Intelligent calculation system and method for logistics freight of cross-border e-commerce

Abstract

The present invention relates to the field of logistics freight accounting technology, and in particular to a system and method for intelligent logistics freight accounting for cross-border e-commerce. The method collects logistics data through APIs and integrates customs and logistics company information; obtains tax data in real time, and uses genetic algorithms to optimize transportation plans; uses machine learning to build a dynamic cost model for preliminary cost estimation; establishes a risk assessment mechanism to monitor freight influencing factors; during transportation, uses geographic information systems and blockchain technology to achieve transparent tracking and cost feedback; after the goods arrive, the final cost accounting is performed based on the actual transportation data; optimizes the logistics process through deep learning methods, and iteratively updates the cost model and transportation strategy. The present invention not only improves the logistics and transportation efficiency of cross-border e-commerce, but also effectively controls transportation costs, and improves the transparency and predictability of the operation of the entire supply chain.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics freight accounting, and in particular to an intelligent logistics freight accounting system and method for cross-border e-commerce. Background Art

[0002] With the development of the global economy, cross-border e-commerce has risen rapidly and become an important part of international trade. Cross-border e-commerce involves multi-country transactions, and its logistics process includes the transportation of goods from the seller's country to the buyer's country. This process involves multiple modes of transportation (such as air transportation, sea transportation, etc.) and multiple logistics nodes (such as warehouses, transfer stations, etc.). The efficiency and cost of each link directly affect the overall logistics efficiency and cost.

[0003] At present, freight accounting faces many challenges in cross-border e-commerce logistics. The following problems still exist: existing technologies may not be able to effectively integrate and update data from different sources in real time. The differences in tax rates, tariff policies and transportation costs in different countries complicate freight accounting; traditional freight accounting relies on manual operations, which is inefficient and prone to errors. Traditional logistics cost accounting methods do not include self-optimization and iterative update functions, resulting in the inability to identify optimization points and automatically update cost accounting models and transportation strategies based on data collected during the logistics process; traditional methods may rely on relatively static models when optimizing transportation plans and routes, lacking flexibility and adaptability, and cannot effectively find the transportation plan with the lowest cost and the highest efficiency. Summary of the invention

[0004] In order to solve the above problems, the present invention provides a system and method for intelligent calculation of logistics freight for cross-border e-commerce.

[0005] To achieve the above object, the technical solution adopted by the present invention is:

[0006] On the one hand, a method for intelligently calculating logistics freight for cross-border e-commerce includes the following steps:

[0007] Collect logistics data and use APIs to integrate data with logistics companies and customs databases;

[0008] By connecting with international logistics service providers, we can obtain tax data in real time, including customs duties, taxes and other additional costs incurred by cross-border delivery;

[0009] Optimize multiple transportation plans and routes through genetic algorithms to find the transportation plan with the lowest cost and the highest efficiency; the transportation plan includes logistics routes and transportation methods, and the transportation methods include air transportation, sea transportation or land transportation;

[0010] Based on the selected transportation plan, tax data and product specifications, a dynamic cost model is constructed using machine learning algorithms to perform preliminary cost calculations and form preliminary cost estimates;

[0011] Establish a risk assessment mechanism to evaluate and monitor factors affecting freight rates;

[0012] During the transportation of goods, the transparency and real-time tracking of the logistics chain are achieved through geographic information systems and blockchain technology, and the actual cost data in transportation is fed back into the dynamic cost model;

[0013] After the goods arrive at the destination, final cost accounting is performed based on the actual transportation data and the final output of the dynamic cost model to form a final cost estimate;

[0014] Analyze the efficiency of the entire logistics process and cost accounting, identify optimization points through deep learning methods, and automatically iterate and update cost accounting models and transportation strategies.

[0015] Furthermore, the logistics data includes cargo weight, volume, value, origin, destination, mode of transportation and historical freight data.

