A one-stop management system for online foreign trade platforms

Abstract

This invention discloses a one-stop management system for online foreign trade platforms, specifically relating to the field of cross-border logistics cost prediction technology. It addresses the problem of order total logistics cost prediction deviations caused by existing systems neglecting multi-path coupling effects. The system acquires fragmented logistics data such as batch shipments, multimodal transport node sequences, and tariff fluctuation parameters. Based on the node sequences, it constructs a transport sub-unit combination network, identifying path combinations of shared transshipment nodes. It calculates the coupling cost increment triggered by cargo dwell time and tariff fluctuation parameters, generating nonlinear coupling parameters. Based on these parameters, it quantifies the cost transmission strength coefficient between transport sub-units. Finally, it uses these coefficients to correct the initial logistics cost model, outputting the predicted value of the order total logistics cost. This achieves nonlinear coupling modeling of dynamic cost elements in multimodal transport scenarios, significantly improving cost prediction accuracy.

Description

Technical Field

[0001] This invention relates to the field of cross-border logistics cost forecasting technology, and more specifically, to a one-stop management system for an online foreign trade platform. Background Technology

[0002] The globalization of cross-border e-commerce has led foreign trade enterprises to widely adopt online one-stop management systems to integrate core business processes such as order processing, warehousing and logistics, and payment settlement. These systems need to connect to international logistics service providers, cross-border payment platforms, and customs data interfaces of multiple countries to achieve end-to-end control from order generation to goods delivery. Existing technologies can already achieve centralized processing of order data and basic logistics status tracking.

[0003] However, in multi-batch, multi-path cross-border transportation scenarios, the nonlinear coupling effect of dynamic cost elements of different transportation routes is not quantitatively modeled, resulting in a systematic bias in the system's prediction of the total logistics cost of an order. This leads to distorted profit assessment and operational decision-making errors for enterprises. Existing technologies, which isolate the massive dynamic variables (such as batch shipments, multimodal transport, and tariff fluctuations) in fragmented logistics scenarios, cannot reveal the cost transmission mechanism between transportation sub-units, thus affecting and restricting the reliability of one-stop system decision-making. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a one-stop management system for online foreign trade platforms to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A one-stop management system for online foreign trade platforms includes:

[0007] The fragment integration module is used to obtain fragmented logistics data for the current order. The fragmented logistics data includes batch shipments, multimodal transport node sequences, and tariff fluctuation parameters.

[0008] The dynamic networking module is used to construct a combined network of transport sub-units based on the sequence of multimodal transport nodes, and to identify the path combinations of transport sub-units sharing transfer nodes in the combined network;

[0009] The coupling quantization module is used to calculate the coupling cost increment triggered by cargo dwell time and tariff fluctuation parameters at shared transfer nodes for the combination of transportation sub-unit paths and batch shipments, and to generate nonlinear coupling parameters.

[0010] The intensity quantification module is used to quantify the cost transmission intensity between transportation sub-units based on nonlinear coupling parameters and generate a cost transmission intensity coefficient.

[0011] The precision correction module is used to correct the initial logistics cost model using the cost transmission strength coefficient and output the predicted value of the total logistics cost of the order.

[0012] Furthermore, fragmented logistics data for the current order is obtained. This fragmented logistics data includes batch shipments, multimodal transport node sequences, and tariff fluctuation parameters, including:

[0013] Receive transportation route information returned by the international logistics service provider's interface, parse the transshipment station identifiers and connection order in the transportation route information to generate a multimodal transport node sequence;

[0014] Extract the cargo breakdown list and transport vehicle identification for each batch of shipments.

[0015] Obtain the tariff fluctuation parameters identified by each transshipment station through the customs data interface;

[0016] Based on the matching of transport vehicle identification and transshipment station identification with a pre-set station scheduling table, the planned dwell time of each batch of shipments at each transshipment station identification is determined.

[0017] Fragmented logistics data is generated by integrating multimodal transport node sequences, cargo breakdown lists, means of transport identification, tariff fluctuation parameters, and planned dwell time.

[0018] Furthermore, a combined network of transport sub-units is constructed based on the multimodal transport node sequence, and the path combinations of transport sub-units sharing transshipment nodes in the combined network are identified, including:

[0019] Map the transshipment station identifiers in the multimodal transport node sequence to a set of nodes in the combined network;

[0020] Directed edges between nodes are generated based on the connection order between the transshipment station identifiers, forming a combined network of transportation sub-units;

[0021] Traverse the set of nodes in the combined network and extract the transport sub-unit paths containing the same transfer station identifier as candidate path combinations;

[0022] The overlap of nodes in the transportation sub-unit paths of the candidate path combinations is checked, and the path combinations with more than a threshold number of overlapping transfer station identifiers are retained as transportation sub-unit path combinations with shared transfer nodes.

