and generate importance data. Weightedly synthesize the update impact data, priority mapping data and importance data to generate a display optimization data group. Subsequently, optimize the display optimization data group according to the time correlation parameters to generate a final display plan. Record the full process indicator information and update suggestion information to generate system metadata. Use the display optimization data group, the final display plan and the system metadata as the output results of this step.

[0109] In this embodiment, the stability of system operation and the dynamic optimization of results are achieved through quality assessment and feedback optimization methods. Specifically, the time window segmentation method is used to perform segmented analysis on the optimization adjustment data, accurately grasping the timing characteristics of system adjustment; the initial accuracy data is generated by the improved confusion matrix analysis method, providing a reliability measure of the recommendation results; the initial stability data is generated by the volatility detection method, effectively monitoring the system operation status; the hierarchical analysis method is used to generate dimension weight data, realizing the scientific quantification of multi-dimensional quality assessment; the initial credibility data is generated by the evidence theory analysis method, improving the reliability of the assessment results; the cascade effect analysis method is used to generate update propagation data, accurately assessing the impact of system updates; the multi-dimensional monitoring method is used to generate indicator collection data, realizing all-round monitoring of system operation. Not only does it ensure the stability of system operation, but it also realizes the dynamic optimization of results, which is particularly suitable for the high requirements for system reliability and data real-time performance in the cross-border e-commerce environment.

[0110] According to one aspect of the present application, step S11 is specifically:

[0111] Step S111, respectively obtain e-commerce platform data, social media data, corporate official website data, exhibition information data, industry database data and customs data. Perform format consistency check on each type of data and generate format check report data. According to the format check report data, automatically correct the data that does not meet the format requirements and generate format correction data. Subsequently, perform noise detection on the corrected data and generate noise marker data. Clean the original data according to the noise marker data to generate a preprocessed data group.

[0112] Step S112, obtain the preprocessed data group output by step S111. Extract time dimension information from the preprocessed data group, including the first appearance time, the most recent active time and the active period, and generate time feature initial data. Use the multi-scale time segmentation method to hierarchically process the time feature initial data to generate multi-scale time feature data. Then, calculate the continuity index of the time feature to generate time continuity data. Fuse the multi-scale time feature data and the time continuity data to generate a time dimension feature data group.

[0113] Step S113, obtaining the preprocessed data group outputted by step S111. Extracting geographic location information from the preprocessed data group, including longitude data, latitude data and address text data, to generate a location initial data group. Performing word segmentation processing on the address text data in the location initial data group, extracting administrative division information, and generating division standard data. Subsequently, matching and verifying the longitude and latitude data with the division standard data, and correcting the inconsistent data using the density weighting method, to generate location calibration data. Constructing a multi-level geographic code based on the location calibration data, and generating a spatial dimension feature data group.

[0114] Step S114, obtaining the preprocessed data group outputted by step S111. Extract enterprise scale data, industry code data and transaction frequency data from the preprocessed data group to generate an initial attribute data group. Use the recursive feature decomposition method to perform noise reduction on the initial attribute data group to generate attribute noise reduction data. Subsequently, calculate the coupling degree between attributes, use the adaptive threshold method to select highly correlated attributes, and generate attribute related data. Integrate the attribute noise reduction data and attribute related data to generate an entity attribute feature data group.

[0115] Step S115, obtain the time dimension feature data group output by step S112, the space dimension feature data group output by step S113, and the entity attribute feature data group output by step S114. Use the spatiotemporal projection transformation method to unify the three sets of feature data into the same feature space to generate unified feature data. Calculate the mutual information value between features to generate feature association data. Subsequently, construct feature fusion weights based on the feature association data to generate weight allocation data. Perform weighted combination on the unified feature data according to the weight allocation data to generate a spatiotemporal feature data group.

[0116] Step S116, obtain the spatiotemporal feature data group output by step S115 and the preprocessed data group output by step S111. Calculate the data consistency index between different data sources, and use the hierarchical fuzzy evaluation method to generate consistency scoring data. According to the timestamp information of the data, use the exponential decay model to calculate the timeliness index and generate timeliness scoring data. Subsequently, based on the accuracy record of historical data, use the sliding window method to calculate the source reliability index and generate reliability scoring data. Perform nonlinear combination of consistency scoring data, timeliness scoring data and reliability scoring data to generate a credibility scoring data group.

[0117] Step S117, obtain the credibility score data group output by step S116. Calculate the historical contribution of each data source, and use the dynamic programming method to generate data source weight data. Use the improved entity matching algorithm to calculate the similarity of entity information in different data sources and generate entity matching data. Subsequently, based on the data source weight data and entity matching data, use the weighted voting method to calculate the cross-validation coefficient and generate verification coefficient data. Multiply the verification coefficient data with the credibility score data group to generate a cross-validation score data group.

[0118] In this embodiment, the standardized processing and quality assurance of heterogeneous data are achieved through multi-source data collection and preprocessing methods. Specifically, a method combining format consistency check and automatic correction is adopted to effectively handle the format differences of data from different sources; a multi-scale time segmentation method is used to achieve hierarchical expression of time features; a density weighted method is used to calibrate location data to improve the accuracy of geocoding; a recursive feature decomposition method is used to reduce noise in attribute data and reduce data redundancy; a hierarchical fuzzy evaluation method is used to generate consistency scores to achieve quantitative evaluation of data quality; an exponential decay model is used to calculate timeliness indicators to accurately reflect the timeliness characteristics of data; a sliding window method is used to calculate source reliability to dynamically evaluate the credibility of the data source; a dynamic programming method is used to generate data source weights to achieve optimal fusion of multi-source data. This multi-level, adaptive data preprocessing method not only improves data quality, but also establishes a reliable data evaluation system, which is particularly suitable for processing the complex characteristics of multi-source heterogeneous data in a cross-border e-commerce environment.

[0119] According to one aspect of the present application, step S22 is specifically:

[0120] Step S221, obtain a feature importance data set and a feature reconstruction weight data set. Perform local sensitivity analysis on each feature in the feature importance data set, use recursive segmentation to calculate the marginal contribution value of the feature, and generate marginal contribution data. Use an improved information gain ratio method to calculate the degree of complementarity between features and generate feature complementarity data. Subsequently, based on the marginal contribution data and feature complementarity data, construct a feature combination effect matrix to generate combination effect data. Map the combination effect data to the feature reconstruction weight data set to generate a feature association initial data set.

[0121] Step S222, obtaining the feature association initial data group outputted in step S221. Using a multi-level dynamic clustering method, the feature association initial data is grouped to generate association group data. The interaction strength of the intra-group features is calculated, and the intra-group association data is generated using an adaptive weight allocation method. Subsequently, a transfer relationship network of inter-group features is constructed, and the inter-group association data is generated using an improved path analysis method. The intra-group association data and the inter-group association data are fused to generate a multi-level association data group.

[0122] Step S223, obtain the multi-level association data group output by step S222. Construct a feature flow probability matrix, use the improved Markov chain analysis method to calculate the feature migration probability, and generate feature migration data. Based on the feature migration data, use the time series association mining method to identify the feature evolution pattern and generate evolution pattern data. Subsequently, calculate the stability index of the feature evolution, and use the entropy method to generate stability evaluation data. Integrate the feature migration data, evolution pattern data, and stability evaluation data to generate a value association data group.

[0123] Step S224, obtain the value-related data group outputted in step S223. Construct a characteristic state sequence based on a time window, and generate state transition data using a state transition probability calculation method. Perform time series decomposition on the state transition data, and generate time series feature data using a multi-scale analysis method. Subsequently, construct a dynamic evolution network based on the time series feature data, and generate structure evolution data using a network growth model. Optimize and combine the state transition data and the structure evolution data to generate a topology preprocessing data group.

[0124] Step S225, obtaining the topological preprocessing data group outputted by step S224. Using an improved spectral clustering algorithm to perform structural analysis on the topological preprocessing data, generating structural feature data. Calculating the centrality index of the network nodes, and using a multi-index fusion method to generate node importance data. Subsequently, constructing a hierarchical topological structure based on the node importance data, and using a self-organizing mapping method to generate hierarchical structure data. Dynamically optimizing the structural feature data and the hierarchical structure data, generating a dynamic topological structure data group.

[0125] Step S226, obtaining the dynamic topology data group outputted in step S225. Using a time-varying network analysis method, calculate the time evolution characteristics of the topology structure and generate time characteristic data. Construct a network stability evaluation index and use a multi-dimensional evaluation method to generate stability data. Subsequently, based on the time characteristic data and stability data, design a structure optimization strategy and generate optimization strategy data. Adjust and optimize the dynamic topology data group according to the optimization strategy data to generate a final dynamic topology data group.

[0126] Traditional path analysis methods do not consider the timeliness and reliability of the path, resulting in inaccurate evaluation of path importance. The improved algorithm introduces time decay factor and credibility evaluation to achieve dynamic adjustment of path weight. The specific implementation method is as follows: the calculation formula of the weight W(p) of path p is: W(p) = B(p)·T(p)·R(p); where B(p): is the basic path weight; T(p): time decay factor; R(p) is the path credibility; B(p) = Π(wij), (i,j)∈p; where wij is the edge weight from node i to node j in path p; T(p) = exp(-λ·Δt(p)); where λ is the decay coefficient; Δt(p) is the time span of path p; R(p) = min{r(i,j)}, (i,j)∈p; where r(i,j) is the edge credibility from node i to node j.

