A logistics coordination and management method for cross-border trade

Abstract

The application belongs to the technical field of business data processing, and specifically discloses a logistics collaborative management method for cross-border trade, which comprises the following steps: collecting the organization type and business capability data of participants, generating an initial role vector by using a vector embedding method, and obtaining a multi-dimensional role attribute representation; grouping similar participants by using a clustering algorithm according to the role vector and geographic location information, and determining a collaboration hierarchical structure; obtaining a dynamic change event signal, and if the signal indicates business range adjustment, updating the role vector component and judging a new collaboration relationship; extracting an identity recognition feature from the updated role vector, propagating neighborhood information through a graph neural network, and obtaining an enhanced identity identification; and calculating the vector similarity for the enhanced identity identification and the logistics state update, and if the similarity exceeds a preset threshold value. The application aims to solve the problems of low accuracy and efficiency of information distribution in the logistics collaboration in the prior art.

Description

Technical Field

[0001] This invention relates to the technical field of business data processing, and more specifically to a method for collaborative logistics management in cross-border trade. Background Technology

[0002] Cross-border trade, a crucial pillar of the global economy, facilitates the transnational flow of goods, capital, and information. Its logistics management directly impacts trade efficiency and cost control. In this field, efficient collaborative management is not only key to enhancing competitiveness but also crucial for ensuring supply chain stability. However, cross-border trade involves multiple countries and parties, and its logistics processes are complex and dynamically changing. Achieving efficient collaboration among all parties has become a critical challenge that urgently needs to be addressed.

[0003] Currently, while many logistics management methods attempt to improve collaboration efficiency through information technology, they often overlook the diversity and dynamism of the participating roles, leading to inaccurate information transmission and difficulty in flexibly adjusting collaborative relationships. Especially when dealing with newly added participants, existing methods lack effective identification and information matching mechanisms, often resulting in information redundancy or omissions. This limitation makes logistics collaboration in cross-border trade difficult to adapt to rapidly changing business environments, particularly in critical stages involving multi-party collaboration.

[0004] Against this backdrop, the main technical challenges in collaborative management and control of cross-border trade logistics lie in accurately identifying and managing the role attributes of participating parties, and in achieving precise information distribution based on these attributes. The role attributes of participating parties not only include their organizational type and business capabilities, but also involve multiple dimensions such as geographical location and collaboration level. The complexity of these attributes makes a simple static identification method insufficient. A deeper problem is that when the business scope or collaborative relationships of participating parties change, the system often fails to adjust the information distribution rules in a timely manner, resulting in some critical information not reaching the relevant parties. For example, in a cross-border logistics scenario, when a quality inspection agency temporarily expands its business scope to be responsible for inspecting more categories of goods, if the system fails to update its role identification and adjust the information push rules in a timely manner, the agency may miss important logistics status updates, thus affecting the customs clearance progress.

[0005] Therefore, in the dynamic cross-border trade environment, how to build a flexible identification system for the multi-dimensional role attributes of the participants and ensure that information distribution is accurately matched with business needs has become a key issue in improving the efficiency of logistics collaborative management and control. Summary of the Invention

[0006] This invention provides a logistics collaborative management method for cross-border trade, aiming to solve the problems of low accuracy and efficiency in information distribution in existing logistics collaboration technologies.

[0007] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0008] A logistics collaborative management and control method for cross-border trade includes: collecting data on the organizational types and business capabilities of participating parties, generating initial role vectors using a vector embedding method to obtain multi-dimensional role attribute representations; grouping similar participants into groups based on the role vectors and geographical location information using a clustering algorithm to determine the collaborative hierarchy; acquiring dynamic change event signals, and updating role vector components and determining new collaborative relationships if the signals indicate adjustments to the business scope; extracting identity recognition features from the updated role vectors, propagating neighborhood information through a graph neural network to obtain enhanced identity identifiers; calculating vector similarity for enhanced identity identifiers and logistics status updates, and performing an information matching process to determine target recipients if the similarity exceeds a preset threshold; generating distribution paths and obtaining optimized information distribution rules using the target recipient list and a collaborative relationship graph; acquiring real-time logistics status data, filtering irrelevant information according to the optimized information distribution rules, determining the business demand matching degree, and determining the final push content; and obtaining collaborative management and control confirmation feedback through encrypted channel transmission of the final push content and identity identifier verification.

[0009] In one aspect of the invention, the step of collecting data on the organizational type and business capabilities of participating parties, and generating an initial role vector using a vector embedding method to obtain a multi-dimensional role attribute representation includes:

[0010] Data on organizational type and business capabilities is obtained by collecting data from participants.

[0011] The vector embedding method is used to process organization type and business capability data to generate initial role vectors;

[0012] Determine the character vector based on the initial character vector;

[0013] Obtain vector embedding representations from character vectors;

[0014] The embedding representation is obtained through vector embedding representation;

[0015] Cosine similarity is used to calculate the similarity between the embedded representation and the pre-stored role template vector. If the similarity is higher than a preset threshold, the matched role is determined; otherwise, it is marked as a new role.

[0016] If a character is matched, the multi-dimensional character attribute representation is directly output; if it is marked as a new character, the character template vector set is updated.

[0017] In one aspect of the present invention, the step of grouping similar participants using a clustering algorithm based on the role vectors and geographic location information to determine the collaborative hierarchy includes:

[0018] Obtain the role vectors and geographical coordinates of the participants;

[0019] Clustering algorithms are used to process role vectors and geographic location coordinates to generate multiple participant groups;

[0020] Calculate the average role vector of the participants in each group to determine the group center vector of that group;

[0021] Calculate the vector distance based on the group center vector between groups, and simultaneously calculate the spatial distance by obtaining the center point of the geographical location of the group.