[0023] Furthermore, for the route combination of transportation sub-units and batch shipments, the coupling cost increments triggered by cargo dwell time and tariff fluctuation parameters at shared transshipment nodes are calculated, generating nonlinear coupling parameters, including:

[0024] Extract goods with the same transshipment station identifier from the goods splitting list corresponding to the batch shipments;

[0025] The total dwell time of the node is generated by superimposing the planned dwell time of batched goods at the shared transfer node.

[0026] Filter the tariff fluctuation parameters corresponding to the overlapping transshipment station identifiers in the transportation sub-unit route combination;

[0027] The product of the total dwell time of the computing node and the selected tariff fluctuation parameters is used as the coupling cost increment;

[0028] The nonlinear coupling parameters are generated by summing all the coupling cost increments at the shared transfer node.

[0029] Furthermore, extracting goods with the same transshipment station identifier from the goods splitting list corresponding to the batch shipment includes grouping goods with the same first six digits of the customs code under the same transshipment station identifier into the same batch group.

[0030] Furthermore, the cost transmission strength between transportation sub-units is quantified based on nonlinear coupling parameters to generate a cost transmission strength coefficient, including:

[0031] Extract the sequence of transfer station identifiers contained in each path of the transport sub-unit path combination of the shared transfer node;

[0032] The proportion of the increase in coupling cost at the shared transfer node for each transportation sub-unit path to the total value of nonlinear coupling parameters is calculated.

[0033] Calculate the path overlap between transport sub-unit paths with the same transfer station identification sequence;

[0034] The transmission strength factor of each transport subunit path pair is generated by multiplying the proportion of the coupling cost increment to the total value of nonlinear coupling parameters with the path overlap.

[0035] The transmission intensity factor of all transport sub-unit path pairs is normalized to generate the cost transmission intensity coefficient.

[0036] Furthermore, the initial logistics cost model is corrected using a cost transmission strength coefficient, outputting a predicted total logistics cost for the order, including:

[0037] Obtain the independent transportation costs and independent customs duties for each batch of shipments output from the initial logistics cost model;

[0038] Extract the cost transmission intensity coefficient corresponding to the route combination of the transportation sub-unit;

[0039] The cost transmission strength coefficient is multiplied by the coupling cost increment at the shared transfer node to generate the transmission correction amount;

[0040] The adjusted transportation cost is generated by superimposing the correction amount onto the independent transportation costs of each batch of shipments.

[0041] Combine and adjust transportation costs and independent tariff costs to generate a total logistics cost forecast for the order.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] This system constructs a combined network of multimodal transport node sequences through a dynamic networking module and identifies path combinations of shared transshipment nodes, enabling explicit modeling of the topological coupling relationships between transport sub-units. This design overcomes the limitations of traditional logistics systems that rely on independent calculations of multiple paths, accurately capturing the interaction effects of cargo delays and tariff fluctuations at shared nodes. Combined with a coupling quantification module, the system correlates delay duration with tariff parameters as nonlinear coupling parameters, effectively quantifying the dynamic cost increments of multiple shipments at transshipment nodes, fundamentally solving the problem of undercalculated coupled costs due to time-varying policies and operational constraints in cross-border logistics.

[0044] The solution further utilizes an intensity quantification module to generate a cost transmission intensity coefficient, integrating path overlap and economic weight as dual factors to accurately characterize the cost diffusion intensity of shared nodes across multiple paths. The initial cost model is then dynamically corrected by a precision correction module, ultimately outputting a predicted total logistics cost for the order. This entire solution forms a technical closed loop of "network modeling - coupled quantification - transmission correction," significantly improving the accuracy of cost prediction in multimodal transport scenarios. It provides foreign trade enterprises with reliable profit assessment and decision-making support, overcoming the technical shortcomings of existing systems that isolate fragmented dynamic logistics variables. Attached Figure Description

[0045] Figure 1 This is a schematic diagram of the structure of a one-stop management system for an online foreign trade platform according to the present invention. Detailed Implementation

[0046] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0047] Example: Figure 1 A schematic diagram of the structure of a one-stop management system for an online foreign trade platform according to the present invention is provided. The one-stop management system for an online foreign trade platform includes:

[0048] The fragment integration module is used to obtain fragmented logistics data for the current order. The fragmented logistics data includes batch shipments, multimodal transport node sequences, and tariff fluctuation parameters.

[0049] The dynamic networking module is used to construct a combined network of transport sub-units based on the sequence of multimodal transport nodes, and to identify the path combinations of transport sub-units sharing transfer nodes in the combined network;

[0050] The coupling quantization module is used to calculate the coupling cost increment triggered by cargo dwell time and tariff fluctuation parameters at shared transfer nodes for the combination of transportation sub-unit paths and batch shipments, and to generate nonlinear coupling parameters.

[0051] The intensity quantification module is used to quantify the cost transmission intensity between transportation sub-units based on nonlinear coupling parameters and generate a cost transmission intensity coefficient.

[0052] The precision correction module is used to correct the initial logistics cost model using the cost transmission strength coefficient and output the predicted value of the total logistics cost of the order.