[0127] The traditional Markov chain method assumes that the state transition probability is constant and cannot adapt to dynamically changing scenarios. The improved algorithm introduces the time-varying transition matrix and state duration distribution, which can more accurately describe the feature evolution process. The specific implementation method is as follows: suppose the state transfer matrix P(t) at time t is calculated as follows: P(t) = M(t)·D(t)·S(t); where M(t) is the basic transfer matrix; D(t) is the duration adjustment factor; S(t) is the seasonal adjustment factor; M(t) = {mij(t)} = P(Xt+1 = j|Xt = i); where mij(t) is the probability of transitioning from state i to state j at time t; D(t) = diag{d1(t), d2(t), ..., dn(t)}; where di(t) is the duration distribution function of state i; S(t) = {sij(t)} = (1 + α·sin(ωt + φ)); where α is the seasonal fluctuation intensity; ω is the period parameter; and φ is the phase offset.

[0128] In this embodiment, the dynamic expression and accurate characterization of customer value relationships are achieved through the value network construction and topological structure generation method. Specifically, the recursive partitioning method is used to calculate the marginal contribution value, and the importance of the features is accurately quantified; the multi-level dynamic clustering method is used to perform association grouping, which improves the efficiency of network construction; the improved path analysis method is used to generate inter-group association data, and the transmission relationship between features is accurately identified; the improved Markov chain analysis method is used to calculate the feature migration probability, and the value flow law is effectively grasped; the time series association mining method is used to identify the feature evolution mode, and the extraction of dynamic features is realized; the multi-dimensional evaluation method is used to generate stability data, which ensures the reliability of the network structure. This dynamic and multi-dimensional network construction method can not only accurately describe customer value relationships, but also effectively predict value flow trends. It is particularly suitable for complex customer value network analysis needs in cross-border e-commerce environments.

[0129] According to one aspect of the present application, step S31 is specifically:

[0130] Step S311, obtain the optimization feedback data group, feature optimization data group and processing metadata. Adaptively sample each data in the optimization feedback data group, and use the stratified sampling method to generate sample distribution data. Calculate the statistical characteristics of the sample data, and use the multidimensional analysis method to generate feature statistical data. Subsequently, construct data quality assessment indicators based on the feature statistical data, and use the comprehensive weighted method to generate quality assessment data. Optimize and screen the sample distribution data according to the quality assessment data to generate a preprocessing feature data group.

[0131] Step S312, obtaining the preprocessed feature data set outputted by step S311. Using an improved local linear embedding algorithm to reduce the dimension of high-dimensional features, generating reduced-dimensional feature data. Constructing a feature importance assessment model, using an entropy-based feature selection method to generate importance ranking data. Subsequently, designing a feature combination strategy based on the importance ranking data, and using a combination optimization algorithm to generate feature combination data. Fusion the reduced-dimensional feature data with the feature combination data to generate an optimized feature data set.

[0132] Step S313, obtaining the optimized feature data set outputted by step S312. Constructing a multi-level feature mapping network, and using an adaptive weight allocation method to generate feature mapping data. Calculating the correlation coefficient between features, and using an improved correlation analysis method to generate feature association data. Subsequently, based on the feature mapping data and feature association data, designing feature conversion rules, and generating feature conversion data. Performing nonlinear combination on the feature conversion data, and generating a conversion feature data set.

[0133] Step S314, obtain the converted feature data set outputted in step S313. Use the dynamic kernel density estimation method to calculate the local density of the data distribution and generate density distribution data. Construct a density-based cluster center recognition model and use the adaptive threshold method to generate center point data. Subsequently, calculate the distance between the sample point and the cluster center and use the improved distance measurement method to generate distance matrix data. Generate a classification feature data set based on the density distribution data and the distance matrix data.

[0134] Step S315, obtaining the classification feature data group outputted by step S314. Constructing a probability-based category discrimination model, using a multidimensional Gaussian mixture model to generate initial probability data. Calculating the similarity between the sample features and the category features, using an improved cosine similarity method to generate similarity data. Subsequently, based on the initial probability data and the similarity data, designing a probability adjustment strategy, generating probability adjustment data. Normalizing the probability adjustment data, generating a category attribution probability data group.

[0135] Step S316, obtaining the category attribution probability data group outputted in step S315. Constructing a probability consistency test model, and using the cross-validation method to generate verification result data. Calculating the probability interference degree between different categories, and using the mutual information analysis method to generate interference assessment data. Subsequently, based on the verification result data and the interference assessment data, designing a probability optimization strategy, and generating optimization scheme data. Adjusting the category attribution probability data group according to the optimization scheme data, and generating the final category attribution probability data group.

[0136] The improved correlation analysis method mainly solves the problem of measuring nonlinear correlation between features. The improved algorithm combines mutual information and dynamic time warping (DTW) distance, which can more accurately measure the nonlinear correlation between features. The specific implementation method is as follows: Assume that the calculation formula of the correlation measure R(X,Y) of features X and Y is R(X,Y) = α·I(X,Y) + β·D(X,Y); where I(X,Y) is the normalized mutual information; D(X,Y) is the normalized value of the DTW distance; α, β are weight coefficients, and α+β=1; the normalized mutual information is calculated as I(X,Y) = [H(X) + H(Y) - H(X,Y)] / max[H(X),H(Y)]; where H(X),H(Y) are edge entropies; H(X,Y) is joint entropy; the DTW distance is calculated as D(X,Y) = min[Σdp] / L; where dp is the local distance on the optimal path; L is the path length; the dynamic weight adjustment is α = exp(-γ·var(X,Y)); β = 1 –α; where var(X,Y) is the local variance ratio; γ is the adjustment parameter.

[0137] The improved distance measurement method mainly solves the problem of sample distance calculation in multi-source heterogeneous data space. The traditional Euclidean distance cannot effectively handle features of different scales and types. The improved algorithm introduces feature weight adaptation and multi-space projection mechanism to improve the accuracy of distance measurement. Suppose the calculation formula of the comprehensive distance D(i,j) between samples i and j is D(i,j) =Σ(wk·dk(i,j))·F(i,j); where wk is the adaptive weight of the k-th feature; dk(i,j) is the distance of the k-th feature space; F(i,j) is the global adjustment factor; the feature space distance is calculated as dk(i,j) = ||Pk(xi) - Pk(xj)|| / σk; where Pk is the projection operator of the k-th feature; σk is the scale parameter of the feature space; the adaptive weight update is wk(t+1)= wk(t)·exp(-η·dE / dwk); where E is the distance metric loss function; η is the learning rate; the global adjustment factor is calculated as F(i,j) = exp(-λ·|Si - Sj|); where Si,Sj are the global structural features of samples i,j; λ is the attenuation coefficient.

[0138] In this embodiment, the dynamic classification method is used to achieve accurate identification of customer characteristics and adaptive classification of categories. Specifically, the stratified sampling method is used to generate sample distribution data to ensure the representativeness and balance of the sample; the feature statistical data is generated by the multidimensional analysis method to fully capture the statistical characteristics of the data; the improved local linear embedding algorithm is used for dimensionality reduction processing to effectively maintain the essential characteristics of the data; the entropy-based feature selection method is used to accurately identify key features; the feature mapping data is generated by the adaptive weight allocation method to achieve dynamic adjustment of feature importance; the local density is calculated by the dynamic kernel density estimation method to accurately identify the clustering center of the data; the multidimensional Gaussian mixture model is used to generate initial probability data to improve the accuracy of classification; the mutual information analysis method is used to generate interference assessment data to effectively reduce interference between categories. This adaptive and dynamic classification method not only improves the accuracy of customer classification, but also realizes the dynamic update of classification results, which is particularly suitable for the rapid changes in customer characteristics in the cross-border e-commerce environment.

[0139] According to one aspect of the present application, step S33 is specifically:

[0140] Step S331, obtain a trend prediction data group and a value prediction data group. Perform time series decomposition on the trend prediction data, and use the wavelet transform method to generate trend component data. Calculate the rate of change of the trend component, and use the dynamic slope analysis method to generate the rate of change data. Subsequently, based on the trend component data and the rate of change data, construct a trend impact assessment model to generate trend assessment data. Perform weight optimization on the trend assessment data to generate a trend impact data group.

[0141] Step S332, obtain the trend impact data group outputted in step S331. Construct a mapping model for value prediction data, and use a nonlinear projection method to generate value mapping data. Calculate the stability index of the mapping data, and use an entropy value evaluation method to generate stability data. Subsequently, design a mapping optimization strategy based on the stability data, and use an adaptive adjustment method to generate optimized mapping data. Reorganize the optimized mapping data to generate a value feature data group.

[0142] Step S333, obtain the value feature data group output by step S332. Construct an entity interaction strength calculation model, and use the improved association rule mining method to generate initial interaction data. Calculate the influence propagation efficiency between entities, and use the network flow analysis method to generate propagation efficiency data. Subsequently, based on the initial interaction data and propagation efficiency data, design interaction strength evaluation rules and generate interaction evaluation data. Normalize the interaction evaluation data to generate an interaction strength data group.