[0022] If both the vector distance and the spatial distance are less than the preset threshold, then the two groups are determined to belong to the same collaboration level.

[0023] Based on the hierarchical affiliation determination results between groups, a collaborative structure tree with parent-child relationships is constructed;

[0024] In the collaborative structure tree, connecting edges are added to nodes with direct hierarchical relationships to form a complete collaborative network topology.

[0025] In one aspect of the invention, the step of acquiring a dynamic change event signal, and if the signal indicates an adjustment in the business scope, updating the role vector component and determining a new collaborative relationship, includes:

[0026] Acquire dynamic change event signals;

[0027] If a dynamic change event signal indicates an adjustment in the scope of business, then the updated participant role vector component is used to obtain the updated role vector.

[0028] The similarity calculation result is obtained by calculating the vector similarity between each participant using the updated role vectors;

[0029] If the similarity calculation result is higher than the preset threshold, a potential new collaborative relationship is determined;

[0030] Generate candidate collaboration pairs based on potential new collaboration relationships;

[0031] New cooperative connections are obtained by comparing candidate cooperative pairs with the existing cooperative network;

[0032] The updated cooperative network is obtained by updating the cooperative network topology based on the newly added cooperative connections.

[0033] In one aspect of the invention, the step of extracting identity recognition features from the updated role vector and propagating neighborhood information through a graph neural network to obtain an enhanced identity identifier includes:

[0034] By extracting identity distinguishing elements from role vectors, and using automated tools to perform preliminary classification of the elements, classified identity units are obtained.

[0035] Based on the classified identity unit, obtain the surrounding node data associated with it, perform information transmission processing on the node data, and obtain the transmitted node set;

[0036] If the amount of information in the node set after transmission exceeds a preset threshold, the set is filtered to identify the core node group.

[0037] By using the core node group, a graph neural network is employed to perform deep correlation analysis on the information within the group, thereby obtaining enhanced correlation identifiers.

[0038] For the enhanced association identifier, obtain its positional distribution in the network structure, and label the unit after determining the distribution;

[0039] Based on the distributed labeled units, update the identity label content to obtain the final enhanced labeling result;

[0040] If the final enhanced tagging result does not match the pre-established tagging library, the result is subjected to information enhancement processing to determine the updated identity tag.

[0041] In one aspect of the invention, the step of calculating vector similarity for enhanced identity verification and logistics status updates, and performing an information matching process to determine the target recipient if the similarity exceeds a preset threshold, includes:

[0042] Based on the enhanced identity identification and logistics status update, extract the identity vector and status vector;

[0043] For the identity vector and the state vector, cosine similarity is used to calculate the similarity value to obtain the similarity result;

[0044] If the similarity result exceeds the preset threshold, the information matching process is triggered to obtain a set of matching candidates;

[0045] For the matching candidate set, traverse the candidate receiver information, compare the difference between the identity vector and the candidate vector, determine the receiver with the smallest difference, and determine the preliminary receiver;

[0046] Based on the initial recipient, query the associated logistics status update records and obtain the status vector sequence in the records;

[0047] For a sequence of state vectors, calculate the sequence average vector to obtain the aggregated state vector;

[0048] Based on the aggregated state vector and the corresponding identity vector of the enhanced identity identifier, the cosine similarity is calculated again to confirm the similarity and determine the final matching receiver.

[0049] In one aspect of the invention, the step of using the target receiver list, combined with the cooperation relationship graph, to generate a distribution path and obtain optimized information distribution rules includes:

[0050] A distribution path graph containing nodes and edges is constructed by using a list of target recipients and combining it with a collaboration relationship graph.

[0051] For the distribution path graph, traverse all edges and calculate the product of the frequency of cooperation on the edge and the response time to obtain the path weight value;

[0052] Based on the path weight values, sort all possible paths, filter the top-ranked paths, and determine the weighted path set.

[0053] For the weighted path set, Dijkstra's algorithm is used to calculate the shortest weighted path from the starting node to each target receiver, and candidate distribution paths are obtained.

[0054] Based on the candidate distribution paths, query historical distribution records to obtain the number of successful transmissions and the total number of transmissions for each path;

[0055] To obtain the path reliability index, the number of successful transmissions is divided by the total number of transmissions, given the number of successful transmissions and the total number of transmissions.

[0056] If the path reliability index is higher than the preset threshold, the corresponding candidate distribution path is retained; otherwise, the path is removed, and the final optimized information distribution rule is determined.

[0057] In one aspect of the invention, the step of acquiring real-time logistics status data, filtering irrelevant information according to optimized information distribution rules, determining the matching degree of business needs, and determining the final push content includes:

[0058] Acquire real-time logistics status data by collecting the latest location information, transportation progress information, and cargo integrity information from multiple logistics tracking interfaces;

[0059] Based on the optimized information distribution rules, the real-time logistics status data is initially filtered to remove logistics information that is irrelevant to the target recipient and retain logistics information that contains the target cargo identifier.

[0060] For the retained logistics information, analyze the business requirement matching degree and extract the goods type field, delivery time field and abnormal status field from the logistics information;

[0061] By comparing the cargo type field in the logistics information with the cargo type of the preset business requirements, the consistency of cargo type is determined. If the cargo types are consistent, it is marked as a preliminary match; otherwise, it is marked as a mismatch.

[0062] For the initially matched logistics information, the delivery time field is compared with the preset business demand time range. If the delivery time field falls within the preset time range, the matching score is increased; otherwise, the matching score is decreased.