[0053] Obtain fragmented logistics data for the current order. This fragmented logistics data includes batch shipments, multimodal transport node sequences, and tariff fluctuation parameters. The specific implementation is as follows:

[0054] The system receives transportation route information returned from the international logistics service provider's interface. This information includes a list of node codes arranged in the actual transportation sequence and the connection relationships between nodes. The node codes adopt the internationally recognized three-letter airport code format. By parsing the node codes and their order in the transportation route information, an ordered sequence of transshipment terminal identifiers is generated, forming a multimodal transport node sequence. The parsing process includes two core operations: first, removing duplicate node codes to avoid path redundancy; and second, verifying whether the node connection logic conforms to the geographical accessibility principle. For example, when the original transportation route information is "PVG-SIN-SIN-FRA", after parsing, an ordered sequence containing the identifier "PVG" of Shanghai Pudong International Airport, the identifier "SIN" of Singapore Changi Airport, and the identifier "FRA" of Frankfurt Airport is generated, where duplicate "SIN" identifiers are merged. Extract the cargo breakdown list and means of transport identification corresponding to each batch of shipments. The cargo breakdown list records the details of each batch of goods in a structured table format, including the unique cargo number, the cargo category corresponding to the Harmonized System of Customs codes, weight data in kilograms, and volume data in cubic meters. The means of transport identification consists of the means of transport type code and the vehicle number. The means of transport type code adopts a single-letter coding rule, where "V" represents a ship, "A" represents an aircraft, and "T" represents a truck. The vehicle number follows the container numbering rules issued by the International Organization for Standardization. For example, the means of transport identification record corresponding to the batch number B003 is recorded as "V_MSKU742189", indicating the ship type and Maersk Line container number 742189.

[0055] The system retrieves tariff fluctuation parameters for each transshipment terminal from the customs data interface. Specifically, it sends a structured query request to the customs data interface, which includes the transshipment terminal identifier field and the customs code field from the cargo splitting list. The tariff fluctuation parameters returned by the interface contain two data items: the basic tariff rate is a percentage value, and the fluctuation coefficient is the adjustment range based on policy changes over the past twelve months. For example, when querying the commodity code "87032341" corresponding to the transshipment terminal identifier "FRA", the returned parameters are "basic tariff rate 6.5%, fluctuation coefficient ±1.8%". Based on the matching of transport vehicle identifiers and transshipment station identifiers, a pre-set station scheduling table is established. This table is a relational database table storing three sets of key parameters: a standard processing time parameter recording the processing time (in hours) for different types of transport vehicles under standard operating conditions; a current load factor parameter updating the station's real-time operating load rate hourly; and a cargo type correction parameter mapping cargo category to processing priority weights. The matching process executes three steps: First, the base value of the standard processing time is retrieved based on the transport vehicle type code; for example, identifier "A" corresponds to a standard processing time of 2.5 hours for aircraft. Second, the current load factor is obtained, which is determined by the station's... The station operation system collects data in real time via its interface; the third step determines the priority weights based on the cargo categories in the cargo splitting list, with dangerous goods weighted at 1.25, cold chain goods at 1.15, and general cargo at 1.0; finally, the planned dwell time is calculated using multiplication: planned dwell time = standard processing time × (1 + current load factor) × priority weight. For example, if the standard processing time for transport vehicle "A_CXA205" at the "FRA" station is 2.5 hours, the current load factor is 0.3, and the cargo priority weight is 1.15, the calculated dwell time is 2.5 × 1.3 × 1.15 = 3.7375 hours.

[0056] Fragmented logistics data is generated by integrating multimodal transport node sequences, cargo split lists, means of transport identification, tariff fluctuation parameters, and planned dwell time. The integration process establishes a mapping relationship between five sets of data: the multimodal transport node sequences serve as the main chain structure, storing an ordered list of terminal identifications; tariff fluctuation parameters are mapped using key-value pairs based on transshipment terminal identifications, for example, the mapping entry ["FRA"→"6.5%±1.8%"]; planned dwell time is bound to the means of transport identification to form a set of timeliness parameters, for example, the mapping entry ["V_MSKU742189@FRA"→"8.2 hours"]; the cargo split list is grouped and stored in detail according to batch shipment batch numbers; and the means of transport identification set independently stores basic vehicle information. The final generated fragmented logistics data is stored in JSON-LD structured format, containing five data blocks: Node sequence block storing an ordered list ["PVG","SIN","FRA"], Customs duty block storing a dictionary {"PVG":"5.2%±0.7%","SIN":"0%±0%","FRA":"6.5%±1.8%"}, Timeliness block storing a dictionary {"B003@PVG":"4.5 hours","B003@SIN":"3.2 hours","B003@FRA":"8.2 hours"}, Split list block storing a batch index {"B003":[{"Goods Number":"C-88921","Item":"87032341","Weight":"4200kg","Volume":"18.7m³"}]}, and Tool identifier block storing a vehicle index {"B003":"V_MSKU742189"}. Each data block is equipped with a data integrity verification mechanism, which uses a cyclic redundancy check algorithm to generate a check code, and the version number is updated in the format of "year-month-day-serial number".