[0016] Furthermore, the optimization of multiple transportation schemes and routes by genetic algorithm includes the following steps:

[0017] Define a set of initial transportation plans as the population of the genetic algorithm, each plan consists of a logistics route and a transportation method;

[0018] Using collected tax data and product specifications, each transportation option is evaluated for cost and efficiency; each transportation option is assigned a fitness score based on the option's total cost and estimated transportation time;

[0019] Adopt a fitness-based selection mechanism to select the best performing solution from the current population;

[0020] A new transportation scheme is generated by combining the features of two high-fitness schemes through a crossover operation, and key parameters are randomly adjusted in the selected scheme through a mutation operation, wherein the key parameters include the selection of transfer points and the switching of transportation modes;

[0021] Repeat the fitness evaluation, selection, crossover, and mutation steps until the preset number of iterations is reached or when the fitness score becomes stable;

[0022] Output the optimal transportation plan obtained by the genetic algorithm. The final output transportation plan is the plan that performs best during the iteration process.

[0023] Furthermore, the fitness formula of the genetic algorithm is as follows:

[0024] ;

[0025] in, Represents fitness and is also the optimal transportation solution for output. A high value of indicates that the transportation option has lower cost, shorter transportation time and lower risk; represents the total cost of option i; the total cost calculation includes shipping costs, customs duties, other taxes and any additional cross-border delivery costs; represents the total transportation time of plan i; represents the risk score of scenario i; represents the time weight coefficient, a higher value indicates that the time factor has a greater impact on the total fitness; represents the risk weight coefficient, and higher values ​​indicate that the risk score has a greater impact on the total fitness.

[0026] Furthermore, the use of machine learning algorithms to construct a dynamic cost model includes the following steps:

[0027] Clean and standardize the collected logistics data, tax data and commodity specifications;

[0028] Use recurrent neural networks to process time series information in logistics data, capture time dependencies and predict future cost changes;

[0029] Design a conditional neural network that dynamically adjusts its network weights according to input destination, transportation mode, and tax policy changes;

[0030] Simultaneously learning multiple related tasks in the same deep neural network model, wherein the multiple related tasks include simultaneously predicting shipping costs, taxes, and other additional charges;

[0031] The integrated online learning mechanism allows the deep neural network model to be updated instantly when new transportation instance data is received, while adjusting the prediction strategy through real-time feedback.

[0032] Furthermore, the conditional neural network specifically adopts a gated recurrent unit network structure, which dynamically adjusts the network weights according to different input conditions such as specific changes in the destination, mode of transportation and tax policy, and based on a hierarchical training strategy, receives the destination code, mode of transportation and identifiers of tax policy changes through the input layer, the middle layer performs weighted and nonlinear transformation processing on the input, and the output layer generates the corresponding cost prediction.

[0033] Furthermore, the time series information includes historical freight data, tax rate changes, seasonal fluctuations in logistics demand, and market price fluctuations.

[0034] Furthermore, the risk assessment mechanism specifically identifies various risks through a multi-level risk identification framework, including policy risks, operational risks, market risks and natural disasters; scores the impact of each risk factor using a combination of quantitative methods and expert evaluation; based on the scoring results, applies decision tree analysis to determine the specific impact of each risk factor on logistics costs; and dynamically adjusts risk scores in combination with real-time market and policy data.

[0035] On the other hand, a cross-border e-commerce logistics freight intelligent accounting system includes a data acquisition module, a transportation plan optimization module, a cost model construction module, a risk assessment module, a logistics monitoring module and a cost accounting module that are sequentially connected in communication;

[0036] The data collection module is used to collect logistics data, integrate data with logistics companies and customs databases using APIs, and obtain tax data in real time through interface connection with international logistics service providers;

[0037] The transportation scheme optimization module is used to optimize the cost and efficiency of multiple transportation schemes and routes using a genetic algorithm;

[0038] The cost model building module is used to use machine learning algorithms to build and update a dynamic cost model based on transportation plans, tax data and commodity specifications, and to perform preliminary cost accounting to form a preliminary cost estimate;

[0039] The risk assessment module is used to establish a risk assessment mechanism to assess and monitor various risk factors that affect freight rates;

[0040] The logistics monitoring module is used to use geographic information system and blockchain technology to achieve transparency of logistics links and real-time tracking of logistics processes;

[0041] The cost accounting module is used to perform final cost accounting according to the final output of the dynamic cost model and form a final cost estimate.