[0063] For logistics information marked as a preliminary match, check the abnormal status field. If the abnormal status field is empty, further increase the matching score; otherwise, decrease the matching score.

[0064] All matching scores are calculated using a weighted summation method to obtain the overall business demand matching score. All logistics information is then sorted, and the logistics information with the highest overall business demand matching score is selected to determine the final push content.

[0065] In one aspect of the present invention, the step of obtaining collaborative management and control confirmation feedback through the final pushed content and identity verification, using encrypted channel transmission, includes:

[0066] By combining the final pushed content with identity verification, the integrity of the content and the consistency of the identifier are obtained.

[0067] Regarding content integrity and identifier consistency, if the content integrity meets the requirements and the identifier consistency matches, the transmission reliability is determined; otherwise, the transmission is marked as interrupted.

[0068] Encrypted channels are used to transmit push content, thus ensuring channel security.

[0069] Based on channel security, if the channel security meets the requirements, the verification accuracy is confirmed; otherwise, the transmission process is interrupted.

[0070] By verifying accuracy and combining transmission reliability, feedback timeliness is obtained;

[0071] To address the timeliness of feedback, a random forest algorithm is used to classify the effectiveness of feedback, thereby achieving consistency in control.

[0072] Based on the consistency of control and the effectiveness of feedback, if the effectiveness of feedback meets the requirements, a collaborative control confirmation feedback is obtained; otherwise, the push content is retransmitted.

[0073] Compared with the prior art, the present invention has the following beneficial effects:

[0074] This invention generates multi-dimensional role vectors by collecting data on the organizational types and business capabilities of participating parties, and then clusters and groups them using geographic location information to construct a collaborative hierarchical structure. For dynamic event signals, the role vectors are updated in real time, and identity identification is enhanced using a graph neural network. Simultaneously, based on vector similarity and collaborative relationship graphs, the distribution path is optimized, and irrelevant information is filtered out using real-time logistics status data to ensure accurate matching of business needs. Finally, the pushed content is transmitted through an encrypted channel, and feedback is obtained. This invention achieves intelligent management of the entire process from role recognition to information distribution, significantly improving the accuracy and efficiency of information distribution in logistics collaboration, reducing resource waste, and providing innovative technical support for collaborative management in complex scenarios. Attached Figure Description

[0075] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained from these drawings without creative effort.

[0076] Figure 1 This is a flowchart of a logistics collaborative management and control method for cross-border trade according to the present invention. Detailed Implementation

[0077] The present invention will be further described below with reference to embodiments. These embodiments are merely some, not all, of the embodiments of the present invention. Other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are all within the protection scope of the present invention.

[0078] Please see Figure 1 As shown in the figure, this embodiment discloses a logistics collaborative management and control method for cross-border trade, which may specifically include:

[0079] Step 101: By collecting data on the organizational type and business capabilities of participating parties, an initial role vector is generated using a vector embedding method to obtain a multi-dimensional representation of role attributes.

[0080] Organization type and business capability data are obtained by collecting data from participants. Vector embedding is used to process the organization type and business capability data to generate initial role vectors. Role vectors are then determined based on these initial vectors. Vector embedding representations are obtained for each role vector. Embedded representations are then derived from these vector embedding representations. Cosine similarity is used to calculate the similarity between the embedded representation and pre-stored role template vectors. If the similarity is higher than a preset threshold, a matching role is determined; otherwise, it is marked as a new role. If a matching role is found, multi-dimensional role attribute representations are directly output; if a new role is marked, the role template vector set is updated.

[0081] In the process of generating initial role vectors by collecting data on the organizational type and business capabilities of participating parties, the organizational type and business capability information can first be extracted from the participants' public data or internal databases. For example, a participant might be a "technology company" with business capabilities including a "software development capability score of 8.5" and a "data analysis capability score of 7.8". Natural language processing technology is then used to transform this textual information into structured data. Specifically, a pre-trained language model such as BERT is used to encode the text, mapping "technology company" to a 128-dimensional semantic vector. Simultaneously, the business capability scores are proportionally normalized to a range of 0 to 1 (e.g., 8.5 becomes 0.85, 7.8 becomes 0.78), and then concatenated with the organizational type vector to form a comprehensive vector containing both semantic and numerical features. Next, an initial role vector is generated using vector embedding. Specifically, the Word2Vec algorithm is used to reduce the dimensionality of the combined vector of organization type and business capabilities, assuming a 64-dimensional vector is obtained after dimensionality reduction. The first 32 dimensions represent organization type features, and the last 32 dimensions represent business capability features. By calculating the cosine similarity between vectors (e.g., the similarity between technology companies A and B is 0.92), the distribution characteristics of the role vectors are analyzed to ensure that the vectors can effectively distinguish different role types. Finally, a multi-dimensional role attribute representation is generated. Based on the 64-dimensional vector, principal component analysis (PCA) is used to extract the main features. Assuming that the first 10 principal components explain 85% of the variance, these principal components are used as the core dimensions of the role attributes. For example, the first principal component may represent technological innovation capability with a weight of 0.65, and the second principal component may represent business execution capability with a weight of 0.58. In this way, a multi-dimensional role attribute representation is formed. To ensure logical rigor, role attributes can be associated with business scenarios. For example, in a smart manufacturing scenario, roles with higher technological innovation capabilities are given priority in R&D tasks. The weight calculation is based on the inner product of vectors (e.g., the inner product of the innovation capability vector and the task requirement is 0.88), thereby achieving intelligent matching between roles and tasks. The entire process is automated through algorithms, forming a complete chain from data collection to role representation.