[0057] By employing a hash table to map nodes and depth-first search traversal, the space complexity can be reduced to O(n) compared to traditional adjacency matrix storage. Depth-first search reduces invalid accesses compared to breadth-first search when detecting path branches. The hash table achieves O(1) complexity node deduplication, avoiding network expansion caused by redundant nodes; the recursive nature of depth-first search is naturally adapted to path branch records, ensuring that no shared nodes are missed.

[0058] Based on the multimodal transport node sequence, a combined network of transport sub-units is constructed, and the path combinations of transport sub-units sharing transshipment nodes in the combined network are identified. The specific implementation is as follows:

[0059] When constructing a combined network of transport subunits based on multimodal transport node sequences, the transshipment station identifiers in the multimodal transport node sequences are first mapped to a set of nodes in the combined network. The mapping process uses a dictionary data structure to establish a unique mapping relationship between station identifiers and network nodes. Each transshipment station identifier is added to the set as an independent node, and the node attributes record the station's geographical coordinates and its region code. The geographical coordinates are obtained by matching against the International Air Transport Association (IATA) airport database, and the coordinate format uses decimal notation. For example, the transshipment station identifier "PVG" is mapped to the node {Identifier: "PVG", Coordinates: "31.1434, 121.805", Region: "CN"}. After the node set is generated, duplicate identifier detection is performed. When a duplicate station identifier is detected, only the first node is retained and marked as a shared node. For example, the second "SIN" in the sequence ["SIN", "FRA", "SIN"] is identified as a redundant node. Directed edges are generated between nodes based on the connection order between the transshipment station identifiers. The rule for generating directed edges is as follows: establish a one-way connection from the start point to the end point between adjacent nodes according to the node sequence. The edge attributes include the transportation mode code and the distance value. The transportation mode code is automatically derived based on the geographical relationship between the nodes. The derivation rule is: if the region codes of the two nodes are the same, assign the value "T" (truck transportation); if they are separated by ocean, assign the value "V" (ship transportation); if they are separated by land border, assign the value "R" (rail transportation). The distance value is obtained by calculating the spherical distance of the node coordinates using the Haversine formula, and the distance unit is uniformly set to kilometers. For example, a directed edge {start point: "PVG", end point: "SIN", mode: "V", distance: 3827} is generated between nodes PVG (31.1434, 121.805) and SIN (1.3502, 103.994). The final combined network of transportation sub-units contains two basic elements: a set of nodes and a set of edges. The network structure is stored in the form of an adjacency list.

[0060] The process involves traversing the node set of the combined network and extracting transport sub-unit paths containing the same transfer station identifier as candidate path combinations. The traversal process uses a breadth-first search algorithm, starting from the network entry node and recording the complete path to each node during the scan. When a node is detected to be traversed by multiple paths, the candidate path combination extraction operation is triggered. The specific implementation includes four steps: First, initialize the path queue and add the starting node to the queue; second, iteratively pop the head node from the queue and explore its adjacent nodes to generate new paths; third, when a new path reaches a visited node, mark that node as a shared node; fourth, extract all complete paths containing the shared node to form candidate path combinations. For example, when there are three paths in the network: Path 1: PVG→SIN→FRA, Path 2: PVG→SIN→DXB, and Path 3: HKG→SIN→FRA, the candidate combination {Path 1, Path 2, Path 3} is extracted at the shared node "SIN". Candidate path combinations are stored as a list of path objects, each containing a path number, node sequence, and edge attribute set.

[0061] The overlap of nodes in the transportation sub-units of the candidate route combination is checked. The overlap is defined as the ratio of the number of transfer station identifiers shared by two routes to the average number of nodes on the routes. The check process consists of three steps: First, the total number of nodes in each route pair is calculated, for example, route A has 4 nodes and route B has 5 nodes. Second, the number of node identifiers that intersect between the two routes is calculated, that is, the number of times the same station identifier appears. Third, the overlap index is calculated: overlap = number of intersection nodes / [(number of nodes in route A + number of nodes in route B) / 2]. The value of this index ranges from 0 to 2. For example, when the number of intersection nodes between route A and route B is 2 and the average number of nodes is (4+5) / 2 = 4.5, the overlap = 2 / 4.5 ≈ 0.444. Path combinations with more than a threshold number of overlapping transfer station identifiers are retained as transportation sub-unit path combinations of shared transfer nodes. The threshold is set with a fixed benchmark value of 0.4, which is determined based on historical logistics data analysis: the average overlap of 1000 actual transportation path combinations is 0.38, and 0.4 is taken as the screening threshold; when the overlap of all path pairs in a candidate path combination is greater than or equal to 0.4, the entire combination is retained. For example, in the aforementioned candidate combinations, the overlap of path 1-path 2 is 0.444>0.4, the overlap of path 1-path 3 is 0.444>0.4, and the overlap of path 2-path 3 is 0.222<0.4. These combinations are removed because there are non-compliant path pairs.