[0143] Step S334: Obtain the trend impact data group output by step S331, the value feature data group output by step S332, and the interaction intensity data group output by step S333. Construct a multi-dimensional recommendation scoring model and generate initial scoring data using the hierarchical weighting method. Calculate the synergy between the scoring indicators and generate synergy data using the cross-validation method. Subsequently, based on the initial scoring data and synergy data, design a scoring optimization strategy and generate optimized scoring data. Comprehensively process the optimized scoring data to generate a recommended score data group.

[0144] Step S335, obtain the recommendation score data set output by step S334. Construct a recommendation candidate set screening model, and use a multi-objective optimization method to generate candidate set data. Calculate the similarity index between candidate items, and use the improved cosine similarity method to generate similarity data. Subsequently, based on the candidate set data and similarity data, design a diversity optimization strategy to generate diversity evaluation data. Sort and optimize the diversity evaluation data to generate a recommendation sorting data set.

[0145] Step S336, obtain the recommended ranking data group outputted in step S335. Construct a recommendation list generation model, and use the sequence optimization method to generate the initial list data. Calculate the coverage index of the list items, and use the set coverage analysis method to generate the coverage data. Subsequently, based on the initial list data and the coverage data, design a list optimization strategy, and generate the optimization strategy data. Adjust the recommendation list according to the optimization strategy data, and generate an optimized recommendation list.

[0146] The improved association rule mining method mainly solves the problem of pattern discovery in large-scale transaction data. The traditional Apriori algorithm has high computational complexity and easily generates a large number of redundant rules. The improved algorithm introduces a dynamic support threshold and rule pruning mechanism to improve mining efficiency and rule quality.

[0147] Suppose the score Score(R) of association rule R is X→Y, and the calculation formula is Score(R) = conf(R)·lift(R)·nov(R); where conf(R) is the rule confidence; lift(R) is the lift; nov(R) is the novelty; the dynamic support threshold is calculated as min_sup(t) = base_sup·(1 + α·d(t)); where d(t) is the time decay function; α is the adjustment coefficient; the rule confidence is calculated as conf(R) = sup(X∪Y) / sup(X)·(1 + β·time_weight(R)); where time_weight(R) is the time weight; β is the time influence factor; the rule novelty is calculated as nov(R) = 1 - Σ(sim(R,Ri)) / |Rs|; where sim(R,Ri) is the rule similarity; Rs is the existing rule set.

[0148] The improved cosine similarity method mainly solves the problem of high-dimensional sparse vector similarity calculation. Traditional cosine similarity is easily affected by vector sparsity and noise. The improved algorithm introduces feature importance weighting and local sensitive hashing to improve the accuracy and efficiency of similarity calculation. Suppose the improved cosine similarity sim(u,v) of vectors u and v is calculated as, sim(u,v) = Σ(wi·ui·vi) / (sqrt(Σ(wi·ui²))·sqrt(Σ(wi·vi²)))·K(u,v); where wi is the feature importance weight; K(u,v) is the local structure similarity; the feature importance weight is calculated as wi = tf(i)·idf(i)·ent(i); where tf(i) is the feature frequency, idf(i) is the inverse document frequency, and ent(i) is the feature entropy; the local structure similarity is calculated as K(u,v) = exp(-γ·||Lu - Lv||²); where Lu,Lv are local structure feature vectors; γ is the kernel function parameter; the local sensitive hashing is calculated as h(x) = sign(Wx + b); where W is the random projection matrix and b is the random bias vector.

[0149] In this embodiment, the recommendation generation method is used to achieve precision and personalization of recommendation results. Specifically, the wavelet transform method is used to decompose the trend prediction data and accurately extract the trend characteristics; the dynamic slope analysis method is used to generate the rate of change data and effectively grasp the changing law of the trend; the nonlinear projection method is used to generate the value mapping data and maintain the nonlinear characteristics of the data; the improved association rule mining method is used to generate the initial interaction data and accurately identify the association pattern between entities; the network flow analysis method is used to generate the propagation efficiency data and quantify the propagation effect of the influence; the hierarchical weighted method is used to generate the initial scoring data and realize the comprehensive evaluation of multiple dimensions; the multi-objective optimization method is used to generate the candidate set data to ensure the diversity of the recommendation results; the set coverage analysis method is used to generate the coverage data and improve the comprehensiveness of the recommendation. This precise and personalized recommendation method not only improves the accuracy of the recommendation, but also realizes the differentiation of the recommendation results, which is particularly suitable for the high requirements for personalized recommendations in the cross-border e-commerce environment.

[0150] According to one aspect of the present application, step S41 is specifically,

[0151] Step S411, obtain quality control metadata, optimization adjustment data group and optimization recommendation list. Perform segment analysis on the optimization adjustment data, and use the time window segmentation method to generate time series segment data. Calculate the adjustment effect value of each time segment, and use the gain analysis method to generate effect evaluation data. Subsequently, based on the time series segment data and effect evaluation data, construct an adjustment response model to generate response feature data. Normalize the response feature data to generate an adjustment effect data group.

[0152] Step S412, obtaining the adjustment effect data set outputted in step S411. Constructing a recommendation accuracy evaluation model, and using an improved confusion matrix analysis method to generate initial accuracy data. Calculating the accuracy index of the recommendation result, and using a hierarchical evaluation method to generate accuracy data. Subsequently, based on the initial accuracy data and the accuracy data, designing an accuracy optimization strategy, and generating accuracy optimization data. Performing weighted processing on the accuracy optimization data, and generating an accuracy evaluation data set.

[0153] Step S413, obtain the accuracy evaluation data set outputted in step S412. Construct a system stability analysis model, and generate initial stability data using the volatility detection method. Calculate the consistency index of the system response, and generate consistency data using the multi-dimensional comparison method. Subsequently, based on the initial stability data and consistency data, design stability evaluation rules and generate stability evaluation data. Perform feature extraction on the stability evaluation data to generate a stability feature data set.

[0154] Step S414, obtain the adjustment effect data group output by step S411, the accuracy assessment data group output by step S412, and the stability characteristic data group output by step S413. Construct a multi-dimensional quality assessment model, and use the hierarchical analysis method to generate dimension weight data. Calculate the degree of association between each dimension, and use the association analysis method to generate associated characteristic data. Subsequently, based on the dimension weight data and the associated characteristic data, design quality assessment rules and generate assessment rule data. Apply the assessment rule data to each data group to generate a quality assessment data group.

[0155] Step S415, obtaining the quality assessment data set outputted in step S414. Constructing a credibility assessment model, and using the evidence theory analysis method to generate the credibility initial data. Calculating the uncertainty index of the assessment result, and using the probability statistics method to generate the uncertainty data. Subsequently, based on the credibility initial data and the uncertainty data, designing the credibility adjustment strategy, and generating the adjustment scheme data. Optimizing the adjustment scheme data, and generating the credibility adjustment data set.

[0156] Step S416: Obtain the quality assessment data set output from step S414 and the credibility adjustment data set output from step S415. Construct a comprehensive scoring model and generate initial score data using a multi-criteria decision-making method. Calculate the contribution of each scoring dimension and generate contribution feature data using a contribution analysis method. Subsequently, based on the initial score data and contribution feature data, design a score optimization strategy and generate optimization solution data. Apply the optimization solution data to the scoring results to generate a comprehensive quality score data set.

[0157] The improved confusion matrix analysis method mainly solves the evaluation of multi-class imbalanced classification problems. Traditional confusion matrix analysis cannot effectively handle cases of class imbalance and unequal misclassification costs. The improved algorithm introduces cost-sensitive learning and dynamic evaluation indicators, providing a more reasonable classification performance evaluation solution. Suppose the calculation formula of the classification cost matrix C from category i to j is, C(i,j) = base_cost(i,j)·W(i,j)·T(t); where base_cost(i,j) is the basic misclassification cost; W(i,j) is the category weight adjustment factor; T(t) is the time adjustment factor; the category weight is adjusted to W(i,j) = (Ni / Nj)^α·(Vi / Vj)^β; where Ni,Nj are the number of category samples; Vi,Vj are the category importance values; α,β are balance parameters; the comprehensive evaluation index is calculated as Score = (1 + μ²)·(P·R) / (μ²·P + R); where P is the weighted precision; R is the weighted recall; μ is the category importance parameter; the dynamic threshold is adjusted to threshold(i) = base_threshold·(1 + λ·imbalance(i)); where imbalance(i) is the category imbalance measure; λ is the adjustment coefficient. ^ is the superscript symbol.

[0158] In this embodiment, through the quality assessment method, all-round monitoring and evaluation of the system operation quality is achieved. Specifically, the time window segmentation method is used for segmented analysis to accurately grasp the timing characteristics of system adjustment; the improved confusion matrix analysis method is used to evaluate the accuracy and provide a reliable performance measurement; the volatility detection method is used to analyze the system stability and effectively monitor the operation status; the multi-dimensional comparison method is used to generate consistency data to ensure the comprehensiveness of the evaluation; the dimensional weight data is generated by the hierarchical analysis method to achieve scientific weight distribution; the evidence theory analysis method is used to evaluate the credibility and improve the reliability of the evaluation results; the multi-criteria decision-making method is used to generate the initial score data to achieve the scientific nature of the comprehensive evaluation. This systematic and multi-dimensional quality assessment method not only achieves accurate quality assessment, but also establishes a complete quality monitoring system, which is particularly suitable for the strict requirements for system reliability in the cross-border e-commerce environment.

