Customs import and export data analysis technology system based on artificial intelligence

Abstract

The invention relates to the technical field of data analysis, and discloses a customs import and export data analysis technology system based on artificial intelligence, and the system comprises the following steps: a data preprocessing module which is used for collecting original import and export data from a customs import and export database; the multi-objective optimization module is used for optimizing a plurality of optimization objectives of customs import and export data; the uncertainty modeling module is used for modeling the uncertainty in the target function based on variational Bayesian inference; the information geometry optimization module is used for optimizing the high-dimensional data of the target function through an information geometry method; a reinforcement learning module; and a real-time feedback module. According to the method, through technologies such as multi-objective optimization, variational Bayesian inference, uncertainty modeling, information geometric optimization and deep reinforcement learning, high efficiency, accuracy and flexibility of customs import and export data analysis technologies are realized, and the decision support capability of the system in a complex dynamic environment is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and specifically to an artificial intelligence-based customs import and export data analysis technology system. Background Art

[0002] In the context of globalization, the processing and analysis of customs import and export data have become increasingly complex; customs not only need to efficiently monitor the circulation of imported and exported goods, but also ensure that import and export activities comply with the laws and trade policies of various countries. How to identify potential risks in the vast amounts of data, improve regulatory efficiency, and ensure compliance has become an important challenge faced by customs management. With the increase in the volume of trade, traditional manual processing methods and simple automated analysis tools are no longer sufficient to meet the rapidly changing market demands and complex international trade environment. Therefore, developing an intelligent analysis system that can process import and export data from multiple perspectives, at multiple levels, and efficiently has become the key to solving this problem.

[0003] In the prior art, customs data analysis technology systems generally adopt rule-based or single-objective optimization methods, which focus on a single objective during analysis, such as compliance or the accuracy of risk detection. These systems can effectively identify violations or illegal acts and provide compliance checks to help customs departments discover potential problems in a timely manner. At the same time, some systems can also improve processing efficiency according to established rules, thereby reducing review time and costs. These prior arts can usually provide rapid data processing results.

[0004] However, there are some deficiencies in the prior art. First, most traditional methods only focus on a single objective during the optimization process and lack consideration of the balance between multiple objectives, resulting in an inability to provide comprehensive and efficient solutions in complex multi-objective optimization scenarios. Second, most of the prior arts are unable to effectively handle the uncertain factors in the external environment. In addition, existing optimization methods often face problems of high computational complexity and slow convergence speed when dealing with high-dimensional data. Especially when dealing with a large amount of customs data, the efficiency and accuracy of traditional methods are relatively low. Finally, most existing systems lack the ability to adjust optimization strategies according to real-time changes, which makes them relatively rigid and difficult to respond quickly when facing changes in market demands or policy adjustments. Summary of the Invention

[0005] In view of the deficiencies of the prior art, the present invention provides an artificial intelligence-based customs import and export data analysis technology system, aiming to solve the problems of insufficient multi-objective optimization, inability to effectively handle external uncertainties, high computational complexity in high-dimensional data processing, and lack of real-time adjustment strategies in the prior art.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: An artificial intelligence-based customs import and export data analysis technology system, including the following steps:

[0007] A data preprocessing module, used to collect original import and export data from the customs import and export database, perform data cleaning, format conversion, missing value filling, and extract feature data;

[0008] A multi-objective optimization module, used to optimize multiple optimization objectives of customs import and export data, including risk detection objectives, regulatory efficiency objectives, compliance objectives, and trade liquidity objectives, and use artificial intelligence algorithms to optimize the objective function;

[0009] An uncertainty modeling module, used to model the uncertainty in the objective function based on variational Bayesian inference, obtain the posterior distribution of the objective function, and optimize the objective function;

[0010] An information geometry optimization module, used to optimize the high-dimensional data of the objective function through information geometry methods, calculate the natural gradient of the objective function, and update the objective function parameters;

[0011] A reinforcement learning module, used to dynamically adjust the weight of the objective function according to the latest import and export records and dynamically changing market data through deep reinforcement learning algorithms;

[0012] A real-time feedback module, used to adjust the optimization strategy of the objective function in real time based on economic fluctuations, policy changes, international situations, and global market demand changes.

[0013] Preferably, the multi-objective optimization module includes:

[0014] Define an optimization strategy for each objective function, where the objective functions include risk detection, regulatory efficiency, compliance, and trade liquidity;

[0015] Based on the multi-objective optimization algorithm, use the Pareto optimal solution method to handle the conflicts between objectives and find the optimal balance solution for multiple objectives;

[0016] Iteratively update the optimization objectives and adjust the optimization strategy according to the trade-off between objectives.

[0017] Preferably, the uncertainty modeling module includes;

[0018] By obtaining external uncertainty factors, model the random variables that affect the objective function, and the uncertainty factors include economic fluctuations and policy changes;

[0019] Use the variational Bayesian inference method to calculate the approximate posterior distribution of the objective function and optimize the objective function, and maximize the variational lower bound to handle the uncertainty in the objective function.

[0020] Preferably, the information geometry optimization module includes:

[0021] Apply a Riemannian metric to the objective function to describe the geometric structure of the objective function through information geometry methods;

[0022] Use the natural gradient descent method to calculate the natural gradient of the objective function and optimize the parameters of the objective function according to the Riemannian metric.

[0023] Preferably, the reinforcement learning module includes;

[0024] When real-time monitoring import and export data, automatically adjust the weights of the objective functions according to changes in the external environment to optimize the balance between various objective functions;

[0025] Train an agent through a deep reinforcement learning algorithm to generate an optimal policy based on the training data.

[0026] Preferably, the real-time feedback module includes:

[0027] Monitor changes in the external environment and analyze the dynamic fluctuations of import and export data in real time, and adjust and optimize the policy through system feedback;

[0028] Update the optimization policy of the objective function according to real-time data, enabling the system to make optimal decisions in a timely manner according to changes in external conditions.