[0062] The final output of the shared transfer node's transport sub-unit path combinations is stored in a graph database format. Node entities contain an identifier field and a shared tag field, while edge entities contain path number references. During data persistence, three data files are generated: the node file stores all shared node information, the edge file stores path connection relationships, and the path file stores complete path attributes. For example, the validated and retained path combinations form the following storage structure: node file record ["SIN","FRA"], edge file record {"Path ID:TR789":["PVG→SIN","SIN→FRA"]}, path file record {"TR789":{Node sequence:["PVG","SIN","FRA"], Distance:[3827,10253]}}.

[0063] Using the first six digits of the customs code (HS code classification level) reduces the number of calculation groups compared to using the complete code. The first six digits correspond to major commodity categories (such as vehicle category 87), ensuring category relevance while avoiding fragmentation caused by over-subdivision; historical data regression determines a 0.4 threshold, improving the rationality of batching compared to manual experience setting.

[0064] For the combination of transportation sub-unit routes and batch shipments, the coupling cost increment triggered by cargo dwell time and tariff fluctuation parameters at shared transshipment nodes is calculated, and nonlinear coupling parameters are generated. The specific implementation is as follows:

[0065] When calculating the coupled cost increment for transportation sub-unit route combinations and batch shipments, the process first extracts goods with the same transshipment station identifier from the goods splitting list corresponding to the batch shipments. The extraction operation performs field matching based on the goods details of the batch shipments. The matching rule is: goods with the same first six digits of their customs code under the same transshipment station identifier are grouped into the same batch group. For example, batch shipment B003 contains two types of goods at the transshipment station identifier "FRA": goods number C-88921 (customs code 87032341) and C-88922 (customs code 87032342). Because the first six digits "870323" are the same, they are merged into batch group G-01. The batch group data structure records the total weight and total volume of goods within the group. The total weight is in kilograms, and the total volume is in cubic meters. This is obtained by accumulating the weight and volume values ​​of all goods within the group.

[0066] The total dwell time of a batch of goods at a shared transshipment node is generated by overlaying the planned dwell times at each node. The overlay process uses arithmetic summation: the planned dwell times of each batch within the batch group at that station are obtained and added together to generate the total dwell time. The planned dwell time is uniformly measured in hours. For example, if batch group G-01 includes batch B003 (dwell time 3.7 hours) and batch B007 (dwell time 4.2 hours), then the total dwell time = 3.7 + 4.2 = 7.9 hours. When the same batch appears multiple times at a shared node, only the first dwell time value is taken to avoid double counting.

[0067] The process filters tariff fluctuation parameters corresponding to overlapping transshipment terminal identifiers within transport sub-unit route combinations. The filtering operation is based on querying the tariff parameter mapping table generated in the previous steps using shared transshipment node identifiers, extracting parameters containing two data items: a base tariff rate and a fluctuation coefficient. For example, the shared node identifier "FRA" corresponds to tariff fluctuation parameters of {"base tariff rate": 6.5,"fluctuation coefficient": 1.8}, with all units being percentages. The scope of tariff parameter filtering is limited to terminal identifiers actually traversed by the current transport sub-unit route combination; parameters for terminal identifiers not involved are not included in the calculation.

[0068] The product of the total dwell time at the node and the selected tariff fluctuation parameters is used as the coupling cost increment. The calculation process is carried out in two steps: First, the tariff fluctuation parameters are converted into actual impact values. Actual impact value = base tariff rate × (1 + fluctuation coefficient / 100). For example, when the base tariff rate is 6.5 and the fluctuation coefficient is 1.8, the actual impact value = 6.5 × (1 + 0.018) = 6.617. Second, the product is calculated: Coupling cost increment = total dwell time at the node × actual impact value, with the unit being hours·percentage. For example, when the total dwell time is 7.9 hours and the actual impact value is 6.617, the increment = 7.9 × 6.617 ≈ 52.2743 hours·percentage. The calculation result is rounded to two decimal places.

[0069] The nonlinear coupling parameter is generated by summing all coupling cost increments at shared transfer nodes. The summation operation iterates through all shared transfer nodes included in the current transport subunit path combination, accumulating the coupling cost increment value of each node. For example, if the combination includes three shared nodes "FRA", "SIN", and "DXB", with increments of 52.27, 38.45, and 29.83 respectively, the nonlinear coupling parameter = 52.27 + 38.45 + 29.83 = 120.55 hours·percentage. The final parameter is stored as a floating-point value, associated with a list of shared node identifiers and their corresponding increment components.

[0070] A three-dimensional tensor is used to store the strength coefficients (path A × path B × node), which increases the node dimension compared to a two-dimensional matrix. The tensor structure explicitly represents the triple relationship of "path pair-node", avoiding the black-box computation of traditional graph convolutional networks; the dual-factor fusion (economic ratio + physical overlap) makes the transmission strength have both economic logic and topological characteristics.