[0042] The beneficial effects of the present invention are:

[0043] The present invention uses genetic algorithms to optimize transportation plans and routes, which can not only find the lowest cost plan, but also ensure the highest transportation efficiency. This method helps to reduce ineffective costs and time waste in the transportation process and improve overall logistics efficiency. By constructing a dynamic cost model of a machine learning algorithm, it is possible to adjust cost forecasts in a timely manner based on real-time data to better cope with the impact of market and policy changes. In addition, real-time tracking of cost changes during transportation helps to identify problems and make adjustments in a timely manner. The established risk assessment mechanism can effectively monitor and evaluate various factors that may affect freight rates, increasing the ability to control risks. At the same time, the application of geographic information systems and blockchain technology improves the transparency and trust of the logistics chain and ensures the security and accuracy of data. By using APIs to integrate data with logistics companies and customs databases, the latest tax data and additional fees can be obtained in real time, which makes cost estimates more accurate and reduces economic losses caused by data delays or inaccuracies. By analyzing the entire logistics process and applying deep learning methods, the system can identify optimization points and automatically iterate and update cost accounting models and transportation strategies. This not only improves operational efficiency, but also dynamically adjusts transportation strategies according to business development and market demand. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a flow chart of a method for intelligently calculating logistics freight for cross-border e-commerce of the present invention.

[0045] Figure 2 It is a flowchart of step S3 provided in an embodiment of the present invention.

[0046] Figure 3 It is a module schematic diagram of a cross-border e-commerce logistics freight intelligent accounting system of the present invention. DETAILED DESCRIPTION

[0047] See also Figure 1-3 As shown, the present invention relates to a system and method for intelligent calculation of logistics freight for cross-border e-commerce.

[0048] Embodiment 1:

[0049] A method for intelligently calculating logistics freight for cross-border e-commerce includes the following steps:

[0050] S1: Collect logistics data and use APIs to integrate data with logistics companies and customs databases; the logistics data includes cargo weight, volume, value, origin, destination, transportation method and historical freight data.

[0051] Specifically, develop an integration interface that can communicate with the databases of different logistics companies and customs. This interface should be able to handle API responses in different formats, such as JSON or XML, and adapt to various API authentication and security standards (such as OAuth or API keys). Synchronize data through API calls regularly (such as every hour or every day) to ensure that the data in the system is up to date. During the data synchronization process, use multi-threading or asynchronous processing to improve data processing speed and efficiency.

[0052] S2: Interface with international logistics service providers to obtain tax data in real time, including tariffs, taxes and other additional costs incurred by cross-border delivery;

[0053] It should be noted that for different countries' tax rates and fee structures, this requires integration with the APIs of customs and tax authorities in various countries, which can provide information such as tariffs, value-added tax (VAT), goods and services tax (GST), etc. Set the data update frequency according to the frequency of tax rate changes and the business needs of cross-border e-commerce. For most countries, tax rates may not change frequently, so weekly or monthly updates may be sufficient.

[0054] After obtaining tax data, process it in real time to meet the needs of freight accounting. For example, when a change in tax rates is detected, immediately update the relevant data in the system to ensure the accuracy of freight estimates. Apply tax data to cost accounting, including accounting for tariffs and other taxes when generating preliminary freight estimates. This requires embedding tax calculation logic in the cost model to ensure that all additional costs are included in the final cost estimate.

[0055] S3: Optimize multiple transportation plans and routes through genetic algorithms to find the transportation plan with the lowest cost and the highest efficiency; the transportation plan includes logistics routes and transportation methods, and the transportation methods include air transportation, sea transportation or land transportation;

[0056] Wherein, step S3 specifically includes the following steps:

[0057] S31: Define a set of initial transportation plans as the population of the genetic algorithm, each plan consists of a logistics route and a transportation method;

[0058] S32: Using the collected tax data and commodity specifications, evaluate the cost and efficiency of each transportation plan; assign a fitness score to each transportation plan based on the total cost and estimated transportation time of the plan;

[0059] Specifically, obtain tax information related to transportation, such as tariffs, value-added tax, etc. imposed by different countries or regions on imported and exported goods. In addition, collect tax information on the mode of transportation (air, sea, land). Record the specifications of all goods to be transported, including but not limited to weight, volume, type (fragile, perishable, ordinary, etc.), and special storage or transportation requirements (such as temperature control requirements). Calculate the basic freight for each mode of transportation (air, sea, land), and calculate the cost based on the weight, volume and transportation distance of the goods. Calculate the tariffs and other taxes that must be paid based on the type of goods and the tax rate of the destination. Consider the handling fees for special goods (such as refrigeration, insurance), the fees of the transshipment point, and possible delay fines or additional fees. Calculate the total time including loading and unloading, waiting for review and actual transportation. Different modes and routes of transportation will affect the total time. For example, air transportation is usually the fastest, but the cost is higher. Analyze the efficiency of different routes, including the impact of transshipment frequency and the choice of transshipment points on the total transportation time.