[0159] According to one aspect of the present application, step S44 is specifically,

[0160] Step S441, obtain the update frequency data group, the update priority data group and the optimization recommendation list. Construct an update impact analysis model, and use the cascade effect analysis method to generate update propagation data. Calculate the impact range of the update operation, and use the propagation path tracking method to generate the impact range data. Subsequently, based on the update propagation data and the impact range data, design the impact assessment rules and generate the impact assessment data. Quantify the impact assessment data to generate the update impact data group.

[0161] Step S442, obtain the update impact data group output by step S441. Construct a priority mapping model and use a nonlinear transformation method to generate priority feature data. Calculate the timeliness index of the priority and use a time decay analysis method to generate timeliness data. Subsequently, based on the priority feature data and timeliness data, design a priority conversion rule and generate conversion strategy data. Apply the conversion strategy data to the original priority to generate a priority mapping data group.

[0162] Step S443, obtain the priority mapping data group output by step S442. Construct a recommendation importance evaluation model, and use a multi-dimensional weight analysis method to generate initial importance data. Calculate the association strength between recommended items, and use an association rule mining method to generate association strength data. Subsequently, based on the initial importance data and the association strength data, design importance optimization rules and generate optimization rule data. Apply the optimization rule data to the recommended items to generate an importance data group.

[0163] Step S444, obtain the update impact data group output by step S441, the priority mapping data group output by step S442, and the importance data group output by step S443. Construct a display optimization model, and use the multi-objective programming method to generate the initial optimization data. Calculate the coverage index of the display scheme, and use the set coverage analysis method to generate the coverage data. Subsequently, based on the initial optimization data and coverage data, design a display optimization strategy and generate the optimization scheme data. Comprehensively process the optimization scheme data to generate a display optimization data group.

[0164] Step S445, obtaining the display optimization data group outputted in step S444. Constructing a time correlation analysis model, and using a time series association mining method to generate time feature data. Calculating the timeliness index of the display content, and using a dynamic threshold method to generate timeliness evaluation data. Subsequently, based on the time feature data and timeliness evaluation data, designing display scheme generation rules, and generating scheme rule data. Applying the scheme rule data to the display content, and generating a final display scheme.

[0165] Step S446, obtain the final display solution outputted in step S445. Construct a system indicator collection model, and use a multi-dimensional monitoring method to generate indicator collection data. Calculate the performance parameters of the system operation, and use a performance evaluation method to generate performance data. Subsequently, analyze the collected indicator data, and use a trend analysis method to generate trend feature data. Generate update suggestion data based on the performance data and trend feature data. Integrate the indicator collection data, performance data, trend feature data, and update suggestion data to generate system metadata.

[0166] In this embodiment, the optimized presentation and dynamic update of information are achieved through the result display method. Specifically, the cascade effect analysis method is used to evaluate the impact of updates, and the impact range of updates is accurately grasped; the priority feature data is generated by the nonlinear transformation method, and the scientific quantification of priorities is achieved; the time decay analysis method is used to generate timeliness data, which accurately reflects the timeliness of the data; the multi-dimensional weight analysis method is used to evaluate the importance, and the optimized sorting of the display content is achieved; the optimized initial data is generated by the multi-objective planning method to ensure the optimization of the display plan; the time feature data is generated by the time series association mining method, and the time series characteristics of the display content are grasped; the multi-dimensional monitoring method is used to generate indicator collection data, and comprehensive monitoring of the display effect is achieved. This optimized and dynamic display method not only improves the effect of information display, but also realizes the intelligent update of the display content, which is particularly suitable for the requirements of real-time and personalization of information display in the cross-border e-commerce environment.

[0167] Some formulas and models of this application are as follows: Stratified sampling method: Construct stratified sampling function S(X): S(X) = {ni = (Ni / N)·n·(si / s*) α}; Where ni is the sample size of the i-th layer; Ni is the population size of the i-th layer; N is the population size; n is the total sample size; si is the standard deviation of the i-th layer; s* is the population standard deviation; α is the adjustment parameter to control the allocation bias.

[0168] Multi-scale time segmentation method: time series segmentation function T(X,k): T(X,k) = argmin Σ(var(Si)) + λ·k; where Si is the i-th time period; var(Si) is the intra-segment variance; k is the number of segmentations; and λ is the penalty coefficient.

[0169] Density weighted method: Position weight calculation W(p): W(p) = Σ(K(||p-pi|| / h)) / Σ(K(||p-pj|| / h)) ; where K(·) is the kernel function; h is the bandwidth parameter; pi, pj are reference position points.

[0170] Recursive eigendecomposition method, eigendecomposition function D(X): D(X) = {Xi = Fi(X) | ||Xi - X||² <ε} Fi+1(X) = Fi(X) - gi·▽E(Fi(X)); where Fi is the i-th decomposition function; gi is the step size; E is the reconstruction error; and ε is the threshold.

[0171] Adaptive threshold method, threshold function θ(t): θ(t) = μ(t) + α·σ(t)·(1 + β·v(t)) ; where μ(t) is the local mean; σ(t) is the local standard deviation; v(t) is the coefficient of variation; α, β are control parameters;

[0172] Spatiotemporal projection transformation method, projection matrix P: P = W·(X T X + λI) -1 ·X T Y = P·X; where W is the feature weight matrix; λ is the regularization parameter; X is the original feature matrix; and Y is the projected feature matrix.

[0173] Exponential decay model, time-effectiveness weight w(t): w(t) = exp(-λ·t)·(1 + α·sin(ωt +φ)) ; where λ is the decay rate; α is the seasonal intensity; ω is the period parameter; φ is the phase shift;

[0174] Sliding window method, reliability score R(t): R(t) = Σ(wi·vi) / Σwi, i∈[tw,t], where wi is the window weight; vi is the observation value; w is the window size.

[0175] Dynamic programming method, weight optimization function V(n,w): V(n,w) = max{V(n-1,w), V(n-1,w-wi) +vi}; where wi is the i-th weight option; vi is the corresponding value; w is the total weight constraint.

[0176] Network growth model, node growth probability P(k): P(k) = (k + α)^β / Σ(j + α)^β; where k is the node degree; α is the initial attraction; β is the priority connection index;

[0177] The weight vector of the competition layer neuron is updated to Wi(t+1) = Wi(t) + η(t)·h(i,j)·[X(t) - Wi(t)]; where Wi is the weight vector of the i-th neuron; η(t) is the learning rate, η(t) = η0·exp(-t / τ1); h(i,j) is the neighborhood function, h(i,j) = exp(-||ri-rj|| 2 / 2σ 2 (t)); σ(t) is the neighborhood radius, σ(t) = σ0·exp(-t / τ 2 ).

[0178] Multi-objective optimization method, the objective function set is F(x) = [f1(x), f2(x), ..., fm(x)]; the Pareto optimal solution is P = {x | does not exist: F(y) < F(x)}; the dominance calculation is dom(x) = |{y | F(x) <F(y)}|; where x, y are decision vectors; it should be noted that < is the dominance relationship; |·| is the set cardinality;

[0179] Sequence optimization method, sequence score S(X): S(X) = Σ(wi·si) - λ·D(X); the update rule is X(t+1) = X(t) + α·▽S(X(t)); where wi is the item weight; si is the single item score; D(X) is the diversity penalty term; and α is the learning rate.

[0180] Set cover analysis method, the coverage is calculated as C(S) = |∪Si| / |U|; the gain function is gain(s) = |s ∩ (U \ C)| / cost(s), where S is the selected set; U is the entire set; cost(s) is the selection cost; C is the current coverage.

[0181] Volatility detection method, the volatility index is calculated as V(t) = sqrt(Σ(xi - μ)² / n)·(1 + β·|Δμ / μ|); the anomaly is determined as A(t) = {x | |x - μ| > k·V(t)}; where μ is the local mean; Δμ is the mean change; β is the sensitivity parameter; and k is the anomaly threshold.

[0182] Cascade effect analysis method, the propagation intensity matrix is ​​P = [pij], pij = wij·exp(-γ·dij); the cumulative impact is calculated as I(t) = Σ(P^k·I0), k = 1,2,...,t; among them, wij is the direct impact weight; dij is the node distance; γ is the attenuation coefficient; I0 ​​is the initial impact vector.

[0183] The mapping function M(X) of the time series mapping method is: M(X) = φ(W·X + b)·exp(-λ·Δt); the time series correlation is R(τ) = Σ(xt·xt+τ) / sqrt(Σxt²·Σxt+τ²), where φ is the activation function; W, b are mapping parameters; Δt is the time interval; λ is the timeliness parameter; τ is the time delay.

