Goods value risk control method and system based on imported customs declaration price data

Abstract

The invention discloses a goods value risk control method and system based on imported customs declaration price data, and relates to the technical field of goods value risk control. The goods value risk control method comprises the steps of obtaining existing data; historical customs declaration price data, cargo data and historical conversion data of imported cargos are extracted from a customs declaration system, and the historical customs declaration price data and the cargo data are represented as heterogeneous data. According to the method, the simulation database is established, the change possibility of the actual price associated data is calculated based on the calculation tool, when the actual price associated data is changed, the corresponding simulation data is directly called from the simulation database based on the changed data, and the original goods value evaluation data is covered by the simulation data; according to the method, a large number of cargos are directly covered, the cargo evaluation data can be quickly and timely updated according to the dynamic information, the condition of underestimated or overestimated cargo value during customs declaration is avoided, and the risk value calculation accuracy is further ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of cargo value risk control, and in particular to a cargo value risk control method and system based on import customs declaration price data. Background Art

[0002] Import declaration price data refers to the price data reviewed and determined by the customs in accordance with relevant regulations during the declaration process of imported goods. The cargo value risk control method refers to a series of measures and methods taken by enterprises to reduce or eliminate the risks related to the value of goods. The cargo value risk control method based on import declaration price data mainly identifies potential risk points and takes corresponding risk control measures by analyzing, comparing and monitoring the import declaration price data.

[0003] The patent publication number is "CN115860747A" and the name is "A method and system for customs import and export commodity risk management based on big data". According to the model output results, namely commodity risk analysis information, commodity quality risk analysis information, and commodity transaction risk analysis information, the customs import and export commodity risk information is determined; when the customs import and export commodity risk information exceeds the preset commodity risk threshold, the customs import and export commodity risk warning management is carried out. The technical effect of improving the accuracy of customs commodity risk identification and processing efficiency, thereby ensuring the timeliness of customs commodity risk warning.

[0004] When goods are imported, they need to be declared. Due to the constant changes in market prices, transportation costs, exchange rates, etc., consumption costs continue to increase. For example, goods in cold chain logistics need to be powered and refrigerated at all times before transportation. At this time, consumption costs will continue to increase, and the import declaration price and value assessment price of the goods will also change accordingly. The existing system needs to update this dynamic information. However, for some imports with large cargo volumes, the internal formats are different, and it is inconvenient to quickly and timely update the dynamic information of the goods. The declaration may lead to underestimation or overestimation of the value of the goods. The lack of real-time data makes the risk value calculation inaccurate. Therefore, a cargo value risk control method and system based on import declaration price data are proposed. Summary of the invention

[0005] The purpose of the present invention is to provide a cargo value risk control method and system based on import customs declaration price data to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a cargo value risk control method based on import customs declaration price data, the cargo value risk control method comprising: Acquire existing data; extract historical customs declaration price data, goods data and historical conversion data of imported goods from the customs declaration system, where the historical customs declaration price data and goods data are represented in heterogeneous data; Constructing transformation charts: Integrate historical customs declaration price data and cargo data represented by various heterogeneous data through data lake technology to form a unified multi-source heterogeneous data view. Based on historical conversion data, construct transformation data and connections between data formats in the multi-source heterogeneous data view to obtain transformation charts. Data conversion: convert the format of cargo data and the format of historical customs declaration price data into the same marking format according to the conversion method and conversion chart; Collect data: obtain the price-related data of the goods and calculate the goods value assessment data; Establish and apply a simulation database: set up a calculation tool for simulation calculation, the calculation tool for simulation calculation combines the goods data in a unified marking format and the customs declaration price data in a unified marking format to simulate the change of price-related data, obtain the simulated goods value assessment data after the corresponding change, summarize the price-related data and the simulated goods value assessment data after the corresponding change, and obtain a simulation database, in which the data position in the database is displayed and called in a multi-dimensional coordinate format; Based on the actual price-related data, the actual price-related data is represented as multi-dimensional coordinate points, and corresponding simulation data is obtained from the simulation database according to the multi-dimensional coordinate points and the multi-dimensional coordinate system, and the simulation data covers the cargo value assessment data to obtain new cargo value assessment data; Based on the continuous actual price-related data, the forecasted price-related data are obtained through a forecasting tool, and the corresponding forecasting simulation data are obtained from a simulation database based on the forecasted price-related data, and the forecasting simulation data are overwritten with the cargo value assessment data to obtain new forecasting cargo value assessment data; Risk assessment: Establish actual warning thresholds and forecast warning thresholds, process new cargo value assessment data through risk calculation to obtain actual risk values, compare actual risk values ​​with actual warning thresholds, and issue a No. 1 alarm when the actual risk value exceeds the actual warning threshold; The predicted risk value is obtained by processing the predicted cargo value assessment data through risk calculation, and the predicted risk value is compared with the predicted warning threshold. When the predicted risk value exceeds the predicted warning threshold, a second alarm is issued; Risk management: adopt strategy number one for alert number one and adopt strategy number two for alert number two.