[0082] Step 102: Based on the role vectors and geographical location information, a clustering algorithm is used to group similar participants and determine the collaboration hierarchy.

[0083] Obtain the role vectors and geographic coordinates of the participants. A clustering algorithm is used to process the role vectors and geographic coordinates, generating multiple participant groups. The average role vector of each participant within each group is calculated to determine the group's cluster center vector. Vector distances are calculated based on the cluster center vectors between groups, and spatial distances are calculated using the center points of the group's geographic locations. If both the vector distance and spatial distance are less than a preset threshold, the two groups are determined to belong to the same collaboration level. Based on the hierarchy determination results between groups, a collaboration structure tree with parent-child relationships is constructed. In the collaboration structure tree, connecting edges are added to nodes with direct hierarchical relationships to form a complete collaboration network topology.

[0084] In the process of grouping participants and determining the collaborative hierarchy based on role vectors and geographic location information, a series of information technology methods can be used to automate the process. First, assuming the existing role vector is 64-dimensional, geographic location data (such as latitude and longitude coordinates, for example, a participant's location is latitude 39.9042, longitude 116.4074) is combined to transform the geographic location information into a two-dimensional numerical vector. Through standardization, the latitude and longitude values ​​are mapped to the range of 0 to 1, forming a 66-dimensional composite vector, where the first 64 dimensions represent role features and the last 2 dimensions represent location features. Next, the K-means clustering algorithm is used to group these composite vectors. The initial number of cluster centers is set to 5. The distance between participants and cluster centers is calculated using Euclidean distance. For example, if a participant's distance to center 1 is 3.25 and its distance to center 2 is 5.67, the system automatically assigns it to the nearest center 1. After iterative optimization, five stable clusters are formed, with the average distance between participants within each cluster being less than 2.5, ensuring the rationality of the grouping. Subsequently, the distribution characteristics of role vectors within each cluster are analyzed. A hierarchical clustering algorithm is used to further determine the collaborative hierarchy. The cosine similarity between participants within a cluster is calculated (e.g., the similarity between participants A and B is 0.87). Participants with similarity higher than 0.8 are grouped into the same sub-level, while those with similarity lower than 0.8 are divided into different sub-levels, forming a tree-like hierarchical structure. To enhance logical coherence, the hierarchical structure can be aligned with business requirements. For example, in a logistics delivery scenario, sub-levels with geographically close proximity and high role similarity are prioritized for regional delivery tasks. By calculating the distance between the cluster center and the delivery target point (e.g., 4.32 kilometers), a collaborative priority list is automatically generated. The entire process is algorithm-driven, ensuring the scientific nature of grouping and hierarchical division.

[0085] Step 103: Obtain dynamic change event signals. If the signal indicates an adjustment in the business scope, update the role vector component and determine the new collaboration relationship.

[0086] Acquire dynamic change event signals. If the dynamic change event signal indicates an adjustment in the business scope, update the participant role vector component to obtain the updated role vectors. Calculate the similarity between the vectors of each participant using the updated role vectors to obtain the similarity calculation result. If the similarity calculation result is higher than a preset threshold, identify potential new collaborative relationships. Generate candidate collaborative pairs based on the potential new collaborative relationships. Obtain new collaborative connections by comparing the candidate collaborative pairs with the original collaborative network. Update the collaborative network topology based on the new collaborative connections to obtain the updated collaborative network.

[0087] In logistics and delivery scenarios, the system uses information technology to acquire and process dynamically changing event signals, automatically responding to business scope adjustments and updating relevant data and collaborative relationships. First, the system monitors business event signals in real time. For example, it might detect a surge in delivery demand in a certain area through sensors and data interfaces, indicating that the number of orders in that area has increased from an average of 100 to 180 per day, triggering a business scope adjustment signal. Next, the system automatically updates the role vector component based on the signal strength. Assuming the original role vector is 50-dimensional, and the increase in business volume percentage is used as a new dimension, the system calculates a business volume growth rate of 0.75 for a certain participant. After the update, the vector dimension increases to 51 dimensions, and the vector value is adjusted using a weighted average algorithm. For example, setting the weight of the business volume dimension to 0.3, the new vector value for that participant changes from 0.62 to 0.68 after comprehensive calculation. Subsequently, the system uses a support vector machine algorithm to classify and analyze the updated role vectors, determining new collaborative relationships. Setting a classification boundary value of 0.5, the analysis results show that the matching degree between a certain participant and the other three participants increases from 0.45 to 0.58, exceeding the boundary value, and automatically assigning it to a new collaborative group. To ensure logical consistency, the system further integrates business needs and links new collaboration groups with delivery resource scheduling. It automatically calculates the resource matching degree between the participants in the group and the target warehouse. For example, if the matching degree is 0.82, which is higher than the threshold of 0.7, the group is given priority to execute new order delivery tasks. The entire process is completed through algorithms and data-driven approaches, ensuring the efficiency and accuracy of dynamic adjustments.

[0088] Step 104: Extract identity recognition features from the updated role vectors, propagate neighborhood information through a graph neural network, and obtain enhanced identity identifiers.

[0089] Identity distinguishing elements are extracted from role vectors, and these elements are initially classified using automated tools to obtain classified identity units. Based on these classified identity units, surrounding node data associated with them is acquired, and information transfer processing is performed on this node data to obtain a transferred node set. If the amount of information in the transferred node set exceeds a preset threshold, the set is filtered to identify core node groups. Through these core node groups, a graph neural network is used to perform deep association analysis on the information within the group to obtain enhanced association identifiers. For these enhanced association identifiers, their positional distribution within the network structure is obtained to determine distributed labeling units. Based on these distributed labeling units, the identity label content is updated to obtain the final enhanced labeling result. If the final enhanced labeling result does not match the pre-established labeling library, information enhancement processing is performed on the result to determine the updated identity label.