[0071] The cost transmission strength between transportation sub-units is quantified based on nonlinear coupling parameters to generate a cost transmission strength coefficient. The specific implementation is as follows:

[0072] When quantifying the cost transmission intensity between transportation sub-units based on nonlinear coupling parameters, the first step is to extract the sequence of transfer station identifiers contained in each path of the transportation sub-unit path combination sharing a transfer node. The extraction operation is based on the transportation sub-unit path combination data generated in the previous step, traversing the node sequence attributes of each path to obtain a complete ordered list of station identifiers; for example, path number TR789 contains the sequence ["PVG","SIN","FRA"], path number TR845 contains the sequence ["PVG","SIN","DXB"], and path number TR912 contains the sequence ["HKG","SIN","FRA"]. The sequence data is stored as a path dictionary structure, with the path number as the key and the list of station identifiers as the value.

[0073] The proportion of the incremental coupling cost at shared transfer nodes for each transportation sub-unit path to the total value of nonlinear coupling parameters is calculated. This process involves three steps: First, obtain the incremental coupling cost of each shared node calculated in the previous step, for example, the incremental cost at shared node "SIN" is 38.45 hours / percentage. Second, obtain the total value of nonlinear coupling parameters, for example, 120.55 hours / percentage. Third, calculate the proportion: Proportion = Increment at the node / Total value of nonlinear coupling parameters. For example, if path TR789 has an incremental cost of 12.8 hours / percentage at the "SIN" node, then the proportion = 12.8 / 120.55 ≈ 0.106. The calculated proportion is rounded to four decimal places.

[0074] Calculate the path overlap between transport sub-unit paths with the same transfer station identification sequence. The calculation process uses a node sequence comparison method: First, take the transfer station identification sequences of two paths; second, calculate the number of intersection nodes; third, calculate the overlap = number of intersection nodes / [(length of sequence A + length of sequence B) / 2]. For example, path TR789 has a sequence length of 3, path TR845 has a sequence length of 3, and the number of intersection nodes ["PVG", "SIN"] is 2, then the overlap = 2 / [(3+3) / 2] = 2 / 3 ≈ 0.6667. When comparing multiple paths, calculate the pairwise overlap for each path pair.

[0075] The transmission strength factor for each transport subunit path pair is generated by multiplying the ratio of the incremental coupling cost to the total value of nonlinear coupling parameters by the path overlap degree. The generation rule is as follows: for each transport subunit path pair, the average ratio of the two paths at the same shared node is taken and multiplied by the path overlap degree of the path pair. The specific formula is: Transmission strength factor = (Ratio_Path A + Ratio_Path B) / 2 × Path overlap degree. For example, if the ratios of paths TR789 and TR845 at node "SIN" are 0.106 and 0.128 respectively, and the overlap degree is 0.6667, then the transmission strength factor = (0.106 + 0.128) / 2 × 0.6667 ≈ 0.117 × 0.6667 ≈ 0.078. The calculation results are stored as a path pair matrix, with rows and columns representing path numbers.

[0076] The conduction strength factors of all transport sub-unit path pairs are normalized to generate cost conduction strength coefficients. The normalization process uses a sum-proportion method: first, the sum of the conduction strength factors of all path pairs is calculated; second, each factor is divided by the sum to obtain a normalized value. For example, if the factors of three path pairs are 0.078, 0.095, and 0.102, and the sum is 0.275, then the normalized coefficients are 0.078 / 0.275≈0.2836, 0.095 / 0.275≈0.3455, and 0.102 / 0.275≈0.3709, respectively. The final generated coefficients are stored as a three-dimensional tensor data structure. The first dimension is the starting path number, the second dimension is the target path number, and the third dimension is the shared node identifier. For example, the tensor element T[TR789][TR845]["SIN"]=0.2836.

[0077] The initial logistics cost model is corrected using a cost transmission strength coefficient, and the predicted total logistics cost for the order is output. The specific implementation is as follows:

[0078] When retrieving the independent transportation costs and independent customs duties for each batch of shipments output by the initial logistics cost model, the model is calculated based on standard logistics pricing rules. Independent transportation costs include vehicle rental fees, fuel costs, and labor costs, all expressed in US dollars. Independent customs duties are calculated by multiplying the dutiable value of the goods by the customs duty rate, also expressed in US dollars. For example, the independent transportation cost for batch shipment number B003 is US$4200, and the independent customs duty cost is US$780. This retrieval process is achieved by querying the model's output database, where cost data fields are stored indexed by the batch shipment number.

[0079] When extracting the cost transmission intensity coefficient corresponding to the path combination of the transportation subunit, the extraction operation is performed based on the three-dimensional tensor data structure generated in the previous steps. Specifically, the operation is as follows: based on the path combination identifier of the currently processed transportation subunit, the corresponding path number dimension and shared node identifier dimension are retrieved from the tensor data; for example, when processing the path combination PC-01, the tensor element T[TR789][TR845]["SIN"]=0.2836 is extracted. The intensity coefficient data is stored in a distributed in-memory database, and the combination identifier is used as the primary key during querying.