[0007] Furthermore, the actual price-related data includes market price x, transportation cost y and exchange rate z, and the method for displaying the data position in the database in multi-dimensional coordinates includes: establishing a three-dimensional coordinate system according to the market price x, transportation cost y and exchange rate z, obtaining (x1, y1, z1) based on the actual price-related data, and obtaining corresponding simulation data according to the position of (x1, y1, z1) in the three-dimensional coordinate system; Based on continuous actual price correlation data, we obtain (x1, y1, z1), (x2, y2, z2)…(xn, yn, zn), and through continuous actual price correlation data and forecasting tools, we obtain (xm, ym, zm). According to the position of (xm, ym, zm) in the three-dimensional coordinate system, we obtain the corresponding simulation data for forecasting.

[0008] Furthermore, the method for risk calculation to process cargo value assessment data includes: Preset time range T; determine a time range T, with days as the basic unit; Calculate the expected return ER: Based on historical customs price data and cargo value assessment data, use statistical methods to estimate the expected return ER of the cargo within the time frame T; Calculate standard deviation SD: Calculate the standard deviation of commodity price fluctuations based on historical customs declaration price data and cargo value assessment data; Calculate the value-added rate VAR: By analyzing the price growth trend in historical data, calculate the average rate of increase in the value of goods over time to obtain the value-added rate VAR; Preset time: determine the time range value; Introducing risk factor K: The risk factor K ranges from 0.2 to 0.4; Calculating Risk-Adjusted Returns ; Calculating Value at Risk .

[0009] Furthermore, the method of integrating the data lake technology includes: Data access: connect various heterogeneous data to the data platform; Data cleaning and processing: Clean, deduplicate, and delete erroneous data in the data platform to obtain processed heterogeneous data: Data storage: The storage layer of the data lake stores processed heterogeneous data and establishes corresponding classifications based on the representation of each data format in the processed heterogeneous data. Heterogeneous data of the same data format are classified into one category.

[0010] Furthermore, the historical conversion data includes conversion logic and results between different data formats in history, and the customs declaration system includes a customs database, enterprise declaration materials and a third-party data provider.

[0011] Furthermore, the actual warning threshold is established by identifying key risk indicators related to cargo value assessment data based on historical customs declaration price data in a unified format, determining the fluctuation range of each risk indicator based on historical customs declaration price data, and obtaining the standard deviation of the fluctuation range and the corresponding arithmetic mean. The standard deviation is divided by the arithmetic mean to obtain the actual warning threshold. Key risk indicators include price volatility, supply and demand changes, market trends and daily consumption; The prediction warning threshold includes setting a safety value, the safety value is 0.90-0.95, and the actual warning threshold is multiplied by the safety value to obtain the prediction warning threshold.

[0012] Furthermore, the conversion method includes: selecting the data format used by the destination as a marked format according to the destination of the imported goods, so that the original heterogeneous data is divided into a marked format and an unmarked format, and the goods with an unmarked format convert the unmarked format of the goods data into a marked format according to a conversion chart, and the historical customs declaration price data with an unmarked format is converted into a marked format according to the conversion chart, and the historical customs declaration price data and the goods data obtain a unified marked format.

[0013] A cargo value risk control system based on import customs declaration price data adopts the above-mentioned cargo value risk control method based on import customs declaration price data.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The cargo value risk control method and system based on import customs declaration price data establishes a simulation database and, based on a calculation tool, calculates the possibility of changes in the actual price-related data through previous calculations before the actual price-related data changes. When the actual price-related data changes, the corresponding simulation data is directly called from the simulation database based on the changed data. The simulation data covers the original cargo value assessment data. For a large number of cargoes, the direct coverage can quickly update the cargo assessment data with dynamic information, thereby avoiding underestimation or overestimation of the cargo value during customs declaration, thereby ensuring accurate risk value calculation.