[0060] S33: Adopt a fitness-based selection mechanism to select the best performing solution from the current population;

[0061] S34: combining the features of two high fitness solutions through a crossover operation to generate a new transportation solution, and randomly adjusting key parameters in the selected solution through a mutation operation, wherein the key parameters include transshipment point selection and transportation mode switching;

[0062] S35: Repeat the fitness evaluation, selection, crossover and mutation steps until a preset number of iterations is reached or the fitness score tends to be stable;

[0063] S36: Output the optimal transportation plan obtained by the genetic algorithm. The transportation plan finally output is the plan that performs best in the iterative process.

[0064] Furthermore, the fitness formula of the genetic algorithm is as follows:

[0065] ;

[0066] in, Represents fitness and is also the optimal transportation solution for output. A high value of indicates that the transportation option has lower cost, shorter transportation time and lower risk; represents the total cost of option i; the total cost calculation includes shipping costs, customs duties, other taxes and any additional cross-border delivery costs; represents the total transportation time of plan i; represents the risk score of scenario i; represents the time weight coefficient, a higher value indicates that the time factor has a greater impact on the total fitness; represents the risk weight coefficient, and higher values ​​indicate that the risk score has a greater impact on the total fitness.

[0067] S4: Based on the selected transportation scheme, tax data and product specifications, a dynamic cost model is constructed using machine learning algorithms to perform preliminary cost calculations and form a preliminary cost estimate;

[0068] The use of machine learning algorithms to construct a dynamic cost model includes the following steps:

[0069] Clean and standardize the collected logistics data, tax data and commodity specifications;

[0070] A recurrent neural network is used to process the time series information in logistics data to capture time dependencies and predict future cost changes; the time series information includes historical freight data, tax rate changes, seasonal fluctuations in logistics demand, and market price fluctuations;

[0071] Design a conditional neural network that dynamically adjusts its network weights according to input destination, transportation mode, and tax policy changes;

[0072] Furthermore, the conditional neural network specifically adopts a gated recurrent unit network structure, dynamically adjusts network weights according to different input conditions such as specific changes in the destination, mode of transportation and tax policy, and based on a hierarchical training strategy, receives the destination code, mode of transportation and identifiers of tax policy changes through the input layer, the middle layer performs weighted and nonlinear transformation processing on the input, and the output layer generates corresponding cost predictions.

[0073] Specifically, the input layer receives specific input variables, such as destination code, mode of transport, and changes in tax policies. These inputs serve not only as direct inputs to the network, but also as conditions for adjusting the internal parameters of the network.

[0074] The hidden layer uses a gated recurrent unit (GRU) to construct the hidden layer. The GRU layer is particularly suitable for processing time series data because it can effectively capture long-term and short-term dependencies through the gating mechanism. In addition, we introduce conditional dependencies in the hidden layer to dynamically adjust the weights according to the input conditions. This can be achieved through conditional batch normalization or modifying the activation function so that each step of the network's response depends on the current input conditions.

[0075] The output layer generates cost predictions based on the processed information. The output layer needs to be designed to be able to calculate the final cost prediction based on the weighted and transformed information received from the hidden layers.

[0076] At each training step, the network weights are dynamically adjusted based on the input destinations, transportation methods, and tax policy changes. This can be achieved by introducing a conditional weight layer, where each parameter of the network can be viewed as a function of the input condition.