[0184] The characteristic flow probability matrix transition probability is calculated as P(t) = [pij(t)] pij(t) = αij·exp(-β·t)·(1 + γ·sij); The state distribution is updated as π(t+1) = π(t)·P(t), where αij is the basic transition probability; β is the time decay coefficient; γ is the similarity influence factor; sij is the state similarity; π is the state distribution vector;

[0185] Dynamic kernel density estimation, the density estimation function is f(x,t) = Σ(K(||x-xi|| / h(t)) / nh(t)); bandwidth adaptation is h(t) = h0·(1 + α·v(t))·exp(-βt), where (·) is the kernel function; h(t) is the time-varying bandwidth; v(t) is the local coefficient of variation; α, β are adjustment parameters.

[0186] For multi-dimensional evaluation, the comprehensive score is calculated as S = Σ(wi·Di·Ti); the dimension weight is updated as wi(t+1)= wi(t)·exp(η·dE / dwi), where Di is the dimension score; Ti is the timeliness weight; E is the evaluation error; η is the learning rate;

[0187] State transition probability calculation method, the transition matrix is ​​calculated as P(t) = [pij(t)] pij(t) = p0ij·K(t)·F(sij), where p0ij is the basic transition probability; K(t) is the time adjustment function; F(sij) is the state similarity function; sij is the distance between states.

[0188] For multi-scale analysis, the scale decomposition is X(t,s) = Σ(Ψj,k(t)·cj,k(s)) ; the coefficient calculation is cj,k(s) =<X,Ψj,k> ; Where Ψj,k is the scale basis function; j is the scale level; k is the location parameter; s is the scale parameter;

[0189] For network flow analysis, the flow matrix is ​​F = [fij] fij = cij·exp(-α·dij)·(1 + β·wij); the maximum flow is calculated as maxflow = min{Σfsi, Σfit}, where cij is the capacity constraint; dij is the distance attenuation; wij is the weight adjustment; s is the source point; t is the sink point;

[0190] Set coverage analysis, coverage ratio is calculated as CR(S) = |U(S)| / |U|; cost efficiency is E(s) = |U(s)\U(S)| / c(s), where U(S) is the set of covered elements; U is the entire set; c(s) is the set cost; s is the candidate set;

[0191] Entropy evaluation, information entropy is calculated as H = -Σ(pi·log(pi)), and the weight is determined as wi = (1 - Hi) / Σ(1 - Hi), where pi is the probability distribution; Hi is the standardized entropy value; wi is the feature weight;

[0192] Hierarchical weighting, the hierarchical weight is W = [wij] wij = αi·βj·exp(-γ·|ij|); the combined score is S = Σ(Σ(wij·xij)); among them, αi is the hierarchical weight; βj is the indicator weight; γ is the hierarchical attenuation coefficient; xij is the indicator score.

[0193] Cross-validation, the validation score is CV(θ) = Σ(L(yi,f_θ(xi))) / n; parameter optimization is θ* =argmin CV(θ), where L(·) is the loss function; f_θ is the prediction function; n is the number of samples; θ is the model parameter;

[0194] Mutual information analysis, mutual information is calculated as I(X,Y) = Σp(x,y)·log(p(x,y) / (p(x)p(y))), and the normalized mutual information is NI(X,Y) = I(X,Y) / sqrt(H(X)·H(Y)), where p(x,y) is the joint probability; H(X) is the information entropy; p(x), p(y) are the marginal probabilities;

[0195] Time series decomposition, the decomposition model is X(t) = T(t) + S(t) + R(t), and the trend extraction is T(t) = Σ(wi·xi) / Σwi, i∈[tk,t+k], where T(t) is the trend component; S(t) is the seasonal component; R(t) is the random component; wi is the weight coefficient;

[0196] Nonlinear feature transformation, the transformation function is F(x) = σ(W·φ(x) + b)·K(x), and the feature mapping is φ(x) = [φ1(x), φ2(x), ..., φn(x)], where σ is the activation function; W is the weight matrix; K(x) is the kernel function; φi is the basis function;

[0197] The weighted score of the comprehensive weighted method is S = Σ(αi·βi·γi·xi), and the weight is adjusted to αi(t+1) = αi(t) + λ·dL / dαi; where αi is the main weight; βi is the timeliness weight; γi is the credibility weight; and L is the loss function;

[0198] Improved DTW distance calculation, the distance calculation is DTW(X,Y) = min{Σc(xi,yj)}; the local cost is c(xi,yj) = d(xi,yj)·w(i,j), where d(·) is the basic distance metric; w(i,j) is the path weight; min{·} is the optimal path;

[0199] For time-varying network analysis, the network evolution is A(t+1) = F(A(t))·M(t), and the node importance is I(v,t) =Σ(αk·ck(v,t)), where A(t) is the adjacency matrix; F(·) is the evolution function; M(t) is the change matrix; ck is the centrality index;

[0200] The probability density of the multidimensional Gaussian mixture model is p(x) = Σ(πk·N(x|μk,Σk)) , and the parameter update is θ(t+1) = θ(t) + η·▽L(θ), where πk is the mixing coefficient; N(·) is the Gaussian distribution; μk,Σk are the mean and covariance; L(θ) is the likelihood function;

[0201] Time-effective weighted fusion, the fusion function is Y(t) = Σ(wi(t)·Xi(t)), and the weight update is wi(t) =w0i·exp(-λi·t)·si(t), where Xi(t) is the source data; w0i is the initial weight; λi is the decay rate; si(t) is the credibility score;

[0202] Adaptive sampling, sampling probability is p(x) = Z -1 · exp(-E(x) / T(t)) , temperature adjustment is T(t) = T0·α t ; Where E(x) is the energy function; T(t) is the time-varying temperature; Z is the normalization factor; α is the cooling rate;

[0203] For spatiotemporal feature extraction, the feature vector is F(x,t) = [Fs(x), Ft(t), Fc(x,t)], and the combination function is Y = Φ(W·F + b)·K(x,t), where Fs is the spatial feature; Ft is the temporal feature; Fc is the cross feature; Φ is the activation function; and K is the kernel function.

[0204] The spatiotemporal projection transformation method uses the projection function P(x,t) = W(t)·Φ(x)·T(t) and the feature transformation is Y= P(x,t)·X + b. Among them, W(t) is the time-varying weight matrix; Φ(x) is the spatial projection operator; T(t) is the time modulation function; X is the original feature matrix; and b is the bias term.

[0205] The weight update is W(t+1) = W(t) + η·▽L(W) T(t+1) = T(t)·exp(-λ·Δt); where η is the learning rate; L is the loss function; λ is the time decay coefficient; Δt is the time interval;

[0206] The projection space is optimized as J = ||Y - P(x,t)·X|| 2 + α·Ω(W) + β·Ψ(T); where Ω(W) is the spatial regularization term; Ψ(T) is the temporal regularization term; α, β are equilibrium parameters;

[0207] In one scenario of this application, the data processing flow is as follows:

[0208] S1, multi-source heterogeneous data collection and preprocessing stage;

[0209] Read the original data D1 of the e-commerce platform; social media data D2; corporate official website data D3; exhibition information D4; industry database information D5; customs data D6; construct the spatiotemporal feature tensor T(x, y, t); where x is the spatial dimension vector [longitude, latitude, geographic level code]; y is the entity attribute vector [company size, industry code, transaction frequency]; t is the time feature vector [first appearance time, last active time, active period]. Construct the credibility score matrix R = [rij]mxn; where rij = α·f(cij) + β·g(tij) + γ·h(sij); cij is the data consistency index; tij is the timeliness index; sij is the source reliability index; f, g, h are the nonlinear mapping functions of each index; α, β, γ are dynamic weight coefficients, satisfying α+β+γ=1; calculate the cross-validation score: CVS(i) = ∑j(wj·δ(i,j)·rij) / ∑jwj; where wj is the data source weight; δ(i,j) is the entity matching function; obtain the standardized spatiotemporal feature tensor T; credibility score matrix R; cross-validation score vector CVS.

[0210] Read the spatiotemporal feature tensor T and the credibility score matrix R. Construct the timeliness decay function, D(t) = exp(-λ(t)·Δt); λ(t) = ∑i(ai·ti); where Δt is the time difference; λ(t) is the adaptive decay coefficient; ai is the polynomial coefficient, determined by optimization; calculate the timeliness weight: W(t) = D(t)·(1 + σ·sin(ωt + φ)); where σ is the seasonal fluctuation intensity; ω is the period parameter; φ is the phase shift; obtain the timeliness weighted feature tensor Ta and the timeliness weight matrix W.

[0211] Read the timeliness weighted feature tensor Ta and the cross-validation score CVS; construct the association strength matrix C = [cij]nxn; cij = μ·p(i→j) + ν·q(j→i) + τ·k(i,j); where p(i→j) is the positive association probability; q(j→i) is the reverse association probability; k(i,j) is the co-occurrence association strength; μ, ν, τ are weight coefficients; calculate the association credibility as RC(i,j) = C(i,j)·sqrt(CVS(i)·CVS(j)); obtain the association strength matrix C and the association credibility matrix RC.

[0212] Read the timeliness weighted feature tensor Ta, the associated credibility matrix RC, and the timeliness weight matrix W. Construct the fusion feature matrix F = H(Ta,RC,W); where H(x,y,z) = ∑i(πi·fi(x,y,z)); fi is the nonlinear feature conversion function; πi is the adaptive weight coefficient; obtain the fusion feature matrix F (as input to the S2 stage) and the metadata index M (including information such as data source, processing process, credibility, etc.).