[0015] At the same time, data lake technology is used to integrate various heterogeneous data, including historical customs declaration price data, cargo data, etc., to form a unified multi-source heterogeneous data view. This method can fully and accurately reflect the true value of goods. By setting up simulation calculation tools and forecasting tools, combining historical data and real-time data, simulating price data changes and predicting future price trends, it provides a scientific basis for cargo value assessment.

[0016] Establish actual warning thresholds and forecast warning thresholds, process cargo value assessment data through risk calculation, and obtain actual risk values ​​and forecast risk values. When the risk value exceeds the warning threshold, issue an alarm in time to provide a decision-making basis for risk management. Different risk management strategies are adopted according to different alarm levels. Alarm No. 1 corresponds to Strategy No. 1, which is the corresponding strategy based on the actual situation. Alarm No. 2 corresponds to Strategy No. 2, which is the corresponding strategy adopted based on the forecast situation. This method enhances risk control capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 Schematic diagram of the cargo value risk control method of the present invention; Figure 2 is a schematic diagram of a multi-dimensional coordinate system of the present invention; Figure 3 is a schematic diagram of a conversion chart of the present invention; Figure 4 Schematic diagram of the establishment of the conversion chart of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] like Figure 1 - Figure 4 As shown, the present invention provides a technical solution: a cargo value risk control method based on import customs declaration price data, the cargo value risk control method comprising: Acquire existing data; extract historical customs declaration price data, goods data and historical conversion data of imported goods from the customs declaration system, where the historical customs declaration price data and goods data are represented in heterogeneous data; Constructing transformation charts: Integrate historical customs declaration price data and cargo data represented by various heterogeneous data through data lake technology to form a unified multi-source heterogeneous data view. Based on historical conversion data, construct transformation data and connections between data formats in the multi-source heterogeneous data view to obtain transformation charts. Data conversion: convert the format of cargo data and the format of historical customs declaration price data into the same marking format according to the conversion method and conversion chart; Collect data: obtain the price-related data of the goods and calculate the goods value assessment data; Establish and apply a simulation database: set up a calculation tool for simulation calculation, the calculation tool for simulation calculation combines the goods data in a unified marking format and the customs declaration price data in a unified marking format to simulate the change of price-related data, obtain the simulated goods value assessment data after the corresponding change, summarize the price-related data and the simulated goods value assessment data after the corresponding change, obtain the simulation database, and the data position in the database is displayed and called in a multi-dimensional coordinate format; Based on the actual price-related data, the actual price-related data is represented as multi-dimensional coordinate points, and corresponding simulation data is obtained from the simulation database according to the multi-dimensional coordinate points and the multi-dimensional coordinate system, and the simulation data covers the cargo value assessment data to obtain new cargo value assessment data; Based on the continuous actual price-related data, the forecasted price-related data are obtained through a forecasting tool, and the corresponding forecasting simulation data are obtained from a simulation database based on the forecasted price-related data, and the forecasting simulation data are overwritten with the cargo value assessment data to obtain new forecasting cargo value assessment data; Risk assessment: Establish actual warning thresholds and forecast warning thresholds, process new cargo value assessment data through risk calculation to obtain actual risk values, compare actual risk values ​​with actual warning thresholds, and issue a No. 1 alarm when the actual risk value exceeds the actual warning threshold; The predicted risk value is obtained by processing the predicted cargo value assessment data through risk calculation, and the predicted risk value is compared with the predicted warning threshold. When the predicted risk value exceeds the predicted warning threshold, a second alarm is issued; Risk management: adopt strategy number one for alert number one and adopt strategy number two for alert number two.

[0020] The actual price-related data include market price x, transportation cost y and exchange rate z. The method for displaying the data position in the database in multi-dimensional coordinates includes: establishing a three-dimensional coordinate system according to the market price x, transportation cost y and exchange rate z, obtaining (x1, y1, z1) based on the actual price-related data, and obtaining corresponding simulation data according to the position of (x1, y1, z1) in the three-dimensional coordinate system; Based on continuous actual price correlation data, we obtain (x1, y1, z1), (x2, y2, z2)…(xn, yn, zn), and through continuous actual price correlation data and forecasting tools, we obtain (xm, ym, zm). According to the position of (xm, ym, zm) in the three-dimensional coordinate system, we obtain the corresponding simulation data for forecasting.