[0077] Specifically, the formula of the conditional neural network is as follows:

[0078] ;

[0079] in, represents a preliminary cost estimate, including shipping, taxes and other additional charges; represents the output layer weight matrix, which is used to generate the final cost prediction; Represents the output layer bias vector, which is used to adjust the deviation of the final output; Represents the hidden state of the current time step, capturing the combined influence of time dependency and input conditions, and its calculation formula is as follows:

[0080] ;

[0081] in, is the hidden state of the previous time step, representing the previous historical information; is the update gate, calculated by the sigmoid activation function; Represents the candidate hidden state, which is calculated by the tanh activation function and is used to generate the candidate value of the hidden state at the current time step;

[0082] Update Gate and reset gate Calculated by the following formulas:

[0083] ;

[0084] ;

[0085] in, The input vector representing the current time step includes the following specific data: destination, mode of transport, and tax policy changes; Represents the weight matrix for calculating the update gate; Represents the weight matrix for calculating the reset gate; express An activation function is used to calculate an update gate and a reset gate. Multiple related tasks are learned simultaneously in the same deep neural network model, wherein the multiple related tasks include simultaneously predicting shipping costs, taxes, and other additional costs;

[0086] The integrated online learning mechanism allows the deep neural network model to be updated instantly when new transportation instance data is received, while adjusting the prediction strategy through real-time feedback.

[0087] S5: Establish a risk assessment mechanism to evaluate and monitor factors affecting freight rates;

[0088] Among them, the risk assessment mechanism specifically identifies various risks through a multi-level risk identification framework, including policy risks, operational risks, market risks and natural disasters; uses a combination of quantitative methods and expert evaluation to score the impact of each risk factor; based on the scoring results, decision tree analysis is used to determine the specific impact of each risk factor on logistics costs; and dynamically adjusts risk scores in combination with real-time market and policy data.

[0089] It should be noted that a multi-level risk identification framework should be constructed to systematically identify and classify potential risks. This framework should include risks at the macro level (such as international trade policies, global economic conditions), industry level (such as specific risks in the logistics industry) and enterprise level (such as operational risks, technical risks). A continuous monitoring mechanism should be established to update information on policy changes, natural disasters or market dynamics in real time through web crawlers, news subscriptions and official announcements.

[0090] Use statistical and probabilistic models to quantify the likelihood and impact of risks, such as using probability distributions to estimate the frequency of natural disasters and the scope of losses that may result. Organize regular expert assessment meetings to allow industry experts to evaluate and discuss newly identified risks, and adjust risk scores based on their insights. Assign a risk score to each risk based on the likelihood of the risk occurring and the severity of the impact. Use techniques such as weighted scoring to integrate the impact of multiple factors. Score the impact of each risk factor to assess the specific impact they have on logistics costs. Use decision tree models to analyze decision paths and countermeasures for different risk factors. This includes establishing policy adjustments based on different risk levels, how to adopt conservative strategies when facing high risks, and adopt more cost-effective strategies when low risks. Based on the output of the decision tree, develop specific operational strategies, such as adjusting transportation routes or alternative supply chain options in advance when major natural disasters are predicted. Use decision tree models to analyze decision paths and countermeasures for different risk factors. This includes establishing policy adjustments based on different risk levels, how to adopt conservative strategies when facing high risks, and adopt more cost-effective strategies when low risks. Based on the output of the decision tree, specific operational strategies are formulated, such as adjusting transportation routes or alternative supply chain solutions in advance when major natural disasters are predicted.

[0091] S6: During the transportation of goods, the transparency and real-time tracking of the logistics chain are achieved through geographic information system and blockchain technology, and the actual cost data in transportation is fed back to the dynamic cost model;

[0092] Specifically, use geographic information system (GIS) data analysis tools to optimize the transportation routes of goods. Analyze historical data and real-time traffic conditions to automatically adjust routes to avoid congested areas or regional risk points (such as areas with bad weather). Use blockchain technology to record every movement and status change of goods to ensure the authenticity and immutability of the records. Each logistics record is added to the chain as a block and is permanently saved after network verification. Through blockchain technology, all participants (including suppliers, logistics companies, customs and buyers) can access the same logistics information to achieve transparency throughout the chain. This transparency improves trust and reduces transaction friction. Deploy smart contracts to automatically handle certain logistics operations, such as automatically paying the transportation company when the goods arrive at a certain checkpoint, or triggering insurance claims when certain conditions are met. Track the transportation costs of each step, including fuel costs, labor costs, tolls, etc., through GIS and blockchain technology. These data are recorded and updated in real time. Feed the real-time tracked cost data into the dynamic cost model. This model adjusts the cost budget and cost estimate based on real-time data to provide more accurate cost analysis. When costs or routes are abnormal, the system can automatically identify and issue warnings, and logistics managers can take timely measures to make adjustments, such as replanning routes or adjusting budgets.