[0029] Preferably, the data preprocessing module includes:

[0030] Extract raw data from the customs import and export database and clean it, removing redundant data and correcting format errors;

[0031] Standardize the data so that import and export data from different sources can be processed and analyzed in the same data space.

[0032] Preferably, the multi-objective optimization module is used to optimize multiple optimization objectives of customs import and export data, including risk detection objectives, supervision efficiency objectives, compliance objectives, and trade liquidity objectives. The specific optimization of the objective function using artificial intelligence algorithms includes:

[0033] The objective function weighted adjustment unit is used to weightedly adjust the objective function according to changes in the external environment, and the weighted adjustment is based on the weight dynamic adjustment mechanism of multi-objective optimization;

[0034] The conflict handling unit is used to handle conflicts between multiple objective functions, and by using the Pareto optimal solution method, a balanced solution is found in the multi-objective optimization process.

[0035] Preferably, the uncertainty modeling module is used to model the uncertainty in the objective function based on variational Bayesian inference, obtain the posterior distribution of the objective function, and optimize the objective function, specifically including:

[0036] An external environment dynamic analysis unit for analyzing the impact of external uncertainty factors on the objective function;

[0037] An uncertainty optimization control unit for adjusting the prior distribution used in variational Bayesian inference according to the external environment dynamic analysis result, optimizing the uncertainty in the objective function, and feeding it back to the multi-objective optimization module according to the optimized objective function.

[0038] Preferably, the information geometry optimization module is used to optimize the high-dimensional data of the objective function by the information geometry method, calculate the natural gradient of the objective function and update the objective function parameters, specifically including:

[0039] A geometric manifold adjustment unit for adjusting the Riemannian metric according to the high-dimensional properties of the objective function;

[0040] A gradient update unit for updating the parameters of the objective function by the natural gradient method.

[0041] The present invention provides an artificial intelligence-based customs import and export data analysis technology system. Having the following

[0042] Beneficial effects:

[0043] 1. The present invention adopts a multi-objective optimization technical solution, and by simultaneously optimizing multiple objectives such as risk detection, supervision efficiency, compliance, and trade liquidity, it achieves the effect of finding the optimal balance among multiple objectives. Compared with the single-objective optimization method in the prior art, the present invention can achieve more efficient comprehensive optimization among conflicting objectives, and solves the problems of objective conflict and insufficient trade-off often occurring in multi-objective optimization by traditional methods.

[0044] 2. The present invention introduces variational Bayesian inference to model the uncertainty in the objective function and optimize the objective function, achieving the effect of providing reliable optimization results in an uncertain environment. Compared with the traditional optimization methods in the prior art that fail to effectively handle uncertainty, the present invention can cope with uncertain factors such as external economic fluctuations and policy changes, and improves the robustness and adaptability of the system in a dynamic environment.

[0045] 3. The present invention adopts information geometry optimization technology to optimize the objective function through the Riemannian metric and the natural gradient method, and efficiently performs optimization in the high-dimensional data space, achieving the effect of improving the optimization speed and accuracy. Compared with the optimization methods using the traditional gradient descent method in the prior art, the present invention reduces the computational complexity and accelerates the convergence process when dealing with complex high-dimensional data, ensuring efficient execution under large-scale data.

[0046] 4. The present invention combines deep reinforcement learning technology to dynamically adjust the weights of the objective function according to the latest import and export records and market data, achieving the effect of real-time optimizing the objective weights and adapting to changes in the external environment. Compared with the static objective function weights in the prior art, the present invention can adjust and optimize the strategy in real time according to real-time changes such as external economic fluctuations and policy changes, improving the flexibility and real-time decision-making ability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic diagram of the system architecture of the present invention;

[0048] Figure 2 It is a schematic diagram of the architecture of the multi-objective optimization module of the present invention;

[0049] Figure 3 It is a schematic diagram of the architecture of the uncertainty modeling module of the present invention;

[0050] Figure 4 It is a schematic diagram of the architecture of the information geometry optimization module of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0051] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0052] Please refer to the attached Figure 1 - attached Figure 4 , the embodiments of the present invention provide an artificial intelligence-based customs import and export data analysis technology system, including the following steps:

[0053] A data preprocessing module, which is used to collect original import and export data from the customs import and export database, perform data cleaning, format conversion, missing value filling, and extract feature data;

[0054] The data preprocessing module is the first key step. It is responsible for collecting original data from the customs import and export database and converting this data into a format suitable for subsequent processing by the multi-objective optimization module. The role of the data preprocessing module is not limited to cleaning and converting data, but also includes filling missing values and feature extraction. This process is the basis for ensuring the normal operation of the entire system, improving the efficiency and accuracy of data analysis technology.

[0055] Generally, the original customs import and export data may come from multiple data sources, including customs databases, trading companies, logistics companies, etc. These data usually vary in format, type, accuracy, and integrity. Therefore, data preprocessing is not only a necessary step to improve data quality but also ensures the accuracy and consistency of the system.

[0056] The data preprocessing module first obtains the original data from the customs import and export database. The original data usually includes multiple dimensions, such as commodity name, quantity, value, transportation mode, trading country, etc., and these data are stored in different tables or files. After collecting the original data, the data is first cleaned and its format is converted.

[0057] During the data cleaning process, the system will automatically identify and remove duplicate records. For example, if the same transaction data is recorded multiple times, the system will filter out these duplicate records to ensure that each data entry appears only once. Then, the system will identify data items with format errors, such as inconsistent date formats or numerical data stored as text, and correct them. All data will be uniformly converted into a standard format so that they can be processed consistently in subsequent optimization steps.

[0058] As an option, the data cleaning module will also verify the rationality of the data. For example, a consistency check is performed on the matching of commodity quantity and amount. If there is an unreasonable relationship between the quantity and amount of certain commodities, these data will be marked as abnormal and await manual confirmation or further processing.

[0059] In the next step of data preprocessing, the system fills in the missing values. In the original customs import and export data, some data are prone to be missing, especially when it comes to specific commodities or special trade processes. To ensure data integrity, the system needs to reasonably fill in these missing values.