[0213] S2, feature extraction and value network construction stage;

[0214] Read the fusion feature matrix F and metadata index M; construct the feature importance matrix I = [iij]kxl; iij = Ψ(μij)·Φ(σij)·θ(ρij); where Ψ(μij) is the mean contribution function; Φ(σij) is the variance influence function; θ(ρij) is the correlation weight function; k is the feature dimension; l is the observation dimension. Calculate the feature reconstruction weight: W(f) = ∑i(αi·I[i,]) / ∑iαi; where αi is the adaptive weight coefficient; I[i,] is the feature vector of the i-th row; obtain the feature importance matrix I and the feature reconstruction weight vector W(f).

[0215] Read the feature importance matrix I and the feature reconstruction weight vector W(f); construct the value association tensor V = [vijk]nxnxm; vijk = η·g(iij) + λ·h(wik) + ξ·f(eij); where g(iij) is the feature association function; h(wik) is the weight mapping function; f(eij) is the marginal effect function; η, λ, ξ are the balance coefficients; n is the number of entities; m is the value dimension; generate a dynamic topological structure: T(t) = Γ(V,t)·Ω(t); where Γ(V,t) is the time-varying topological operator; Ω(t) is the structural optimization matrix; obtain the value association tensor V; and the dynamic topological structure T(t).

[0216] Read the value-related tensor V and dynamic topological structure T(t); construct the value scoring matrix S = [sij]nxk; sij =∑k(βk·v(i,j,k)·τ(k)); where v(i,j,k) is the value component function; τ(k) is the time decay function; βk is the dimension weight; calculate the comprehensive value score CV(i) = ∑j(wj·sij) + γ·▽T(i); where wj is the feature weight; ▽T(i) is the topological gradient; γ is the structural influence coefficient; obtain the value scoring matrix S and the comprehensive value score vector CV.

[0217] Read the value scoring matrix S and the comprehensive value score vector CV; construct the optimization feedback matrix O = [oij]nxn; oij = μ·f(CVi,CVj) + ν·g(Si,Sj) + ω·h(Tij); where f(CVi,CVj) is the value-related function; g(Si,Sj) is the scoring similarity function; h(Tij) is the topological association function; μ, ν, ω are weight coefficients; calculate the feature optimization vector FO(i) = ∑j(πj·oij) / ∑jπj; where πj is the adaptive optimization weight; obtain the optimization feedback matrix O, the feature optimization vector FO, and process the metadata P.

[0218] S3, customer classification and value prediction stage;

[0219] Read the optimization feedback matrix O, feature optimization vector FO, and process metadata P; construct the classification feature matrix C = [cij] nxm; cij = α·f(oij) + β·g(foi) + γ·h(pij); where f(oij) is the optimization feedback mapping function; g(foi) is the feature optimization mapping function; h(pij) is the metadata influence function; α, β, γ are dynamic weight coefficients; calculate the category belonging probability P(k|i) = ∑j(wj·φ(cij,μk,Σk)); where φ(x,μ,Σ) is the multidimensional probability density function; μk is the center vector of category k; Σk is the covariance matrix of category k; wj is the feature weight; obtain the classification feature matrix C and the category belonging probability matrix P(k|i).

[0220] Read the classification feature matrix C and the category attribution probability matrix P(k|i), and construct the trend prediction tensor T = [tijk]nxmxl; tijk = λ·f(cij,k) + μ·g(pi,k) + ν·h(τk); where f(cij,k) is the feature trend function; g(pi,k) is the probability evolution function; h(τk) is the time influence function; λ, μ, ν are the balance coefficients; l is the time window length; calculate the value prediction matrix V(t+Δt) = Ψ(T,t)·Ω(Δt); where Ψ(T,t) is the trend mapping operator; Ω(Δt) is the time evolution matrix; Δt is the prediction time step; obtain the trend prediction tensor T and the value prediction matrix V(t+Δt).

[0221] Read the trend prediction tensor T and the value prediction matrix V(t+Δt), and construct the recommendation score tensor R = [rijk]nxmxp; rijk = α·f(tijk) + β·g(vij) + γ·h(ik); where f(tijk) is the trend influence function; g(vij) is the value mapping function; h(ik) is the interaction intensity function; α, β, γ are weight coefficients; p is the recommendation dimension; generate the optimized recommendation list RL(i) = argmax_k{∑j(wj·rijk) | k ∈ K}; where wj is the feature weight; K is the candidate recommendation set; obtain the recommendation score tensor R and the optimized recommendation list RL.

[0222] Read the recommendation score tensor R, optimize the recommendation list RL, and construct the feedback optimization matrix F = [fij]nxm; fij =μ·g(rij) + ν·h(rli) + ω·k(eij); where g(rij) is the recommendation effect function; h(rli) is the list influence function; k(eij) is the error correction function; μ, ν, ω are adjustment coefficients; calculate the optimized adjustment vector A(i) = ∑j(π j·fij) + σ·▽E(i); where πj is the weight coefficient; ▽E(i) is the error gradient; σ is the learning rate; obtain the feedback optimization matrix F, the optimized adjustment vector A, and the quality control metadata Q.

[0223] S4, quality control and result optimization stage;

[0224] Read quality control metadata Q, optimize adjustment vector A, optimize recommendation list RL; construct quality assessment tensor Q = [qijk] nx mxl; qijk = α·f(aij) + β·g(rlik) + γ·h(tk); where f(aij) is the adjustment effect function; g(rlik) is the recommendation accuracy function; h(tk) is the time stability function; α, β, γ are weight coefficients; l is the number of evaluation dimensions. Calculate the comprehensive quality score QS(i) = ∑jk(wjk·qijk)·θ(i); where wjk is the dimension weight; θ(i) is the credibility adjustment function; obtain the quality assessment tensor Q and the comprehensive quality score vector QS.

[0225] Read the quality assessment tensor Q and the comprehensive quality score vector QS; construct the anomaly detection matrix E = [eij]nxm; eij= μ·f(qij) + ν·g(qsi) + ω·h(δij); where f(qij) is the quality deviation function; g(qsi) is the score anomaly function; h(δij) is the threshold adjustment function; μ, ν, ω are weight coefficients; generate the anomaly handling strategy S(i) =Φ(E[i,])·Ψ(QS[i]); where Φ(x) is the anomaly handling mapping function; Ψ(x) is the strategy optimization function; obtain the anomaly detection matrix E and the processing strategy matrix S.

[0226] Read the anomaly detection matrix E and the processing strategy matrix S, and construct the update frequency matrix U = [uij]nxm; uij =λ·f(eij) + μ·g(sij) + ν·h(τij); where f(eij) is the anomaly impact function; g(sij) is the strategy effect function; h(τij) is the time sensitivity function; λ, μ, ν are weight coefficients; Calculate the update priority P(i) = ∑j(wj·uij)·φ(t); where wj is the feature weight; φ(t) is the time decay function; obtain the update frequency matrix U and the update priority vector P.

[0227] Read the update frequency matrix U, update the priority vector P, and optimize the recommendation list RL. Construct the display optimization matrix D = [dij] nxk; dij = α·f(ui) + β·g(pi) + γ·h(rlij); where f(ui) is the update impact function; g(pi) is the priority mapping function; h(rlij) is the recommendation importance function; α, β, γ are weight coefficients; k is the number of display dimensions; generate the final display plan V(i) = Ω(D[i,])·Φ(t); where Ω(x) is the display optimization function; Φ(t) is the time correlation function; obtain the display optimization matrix D, the final display plan V, and the system metadata M.