[0021] The methods used by risk calculation to process cargo value assessment data include: Preset time range T; determine a time range T, T is based on days; Calculate the expected return ER: Based on historical customs price data and cargo value assessment data, use statistical methods to estimate the expected return ER of the cargo within the time frame T; Calculate standard deviation SD: Calculate the standard deviation of commodity price fluctuations based on historical customs declaration price data and cargo value assessment data; Calculate the value-added rate VAR: By analyzing the price growth trend in historical data, calculate the average rate of increase in the value of goods over time to obtain the value-added rate VAR; Preset time: determine the time range value; Introducing risk factor K: The risk factor K ranges from 0.2 to 0.4; Calculating Risk-Adjusted Returns ; Calculating Value at Risk .

[0022] Data lake technology integration methods include: Data access: connect various heterogeneous data to the data platform; Data cleaning and processing: Clean, deduplicate, and delete erroneous data in the data platform to obtain processed heterogeneous data: Data storage: The storage layer of the data lake stores processed heterogeneous data and establishes corresponding classifications based on the representation of each data format in the processed heterogeneous data. Heterogeneous data of the same data format are classified into one category.

[0023] Historical conversion data includes the conversion logic and results between different data formats in history. The customs declaration system includes the customs database, enterprise declaration materials and third-party data providers.

[0024] The actual method of establishing the early warning threshold is: based on the historical customs price data in a unified format, identify the key risk indicators related to the cargo value assessment data, determine the fluctuation range of each risk indicator based on the historical customs price data, obtain the standard deviation of the fluctuation range and the corresponding arithmetic mean, The standard deviation is divided by the arithmetic mean to obtain the actual warning threshold. Key risk indicators include price volatility, supply and demand changes, market trends and daily consumption; The prediction warning threshold includes setting a safety value, the safety value is 0.90-0.95, and the actual warning threshold is multiplied by the safety value to obtain the prediction warning threshold.

[0025] The conversion method includes: selecting the data format used by the destination as the marked format according to the destination of the imported goods, so that the original heterogeneous data is divided into marked format and unmarked format, and the goods with unmarked format are converted from the unmarked format of the goods data into the marked format according to the conversion chart, and the historical customs declaration price data with unmarked format is converted into the marked format according to the conversion chart, and the historical customs declaration price data and the goods data obtain a unified marked format.

[0026] A cargo value risk control system based on import customs declaration price data adopts the above-mentioned cargo value risk control method based on import customs declaration price data.

[0027] Import declaration price data refers to the price information of goods reported by enterprises to the customs during the import process. This information usually includes the transaction price of goods, transportation costs, insurance costs, etc. These data play an important role in customs tax collection and management, risk assessment and goods supervision. They are the basis for customs to conduct goods value assessment and tax calculation. By accurately declaring the price information of imported goods, enterprises can ensure compliance with import regulations and avoid unnecessary tax risks and legal disputes. Customs can also effectively supervise and manage imported goods based on these data to maintain national tax security and market order. Heterogeneous data refers to data in multiple formats.

[0028] The multi-source heterogeneous fusion database integrates data from multiple sources such as customs, suppliers, logistics companies, and financial institutions. These data include import declaration prices, cargo descriptions, transportation information, payment records, etc. Through data cleaning, data conversion, and data integration technologies, these data in different formats and standards are integrated to form a unified multi-source heterogeneous data view. Based on historical conversion data, the conversion data and connections between multi-source heterogeneous data views are constructed to obtain conversion charts. Data lake technology is a centralized repository for storing, managing, and analyzing a large amount of data of different types and formats. Data lake technology is used to process data of different types and formats and classify them for management. On the basis of data lake technology, historical conversion data is used to connect data of different formats. Historical conversion data includes the conversion logic and results between different data formats in history. Through the conversion logic and results, the connection between data of different formats is realized, so that the non-tagged format can be converted into the tagged format later. Historical conversion data includes the conversion logic and results between multi-source heterogeneous data in history. Based on these conversion data, the data formats in the data view can be connected.

[0029] Relying on the integration of multi-source data, dynamic monitoring of the value of goods is achieved by extracting historical customs declaration data and real-time price-related data. By establishing a conversion chart, heterogeneous data is unified into a labeled format, making subsequent analysis more concise and efficient. Using data lake technology, multiple heterogeneous data are integrated into a unified view, and conversion between formats is achieved by building a conversion chart. This integration ensures the availability of data.