[0060] Multiple methods are used for filling in the missing values. In some cases, the system will fill in the missing values based on the average or median of the data. For example, when the commodity quantity or trade amount field is missing, the system will calculate the average quantity or amount of all similar commodities and use this value to fill in the missing item. In other cases, especially for the missing data of certain specific commodities or regions, the system may adopt a filling method based on correlation analysis. For example, if the import and export volume of a certain commodity is highly correlated with that of other commodities, the system can predict and fill in the missing value based on the data of related commodities.

[0061] In certain specific cases, if the error in filling in the missing values is large, the system can also use interpolation methods or predict the values of the missing data through machine learning models (such as regression models or k-nearest neighbor algorithms). Specifically, the system will select data items with strong correlation with the missing values for predictive filling.

[0062] After data cleaning and missing value imputation, the system needs to extract valuable feature data from the original customs import and export data. Feature extraction is the transformation of complex raw data into a format that can be effectively utilized by machine learning and optimization algorithms.

[0063] Specifically, the system extracts information from various dimensions of the dataset. For example, the system extracts information such as commodity name, commodity category, import / export country, transportation mode, timestamp, trade amount, etc., and converts this information into numerical features as the input for the subsequent multi-objective optimization module.

[0064] In some embodiments, feature extraction also includes converting categorical data into numerical data (such as encoding countries or commodity categories), and processing data with different dimensions through standardization and normalization. For example, features such as import / export amount, quantity, transportation mode, etc., may have different dimensions and ranges. The system standardizes these data to give them a unified scale, ensuring that the multi-objective optimization is not affected by the relatively large dimensions of certain features.

[0065] During the data preprocessing process, the mathematical model for standardizing the feature data can be expressed as:

[0066]

[0067] where x i is the standardized feature data; μ i is the mean of feature i; σ i is the standard deviation of feature i.

[0068] Through this formula, the system can ensure that the data ranges of all features are relatively consistent, which helps with subsequent machine learning and optimization processes.

[0069] The design of the data preprocessing module is closely connected to the subsequent modules in the system. The original data that has undergone data cleaning, format conversion, and missing value filling becomes the input data for the multi-objective optimization module. Since the multi-objective optimization module relies on high-quality data to train the optimization model, the accuracy and effectiveness of data preprocessing directly affect the final optimization result of the system.

[0070] In addition, the data after feature extraction will be passed to the uncertainty modeling module, information geometric optimization module, and reinforcement learning module for further analysis and processing. Through accurate and consistent data preprocessing, the system can ensure that each module uses high-quality data when optimizing the objective function, thereby improving the overall performance of the system.

[0071] A multi-objective optimization module is used to optimize multiple optimization objectives of customs import and export data, including risk detection objectives, regulatory efficiency objectives, compliance objectives, and trade liquidity objectives, and uses artificial intelligence algorithms to optimize the objective function;

[0072] In the customs import and export data analysis technology system of the present invention, the multi-objective optimization module is one of the key processing links. The main task of this module is to optimize multiple optimization objectives, including risk detection, regulatory efficiency, compliance, and trade liquidity. Since there are conflicts and mutual influences among these objectives, how to effectively balance them and achieve the optimal solution is the core issue in system design. For this reason, this module uses artificial intelligence algorithms, especially the multi-objective optimization method based on Pareto optimal solutions, to achieve the balance and optimization among the objectives.

[0073] In this embodiment, the multi-objective optimization module is closely connected to the aforementioned data preprocessing module. The data after data cleaning, format conversion, and missing value filling will be used as the input of the optimization module. The quality of data preprocessing directly affects the accuracy of subsequent optimization results. Therefore, the accuracy of data processing is crucial for the optimization module. After obtaining high-quality data, the optimization module processes the data according to multiple objective functions and generates a set of Pareto optimal solutions, thereby achieving the optimization of multiple objectives.

[0074] Generally, there are conflicts among multiple objectives. For example, during the risk detection process, increasing the strictness of detection may lead to a decrease in regulatory efficiency, while increasing efficiency may lead to missed risk reports. Therefore, the system needs to find a balance among these objectives to meet the optimal requirements of each objective.

[0075] Specifically, in this embodiment, the method based on Pareto optimal solutions is used to optimize these objective functions. Pareto optimal solution is a commonly used method in multi-objective optimization, aiming to find a solution set where each solution is optimal in at least one objective and no worse than any other solution in other objectives. In other words, the system will make trade-offs among the objectives to ensure the best balance among different objectives.

[0076] In a possible implementation manner, the multi-objective optimization module first defines each objective. The defined objectives include:

[0077] The risk detection objective is used to measure the accuracy of the customs in identifying risks in import and export data. This objective requires minimizing the risks of missed reports and false reports through in-depth analysis and mining of the data.

[0078] The regulatory efficiency objective is used to measure the time required for the customs to review import and export data. This objective requires optimizing the data processing flow so that the customs can complete the regulatory tasks more quickly.

[0079] The compliance objective is used to ensure that import and export activities comply with national laws and regulations and meet the requirements of customs policies.

[0080] The trade liquidity objective is used to ensure that the customs' supervision does not impede normal trade flows, optimize the data review process, and enable import and export activities to proceed more smoothly.

[0081] Each objective function is adjusted according to the actual customs data and external environment during optimization. During the balancing process between objective functions, the system dynamically adjusts the weights of each objective to meet different optimization requirements.

[0082] In this embodiment, the system uses a multi-objective genetic algorithm for optimization. The genetic algorithm is an optimization algorithm based on the principles of natural selection and genetics. It gradually approaches the optimal solution by simulating the biological evolution process. The multi-objective genetic algorithm generates a set of solutions containing multiple Pareto optimal solutions by simultaneously optimizing multiple objective functions.

[0083] In each generation, the algorithm generates a set of "populations", and each individual represents a potential solution. Each individual is evaluated through a fitness function, which measures the performance of the individual on all objectives. Through operations such as crossover and mutation, the genetic algorithm continuously generates new generations of solutions until it converges to the Pareto optimal solution set.

