Agricultural product export intelligent customs declaration receipt generation method

Abstract

The invention relates to the technical field of logistics management, in particular to an agricultural product export intelligent customs declaration receipt generation method, which comprises the following steps of: acquiring weighing data of a target agricultural product batch in a processing plant delivery weighbridge, a warehouse warehousing weighbridge and a container encasement link; and extracting an exporter AEO authentication level, a historical error rate and a declaration goods value corresponding to the batch, and establishing a to-be-processed comprehensive data file. According to the invention, based on weighing data acquisition of agricultural product batches at each logistics node, multi-dimensional information such as the weighing data, the exponent authentication level, the historical error rate, the declaration goods value and the like is subjected to fusion analysis, the theoretical weight is predicted through calculation of the theoretical moisture evaporation rate, iterative calibration of the weighing data is carried out, and the weighing accuracy of the agricultural product batches is improved. More accurate declaration weight prediction is realized, and the problem of large error of export declaration weight is solved.

Description

Technical Field

[0001] This invention relates to the field of logistics management technology, and in particular to a method for generating intelligent customs declaration documents for agricultural product exports. Background Technology

[0002] The field of logistics management technology refers to the management technology of the flow of goods, information and funds involved in the entire process from raw material procurement and product production to final product distribution, through scientific methods, information technology and system planning.

[0003] Current technologies lack calculations for weight loss between different logistics nodes in practice. Weight data from each node is used independently without iterative calibration and fusion, leading to discrepancies between the actual and declared weights. This can easily trigger inspection risks during customs clearance. Furthermore, relying solely on simple risk indicators or subjective judgment based on human experience results in inaccurate risk assessments for export inspections, leading to adverse consequences such as cargo delays at ports, low customs clearance efficiency, and increased costs for exporting companies. Therefore, improvements are needed. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a method for generating intelligent customs declaration documents for agricultural product exports.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a method for generating intelligent customs declaration documents for agricultural product exports, comprising the following steps: Collect weighing data of target agricultural product batches at the processing plant's outbound weighbridge, warehouse inbound weighbridge, and container loading stages; extract the exporter's AEO certification level, historical error rate, and declared value corresponding to the batch; and establish a comprehensive data archive to be processed. Based on the comprehensive data archive to be processed, the theoretical moisture evaporation rate is calculated. Based on the moisture evaporation rate and the weight and time of the factory's outgoing weighbridge slip, the theoretical weight of the next logistics node is predicted, and the theoretical weight prediction value between nodes is obtained. Based on the theoretical weight prediction value between nodes and the weighing data of the warehouse inbound weighbridge, the optimal estimated declared weight is generated. Based on the comprehensive data archive to be processed, the exporter's AEO certification level and historical error rate are extracted to generate an exporter rating score, the product HS code sensitivity is extracted to generate a product sensitivity score, the destination port risk coefficient is extracted to generate a regional risk score, a multi-dimensional risk set is obtained, the deviation between the declared value and the average price in the system is calculated, the consistency of invoices, packing lists and contract data is compared to obtain a document consistency score, and the document consistency score is calculated with the multi-dimensional risk set to obtain an inspection intervention index; Based on the optimal estimated declared weight and the comprehensive data archive to be processed, the optimal estimated declared weight is filled into the weight field of the customs declaration form and compiled into basic customs declaration document content. The inspection intervention index is compared with the set low-risk threshold and high-risk threshold for judgment. Based on the judgment result, the product usage description, production flow chart or risk self-assessment result is selectively added to the basic customs declaration document content to generate an intelligent export customs declaration document combination.