Claims

1. A retrieval method for efficiently collecting accurate global potential customer information, characterized in that: The steps include: Step S1, taking multi-source heterogeneous data and performing spatiotemporal feature extraction and credibility assessment to generate a spatiotemporal feature data set, a credibility score data set and a cross-validation score data set; then performing timeliness optimization processing to generate a timeliness weighted feature data set; Based on the pre-built association relationships between entities, the association strength data set and the association credibility data set are generated and fused to obtain the fused feature data set and metadata index; multi-source heterogeneous data include e-commerce platform data, social media data, corporate official website data, exhibition information, industry database information and customs data; Step S2, obtaining the fusion feature data set and metadata index, performing feature decomposition and reconstruction processing, and constructing a value association network, generating a value association data set and a dynamic topology structure data set and evaluating multi-dimensional value, outputting a value scoring data set and a comprehensive value score data set and optimizing the processing, generating an optimization feedback data set, a feature optimization data set and processing metadata; Step S3, obtaining an optimization feedback data group, a feature optimization data group, and processing metadata, and performing dynamic classification processing to generate a classification feature data group and a category attribution probability data group, and predicting value trends to obtain prediction results including a trend prediction data group and a value prediction data group; performing precise recommendation analysis on the prediction results, outputting a recommendation score data group and an optimization recommendation list, and providing feedback optimization to generate a feedback optimization data group, an optimization adjustment data group, and quality control metadata; Step S4: Acquire quality control metadata, optimization adjustment data set and optimization recommendation list, perform system quality assessment, generate quality assessment data set and comprehensive quality score data set; perform anomaly detection and processing on the assessment results, generate anomaly detection data set and processing strategy data set; Establish a timeliness update mechanism to generate update frequency data groups and update priority data groups; integrate and display all results to generate display optimization data groups, final display plans and system metadata; The step S4 is specifically as follows: Step S41, obtaining quality control metadata, optimization adjustment data set and optimization recommendation list; Perform segmented analysis on the optimized adjustment data to generate time series segment data; calculate the adjustment effect value of each time segment to generate effect evaluation data; build an adjustment response model based on the time series segment data and effect evaluation data to generate response feature data; normalize the response feature data to generate an adjustment effect data group; build a recommendation accuracy evaluation model to generate an accuracy evaluation data group; perform weighted synthesis on the adjustment effect data group and the accuracy evaluation data group to generate a quality evaluation data group; perform credibility adjustment on the quality evaluation data group to generate a comprehensive quality score data group; Step S42, obtaining a quality assessment data group and a comprehensive quality score data group; performing deviation analysis on the quality assessment data to generate quality deviation data; analyzing abnormal points of the score data to generate score abnormality data; Determine the threshold range for abnormality determination and generate threshold adjustment data; perform weighted synthesis on quality deviation data, score abnormality data and threshold adjustment data to generate an abnormality detection data group; generate a processing strategy data group based on the abnormality detection results and quality scores; Step S43, obtaining anomaly detection data group and processing strategy data group; analyzing the impact of abnormal data on the system and generating abnormal impact data; evaluating the execution effect of the processing strategy and generating strategy effect data; calculating the time sensitivity of the data and generating time sensitive data; The abnormal impact data, strategy effect data and time-sensitive data are weighted and synthesized to generate an update frequency data group; the update frequency data group is prioritized in combination with the time decay factor to generate an update priority data group; Step S44, obtaining an update frequency data group, an update priority data group and an optimization recommendation list; Analyze the impact of update frequency on display effect and generate update impact data; convert priority information into display parameters and generate priority mapping data; calculate the importance of recommendation results and generate importance data; weightedly synthesize update impact data, priority mapping data and importance data to generate display optimization data group; optimize the display optimization data group according to time correlation parameters to generate the final display plan; record full process indicator information and update suggestion information to generate system metadata.

2. The retrieval method for efficiently collecting global accurate potential customer information according to claim 1 is characterized in that: The step S1 is specifically as follows: Step S11, obtaining multi-source heterogeneous data, including e-commerce platform data, social media data, corporate official website data, exhibition information, industry database information and customs data; performing format standardization and noise cleaning to generate a preprocessed data set; extracting time dimension features, space dimension features and entity attribute features from the preprocessed data set; using the time-space projection transformation method to unify the feature space and generate a time-space feature data set; calculating data consistency value, timeliness value and source reliability value, and generating a credibility score data set through nonlinear combination; Based on the importance of data sources and the matching degree of entity information, the credibility score is weighted and calculated to generate a cross-validation score data set; Step S12, obtaining a spatiotemporal feature data group and a credibility score data group; calculating the timeliness attenuation value of the data, and optimizing it using a polynomial method; generating timeliness weight data in combination with seasonal fluctuation data, wherein seasonal fluctuations are obtained by calculating periodic parameters and phase offset values; multiplying the spatiotemporal feature data group with the timeliness weight data to generate a timeliness weighted feature data group; Step S13, obtaining a timeliness weighted feature data set and a cross-validation score data set; calculating the forward association probability value, reverse association probability value and co-occurrence association strength value between entities, and generating an association strength data set by weighted synthesis; performing a product operation on the association strength data set and the cross-validation score of the corresponding entity to generate an association credibility data set; Step S14, obtaining a timeliness weighted feature data set and timeliness weight data, as well as an associated credibility data set; processing them using a nonlinear feature conversion method, and then synthesizing them through adaptive weighting to generate a fused feature data set; Record data sources, processing procedures and credibility information, and generate metadata indexes.

3. The retrieval method for efficiently collecting global accurate potential customer information according to claim 2 is characterized in that: The step S2 is specifically as follows: Step S21, obtaining a fusion feature data set and a metadata index; calculating the mean contribution value, variance influence value and correlation weight value of each feature; weighting and synthesizing the mean contribution value, variance influence value and correlation weight value to generate a feature importance data set; The feature importance data set is processed using adaptive weights to generate a feature reconstruction weight data set; Step S22, obtaining a feature importance data set and a feature reconstruction weight data set; Calculate feature association values, weight mapping values ​​and marginal effect values; perform weighted synthesis on feature association values, weight mapping values ​​and marginal effect values ​​to generate a value association data set; perform time series analysis on the value association data set, and combine it with structural optimization parameters to generate a dynamic topological structure data set; Step S23, obtaining a value-related data group and a dynamic topological structure data group; calculating the value component value of each dimension and performing time decay processing to generate a decayed value component data group; Perform weighted synthesis on the attenuated value component data set to generate a value score data set; perform optimization calculation on the value score data set in combination with the gradient information of the topological structure to generate a comprehensive value score data set; Step S24, obtain the value score data group and the comprehensive value score data group; calculate the value relevance value, score similarity value and topological association value; perform weighted synthesis on the value relevance value, score similarity value and topological association value to generate an optimization feedback data group; use adaptive optimization weights to process the optimization feedback data group to generate a feature optimization data group; record the optimization process information and parameter selection information, and generate processing metadata.

4. The retrieval method for efficiently collecting global accurate potential customer information according to claim 3 is characterized in that: The step S3 is specifically as follows: Step S31, obtaining an optimization feedback data group, a feature optimization data group and processing metadata; performing a mapping transformation on the optimization feedback data to generate feedback mapping data; performing a mapping transformation on the feature optimization data to generate feature mapping data; Performing mapping transformation on the processed metadata to generate metadata mapping data; Performing weighted synthesis on the feedback mapping data, the feature mapping data and the metadata mapping data to generate a classification feature data group; Calculate the probability of each entity belonging to different categories and generate a category belonging probability data set; Step S32, obtaining a classification feature data group and a category attribution probability data group; analyzing the change trend of each feature in different time windows to generate feature trend data; analyzing the evolution law of the category attribution probability to generate probability evolution data; analyzing the influence of the time factor to generate time influence data; Perform weighted synthesis of characteristic trend data, probability evolution data and time impact data to generate a trend prediction data group; perform time series mapping processing on the trend prediction data group and combine it with time evolution parameters to generate a value prediction data group; Step S33, obtaining a trend prediction data group and a value prediction data group; performing an impact analysis on the trend prediction data to generate trend impact data; performing a mapping conversion on the value prediction data to generate value mapping data; Calculate the interaction intensity between entities to generate interaction intensity data; perform weighted synthesis on trend impact data, value mapping data, and interaction intensity data to generate a recommendation score data set; According to the recommendation score data set and the preset feature weights, the best recommendation items are selected from the candidate recommendation set to generate an optimized recommendation list; Step S34, obtaining a recommendation score data group and an optimized recommendation list; Evaluate the recommendation effect and generate effect evaluation data; Analyze the impact of the recommendation list and generate list impact data; count the errors that occur and generate error statistics; The effect evaluation data, list impact data and error statistics are weighted and synthesized to generate a feedback optimization data group; the feedback optimization data group is optimized in combination with the error gradient information to generate an optimization adjustment data group; Record optimization effect information and adjustment suggestion information, and generate quality control metadata.

5. The retrieval method for efficiently collecting accurate global potential customer information according to claim 4 is characterized in that: The step S11 is specifically as follows: Step S111, obtaining e-commerce platform data, social media data, corporate official website data, exhibition information data, industry database data and customs data; performing format consistency check on each type of data and generating format check report data; automatically correcting data that does not meet format requirements based on the format check report data and generating format correction data; Perform noise detection on the corrected data to generate noise labeled data; Clean the original data according to the noise labeled data to generate a preprocessed data group; Step S112, obtaining a preprocessed data group; extracting time dimension information from the preprocessed data group to generate initial time feature data; The multi-scale time segmentation method is used to hierarchically process the initial time feature data to generate multi-scale time feature data; the continuity index of the time feature is calculated to generate time continuity data; the multi-scale time feature data and the time continuity data are fused to generate a time dimension feature data group; Step S113, obtaining a preprocessed data group; extracting geographic location information from the preprocessed data group to generate an initial location data group; Perform word segmentation on the address text data in the location initial data group, extract administrative division information, and generate division standard data; The latitude and longitude data are matched and verified with the zoning standard data, and the inconsistent data are corrected using the density weighting method to generate position calibration data; multi-level geocoding is constructed based on the position calibration data to generate a spatial dimension feature data group; Step S114, obtaining a preprocessed data group; extracting enterprise scale data, industry code data and transaction frequency data from the preprocessed data group to generate an attribute initial data group; performing noise reduction processing on the attribute initial data group using a recursive feature decomposition method to generate attribute noise reduction data; Calculate the coupling degree between attributes, use the adaptive threshold method to screen highly correlated attributes, and generate attribute-related data; integrate the attribute denoising data and attribute-related data to generate entity attribute feature data sets; Step S115, obtaining a time dimension feature data group, a space dimension feature data group, and an entity attribute feature data group; unifying the three sets of feature data into the same feature space using a time-space projection transformation method to generate unified feature data; Calculate the mutual information value between features and generate feature association data; Construct feature fusion weights based on feature association data to generate weight distribution data; perform weighted combination of unified feature data based on weight distribution data to generate a spatiotemporal feature data group; Step S116, obtaining a spatiotemporal feature data group and a preprocessed data group; calculating data consistency indicators between different data sources to generate consistency score data; calculating timeliness indicators using an exponential decay model based on the timestamp information of the data to generate timeliness score data; calculating source reliability indicators using a sliding window method based on the accuracy records of historical data to generate reliability score data; performing a nonlinear combination of consistency score data, timeliness score data, and reliability score data to generate a credibility score data group; Step S117, obtaining a credibility score data group; Calculate the historical contribution of each data source and generate data source weight data; An improved entity matching algorithm is used to calculate the similarity of entity information in different data sources and generate entity matching data; Based on the data source weight data and entity matching data, the weighted voting method is used to calculate the cross-validation coefficient to generate the validation coefficient data; the validation coefficient data is multiplied by the credibility score data group to generate the cross-validation score data group.