[0084] During implementation, the optimization algorithm will adopt the following steps:

[0085] Initialize the population: Randomly generate a set of potential solutions and evaluate the fitness of each solution.

[0086] Selection operation: Select individuals with higher fitness according to the performance of each solution on various objectives.

[0087] Crossover and mutation operations: Generate new generations of individuals through crossover and mutation operations to improve the diversity of the population.

[0088] Fitness evaluation and selection: Evaluate the fitness of the new generation of individuals and select the optimal individuals based on fitness to enter the next generation.

[0089] Iterative update: Repeat the above steps until the predetermined stopping condition is reached.

[0090] In this optimization process, each individual represents a solution, and each solution contains the weights and decision parameters of multiple objectives. Through the evolutionary process of the genetic algorithm, the system can obtain a set of Pareto optimal solutions, providing multiple options for customs managers to choose from.

[0091] Mathematical Modeling and Optimization Formulas

[0092] In the process of multi-objective optimization, the mathematical model of the objective function can be expressed as:

[0093] min{R(x), T(x), C(x), L(x)};

[0094] where R(x) represents the risk detection objective function; T(x) represents the supervision efficiency objective function; C(x represents the compliance objective function; L(x) represents the trade liquidity objective function; and x is the decision variable containing multiple characteristic data.

[0095] In the process of multi-objective optimization, the output of each objective function is a vector, and these vectors represent the weights and contribution degrees of different objectives. Through the operations of the genetic algorithm, the system continuously adjusts the weights and parameters of these objective functions to optimize the overall performance.

[0096] For example, the system may calculate the weighted sum of each objective function through the following formula to determine the optimal solution:

[0097] F(x) = w 1 R(x) + w 2 T(x) + w 3 C(x) + w 4 L(x);

[0098] where w 1 , w 2 , w 3 , w 4 are the weight coefficients of each objective function; and F(x) is the comprehensive objective function.

[0099] These weight coefficients are dynamically adjusted according to real-time data and changes in the external environment. By adjusting the weights, the system can preferentially optimize certain objectives according to the needs of the customs, thus achieving the optimal balance in multi-objective optimization.

[0100] The multi-objective optimization module is closely connected with the aforementioned data preprocessing module and uncertainty modeling module. The cleaned and transformed data provided by the data preprocessing module is used as input. After passing through the multi-objective optimization module, multiple Pareto optimal solutions are generated. Then, these solutions are passed to the uncertainty modeling module and the subsequent reinforcement learning module for further optimization and adjustment.

[0101] The multi-objective optimization module, through precise optimization methods, not only ensures the reasonable optimization of each objective but also can adapt to changes in different customs data and external environments. The accuracy and real-time nature of the optimization results guarantee the comprehensive performance of customs management in aspects such as risk control, efficiency improvement, compliance inspection, and trade liquidity management, thus enhancing the overall performance of the customs import and export data analysis technology system.

[0102] An uncertainty modeling module, used to model the uncertainty in the objective function based on variational Bayesian inference, obtain the posterior distribution of the objective function, and optimize the objective function;

[0103] In the customs import and export data analysis technology system, the uncertainty modeling module plays an important role. Since the analysis of import and export data is affected by multiple external factors, traditional deterministic methods often cannot effectively handle these uncertainties. Therefore, the system introduces the variational Bayesian inference method to model the uncertainty in the objective function, thereby improving the robustness and adaptability of the optimization process.

[0104] The uncertainty modeling module is closely connected to the aforementioned data preprocessing module and multi-objective optimization module. After the customs import and export data is cleaned, transformed, and missing values are filled through the data preprocessing module, the generated high-quality data will be passed as input to the uncertainty modeling module. After the multi-objective optimization module generates a preliminary objective function, this module will further process the uncertainty in the objective function, thus providing more accurate and reliable decision-making support for subsequent optimization.

[0105] Generally, the analysis of customs import and export data is affected by external factors, such as global economic fluctuations, policy changes, market demand fluctuations, etc. These external uncertainty factors cannot be directly quantified, so Bayesian methods need to be used for modeling. Through Bayesian inference, we can introduce these uncertainty factors as random variables into the objective function, thus considering the influence of these factors in the optimization process.

[0106] Specifically, the variational Bayesian inference method is used in this embodiment. Bayesian inference can effectively handle uncertainty by calculating the posterior distribution of the objective function. However, since calculating the posterior distribution involves high-dimensional integration, the computational amount is often large and cannot be directly solved. Therefore, the variational inference method is adopted to approximate the posterior distribution by introducing a variational distribution q(x, ξ). The key objective of variational Bayesian inference is to maximize the variational lower bound (ELBO), making the variational distribution as close as possible to the true posterior distribution.

[0107] In a possible implementation, the system first obtains external uncertainty factors related to the objective function, such as economic fluctuations, policy changes, etc. These external factors may affect the customs import and export data, resulting in uncertainty in the output of the objective function. Then, the system uses the variational Bayesian inference method to model the objective function and obtain the posterior distribution of the objective function.

[0108] Calculation and Optimization of Posterior Distribution

[0109] In this embodiment, the process of variational Bayesian inference includes two parts: deriving the variational distribution and maximizing the variational lower bound. First, by representing the objective function as a conditional probability model, the uncertain part in the objective function is modeled as a latent variable ξ, thus introducing uncertainty into the model. The specific mathematical expression can be written as:

[0110] p(x,ξ)=p(x)p(ξ∣x);

[0111] where x is the feature vector of the import and export data, representing various information in the customs data; ξ is the external uncertainty factor related to the import and export data, representing global economic fluctuations, policy changes, etc.; p(x) is the prior distribution of the customs import and export data; p(ξ∣x) is the external uncertainty distribution given the data feature x. Next, use the variational distribution q(x,ξ) to approximate the posterior distribution p(x,ξ), and solve for the optimal variational distribution by maximizing the variational lower bound (ELBO):

[0112]

[0113] where is the variational lower bound, representing the optimized objective function; is the expectation under the variational distribution; y is the observed value of the import and export data; θ is the parameter of the model.