[0030] like Figure 3As shown, data formats 1, 2, 3, 4 and 5 are obtained through integration by data lake technology. Then, the connection between different data formats can be made based on historical conversion data to obtain a conversion chart. In the use of the conversion chart, the data format used by the destination is the marking format. For example, data format 3 is regarded as the marking format used by the destination, and the remaining data formats need to convert their own data formats into the representation of data format 3. Data format 1 and data format 5 can be directly converted into data format 3, while data format 2 and data format 4 are converted into data format 3 using the shortest path method. For example, for data format 4, under the shortest path method, data format 4 is first converted into data format 1, and then data format 1 is converted into data format 3, while data format 4 is first converted into data format 1, data format 1 is converted into data format 5, and data format 5 is converted into data format 3. The latter path is too long and the conversion is too long and time-consuming, so it will not be used. A variety of heterogeneous data, including historical customs price data, cargo data, etc., are integrated through data lake technology to form a unified multi-source heterogeneous data view. This method can fully and accurately reflect the true value of the goods. By setting up simulation calculation tools and forecasting tools, combining historical data and real-time data, it simulates changes in price data and predicts future price trends, providing a scientific basis for cargo value assessment.

[0031] Destination data format. Since the goods are imported, there is a destination, that is, the goods will be sent to a location. Due to different regions, the resulting data format will be different. The data format used by the destination is taken as the standard, and the data format used is regarded as the marked format. The non-marked format of the remaining data is converted into the marked format according to the conversion chart.

[0032] Since the exchange rate is based on one of the basic units, the value of the goods is reduced to unit 1, and the corresponding value of the goods is reduced synchronously, thereby obtaining a reference value. This reference value can be regarded as a coordinate value. Since this reference value may have infinite decimals, the specific value of the reference value can be adjusted based on the actual exchange rate. The same is true for other market prices, transportation costs, and consumption costs. The calculation tool is based on the change of coordinate values. The calculation formula in the calculation tool exists in the prior art and is not specifically described here. The value of goods data can be calculated based on the existing calculation formula based on the coordinate values. The prediction tool can predict the change of subsequent values ​​based on the change of coordinate values. At the same time, there will be different specific prediction methods for numerical attributes. For example, in cold chain transportation, the longer the frozen goods stay at the checkpoint, the greater the consumption cost. Therefore, the predicted value of the consumption cost will always be greater than the current consumption cost. The specific values ​​of data such as exchange rates and market prices will fluctuate up and down. Therefore, different prediction tools are used for different numerical attributes. The prediction method used by the prediction tool has existing technologies, which will not be explained in detail here. The corresponding prediction simulation data is obtained from the simulation database by predicting price-related data, and risk assessment is performed on the prediction simulation data.

[0033] like Figure 2 As shown, the transportation cost and consumption cost can be classified into the cost data m, and the market price and exchange rate can be classified into the fluctuation data n, so as to construct the m and n coordinate systems. Based on the changes in the actual data, (m1, n1) can be obtained, and the simulation data represented by (m1, n1) can be obtained. The new cargo value assessment data can be obtained through the simulation data represented by (m1, n1) and the calculation tool. Due to the uncertainty of the prediction, the corresponding coordinate points can be obtained through the continuous actual price correlation data. The figure has given five known coordinate points: (m1, n1), (m2, n2), (m3, n2), (m4, n4), and (m5, n5). The five known coordinate points are represented by The cost data m will increase with the increase of time. For example, in cold chain logistics, the longer the time spent at the checkpoint and the longer the transportation time, the more cost will be consumed, which makes the increase of cost data m and the increase of time synchronous. The above five coordinates are processed by the prediction tool to obtain the predicted coordinate point (m6, n6). The predicted coordinate point is obtained through continuous coordinate points and prediction tools. The corresponding predicted cargo value assessment data is obtained through the position of the predicted coordinate point on the coordinate system. The new cargo value assessment data is compared with the actual warning threshold, and the predicted cargo value assessment data is compared with the predicted risk value. Based on the comparison results, the corresponding strategy is used.