[0080] The cost transmission strength coefficient is multiplied by the coupling cost increment at the shared transit node to generate the transmission correction. The calculation process is implemented in two steps: First, the coupling cost increment value at the shared transit node is obtained, which comes from the node coupling cost increment record calculated in the previous step; for example, the coupling cost increment at the shared node "SIN" is 38.45 hours·percentage; Second, a multiplication operation is performed: Transmission correction = Cost transmission strength coefficient × Coupling cost increment; for example, when the strength coefficient is 0.2836 and the coupling cost increment is 38.45, the transmission correction = 0.2836 × 38.45 ≈ 10.90462 hours·percentage. The calculation result is converted to the currency unit US dollars, with the conversion rule being: 1 hour·percentage corresponds to a US dollar exchange rate coefficient of 0.15, which is derived from the analysis of historical logistics cost data; for example, 10.90462 hours·percentage × 0.15 ≈ 1.635693 US dollars.

[0081] The adjusted transportation cost is generated by adding the transmission correction amount to the independent transportation cost of each batch of shipments. The addition process uses an arithmetic summation principle: Adjusted Transportation Cost = Independent Transportation Cost + Transmission Correction Amount (monetary value); for example, when the independent transportation cost of batch number B003 is $4200 and the transmission correction amount is $1.635693, the adjusted transportation cost = $4200 + $1.635693 = $4201.635693. When multiple transmission correction amounts apply to the same batch of shipments, all relevant correction amounts are added together; for example, when a batch is simultaneously affected by two correction amounts of $1.63 and $0.87, the total correction amount = $2.50.

[0082] The total logistics cost forecast for an order is generated by merging and adjusting transportation costs and independent customs duties. The merging operation performs an addition calculation: Total Logistics Cost Forecast = Adjusted Transportation Cost + Independent Customs Duty. For example, when batch number B003 has an adjusted transportation cost of $4201.64 and an independent customs duty cost of $780, the forecast value = $4201.64 + $780 = $4981.64. When an order contains multiple batches, the forecast values ​​for all batches are summed. For example, when an order contains batches B003 ($4981.64) and B007 ($5210.20), the total forecast value = $4981.64 + $5210.20 = $10191.84. The final forecast value is stored as a floating-point number, linked to the cost component details of each batch.

[0083] In implementation, the exchange rate coefficient was determined as follows: 10,000 sets of international logistics cost data samples from the past three years were collected, and the linear regression relationship between unit-time tariff costs and actual currency costs was statistically analyzed, yielding a regression coefficient of 0.15 USD / hour·percentage and a correlation coefficient of 0.92. For boundary handling of the transmission correction: when the calculation result is negative, it is forcibly reset to zero to avoid cost deductions, as logistics cost transmission can only increase additional expenses. Data persistence uses a columnar storage format, with each record containing four basic fields: order number, batch number, cost type, and currency value.

[0084] Columnar storage of order cost data (order ID + batch ID + cost type + value) saves storage space compared to row-based storage. Column compression technology optimizes the efficiency of multi-batch queries, reducing the time required for aggregation calculations of tens of thousands of batches; component-detailed associated storage supports cost traceability, improving data location speed during auditing.

[0085] In the field of logistics cost forecasting, traditional solutions typically calculate the transportation and tariff costs of independent batches based on static rate models, failing to effectively quantify the coupling effects caused by shared nodes in multimodal transport scenarios. This solution achieves explicit modeling of node coupling between paths by constructing a combined network of transport sub-units: transshipment station identifiers are mapped to network nodes and directed connections are established; shared node path combinations are extracted through depth-first traversal, differing from the conventional independent path calculation model. In particular, the node overlap verification mechanism combined with dynamic threshold settings accurately captures highly correlated path groups, overcoming the limitations of existing technologies that treat physical topology and economic parameters separately. Furthermore, it innovatively proposes the product of tariff fluctuation parameters and dwell time as the coupling cost increment, and establishes a dynamic association between goods and nodes based on the first six digits of the customs code as a batching rule, solving the coupling modeling problem of time-varying tariffs and goods collection and distribution in cross-border logistics. Finally, the transmission strength coefficient of a three-dimensional tensor structure is used to integrate path economic weights and topological overlap, achieving quantitative correction of nonlinear cost transmission.

[0086] All calculations involved in the embodiments are dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to the actual situation.

[0087] It should be noted that this invention can be deployed on the device itself to realize embedded applications, or it can run on a PC or other terminal with a user interface, thereby meeting various hardware environments and usage requirements.

[0088] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wireless or wired transmission; wired transmission methods include optical fiber, twisted pair, coaxial cable, etc.; wireless transmission includes infrared, microwave, etc. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center containing one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0089] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0090] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of apparatuses or modules may be electrical, mechanical, or other forms.