[0114] By optimizing this lower bound, the system can obtain an approximate posterior distribution q(x,ξ) and use this distribution to optimize the objective function. In this way, the system can dynamically adjust the objective function according to the changes in external uncertainty factors, thereby improving the stability and reliability of the optimization results.

[0115] In this embodiment, the uncertainty modeling module works closely with the multi-objective optimization module. During the multi-objective optimization process, the system will adjust according to the weights between different objectives and make corresponding decisions based on the optimization results of the objective function. However, these objective functions are often affected by external uncertainty factors, so it is necessary to adjust the objective function through the uncertainty modeling module to ensure that the optimization results are still optimal in the presence of uncertainty.

[0116] Based on the multi-objective optimization module, the uncertainty modeling module further processes the randomness of the objective function. For example, in the risk detection objective, external uncertainties (such as policy changes) may cause changes in the risk assessment of certain commodities. By modeling and optimizing these uncertainty factors, the system can more accurately evaluate risks and provide optimized decisions. The optimized objective function is passed to the multi-objective optimization module for further optimization processing.

[0117] When performing uncertainty modeling, the system optimizes the posterior distribution of the objective function to obtain the optimized objective function. Specifically, the system calculates through the following steps:

[0118] Model the randomness of the objective function: By introducing external uncertainty factor ξ and modeling the objective function through Bayesian inference.

[0119] Calculate the posterior distribution: Calculate the posterior distribution p(x, ξ) of the objective function through the variational Bayesian inference method.

[0120] Feedback of the optimization result: The optimized objective function is fed back to the multi-objective optimization module for the next optimization process.

[0121] In this embodiment, the uncertainty modeling module can effectively introduce external uncertainty factors into the objective function and optimize the objective function through the variational Bayesian inference method. This process ensures that the system can make more accurate decisions in the face of uncertainties. Through cooperation with the multi-objective optimization module, the uncertainty modeling module can provide a more reliable objective function for multi-objective optimization, thereby improving the overall performance and robustness of the customs import and export data analysis technology system.

[0122] The information geometry optimization module is used to optimize the high-dimensional data of the objective function through the information geometry method, calculate the natural gradient of the objective function, and update the objective function parameters;

[0123] The information geometry optimization module optimizes the high-dimensional data of the objective function by adopting the information geometry method, solving the computational complexity problem in high-dimensional data processing. By calculating the natural gradient of the objective function and updating the objective function parameters, this module can effectively achieve optimization in the high-dimensional data space, thereby improving the processing efficiency and optimization accuracy of the system.

[0124] The information geometry optimization module is closely connected with the data preprocessing module, the multi-objective optimization module, and the uncertainty modeling module. After the above-mentioned modules perform data preprocessing, the customs import and export data that has been cleaned, standardized, and had missing values filled is provided as input to the multi-objective optimization module. The objective function generated by the multi-objective optimization module is then optimized by this module to ensure that the objective function can be optimized efficiently and stably in the high-dimensional data space. The finally optimized objective function will be fed back to the multi-objective optimization module for further trade-off and iterative optimization to ensure that the system can output the best decision-making support results.

[0125] Generally, the characteristics of customs import and export data are usually multi-dimensional and complex. Especially when multiple objective functions are involved, the dimension of the data will further increase. Traditional optimization methods often face problems such as high computational complexity and slow convergence speed when dealing with these high-dimensional data. Therefore, the present invention introduces the information geometry method, which uses the Riemannian metric to describe the geometric structure of the data in the high-dimensional space, thus effectively solving these problems.

[0126] Specifically, the information geometry optimization method represents the optimization path of the objective function by applying a geometric manifold in the high-dimensional space. Each point in the high-dimensional space represents an optimization solution, and the update of the objective function is based on the geometric characteristics of this point. The information geometry optimization module uses the natural gradient descent method to perform gradient update by utilizing the geometric characteristics of the objective function on the manifold.

[0127] In a possible implementation, after the above-mentioned data preprocessing and uncertainty modeling, the objective function already contains various characteristics of the customs import and export data and external uncertainty factors. To further optimize this objective function, the information geometry optimization module updates the parameters of the objective function according to the Riemannian metric by calculating the natural gradient of the objective function. This process can more precisely capture the optimal direction of the objective function and accelerate the optimization process by introducing the structure of the geometric manifold.

[0128] In this embodiment, the information geometry optimization module calculates the natural gradient of the objective function and uses the Riemannian metric to describe the geometric structure of the high-dimensional data. The Riemannian metric is an inner product space used to calculate the distance between two points on the manifold. In high-dimensional data, the Riemannian metric can effectively capture the geometric characteristics of the objective function, making the optimization process more precise.

[0129] Specifically, the natural gradient update process of the objective function f(x t ) is as follows:

[0130]

[0131] where, x t is the parameter value at step t; α t is the learning rate, controlling the step size of the update; is the Riemannian metric matrix at point x t , which represents the geometric properties of the objective function at this point; is the gradient of the objective function f(x t ) at point x t .

[0132] Through this formula, the system can update the parameters of the objective function according to the natural gradient, thereby effectively iterating in the direction of the optimal solution. Different from the traditional gradient descent method in Euclidean space, the natural gradient takes into account the geometric structure of the objective function in the manifold and can converge more quickly and accurately in high-dimensional data.

[0133] In the information geometry optimization module, the optimization steps of the objective function are as follows:

[0134] Initialize parameters: The system first obtains the objective function from the multi-objective optimization module and initializes it according to the initial parameter x 0 .

[0135] Calculate the natural gradient: Use the Riemannian metric to calculate the natural gradient of the current parameter x t and update the gradient of the objective function

[0136] Update parameters: According to the natural gradient and the learning rate α t , use the formula to update the parameters and obtain the new parameter value x t+1 .

[0137] Convergence detection: By calculating the change amount of the objective function or the norm of the gradient, determine whether the convergence condition is satisfied. If it converges, stop the iteration and return the optimal solution; otherwise, continue the iteration.