[0006] Preferably, the step of obtaining the comprehensive data archive to be processed is as follows: Weighing data of the target agricultural product batches are collected by weighing equipment in the processing plant, warehouse and container packing process. At the same time, the accuracy level of the weighing equipment and the current calibration status information corresponding to the weighing data are obtained from the weighing equipment label or calibration record. In addition, the ambient temperature and humidity data of each process are collected in real time by temperature and humidity sensors to obtain the initial weighing and environmental data. Based on the initial weighing and environmental data, the product type information of the corresponding batch is retrieved from the business system using the agricultural product batch number as the associated field. The AEO certification level of the exporter of the batch, the historical error rate of export declaration, and the declared value information are also queried to obtain the batch-related business information. Based on the batch-related business information, the initial weighing and environmental data are integrated with the batch-related business information into a unified data archive template. The data items corresponding to each weighing step are marked and associated one by one according to the time sequence to form a comprehensive data archive to be processed.

[0007] Preferably, the step of obtaining the theoretical weight prediction value between nodes is as follows: Based on the comprehensive data archive to be processed, the product types marked in the archive are extracted, and the environmental temperature and humidity data are obtained from the corresponding product type records. The environmental temperature and humidity data are matched and associated one by one according to the time points corresponding to the weighing data to form the association information between product types and corresponding environmental temperature and humidity. Based on the product type and the corresponding environmental temperature and humidity information, the standard for water evaporation rate corresponding to the agricultural product type is called. The theoretical water evaporation amount of each batch of agricultural products in each time period is calculated according to the combination of environmental temperature and humidity values. The theoretical water evaporation rate is determined by the ratio between the theoretical water evaporation amount and the actual weight of adjacent time periods, thus forming the theoretical water evaporation rate. Based on the theoretical moisture evaporation rate, the weight data recorded on the factory's weighbridge slip is used as the initial weight benchmark value. The weighing time of the factory's weighbridge slip and the planned arrival time of the next logistics node are called to calculate the total moisture loss value corresponding to the theoretical moisture evaporation rate during the period. The total moisture loss value is then subtracted from the initial weight benchmark value to obtain the theoretical weight prediction value between nodes.

[0008] Preferably, the step of obtaining the optimal estimated declared weight is as follows: Extract the theoretical weight prediction values ​​between the nodes, the weighing data of the warehouse weighbridge, and the corresponding timestamp information. Pair the theoretical weighing data with the actual weighing data one by one by comparing the timestamps. Call up the accuracy level and calibration status of the measuring equipment corresponding to each pair of data, retrieve and standardize them into relative uncertainty values, and obtain a ternary combination set of theoretical weighing value, actual weighing value, and uncertainty value. Based on the ternary combination set of the theoretical weighing value, the actual weighing value, and the uncertainty value, the optimal estimated declared weight is calculated. Based on the optimal estimated declared weight, and combining all the weighing point numbers paired in the dataset with the list of actual weighing data participating in the fusion, the corrected weighing basis is summarized to obtain the optimal estimated declared weight set.

[0009] Preferably, the steps for obtaining the multi-dimensional risk set are as follows: Based on the comprehensive data archive to be processed, the exporter's AEO certification level field value and historical error rate percentage are analyzed. According to the unified credit rating mapping table, the exporter's AEO certification level field value and historical error rate percentage are converted into a standard credit score and normalized to generate a normalized exporter rating score. The results are then summarized to obtain a multi-dimensional risk set.

[0010] Preferably, the step of obtaining the inspection intervention index is as follows: Based on the normalized exporter rating score in the multi-dimensional risk set, the sensitivity level corresponding to the product HS code and the historical inspection risk level of the destination port are extracted and converted into a risk standard score in the range of 0 to 1. After normalization, these scores are recorded as product sensitivity score and regional risk score. The commodity name, category, quantity and currency fields in the invoice, packing list and contract are parsed, and the content consistency ratio is compared according to the field dimension and converted into a normalized score to obtain the document consistency score. Based on the normalized exporter rating score, product sensitivity score, regional risk score, document consistency score, and declared value and system average price, the inspection intervention index is calculated.