[0093] S7: After the goods arrive at the destination, final cost accounting is performed based on the actual transportation data and the final output of the dynamic cost model to form a final cost estimate;

[0094] Specifically, when the goods arrive at the destination, collect all relevant transportation data, including but not limited to transportation time, route changes, actual time consumption, accident records, etc. Ensure that all collected data is accurate and verify the consistency and completeness of the data by comparing it with the data recorded in GIS and blockchain. Analyze key data points in the actual transportation process, such as dwell time, acceleration and deceleration frequency, which can help evaluate transportation efficiency and potential cost-increasing factors. Input the actual transportation data into the trained dynamic cost model. This model may be based on a machine learning algorithm that can adjust and optimize cost estimates based on new inputs. Use the dynamic cost model to calculate all relevant costs, including transportation costs, taxes, surcharges, etc. The model takes into account actual events such as delays or expedited fees to ensure the accuracy of cost accounting. Check the cost estimates output by the model with the actual costs incurred, conduct a difference analysis, and ensure that there are no major deviations. Generate a detailed cost report based on the output of the dynamic cost model. The report not only includes the total cost, but also lists in detail the components of each cost and the calculation basis of each part. To improve transparency, the cost estimate report should clearly show the calculation method and source of all costs, including taxes, transportation fees, surcharges, etc. The report should provide enough information to help management make better transportation and financial decisions, such as choosing a more cost-effective mode of transportation or adjusting transportation strategies to reduce costs. Adjust the dynamic cost model based on the results of the final cost accounting. Use this new actual data to train and improve the model to improve the accuracy and efficiency of future accounting. Monitor the difference between actual transportation costs and estimated costs, and continuously optimize the cost model and transportation strategy based on this data to reduce future deviations and overall costs.

[0095] S8: Analyze the efficiency of the entire logistics process and cost accounting, identify optimization points through deep learning methods, and automatically iterate and update cost accounting models and transportation strategies.

[0096] Specifically, aggregate data from the entire logistics chain, including transportation time, cost, route changes, delay information, customer feedback, etc. This data needs to be cleaned and standardized to meet the needs of deep learning models. Select appropriate deep learning models, such as convolutional neural networks (CNN) for image recognition (such as cargo damage) and recurrent neural networks (RNN) for sequence data processing (such as time series analysis), to analyze the data and identify patterns and trends. Use the model to identify key influencing factors and bottlenecks, such as which specific routes or time periods are often delayed, and which operational processes lead to increased costs. Use model outputs to identify inefficiencies in the logistics process, such as unnecessary transfer points, suppliers with frequent problems, or unreasonable packaging methods. Identify potential measures for cost control, such as more reasonable allocation of transportation modes (air, sea, land), and optimizing inventory levels to reduce warehousing costs. Feed back newly identified optimization points into cost accounting models and transportation strategies. Automatically adjust parameters or strategies based on the analysis results of the deep learning model to continuously improve performance. Automatically update transportation strategies and accounting processes based on the model's recommendations. For example, if the model finds that a certain transportation route is often delayed in winter, the system can automatically adjust the route strategy during this period. Regularly evaluate the effectiveness of the updated strategy, including cost savings, time efficiency, customer feedback and other dimensions. Based on the evaluation results, continue to adjust and optimize the deep learning model and transportation strategy. Ensure that the entire system can adapt to market changes and changes in business needs.