[0138] Output the optimal parameters: The finally optimized parameter value x * is the optimal solution and is fed back to the multi-objective optimization module for further decision support.

[0139] Connection with the foregoing module

[0140] There is a close collaborative relationship between the information geometry optimization module and the aforementioned data preprocessing module, multi-objective optimization module, and uncertainty modeling module. In the data preprocessing module, the system cleans and standardizes the customs import and export data to generate data suitable for optimization. Next, after the uncertainty modeling module performs uncertainty modeling on the objective function, the system will obtain the objective function and its related external factors.

[0141] In the multi-objective optimization module, the objective function will be iteratively optimized to generate a set of solutions, while the information geometry optimization module is responsible for further optimization in the high-dimensional data space by updating the parameters of the objective function through natural gradients. The optimized objective function will be returned to the multi-objective optimization module for further trade-off and iteration, and finally the optimal solution will be obtained.

[0142] During the information geometry optimization process, the system uses Riemannian metrics for optimization and updates the parameters by calculating the natural gradient of the objective function. For multi-objective optimization problems, the system optimizes by integrating multiple objective functions:

[0143]

[0144] where f i (x t ) is the i-th objective function; w i is the weight coefficient of the objective function; is the gradient of the comprehensive objective function.

[0145] Through such an optimization process, the system can more precisely adjust the parameters of the objective function in multi-objective optimization, thereby obtaining the optimal solution.

[0146] By adopting the information geometry optimization method, the system can efficiently optimize in the high-dimensional data space, overcoming the problem of excessively high computational complexity of traditional optimization methods in high-dimensional spaces. By calculating the natural gradient of the objective function and updating the parameters according to Riemannian metrics, the system can more precisely converge towards the optimal solution and finally obtain the optimal balance solution of multiple objective functions. This process improves the optimization efficiency of the customs import and export data analysis technology system, ensuring that the system can converge stably and quickly in complex multi-objective optimization tasks.

[0147] The reinforcement learning module is used to dynamically adjust the weight of the objective function according to the latest import and export records and dynamically changing market data through the deep reinforcement learning algorithm;

[0148] The reinforcement learning module plays a crucial role. The main task of this module is to dynamically adjust the weights of the objective function according to the latest import and export records and dynamically changing market data through deep reinforcement learning algorithms. Since the objective functions faced by the customs data analysis technology system are usually multi-objective and mutually influential, the weights of the objective function may need to be adjusted according to the actual situation at different time nodes. The introduction of the reinforcement learning module enables the system to respond to external changes in real time, optimize the balance between objectives, and provide intelligent decision-making support.

[0149] The reinforcement learning module is closely connected with the aforementioned data preprocessing module, multi-objective optimization module, and uncertainty modeling module. The data cleaned and transformed by the data preprocessing module will be supplied as input to the multi-objective optimization module for optimizing the objective function. The uncertainty modeling module further optimizes the objective function and provides an impact assessment of external uncertainty factors. Finally, the reinforcement learning module dynamically adjusts the weights of the objective function according to the latest import and export records and market changes, and ensures that the multi-objective optimization module can conduct objective trade-offs and make decisions in an optimal state.

[0150] Generally, the customs import and export data analysis technology system faces complex external environmental changes, such as policy adjustments, market demand fluctuations, etc. These changes will directly affect multiple objective functions in the system. To cope with these dynamic changes, the system needs to have the ability to adjust the weights of the objective function in real time. Reinforcement learning is a learning method based on trial and error and reward mechanisms, which can adjust the behavior of the system according to the state of the current environment to achieve long-term optimization.

[0151] The core idea of reinforcement learning is to continuously learn the optimal strategy through interaction with the environment. In the system, the reinforcement learning module continuously interacts with the environment (including import and export records, market data, economic fluctuations, etc.) to learn how to adjust the weights of the objective function to achieve the optimal decision-making result. Specifically, the system conducts reinforcement learning by defining the state space, action space, and reward function.

[0152] In the reinforcement learning module, the state space represents the environmental state of the system at each moment. In the customs import and export data analysis technology system, the state space usually includes the latest import and export records, market data, external uncertainty factors, etc. For example, data such as the quantity, value, and transportation mode of current imported and exported goods, as well as external factors such as global economic fluctuations and policy changes, will constitute the current state space.

[0153] The action space is the actions that the system can take in each state. In this embodiment, the action space refers to the adjustment of the weights of each objective function. For example, the system may adjust the weights of risk detection goals, regulatory efficiency goals, compliance goals, and trade liquidity goals based on real-time data. In this way, the reinforcement learning module can dynamically balance the priorities between multiple objective functions.

[0154] In this embodiment, the system uses a deep Q-learning algorithm for reinforcement learning. Deep Q-learning combines deep learning and Q-learning, and uses a deep neural network to approximate the Q-value function, solving the problem of high computational complexity of traditional Q-learning in high-dimensional state space.

[0155] The basic goal of the Q-learning algorithm is to select the optimal action by learning the state-action value function Q(s,a). The state-action value function represents the expected reward obtained by taking action a in state s. Specifically, the Q-learning algorithm learns the optimal strategy by continuously updating the Q value so that the optimal action can be selected in each state.

[0156] In this embodiment, the update formula of the Q value is as follows:

[0157] Q(s t ,a t )←Q(s t ,a t )+α(R t+1 +γmax a′ Q(s t+1 ,a′)-Q(s t ,a t ));

[0158] Among them, Q(s t ,a t ) is in state s t Take action a t Q value; R t+1 Is taking action a t The immediate reward obtained after the game; γ is the discount factor, which is used to balance the current reward and future rewards; a is the learning rate, which controls the speed of learning; max a′ Q(s t+1 ,a′) is in the next state s t+1 The Q value of the optimal action a′ is selected.

[0159] Through this formula, the system updates the Q value at each moment according to the current state and the action taken, thereby continuously adjusting the weight of the objective function, so that the overall optimization effect of the system is gradually improved.