[0090] In logistics and delivery scenarios, the system extracts identity features from updated role vectors using information technology and leverages graph neural networks to propagate neighborhood information to generate enhanced identity identifiers. First, the system performs feature selection on the 51-dimensional role vectors of each participant, using principal component analysis to extract the top 10 key identity features. These features have a cumulative contribution rate of 0.87. For example, a delivery person's vector scores 0.91 in reliability, 0.85 in timeliness, and 0.76 in coverage area. After filtering with a threshold of 0.7, the system retains 8 core features, forming the initial identity identifier vector. Subsequently, the system constructs a collaboration graph, treating participants as nodes and historical collaboration counts as edge weights. For example, the edge weight between node A and node B is 45 collaborations, and between node A and node C it is 12 collaborations. A two-layer graph neural network is used for information propagation. The first layer uses an aggregation function to calculate a weighted average of neighborhood features, updating the feature value of node A to the original value of 0.68 plus the neighborhood contribution of 0.24 multiplied by the normalization weight of 0.6, resulting in an intermediate representation of 0.824. The second layer further integrates global information and calculates neighborhood weights through an attention mechanism. For example, the attention score of node B to A is 0.42, and that of node C is 0.31, ultimately enhancing the identity vector value to 0.89. The system then calculates the similarity of the enhanced identity and uses the cosine similarity algorithm to analyze the matching with existing collaborative groups. For example, the similarity between the delivery person and the peak-hour rapid response group increases from 0.53 to 0.81, exceeding the preset threshold of 0.75. The system automatically confirms the enhanced identity as a "peak-hour priority responder" and adjusts the priority of delivery task allocation accordingly, adding the delivery person to the real-time order response queue. The entire process uses graph neural networks and feature analysis to achieve dynamic enhancement and accurate identification of the identity.

[0091] Step 105: For enhanced identity identification and logistics status updates, calculate vector similarity. If the similarity exceeds a preset threshold, perform an information matching process to determine the target recipient.

[0092] Based on the enhanced identity identifier and logistics status update, identity vectors and status vectors are extracted. For both identity and status vectors, cosine similarity is used to calculate the similarity score. If the similarity score exceeds a preset threshold, an information matching process is triggered to obtain a matching candidate set. For the matching candidate set, the candidate recipient information is traversed, and the difference between the identity vector and the candidate vectors is compared. The recipient with the smallest difference is determined, identifying the initial recipient. Based on the initial recipient, the associated logistics status update records are queried to obtain the status vector sequence from the records. For the status vector sequence, the sequence average vector is calculated to obtain the aggregated status vector. Based on the aggregated status vector and the identity vector corresponding to the enhanced identity identifier, cosine similarity is used again to confirm the similarity and determine the final matched recipient.

[0093] In logistics and delivery scenarios, the system leverages information technology to intelligently match enhanced identity verification with logistics status updates, integrating various implementation methods. First, the system retrieves delivery personnel identity vectors from the database. For example, a delivery person's vector contains 32 dimensions, with key dimensions such as delivery efficiency (0.88), customer satisfaction (0.82), and on-time performance (0.79) as key dimensions. The system uses the Euclidean distance algorithm to perform preliminary standardization on these vectors, mapping each dimension value to the 0-1 range, calculating a standardized comprehensive score of 0.83. Simultaneously, the system collects real-time logistics status data, such as the current order's urgency score (0.75), delivery distance (12.5 km), and estimated delivery time (35 minutes). This data is weighted to form a status vector with weights of 0.4, 0.3, and 0.3, resulting in a comprehensive status value of 0.68. Next, the system uses the Pearson correlation coefficient algorithm to calculate the similarity between the identity vector and the logistics status vector, obtaining a similarity value of 0.78. This similarity is then compared to a preset threshold of 0.72. If the similarity exceeds the threshold, the matching process is automatically triggered. To ensure matching accuracy, the system further incorporates historical delivery data analysis, extracting data showing that the delivery person's emergency order completion rate reached 85% in the past 30 days. Combined with the current order's urgency, a matching priority score of 0.81 is calculated. Finally, based on the priority score, the system uses its built-in rule engine to determine the target recipient, assigns the order to the delivery person, and updates their task queue. The proportion of emergency orders in the queue increases from 40% to 55%, thus achieving precise matching between logistics tasks and identity identifiers. The entire process is automated through algorithms and data analysis, forming a complete logical chain from vector calculation to task allocation.

[0094] Step 106: Using the target recipient list and the collaboration relationship graph, a distribution path is generated to obtain optimized information distribution rules.

[0095] A distribution path graph containing nodes and edges is constructed using a list of target receivers and a collaboration relationship graph. For the distribution path graph, all edges are traversed, and the product of collaboration frequency and response time on each edge is calculated to obtain the path weight value. Based on the path weight values, all possible paths are sorted, and the top-ranked paths are selected to determine the weighted path set. For the weighted path set, Dijkstra's algorithm is used to calculate the shortest weighted path from the starting node to each target receiver, resulting in candidate distribution paths. Based on the candidate distribution paths, historical distribution records are queried to obtain the number of successful transmissions and the total number of transmissions for each path. The number of successful transmissions is divided by the total number of transmissions to obtain the path reliability index. If the path reliability index is higher than a preset threshold, the corresponding candidate distribution path is retained; otherwise, the path is removed, and the final optimized information distribution rules are determined.