[0091] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0092] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0093] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0094] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0095] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A one-stop management system for an online foreign trade platform, characterized in that, include: The fragment integration module is used to obtain fragmented logistics data for the current order. The fragmented logistics data includes batch shipments, multimodal transport node sequences, and tariff fluctuation parameters. The dynamic networking module is used to construct a combined network of transport subunits based on the sequence of multimodal transport nodes, and to identify the path combinations of transport subunits sharing transshipment nodes in the combined network, including: Map the transshipment station identifiers in the multimodal transport node sequence to a set of nodes in the combined network; Directed edges between nodes are generated based on the connection order between the transshipment station identifiers, forming a combined network of transportation sub-units; Traverse the set of nodes in the combined network and extract the transport sub-unit paths containing the same transfer station identifier as candidate path combinations; Perform node overlap verification on the transportation sub-unit paths in the candidate path combinations, and retain the path combinations with more than a threshold number of overlapping transfer station identifiers as transportation sub-unit path combinations with shared transfer nodes. The coupling quantization module is used to calculate the coupling cost increment triggered by cargo dwell time and tariff fluctuation parameters at shared transfer nodes for the combination of transportation sub-unit paths and batch shipments, and to generate nonlinear coupling parameters. The intensity quantification module is used to quantify the cost transmission intensity between transportation sub-units based on nonlinear coupling parameters and generate a cost transmission intensity coefficient. The precision correction module is used to correct the initial logistics cost model using the cost transmission strength coefficient and output the predicted value of the total logistics cost of the order.

2. The one-stop management system for an online foreign trade platform according to claim 1, characterized in that, Obtain fragmented logistics data for the current order. This fragmented logistics data includes batch shipments, multimodal transport node sequences, and tariff fluctuation parameters, including: Receive transportation route information returned by the international logistics service provider's interface, parse the transshipment station identifiers and connection order in the transportation route information to generate a multimodal transport node sequence; Extract the cargo breakdown list and transport vehicle identification for each batch of shipments. Obtain the tariff fluctuation parameters identified by each transshipment station through the customs data interface; Based on the matching of transport vehicle identification and transshipment station identification with a pre-set station scheduling table, the planned dwell time of each batch of shipments at each transshipment station identification is determined. Fragmented logistics data is generated by integrating multimodal transport node sequences, cargo breakdown lists, means of transport identification, tariff fluctuation parameters, and planned dwell time.

3. The one-stop management system for an online foreign trade platform according to claim 2, characterized in that, For the combination of transportation sub-unit routes and batch shipments, the coupling cost increments triggered by cargo dwell time and tariff fluctuation parameters at shared transshipment nodes are calculated, generating nonlinear coupling parameters, including: Extract goods with the same transshipment station identifier from the goods splitting list corresponding to the batch shipments; The total dwell time of the node is generated by superimposing the planned dwell time of batched goods at the shared transfer node. Filter the tariff fluctuation parameters corresponding to the overlapping transshipment station identifiers in the transportation sub-unit route combination; The product of the total dwell time of the computing node and the selected tariff fluctuation parameters is used as the coupling cost increment; The nonlinear coupling parameters are generated by summing all the coupling cost increments at the shared transfer node.

4. The one-stop management system for an online foreign trade platform according to claim 3, characterized in that, Extracting goods from the split shipment list corresponding to the batch shipments that have the same transshipment station identification includes grouping goods with the same first six digits of the customs code under the same transshipment station identification into the same batch group.

5. The one-stop management system for an online foreign trade platform according to claim 3, characterized in that, The cost transmission strength between transportation sub-units is quantified based on nonlinear coupling parameters, and a cost transmission strength coefficient is generated, including: Extract the sequence of transfer station identifiers contained in each path of the transport sub-unit path combination of the shared transfer node; The proportion of the increase in coupling cost at the shared transfer node for each transportation sub-unit path to the total value of nonlinear coupling parameters is calculated. Calculate the path overlap between transport sub-unit paths with the same transfer station identification sequence; The transmission strength factor of each transport subunit path pair is generated by multiplying the proportion of the coupling cost increment to the total value of nonlinear coupling parameters with the path overlap. The transmission intensity factor of all transport sub-unit path pairs is normalized to generate the cost transmission intensity coefficient.

6. The one-stop management system for an online foreign trade platform according to claim 5, characterized in that, The initial logistics cost model is corrected using a cost pass-through strength coefficient, and the predicted total logistics cost for the order is output, including: Obtain the independent transportation costs and independent customs duties for each batch of shipments output from the initial logistics cost model; Extract the cost transmission intensity coefficient corresponding to the route combination of the transportation sub-unit; The cost transmission strength coefficient is multiplied by the coupling cost increment at the shared transfer node to generate the transmission correction amount; The adjusted transportation cost is generated by superimposing the correction amount onto the independent transportation costs of each batch of shipments. Combine and adjust transportation costs and independent tariff costs to generate a total logistics cost forecast for the order.

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