[0160] In reinforcement learning, the design of the reward function is crucial. The reward function is used to evaluate the effect of the system taking a certain action in a certain state. In this embodiment, the design of the reward function takes into account the multi-dimensional performance of the objective function, including multiple aspects such as risk detection, regulatory efficiency, compliance, and trade liquidity.

[0161] Specifically, the reward function R t can be designed according to the optimization results of the objective function, for example:

[0162] R t = w 1 ·ΔR(x t ) + w 2 ·ΔT(x t ) + w 3 ·ΔC(x t ) + w 4 ·ΔL(x t );

[0163] Among them, ΔR(x t ) represents the change in the risk detection objective; ΔT(x t ) represents the change in the regulatory efficiency objective; ΔC(x t ) represents the change in the compliance objective; ΔL(x t ) represents the change in the trade liquidity objective; w 1 w 2 , w 3 , w 4 are the weight coefficients of each objective.

[0164] By adjusting the weights w 1 w 2 , w 3 , w 4 of the objective function, the system can dynamically optimize the weights of the objective function according to the changes in real-time import and export records and market data to achieve the best balance.

[0165] The reinforcement learning module works closely with the aforementioned multi-objective optimization module, uncertainty modeling module, and information geometry optimization module. In the multi-objective optimization module, the system has generated a preliminary objective function and optimized it. Then, the uncertainty in the objective function is processed through the uncertainty modeling module, enabling the objective function to better adapt to changes in the external environment.

[0166] Next, the reinforcement learning module adjusts the weights of the objective function in real time according to the latest import and export records and dynamic market data through a deep reinforcement learning algorithm. The optimized objective function is fed back to the multi-objective optimization module to further optimize the overall performance of the system. The introduction of the reinforcement learning module ensures that the system can maintain efficient decision-making capabilities in the face of a constantly changing external environment.

[0167] A real-time feedback module for adjusting the optimization strategy of the objective function in real time based on economic fluctuations, policy changes, international situations, and changes in global market demands.

[0168] The role of the real-time feedback module is crucial. This module can adjust the optimization strategy of the objective function in real time according to changes in the external environment, especially the dynamic changes in economic fluctuations, policy changes, international situations, and global market demands. Through this real-time feedback mechanism, the system can flexibly respond to external changes, ensuring that the multi-objective optimization module can make optimal decisions in a constantly changing environment.

[0169] In this embodiment, the real-time feedback module works closely with the aforementioned data preprocessing module, multi-objective optimization module, uncertainty modeling module, and reinforcement learning module. Through the data preprocessing module, the customs import and export data is cleaned, formatted, and missing values are filled, and then passed to the multi-objective optimization module. The multi-objective optimization module optimizes the objective function, but the optimization of these objectives needs to be adjusted according to the real-time changes in the external environment. The real-time feedback module monitors external economic, policy, and market data in real time and updates the optimization strategy in a timely manner, thus ensuring that the entire system always operates in an optimal state.

[0170] Generally, the external environmental factors faced by the customs import and export data analysis technology system are complex and changeable. For example, fluctuations in the global economy may affect the demand for certain goods, policy changes may change the customs inspection standards, and changes in the international situation may also affect the liquidity of cross-border trade. To ensure that the system can cope with these changes, the real-time feedback module needs to be able to collect and analyze these external data in real time and dynamically adjust the weights of the objective function according to the analysis results.

[0171] The real-time feedback module will monitor the following key external factors in real time:

[0172] Economic fluctuations: Global economic changes (such as inflation, economic recession, currency fluctuations, etc.) have a direct impact on international trade and import and export liquidity.

[0173] Policy changes: Government policy adjustments (such as tariff changes, trade barrier settings, etc.) may affect the customs inspection standards for import and export data.

[0174] International situation: Changes such as trade wars and international agreements may affect international import and export flows.

[0175] Global market demand changes: Changes in the demand for certain goods or services may lead to imbalances in trade flows, thereby affecting goals such as risk detection and regulatory efficiency.

[0176] The collaborative relationship between the real-time feedback module and the uncertainty modeling module and the reinforcement learning module is particularly crucial. Through the aforementioned uncertainty modeling module, the system has modeled external uncertainties and optimized the objective function accordingly. However, the dynamic changes in the external environment, especially changes in the economy, policies, international situation, and market demand, are often unpredictable in advance. Therefore, the role of the real-time feedback module is to obtain real-time information on these external environmental changes and adjust the optimization strategy of the objective function based on this information to ensure that the system can make optimal decisions in the actual environment.

[0177] Specifically, the real-time feedback module works in collaboration with other modules through the following steps:

[0178] Real-time data acquisition and monitoring: The real-time feedback module obtains real-time economic, policy, international situation, and market demand data related to customs import and export data through API interfaces or other data sources.

[0179] Analyze external environmental changes: By analyzing external environmental data, the system identifies factors that have a greater impact on the objective function. These factors may include large fluctuations in market demand, changes in international trade policies, etc.

[0180] Adjust the weights of the objective function: According to external environmental changes, the real-time feedback module will adjust the weights in the objective function. For example, when the global economy shows a downturn, it may be necessary to increase the attention to regulatory efficiency and reduce the weight of the risk detection goal; when policies change, the weight of the compliance goal may need to be increased.

[0181] Update the optimization strategy: The updated weights of the objective function will be fed back to the multi-objective optimization module for re-optimization calculations.

[0182] In this embodiment, the real-time feedback module can adjust the weights of the objective function according to the real-time monitoring and analysis of external environmental changes. Specifically, the feedback mechanism makes the balance between various goals more in line with actual needs by adjusting the weight coefficients of the objective function.

[0183] The weight update formula of the objective function after feedback can be expressed as:

[0184]

[0185] Where, is the weight of the adjusted objective function i; is the weight of the original objective function i; Δw i is the weight adjustment value calculated based on external environment changes (such as economic fluctuations, policy changes, etc.).

[0186] In some embodiments, Δw i can be calculated based on the influence degree of external factors. For example, if an economic recession has a greater impact on market demand, the system may increase the weight of the trade liquidity target, thereby optimizing the system's response strategy.