[0096] In logistics and delivery scenarios, the system intelligently integrates the target recipient list with the collaborative relationship graph through information technology to generate optimized information distribution paths. The specific implementation methods are integrated into one. First, the system extracts a list of matched target recipients from a relational database, for example, containing the IDs of five delivery personnel and their priority scores of 0.81, 0.76, 0.72, 0.69, and 0.65 respectively. Simultaneously, it constructs a collaborative relationship graph, using a graph database to store historical collaboration data between nodes. The weight of each edge represents the percentage of orders completed jointly in the past 60 days, with the highest edge weight reaching 0.92. The system uses Dijkstra's algorithm combined with the reciprocal of the edge weights as the path cost to traverse the graph. Starting from the order initiation node, it calculates the shortest weighted path to each target recipient; for example, the path cost to the first delivery person is 1.34, and to the second is 1.58. Next, the system introduces collaboration frequency analysis, scoring the collaboration degree of transit delivery personnel involved in the path. The calculation formula is the average edge weight of the path multiplied by the recipient's priority; for example, the collaboration degree score for the first delivery person is 0.87. The system further optimizes the global path using the Floyd-Warshall algorithm, identifying potential loops and eliminating invalid branches with a cost higher than 2.0. The final generated distribution path sequence is a combination of Receiver 1 - Relay 2 - Receiver 3, improving the overall path optimization score to 0.89. Subsequently, the system formulates information distribution rules based on this path, using a rule engine to automatically set the push order and backup mechanism. If the first receiver's response timeout exceeds 10 minutes, it automatically switches to the suboptimal path, ensuring that information distribution delays are controlled within 5 minutes. The entire process is completed automatically through graph algorithms and the rule engine, forming a complete logical chain from list fusion to path optimization.

[0097] Step 107: Obtain real-time logistics status data, filter irrelevant information according to the optimized information distribution rules, determine the matching degree of business needs, and determine the final push content.

[0098] Real-time logistics status data is acquired by collecting the latest location information, transportation progress information, and cargo integrity information from multiple logistics tracking interfaces. Based on optimized information distribution rules, the real-time logistics status data is initially filtered, removing logistics information irrelevant to the target recipient and retaining logistics information containing the target cargo identifier. For the retained logistics information, the business requirement matching degree is analyzed, extracting the cargo type field, delivery time field, and abnormal status field from the logistics information. By comparing the cargo type field in the logistics information with the preset business requirement cargo type, cargo type consistency is determined. If the cargo types match, it is marked as a preliminary match; otherwise, it is marked as a mismatch. For the initially matched logistics information, the delivery time field is compared with the preset business requirement time range. If the delivery time field falls within the preset time range, the matching score is increased; otherwise, the matching score is decreased. For the logistics information marked as initially matched, the abnormal status field is checked. If the abnormal status field is empty, the matching score is further increased; otherwise, the matching score is decreased. All matching scores are calculated using a weighted summation method to obtain the overall business demand matching score. All logistics information is then sorted, and the logistics information with the highest overall business demand matching score is selected to determine the final push content.

[0099] In logistics and delivery scenarios, the system acquires real-time logistics status data through information technology and performs intelligent filtering and matching based on optimized information distribution rules to ultimately determine the content to be pushed. The specific implementation methods are integrated into one system. First, the system pulls real-time logistics status data from vehicle GPS devices and warehouse IoT sensors via API interfaces. For example, the location coordinates of the five delivery vehicles involved in the current order, their remaining load percentages are 0.85, 0.62, 0.78, 0.91, and 0.57 respectively, and the real-time road congestion index is 1.45. Simultaneously, the system loads optimized information distribution rules from the rule engine, including path sequence priority and response timeout thresholds. Then, a cosine similarity algorithm is used to calculate the matching degree between each recipient's state vector and the order demand vector. The vector dimensions cover distance, load, and historical on-time rate. For example, the cosine similarity between the first delivery person's state vector and the demand vector is 0.93, and for the second, it is 0.81. The system further introduces a dynamic filtering mechanism, directly excluding irrelevant information from recipients with a matching score below 0.75. Simultaneously, it sorts the remaining recipients by their demand matching score, calculated as similarity multiplied by a real-time availability coefficient (the availability coefficient is weighted by the weighted average of the remaining load ratio and the inverse of the congestion index), resulting in a first-place matching score of 0.88 after adjustment. The system then analyzes the urgency of business needs using a decision tree model. For example, if an order requires completion within 30 minutes, the weight of high-matching recipients is increased by 0.15. Ultimately, the system determines the push content to be detailed order information combined with real-time route adjustment suggestions, pushing only a simplified task description and complete map data to the top three delivery personnel with the highest matching scores. This ensures accurate content and keeps the information volume within a reasonable range. The entire process is automatically completed through vector calculation, rule filtering, and model decision-making, forming a closed-loop logical chain from status acquisition to content determination.

[0100] Step 108: Through the final push content and identity verification, the data is transmitted via an encrypted channel to obtain collaborative management confirmation feedback.

[0101] By combining the final pushed content with identity verification, the integrity of the content and the consistency of the identifier are obtained. Regarding content integrity and identifier consistency, if the content integrity meets the requirements and the identifier consistency matches, the transmission reliability is determined; otherwise, the transmission is marked as interrupted. Push content is transmitted through an encrypted channel to obtain channel security. Based on channel security, if the channel security meets the requirements, the verification accuracy is determined; otherwise, the transmission process is interrupted. By combining verification accuracy with transmission reliability, the timeliness of feedback is obtained. Regarding feedback timeliness, a random forest algorithm is used to classify feedback validity, thus obtaining control consistency. Based on control consistency and feedback validity, if the feedback validity meets the requirements, collaborative control confirmation feedback is obtained; otherwise, the pushed content is retransmitted.