[0034] Through the establishment of a simulation database and based on calculation tools, before the actual price-related data changes, the possibility of changes in the actual price-related data is calculated through previous calculations. When the actual price-related data changes, the corresponding simulation data is directly called from the simulation database based on the changed data. The simulation data covers the original cargo value assessment data. For a large number of cargoes, direct coverage can be performed, and the cargo assessment data can be quickly and timely updated with dynamic information, avoiding underestimation or overestimation of the cargo value during customs declaration, thereby ensuring accurate risk value calculation.

[0035] The strategy should include risk avoidance, risk reduction, risk transfer and risk acceptance to ensure that risks can be responded to quickly and effectively when they occur. Since the causes of the No. 1 alarm and the No. 2 alarm are different, the specific details of the No. 1 strategy and the No. 2 strategy will be significantly different in actual implementation. Since the implementation of the strategy is a specific operation, the specific contents of the No. 1 strategy and the No. 2 strategy are not explained here. The actual and predicted warning thresholds are set, and the No. 1 and No. 2 alarms are dynamically generated based on the risk calculation results. When the actual or predicted risk exceeds the threshold, the corresponding strategy is quickly adopted for management to ensure timely response to potential risks, establish the actual warning threshold and the predicted warning threshold, and process the cargo value assessment data through risk calculation to obtain the actual risk value and the predicted risk value. When the risk value exceeds the warning threshold, an alarm is issued in time to provide a decision-making basis for risk management. Different risk management strategies are adopted according to different alarm levels. The No. 1 alarm corresponds to the No. 1 strategy, which is the corresponding strategy based on the actual situation. The No. 2 alarm corresponds to the No. 2 strategy, which is the corresponding strategy adopted based on the predicted situation. This method enhances the risk control ability.

[0036] Price-related data also include additional costs, including insurance costs, customs clearance fees, etc. The value-added rate more accurately reflects the rate of increase in the value of goods over time, making risk assessment closer to the actual investment situation. By introducing the risk coefficient K, investors can adjust the impact of the standard deviation according to their own risk tolerance. The impact of the time range T on the risk is considered, and √T is used to adjust the standard deviation. The revised method intuitively represents the risk level of the goods by calculating the risk value. The closer the RV is to 0, the lower the risk; the closer the RV is to 1, the higher the risk. Based on the setting of the actual warning threshold, the actual warning threshold is between 0 and 1, and the actual warning threshold can be prioritized as 0.4. When the RV is greater than 0.4, it means that a warning is needed. The specific value of the actual warning threshold can be selected by the user according to the actual situation. Through more accurate value-added rate calculation and flexible risk adjustment, the revised method can more accurately assess the risk level of the goods and provide investors with a more reliable decision-making basis. Through intuitive risk value representation, users can more clearly understand the risk status of the goods. Taking into account the time factor and the changes in the risk tolerance of investors, risk assessment is more flexible and dynamic, and can adapt to changes in different market environments.

[0037] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is limited by the attached embodiments and their equivalents.

Claims

1. A cargo value risk control method based on import customs declaration price data, characterized in that: The cargo value risk control method includes: Acquire existing data; extract historical customs declaration price data, goods data and historical conversion data of imported goods from the customs declaration system, where the historical customs declaration price data and goods data are represented in heterogeneous data; Constructing transformation charts: Integrate historical customs declaration price data and cargo data represented by various heterogeneous data through data lake technology to form a unified multi-source heterogeneous data view. Based on historical conversion data, construct transformation data and connections between data formats in the multi-source heterogeneous data view to obtain transformation charts. Data conversion: convert the format of cargo data and the format of historical customs declaration price data into the same marking format according to the conversion method and conversion chart; Collect data: obtain the price-related data of the goods and calculate the goods value assessment data; Establish and apply a simulation database: set up a calculation tool for simulation calculation, the calculation tool for simulation calculation combines the goods data in a unified marking format and the customs declaration price data in a unified marking format to simulate the change of price-related data, obtain the simulated goods value assessment data after the corresponding change, summarize the price-related data and the simulated goods value assessment data after the corresponding change, and obtain a simulation database, in which the data position in the database is displayed and called in a multi-dimensional coordinate format; Based on the actual price-related data, the actual price-related data is represented as multi-dimensional coordinate points, and corresponding simulation data is obtained from the simulation database according to the multi-dimensional coordinate points and the multi-dimensional coordinate system, and the simulation data covers the cargo value assessment data to obtain new cargo value assessment data; Based on the continuous actual price-related data, the forecasted price-related data are obtained through a forecasting tool, and the corresponding forecasting simulation data are obtained from a simulation database based on the forecasted price-related data, and the forecasting simulation data are overwritten with the cargo value assessment data to obtain new forecasting cargo value assessment data; Risk assessment: Establish actual warning thresholds and forecast warning thresholds, process new cargo value assessment data through risk calculation to obtain actual risk values, compare actual risk values ​​with actual warning thresholds, and issue a No. 1 alarm when the actual risk value exceeds the actual warning threshold; The predicted risk value is obtained by processing the predicted cargo value assessment data through risk calculation, and the predicted risk value is compared with the predicted warning threshold. When the predicted risk value exceeds the predicted warning threshold, a second alarm is issued; Risk management: adopt strategy number one for alert number one and adopt strategy number two for alert number two.