[0187] As an option, the real-time feedback module can adopt an adaptive mechanism, enabling the weight adjustment of the objective function not only based on real-time external environment changes but also making decisions according to historical data. For example, the system can use historical data analysis techniques to identify which economic fluctuations, policy changes, or market demand changes have affected the optimization results of the objective function and predict the most appropriate adjustment strategy currently based on this historical data.

[0188] In a possible implementation, the real-time feedback module dynamically adjusts the optimization strategy of the objective function through learning historical data and real-time monitoring of external environment data, enabling the system to continuously optimize and adapt to new changes.

[0189] After dynamically adjusting the weight of the objective function through the real-time feedback mechanism, the optimization result is fed back to the multi-objective optimization module. After further optimization by the multi-objective optimization module, a set of Pareto optimal solutions is finally generated and provided to the customs administrator for decision support. In this way, the real-time feedback module can ensure that the system can still maintain the best optimization state when facing a complex and dynamic external environment.

[0190] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. The customs import and export data analysis technology system based on artificial intelligence is characterized by: The following steps are involved: The data preprocessing module is used to collect raw import and export data from the customs import and export database, perform data cleaning, format conversion, fill in missing values, and extract feature data; The multi-objective optimization module is used to optimize multiple optimization objectives of customs import and export data, including risk detection objectives, regulatory efficiency objectives, compliance objectives and trade liquidity objectives, and uses artificial intelligence algorithms to optimize objective functions; Uncertainty modeling module, used to model the uncertainty in the objective function based on variational Bayesian inference, obtain the posterior distribution of the objective function and optimize the objective function; The information geometry optimization module is used to optimize the high-dimensional data of the objective function through the information geometry method, calculate the natural gradient of the objective function and update the objective function parameters; Reinforcement learning module, which is used to dynamically adjust the weight of the objective function based on the latest import and export records and dynamically changing market data through deep reinforcement learning algorithms; The real-time feedback module is used to adjust the optimization strategy of the objective function in real time based on economic fluctuations, policy changes, international situation and changes in global market demand.

2. The artificial intelligence-based customs import and export data analysis technology system according to claim 1 is characterized in that: The multi-objective optimization module includes: Defining optimization strategies for each objective function, including risk detection, regulatory efficiency, compliance, and trade liquidity; Based on the multi-objective optimization algorithm, the Pareto optimal solution is used to deal with the conflicts between objectives and find the optimal balance solution for multiple objectives; The optimization objectives are iteratively updated, and the optimization strategy is adjusted according to the trade-offs between the objectives.

3. The artificial intelligence-based customs import and export data analysis technology system according to claim 1 is characterized in that: The uncertainty modeling module includes: Modeling random variables that affect the objective function by acquiring external uncertainties, including economic fluctuations and policy changes; Using the variational Bayesian inference method, the approximate posterior distribution of the objective function is calculated and the objective function is optimized, maximizing the variational lower bound to deal with the uncertainty in the objective function.

4. The artificial intelligence-based customs import and export data analysis technology system according to claim 1 is characterized in that: The information geometry optimization module includes: Apply Riemannian metric to the objective function and describe the geometric structure of the objective function by information geometry method; The natural gradient descent method is used to calculate the natural gradient of the objective function, and the parameters of the objective function are optimized according to the Riemannian metric.

5. The artificial intelligence-based customs import and export data analysis technology system according to claim 1 is characterized in that: The reinforcement learning module includes: When monitoring import and export data in real time, the weight of the objective function is automatically adjusted according to changes in the external environment to optimize the balance between the objective functions; The agent is trained through a deep reinforcement learning algorithm to generate the optimal strategy based on the training data.

6. The artificial intelligence-based customs import and export data analysis technology system according to claim 1 is characterized in that: The real-time feedback module comprises: Monitor changes in the external environment and analyze the dynamic fluctuations of import and export data in real time, and adjust optimization strategies through system feedback; The optimization strategy of the objective function is updated according to real-time data, so that the system can make the best decision in time according to the changes in external conditions.

7. The artificial intelligence-based customs import and export data analysis technology system according to claim 1 is characterized in that: The data preprocessing module comprises: Extract raw data from the customs import and export database and clean it to remove redundant data and correct format errors; The data is standardized so that import and export data from different sources can be processed and analyzed in the same data space.

8. The artificial intelligence-based customs import and export data analysis technology system according to claim 1 is characterized in that: The multi-objective optimization module is used to optimize multiple optimization objectives of customs import and export data, including risk detection objectives, regulatory efficiency objectives, compliance objectives and trade liquidity objectives. The optimization of the objective function using artificial intelligence algorithms specifically includes: An objective function weighted adjustment unit, used to perform weighted adjustment on the objective function according to changes in the external environment, wherein the weighted adjustment is based on a dynamic weight adjustment mechanism of multi-objective optimization; The conflict handling unit is used to handle the conflicts between multiple objective functions and find a balanced solution in the multi-objective optimization process by adopting the Pareto optimal solution.

9. The artificial intelligence-based customs import and export data analysis technology system according to claim 1 is characterized in that: The uncertainty modeling module is used to model the uncertainty in the objective function based on variational Bayesian inference, obtain the posterior distribution of the objective function and optimize the objective function, specifically including: External environment dynamic analysis unit, used to analyze the impact of external uncertainties on the objective function; The uncertainty optimization control unit is used to adjust the prior distribution used in variational Bayesian inference according to the results of dynamic analysis of the external environment, optimize the uncertainty in the objective function, and feed back the optimized objective function to the multi-objective optimization module.

10. The artificial intelligence-based customs import and export data analysis technology system according to claim 1 is characterized in that: The information geometry optimization module is used to optimize the high-dimensional data of the objective function by using the information geometry method, calculate the natural gradient of the objective function and update the objective function parameters, specifically including: A geometric manifold adjustment unit, used to adjust the Riemannian metric according to the high-dimensional nature of the objective function; Gradient update unit, used to update the parameters of the objective function through the natural gradient method.

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