[0102] In logistics and delivery scenarios, the system automates the entire process of encrypted transmission and collaborative control confirmation feedback of the final push content through information technology. First, the system verifies the recipient's identity using an identity verification module. This is done by using a hash algorithm to check the recipient's device ID and generate a unique verification code (e.g., X9Y2K7). The code must match the device identifier pre-stored in the database by 100%; otherwise, transmission is automatically rejected. Next, the system encrypts the push content using the AES-256 encryption algorithm to ensure data security during transmission. The encryption key is 256 bits long, and the data is transmitted to the target recipient device through a pre-defined encrypted channel. For example, the data packet size is 2.5MB, and the transmission rate is controlled within 1.2MB / s to ensure stability. Next, after the receiving device decrypts the data, the system automatically triggers a feedback mechanism to obtain confirmation information through a preset collaborative management protocol. For example, if the receiving device returns a confirmation code of C4M9P1, the system compares it with the expected code. The matching degree must exceed 98%; if it is lower than this value, the data packet is automatically retransmitted. Simultaneously, the system uses timestamps to record feedback delays. For example, if the current feedback time is 0.8 seconds, and it exceeds a preset threshold of 1.5 seconds, a backup channel transmission is triggered to ensure timeliness. To form a closed-loop logic, the system stores all transmission and feedback data in a log database, automatically analyzes the transmission success rate (e.g., the current batch success rate is 99.3%), and adjusts the load balancing strategy of the encrypted channel based on historical data to ensure subsequent transmission efficiency. This fully automated process forms a tight logical chain through identity verification, encrypted transmission, feedback confirmation, and data analysis, ensuring efficient collaboration in logistics and delivery tasks.

[0103] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A logistics collaborative management and control method for cross-border trade, characterized in that, include: By collecting data on the organizational type and business capabilities of participating parties, an initial role vector is generated using a vector embedding method to obtain a multi-dimensional representation of role attributes. Based on the role vectors and geographical location information, a clustering algorithm is used to group similar participants and determine the collaboration hierarchy. Acquire dynamic change event signals; if the signal indicates an adjustment in the business scope, update the role vector component and determine the new collaboration relationship. Identity recognition features are extracted from the updated role vectors, and neighborhood information is propagated through a graph neural network to obtain enhanced identity identifiers; For enhanced identity verification and logistics status updates, vector similarity is calculated. If the similarity exceeds a preset threshold, an information matching process is performed to determine the target recipient. By using a list of target recipients and combining it with a collaboration relationship graph, distribution paths are generated, resulting in optimized information distribution rules. Obtain real-time logistics status data, filter irrelevant information according to optimized information distribution rules, determine the degree of matching with business needs, and determine the final push content; The final pushed content and identity verification are transmitted through an encrypted channel to obtain collaborative management and control confirmation feedback.

2. The logistics collaborative management and control method for cross-border trade according to claim 1, characterized in that, The process involves collecting data on the organizational type and business capabilities of participating parties, and then using vector embedding to generate initial role vectors, resulting in multi-dimensional role attribute representations, including: Data on organizational type and business capabilities is obtained by collecting data from participating parties; The vector embedding method is used to process organization type and business capability data to generate initial role vectors; Determine the character vector based on the initial character vector; Obtain vector embedding representations from character vectors; The embedding representation is obtained through vector embedding representation; Cosine similarity is used to calculate the similarity between the embedded representation and the pre-stored role template vector. If the similarity is higher than a preset threshold, the matched role is determined; otherwise, it is marked as a new role. If a match is found, the multi-dimensional character attribute representation is directly output; if it is marked as a new character, the character template vector set is updated.

3. The logistics collaborative management and control method for cross-border trade according to claim 1, characterized in that, The step of grouping similar participants into groups based on the role vectors and geographical location information, and determining the collaboration hierarchy, includes: Obtain the role vectors and geographical coordinates of the participants; Clustering algorithms are used to process role vectors and geographic location coordinates to generate multiple participant groups; Calculate the average role vector of the participants in each group to determine the group center vector of that group; Calculate the vector distance based on the group center vector between groups, and simultaneously calculate the spatial distance by obtaining the center point of the geographical location of the group. If both the vector distance and the spatial distance are less than the preset threshold, then the two groups are determined to belong to the same collaboration level. Based on the hierarchical affiliation determination results between groups, a collaborative structure tree with parent-child relationships is constructed; In the collaborative structure tree, connecting edges are added to nodes with direct hierarchical relationships to form a complete collaborative network topology.

4. The logistics collaborative management and control method for cross-border trade according to claim 1, characterized in that, The process of acquiring dynamic change event signals, and if the signal indicates an adjustment in the business scope, updating the role vector component and determining new collaborative relationships, includes: Acquire dynamic event signals; If a dynamic change event signal indicates an adjustment in the scope of business, then the updated participant role vector component is used to obtain the updated role vector. The similarity calculation result is obtained by calculating the vector similarity between each participant using the updated role vectors; If the similarity calculation result is higher than the preset threshold, a potential new collaborative relationship is determined; Generate candidate collaboration pairs based on potential new collaboration relationships; New cooperative connections are obtained by comparing candidate cooperative pairs with the existing cooperative network; The updated cooperative network is obtained by updating the cooperative network topology based on the newly added cooperative connections.