[0011] Preferably, the steps for obtaining the basic customs declaration document content are as follows: Based on the optimal estimated declared weight and the comprehensive data file to be processed, the customs declaration template of the corresponding batch in the comprehensive data file to be processed is called. According to the field mapping relationship of the customs declaration template, the optimal estimated declared weight value is filled into the weight field of the customs declaration template to form the initial weight-filled customs declaration. Based on the initial weight-filled customs declaration, the factory's outgoing weighbridge weight, warehouse's incoming weighbridge weight, and container loading dynamic weighing data in the pending comprehensive data archive are called one by one. The numerical differences between the weight data at each node are compared in turn, and the weighing node, weight difference, and adjustment method corresponding to the numerical difference are recorded. The data are then compiled to form the basic customs declaration content.

[0012] Preferably, the steps for obtaining the intelligent export customs declaration document combination are as follows: Based on the basic customs declaration documents and the customs declaration filled with the initial weight, the inspection intervention index is compared with the preset low-risk threshold and high-risk threshold one by one. If the inspection intervention index is less than the low-risk threshold, no additional explanation is added. If the inspection intervention index is between the low-risk threshold and the high-risk threshold, a product usage description and production flow chart are added. If the inspection intervention index is greater than or equal to the high-risk threshold, a risk self-assessment result description is added, thus forming an intelligent export customs declaration document combination.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are as follows: This invention is based on the collection of weighing data from agricultural product batches at various logistics nodes. It integrates and analyzes this weighing data with multi-dimensional information such as exporter certification levels, historical error rates, and declared value. By calculating the theoretical moisture evaporation rate, it predicts the theoretical weight and iteratively calibrates the weighing data, achieving more accurate predicted declared weight and solving the problem of large errors in export declared weight. Simultaneously, it integrates multi-dimensional risk factors such as exporter ratings, product sensitivity, regional risk, price deviation, and document consistency to dynamically generate an inspection intervention index, assessing the risk level of export declarations and improving the accuracy of risk assessment and export compliance. Furthermore, through automated risk grading logic, it selectively supplements document content according to different risk levels, specifically strengthening the credibility and completeness of customs declaration content, reducing the inspection frequency in the export customs declaration process, and improving the customs clearance efficiency of the export logistics process. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the steps of the present invention. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0016] Please see Figure 1 This invention provides a technical solution: a method for generating intelligent customs declaration documents for agricultural product exports, comprising the following steps: Collect weighing data of target agricultural product batches at the processing plant's outbound weighbridge, warehouse inbound weighbridge, and container loading stages; extract the exporter's AEO certification level, historical error rate, and declared value corresponding to the batch; and establish a comprehensive data archive to be processed. Based on the comprehensive data archive to be processed, the theoretical moisture evaporation rate is calculated. Based on the moisture evaporation rate and the weight and time of the factory's outgoing weighbridge slip, the theoretical weight of the next logistics node is predicted, and the theoretical weight prediction value between nodes is obtained. Based on the theoretical weight prediction value between nodes and the weighing data of the warehouse inbound weighbridge, the optimal estimated declared weight is generated. Based on the comprehensive data archive to be processed, the exporter's AEO certification level and historical error rate are extracted to generate an exporter rating score, the product HS code sensitivity is extracted to generate a product sensitivity score, the destination port risk coefficient is extracted to generate a regional risk score, a multi-dimensional risk set is obtained, the deviation between the declared value and the average price in the system is calculated, the consistency of invoices, packing lists and contract data is compared to obtain a document consistency score, and the document consistency score is calculated with the multi-dimensional risk set to obtain the inspection intervention index; Based on the optimal estimated declared weight and the comprehensive data archive to be processed, the optimal estimated declared weight is filled into the weight field of the customs declaration form and compiled into the basic customs declaration document content. The inspection intervention index is compared with the set low-risk threshold and high-risk threshold for judgment. Based on the judgment result, the product use description, production flow chart or risk self-assessment result is selectively added to the basic customs declaration document content to generate an intelligent export customs declaration document combination.