2. A cargo value risk control method based on import customs declaration price data according to claim 1, characterized in that: The actual price-related data include market price x, transportation cost y and exchange rate z. The method for displaying the data position in the database in multi-dimensional coordinates includes: establishing a three-dimensional coordinate system according to the market price x, transportation cost y and exchange rate z, obtaining (x1, y1, z1) based on the actual price-related data, and obtaining corresponding simulation data according to the position of (x1, y1, z1) in the three-dimensional coordinate system; Based on continuous actual price correlation data, we obtain (x1, y1, z1), (x2, y2, z2)…(xn, yn, zn), and through continuous actual price correlation data and forecasting tools, we obtain (xm, ym, zm). According to the position of (xm, ym, zm) in the three-dimensional coordinate system, we obtain the corresponding simulation data for forecasting.

3. A cargo value risk control method based on import customs declaration price data according to claim 1, characterized in that: The methods used by risk calculation to process cargo value assessment data include: Preset time range T; determine a time range T, T is based on days; Calculate the expected return ER: Based on historical customs price data and cargo value assessment data, use statistical methods to estimate the expected return ER of the cargo within the time frame T; Calculate standard deviation SD: Calculate the standard deviation of commodity price fluctuations based on historical customs declaration price data and cargo value assessment data; Calculate the value-added rate VAR: By analyzing the price growth trend in historical data, calculate the average rate of increase in the value of goods over time to obtain the value-added rate VAR; Preset time: determine the time range value; Introducing risk factor K: The risk factor K ranges from 0.2 to 0.4; Calculating Risk-Adjusted Returns ; Calculating Value at Risk .

4. A cargo value risk control method based on import customs declaration price data according to claim 1, characterized in that: The data lake technology integration method includes: Data access: connect various heterogeneous data to the data platform; Data cleaning and processing: Clean, deduplicate, and delete erroneous data in the data platform to obtain processed heterogeneous data: Data storage: The storage layer of the data lake stores processed heterogeneous data and establishes corresponding classifications based on the representation of each data format in the processed heterogeneous data. Heterogeneous data of the same data format are classified into one category.

5. The cargo value risk control method based on import customs declaration price data according to claim 1 is characterized by: The historical conversion data includes the conversion logic and results between different data formats in history, and the customs declaration system includes the customs database, enterprise declaration materials and third-party data providers.

6. A cargo value risk control method based on import customs declaration price data according to claim 1, characterized in that: The actual method of establishing the early warning threshold is: based on the historical customs price data in a unified format, identify the key risk indicators related to the cargo value assessment data, determine the fluctuation range of each risk indicator based on the historical customs price data, obtain the standard deviation of the fluctuation range and the corresponding arithmetic mean, The standard deviation is divided by the arithmetic mean to obtain the actual warning threshold. Key risk indicators include price volatility, supply and demand changes, market trends and daily consumption; The prediction warning threshold includes setting a safety value, the safety value is 0.90-0.95, and the actual warning threshold is multiplied by the safety value to obtain the prediction warning threshold.

7. A cargo value risk control method based on import customs declaration price data according to claim 1, characterized in that: The conversion method includes: selecting the data format used by the destination as a marked format according to the destination of the imported goods, so that the original heterogeneous data is divided into a marked format and an unmarked format, and the goods with an unmarked format are converted into a marked format according to a conversion chart, and the historical customs declaration price data with an unmarked format is converted into a marked format according to the conversion chart, and the historical customs declaration price data and the goods data obtain a unified marked format.

8. A cargo value risk control system based on import customs declaration price data, characterized by: A cargo value risk control method based on import customs declaration price data as described in any one of claims 1 to 7 is adopted.

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