5. The logistics collaborative management and control method for cross-border trade according to claim 1, characterized in that, The process of extracting identity recognition features from the updated role vector and propagating neighborhood information through a graph neural network to obtain enhanced identity identifiers includes: By extracting identity distinguishing elements from role vectors, and using automated tools to perform preliminary classification of the elements, classified identity units are obtained. Based on the classified identity unit, obtain the surrounding node data associated with it, perform information transmission processing on the node data, and obtain the transmitted node set; If the amount of information in the node set after transmission exceeds a preset threshold, the set is filtered to identify the core node group. By using the core node group, a graph neural network is used to perform deep correlation analysis on the information within the group to obtain enhanced correlation identifiers. For the enhanced association identifier, obtain its positional distribution in the network structure, and mark the unit after determining the distribution; Based on the distributed labeled units, update the identity label content to obtain the final enhanced labeling result; If the final enhanced tagging result does not match the pre-established tagging library, the result is subjected to information enhancement processing to determine the updated identity tag.

6. The logistics collaborative management and control method for cross-border trade according to claim 1, characterized in that, For enhanced identity verification and logistics status updates, vector similarity is calculated. If the similarity exceeds a preset threshold, an information matching process is performed to determine the target recipient, including: Based on the enhanced identity identification and logistics status update, extract the identity vector and status vector; For the identity vector and the state vector, cosine similarity is used to calculate the similarity value to obtain the similarity result; If the similarity result exceeds the preset threshold, the information matching process is triggered to obtain a set of matching candidates; For the matching candidate set, traverse the candidate receiver information, compare the difference between the identity vector and the candidate vector, determine the receiver with the smallest difference, and determine the preliminary receiver; Based on the initial recipient, query the associated logistics status update records and obtain the status vector sequence in the records; For a sequence of state vectors, calculate the sequence average vector to obtain the aggregated state vector; Based on the aggregated state vector and the corresponding identity vector of the enhanced identity identifier, the cosine similarity is calculated again to confirm the similarity and determine the final matching receiver.

7. The logistics collaborative management and control method for cross-border trade according to claim 1, characterized in that, The process involves using a target recipient list, combined with a collaboration relationship graph, to generate distribution paths and obtain optimized information distribution rules, including: A distribution path graph containing nodes and edges is constructed by using a list of target recipients and combining it with a collaboration relationship graph. For the distribution path graph, traverse all edges and calculate the product of the frequency of cooperation on the edge and the response time to obtain the path weight value; Based on the path weight values, sort all possible paths, filter the top-ranked paths, and determine the weighted path set. For the weighted path set, Dijkstra's algorithm is used to calculate the shortest weighted path from the starting node to each target receiver, and candidate distribution paths are obtained. Based on the candidate distribution paths, query historical distribution records to obtain the number of successful transmissions and the total number of transmissions for each path; To obtain the path reliability index, the number of successful transmissions is divided by the total number of transmissions, given the number of successful transmissions and the total number of transmissions. If the path reliability index is higher than the preset threshold, the corresponding candidate distribution path is retained; otherwise, the path is removed, and the final optimized information distribution rule is determined.

8. The logistics collaborative management and control method for cross-border trade according to claim 1, characterized in that, The process of acquiring real-time logistics status data, filtering irrelevant information according to optimized information distribution rules, determining the matching degree of business needs, and determining the final push content includes: Acquire real-time logistics status data by collecting the latest location information, transportation progress information, and cargo integrity information from multiple logistics tracking interfaces; Based on the optimized information distribution rules, the real-time logistics status data is initially filtered to remove logistics information that is irrelevant to the target recipient and retain logistics information that contains the target cargo identifier. For the retained logistics information, analyze the business requirement matching degree and extract the goods type field, delivery time field and abnormal status field from the logistics information; By comparing the cargo type field in the logistics information with the cargo type of the preset business requirements, the consistency of cargo type is determined. If the cargo types are consistent, it is marked as a preliminary match; otherwise, it is marked as a mismatch. For the initially matched logistics information, the delivery time field is compared with the preset business demand time range. If the delivery time field falls within the preset time range, the matching score is increased; otherwise, the matching score is decreased. For logistics information marked as a preliminary match, check the abnormal status field. If the abnormal status field is empty, further increase the matching score; otherwise, decrease the matching score. All matching scores are calculated using a weighted summation method to obtain the overall business demand matching score. All logistics information is then sorted, and the logistics information with the highest overall business demand matching score is selected to determine the final push content.

9. A logistics collaborative management and control method for cross-border trade according to claim 1, characterized in that, The process of verifying the final pushed content and identity, transmitting via an encrypted channel, and obtaining collaborative management confirmation feedback includes: By combining the final pushed content with identity verification, the integrity of the content and the consistency of the identifier are obtained. Regarding content integrity and identifier consistency, if the content integrity meets the requirements and the identifier consistency matches, the transmission reliability is determined; otherwise, the transmission is marked as interrupted. Encrypted channels are used to transmit push content, thus ensuring channel security. Based on channel security, if the channel security meets the requirements, the verification accuracy is confirmed; otherwise, the transmission process is interrupted. By verifying accuracy and transmission reliability, feedback timeliness is obtained; To address the timeliness of feedback, a random forest algorithm is used to classify the effectiveness of feedback, thereby achieving consistency in control. Based on the consistency of control and the effectiveness of feedback, if the effectiveness of feedback meets the requirements, a collaborative control confirmation feedback is obtained; otherwise, the push content is retransmitted.

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