6. The retrieval method for efficiently collecting global accurate potential customer information according to claim 4 is characterized in that: The step S22 is specifically as follows: Step S221, obtaining a feature importance data group and a feature reconstruction weight data group; performing local sensitivity analysis on each feature in the feature importance data group, using a recursive partitioning method to calculate the marginal contribution value of the feature, and generating marginal contribution data; using an improved information gain ratio method to calculate the degree of complementarity between features, and generating feature complementarity data; constructing a feature combination effect matrix based on the marginal contribution data and feature complementarity data, and generating combination effect data; mapping and transforming the combination effect data and the feature reconstruction weight data group, and generating a feature association initial data group; Step S222, obtaining a feature association initial data group; grouping the feature association initial data using a multi-level dynamic clustering method to generate association group data; calculating the interaction strength of intra-group features, and using an adaptive weight allocation method to generate intra-group association data; constructing a transfer relationship network of inter-group features, and using an improved path analysis method to generate inter-group association data; fusing the intra-group association data and the inter-group association data to generate a multi-level association data group; Step S223, obtaining a multi-level association data group; Construct the characteristic flow probability matrix, use the improved Markov chain analysis method to calculate the characteristic migration probability, and generate characteristic migration data; Based on feature migration data, the time series association mining method is used to identify feature evolution patterns and generate evolution pattern data; Calculate the stability index of feature evolution and use entropy method to generate stability assessment data; Integrate feature migration data, evolution pattern data and stability assessment data to generate value-related data sets; Step S224, obtaining a value-related data group; constructing a characteristic state sequence based on a time window, and generating state transition data by using a state transition probability calculation method; performing time series decomposition on the state transition data, and generating time series characteristic data by using a multi-scale analysis method; Based on the time series feature data, a dynamic evolution network is constructed, and the structure evolution data is generated using the network growth model; the state transition data and the structure evolution data are optimized and combined to generate a topology preprocessing data set; Step S225, obtaining a topology preprocessing data group; The improved spectral clustering algorithm is used to perform structural analysis on the topological preprocessed data to generate structural feature data; Calculate the centrality index of network nodes and use multi-index fusion method to generate node importance data; A hierarchical topological structure is constructed based on node importance data, and the hierarchical structure data is generated by using the self-organizing mapping method; the structural feature data and the hierarchical structure data are dynamically optimized to generate a dynamic topological structure data group; Step S226, obtaining a dynamic topological structure data group; using a time-varying network analysis method to calculate the time evolution characteristics of the topological structure and generate time characteristic data; constructing a network stability evaluation index and using a multi-dimensional evaluation method to generate stability data; Design structural optimization strategies based on time characteristic data and stability data and generate optimization strategy data; The dynamic topology structure data group is adjusted and optimized according to the optimization strategy data to generate a final dynamic topology structure data group.

7. The retrieval method for efficiently collecting global accurate potential customer information according to claim 4 is characterized in that: The step S31 is specifically as follows: Step S311, obtaining an optimization feedback data group, a feature optimization data group and processing metadata; performing adaptive sampling on each piece of data in the optimization feedback data group, and using a stratified sampling method to generate sample distribution data; calculating statistical features of the sample data, and using a multi-dimensional analysis method to generate feature statistical data; Construct data quality assessment indicators based on feature statistical data, and use comprehensive weighting method to generate quality assessment data; optimize and screen sample distribution data based on quality assessment data to generate preprocessing feature data group; Step S312, obtaining a preprocessing feature data set; An improved local linear embedding algorithm is used to reduce the dimension of high-dimensional features and generate reduced-dimensional feature data; Construct a feature importance evaluation model, and use an entropy-based feature selection method to generate importance ranking data; design a feature combination strategy based on importance ranking data, and use a combination optimization algorithm to generate feature combination data; fuse the reduced-dimensional feature data with the feature combination data to generate an optimized feature data set; Step S313, obtaining an optimized feature data group; constructing a multi-level feature mapping network, and using an adaptive weight allocation method to generate feature mapping data; Calculate the correlation coefficient between features and use the improved correlation analysis method to generate feature association data; Based on the feature mapping data and feature association data, design feature conversion rules and generate feature conversion data; Nonlinearly combining the feature transformation data to generate a transformation feature data group; Step S314, obtaining a conversion feature data group; using a dynamic kernel density estimation method to calculate the local density of data distribution and generate density distribution data; constructing a density-based cluster center recognition model and using an adaptive threshold method to generate center point data; calculating the distance between the sample point and the cluster center and using an improved distance measurement method to generate distance matrix data; Generate a classification feature data group according to density distribution data and distance matrix data; Step S315, obtaining a classification feature data group; constructing a probability-based category discrimination model, using a multidimensional Gaussian mixture model to generate initial probability data; calculating the similarity between sample features and category features, using an improved cosine similarity method to generate similarity data; designing a probability adjustment strategy based on the initial probability data and similarity data, and generating probability adjustment data; Normalizing the probability adjustment data to generate a category belonging probability data group; Step S316, obtain the category attribution probability data group; construct a probability consistency test model, and use the cross-validation method to generate verification result data; calculate the probability interference degree between different categories, and use the mutual information analysis method to generate interference assessment data; based on the verification result data and the interference assessment data, design a probability optimization strategy and generate optimization solution data; adjust the category attribution probability data group according to the optimization solution data to generate the final category attribution probability data group.

8. The retrieval method for efficiently collecting global accurate potential customer information according to claim 4 is characterized in that: The step S33 is specifically as follows: Step S331, obtaining a trend prediction data group and a value prediction data group; performing time series decomposition on the trend prediction data, and using wavelet transform method to generate trend component data; Calculate the rate of change of the trend component and use dynamic slope analysis to generate rate of change data; Based on trend component data and change rate data, a trend impact assessment model is constructed to generate trend assessment data; Optimize the weight of trend assessment data to generate trend impact data group; Step S332, obtaining a trend impact data group; Construct a mapping model for value prediction data and use nonlinear projection method to generate value mapping data; Calculate the stability index of the mapping data and generate stability data using the entropy evaluation method; Design a mapping optimization strategy based on stability data and use an adaptive adjustment method to generate optimized mapping data; Reorganize the optimized mapping data into features to generate a value feature data group; Step S333, obtaining a value feature data group; Construct a calculation model for entity interaction intensity and use an improved association rule mining method to generate initial interaction data; Calculate the influence propagation efficiency between entities and generate propagation efficiency data using network flow analysis; Based on the initial interaction data and the propagation efficiency data, the interaction intensity evaluation rules are designed to generate the interaction evaluation data; the interaction evaluation data is normalized to generate the interaction intensity data group; Step S334, obtaining a trend impact data group, a value feature data group, and an interaction intensity data group; constructing a multi-dimensional recommendation scoring model, and using a hierarchical weighted method to generate initial scoring data; calculating the synergy between scoring indicators, and using a cross-validation method to generate synergy effect data; designing a scoring optimization strategy based on the initial scoring data and the synergy effect data, and generating optimized scoring data; performing comprehensive processing on the optimized scoring data to generate a recommendation score data group; Step S335, obtaining a recommendation score data group; constructing a recommendation candidate set screening model, and using a multi-objective optimization method to generate candidate set data; Calculate the similarity index between candidate items and use the improved cosine similarity method to generate similarity data; design a diversity optimization strategy based on the candidate set data and similarity data to generate diversity evaluation data; sort and optimize the diversity evaluation data to generate a recommended sorting data group; Step S336, obtaining a recommended ranking data group; constructing a recommendation list generation model, and using a sequence optimization method to generate initial list data; Calculate the coverage index of the list items and use the set coverage analysis method to generate coverage data; design a list optimization strategy based on the initial list data and coverage data, and generate optimization strategy data; adjust the recommendation list according to the optimization strategy data to generate an optimized recommendation list.

9. A retrieval system for efficiently collecting accurate global potential customer information, characterized in that: include: at least one processor; as well as, a memory communicatively connected to at least one of the processors; wherein, The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the retrieval method for efficiently collecting global accurate potential customer information as described in any one of claims 1 to 8.

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