[0097] Embodiment 2:

[0098] According to the method for intelligent calculation of logistics and freight of cross-border e-commerce described in Example 1, a system for intelligent calculation of logistics and freight of cross-border e-commerce comprises a data acquisition module, a transportation scheme optimization module, a cost model construction module, a risk assessment module, a logistics monitoring module and a cost calculation module which are sequentially connected in communication;

[0099] The data collection module is used to collect logistics data, integrate data with logistics companies and customs databases using APIs, and obtain tax data in real time through interface connection with international logistics service providers;

[0100] The transportation scheme optimization module is used to optimize the cost and efficiency of multiple transportation schemes and routes using a genetic algorithm;

[0101] The cost model building module is used to use machine learning algorithms to build and update a dynamic cost model based on transportation plans, tax data and commodity specifications, and to perform preliminary cost accounting to form a preliminary cost estimate;

[0102] The risk assessment module is used to establish a risk assessment mechanism to assess and monitor various risk factors that affect freight rates;

[0103] The logistics monitoring module is used to use geographic information system and blockchain technology to achieve transparency of logistics links and real-time tracking of logistics processes;

[0104] The cost accounting module is used to perform final cost accounting according to the final output of the dynamic cost model and form a final cost estimate.

[0105] In summary, this application realizes automated data synchronization and integration through integrated API interfaces with logistics companies and customs databases, improving the processing efficiency and accuracy of logistics data. Multithreading or asynchronous processing technology further accelerates data processing to ensure that the system can respond to market and operational needs in a timely manner. Real-time acquisition and processing of tax data of international logistics service providers makes freight and tax calculations more accurate, helping companies to reasonably plan costs and avoid budget deviations caused by changes in tax rates.

[0106] This application optimizes transportation plans and routes through genetic algorithms, combines cost and efficiency assessments, and finds the lowest cost and most efficient transportation plan. This helps companies minimize logistics costs while ensuring service quality. The dynamic cost model established using machine learning algorithms can dynamically adjust cost estimates based on real-time data, provide more accurate and real-time cost accounting, and help companies make timely financial decisions. The established risk assessment mechanism can systematically identify and evaluate various risks that may be encountered in the logistics process, adjust transportation strategies in a timely manner to deal with possible risks, and protect companies from unexpected losses.

[0107] The application of GIS and blockchain technology in this application realizes the transparency and real-time tracking of the logistics chain, improves the visibility and trust of the transportation process, and helps to improve customer satisfaction and optimize supply chain management. The deep learning method analyzes the data of the entire logistics chain, identifies optimization points, and automatically iterates and updates the cost accounting model and transportation strategy to ensure that the system can adapt to market changes and continuously improve performance.

[0108] The above implementation modes are merely descriptions of the preferred implementation modes of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary engineering and technical personnel in the field shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for intelligent calculation of logistics freight for cross-border e-commerce, characterized in that: The following steps are involved: Collect logistics data and use APIs to integrate data with logistics companies and customs databases; By connecting with international logistics service providers, we can obtain tax data in real time, including customs duties, taxes and other additional costs incurred by cross-border delivery; Optimize multiple transportation plans and routes through genetic algorithms to find the transportation plan with the lowest cost and the highest efficiency; the transportation plan includes logistics routes and transportation methods, and the transportation methods include air transportation, sea transportation or land transportation; Based on the selected transportation plan, tax data and product specifications, a dynamic cost model is constructed using machine learning algorithms to perform preliminary cost calculations and form preliminary cost estimates; Establish a risk assessment mechanism to evaluate and monitor factors affecting freight rates; During the transportation of goods, the transparency and real-time tracking of the logistics chain are achieved through geographic information systems and blockchain technology, and the actual cost data in transportation is fed back into the dynamic cost model; After the goods arrive at the destination, final cost accounting is performed based on the actual transportation data and the final output of the dynamic cost model to form a final cost estimate; Analyze the efficiency of the entire logistics process and cost accounting, identify optimization points through deep learning methods, and automatically iterate and update dynamic cost models and transportation strategies; The optimization of multiple transportation schemes and routes by genetic algorithm includes the following steps: Define a set of initial transportation plans as the population of the genetic algorithm, each plan consists of a logistics route and a transportation method; Using collected tax data and product specifications, each transportation option is evaluated for cost and efficiency; each transportation option is assigned a fitness score based on the option's total cost and estimated transportation time; Adopt a fitness-based selection mechanism to select the best performing solution from the current population; A new transportation scheme is generated by combining the features of two high-fitness schemes through a crossover operation, and key parameters are randomly adjusted in the selected scheme through a mutation operation, wherein the key parameters include the selection of transfer points and the switching of transportation modes; Repeat the fitness evaluation, selection, crossover, and mutation steps until the preset number of iterations is reached or when the fitness score becomes stable; Output the optimal transportation plan obtained by the genetic algorithm. The final output transportation plan is the best plan in the iterative process; The method of using a machine learning algorithm to construct a dynamic cost model includes the following steps: Clean and standardize the collected logistics data, tax data and commodity specifications; Use recurrent neural networks to process time series information in logistics data, capture time dependencies and predict future cost changes; Design a conditional neural network that dynamically adjusts its network weights according to input destination, transportation mode, and tax policy changes; Simultaneously learning multiple related tasks in the same deep neural network model, wherein the multiple related tasks include simultaneously predicting shipping costs, taxes, and other additional charges; Integrated online learning mechanism allows the deep neural network model to be updated instantly when receiving new transportation instance data, while adjusting the prediction strategy through real-time feedback; The risk assessment mechanism specifically identifies various risks through a multi-level risk identification framework, including policy risks, operational risks, market risks and natural disasters; uses a combination of quantitative methods and expert evaluation to score the impact of each risk factor; based on the scoring results, applies decision tree analysis to determine the specific impact of each risk factor on logistics costs, specifically using a decision tree model to analyze the decision paths and countermeasures for different risk factors, which includes establishing strategy adjustments based on different risk levels, adopting conservative strategies when facing high risks, and adopting more cost-effective strategies when facing low risks; and formulating specific operational strategies based on the output of the decision tree, including adjusting transportation routes or alternative supply chain plans in advance when major natural disasters are predicted; and dynamically adjusting risk scores in combination with real-time market and policy data.

2. According to claim 1, a method for intelligent calculation of logistics freight for cross-border e-commerce is characterized in that: The logistics data includes cargo weight, volume, value, origin, destination, mode of transportation and historical freight data.

3. According to claim 1, a method for intelligent calculation of logistics freight for cross-border e-commerce is characterized in that: The fitness formula of the genetic algorithm is as follows: ； in, Represents fitness and is also the optimal transportation solution for output. A high value of indicates that the transportation option has lower cost, shorter transportation time and lower risk; represents the total cost of option i; the total cost includes shipping costs, customs duties, other taxes and any additional cross-border delivery costs; represents the total transportation time of plan i; represents the risk score of scenario i; represents the time weight coefficient, a higher value indicates that the time factor has a greater impact on the total fitness; represents the risk weight coefficient, and higher values ​​indicate that the risk score has a greater impact on the total fitness.

4. According to claim 1, a method for intelligent calculation of logistics freight for cross-border e-commerce is characterized in that: The conditional neural network specifically adopts a gated recurrent unit network structure, dynamically adjusts network weights according to different input conditions, including specific changes in the destination, mode of transportation and tax policy, and based on a hierarchical training strategy, receives the destination code, mode of transportation and identifiers of tax policy changes through the input layer, the middle layer performs weighted and nonlinear transformation processing on the input, and the output layer generates the corresponding cost prediction.

5. According to claim 1, a method for intelligent calculation of logistics freight for cross-border e-commerce is characterized in that: The time series information includes historical freight data, tax rate changes, seasonal fluctuations in logistics demand, and market price fluctuations.

6. An intelligent calculation system for logistics freight of cross-border e-commerce, characterized in that: The system is applied to a method for intelligent calculation of logistics freight of a cross-border e-commerce as claimed in any one of claims 1 to 5, comprising a data acquisition module, a transportation scheme optimization module, a cost model construction module, a risk assessment module, a logistics monitoring module and a cost calculation module which are sequentially connected in communication; The data collection module is used to collect logistics data, integrate data with logistics companies and customs databases using APIs, and obtain tax data in real time through interface connection with international logistics service providers; The transportation scheme optimization module is used to optimize the cost and efficiency of multiple transportation schemes and routes using a genetic algorithm; The cost model building module is used to use machine learning algorithms to build and update a dynamic cost model based on transportation plans, tax data and commodity specifications, and to perform preliminary cost accounting to form a preliminary cost estimate; The risk assessment module is used to establish a risk assessment mechanism to assess and monitor various risk factors that affect freight rates; The logistics monitoring module is used to use geographic information system and blockchain technology to achieve transparency of logistics links and real-time tracking of logistics processes; The cost accounting module is used to perform final cost accounting according to the final output of the dynamic cost model and form a final cost estimate.

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