[0017] The steps for obtaining the comprehensive data archive to be processed are as follows: Weighing data of the target agricultural product batches are collected by weighing equipment in the processing plant, warehouse and container packing process. At the same time, the accuracy level of the weighing equipment and the current calibration status information corresponding to the weighing data are obtained from the weighing equipment label or calibration record. In addition, the ambient temperature and humidity data of each process are collected in real time by temperature and humidity sensors to obtain the initial weighing and environmental data. Based on the initial weighing and environmental data, the product type information of the corresponding batch is retrieved from the business system using the agricultural product batch number as the associated field. The AEO certification level of the exporter of the batch, the historical error rate of export declaration, and the declared value information are also queried to obtain the batch-related business information. Based on batch-related business information, the initial weighing and environmental data are integrated with the batch-related business information into a unified data archive template. The data items corresponding to each weighing step are marked and associated one by one according to the time sequence to form a comprehensive data archive to be processed.

[0018] Specifically, the system uses weighing equipment in processing plants, warehouses, and container loading stages. Utilizing industrial Ethernet or RS-485 bus interfaces and based on Modbus or OPC-UA communication protocols, it collects dynamic and static weighing data of target agricultural product batches in real time, once per second, during the weighing process. Simultaneously, the system automatically queries the equipment management database associated with the weighing equipment ID, retrieves the accuracy level (e.g., Level III) under the OIML R76 standard recorded on its label, and reads the date of the most recent calibration record. The system has a built-in calibration validity judgment rule that compares the calibration record date with the current date. If the time interval exceeds a preset 180-day calibration cycle, the current calibration status is marked as "expired"; otherwise, it is marked as "valid." Next, SHT31 temperature and humidity sensors deployed around each weighing stage, within a five-meter radius of the weighing equipment, collect ambient temperature and humidity data every minute. The system calculates the average of five consecutively collected data sets to smooth out instantaneous reading fluctuations and obtain stable environmental data for that minute. Each piece of raw weighing data in the system undergoes a validity check. This check compares the weighing reading with a preset "reasonable weight range," the upper and lower limits of which are dynamically set according to the product type and weighing process. For example, for a whole pallet of garlic leaving the processing plant, the reasonable weight range is set to 800 kg to 1200 kg. Any reading outside this range will be marked as abnormal data to be reviewed. All data that passes the check is associated with the ambient temperature and humidity data corresponding to the nearest timestamp. Finally, the data is integrated to generate a structured data record containing timestamp, weighing value, equipment accuracy, calibration status, ambient temperature, and ambient humidity, thus obtaining the initial weighing and environmental data.

[0019] Based on the agricultural product batch number contained in the initial weighing and environmental data records, this batch number is entered by the operator using a barcode scanner and bound to the weighing data during the first weighing. The system automatically uses this batch number as the primary key to initiate a Structured Query Language (SQL) request to the Enterprise Resource Planning (ERP) or Warehouse Management System (WMS) database. This request first links to the product master data table, precisely retrieving the product type information corresponding to the batch based on the batch number, such as "Grade 1 clean garlic, 5.5 cm". Subsequently, the query request links to the exporter's profile information table, extracting the AEO certification level pre-registered by the exporter for this batch, with a value such as "Advanced Certified Enterprise" or "General Certified Enterprise". Simultaneously, the system triggers an independent calculation task to obtain the exporter's historical error rate. This task queries all completed customs declaration records within the past 24 months, statistically analyzing those errors caused by discrepancies in weight, quantity, or value declarations. The system records the total number of declarations with inspection problems, then divides it by the total number of declarations made by the exporter during the same period to calculate the specific historical error rate percentage. For example, if 5 declarations had errors in the past 24 months, and the total number of declarations was 500, the historical error rate would be 1.0%. Finally, the query request is linked to the sales order or finance module to retrieve the declared value information that exactly matches the batch number, including the total amount and currency. The system also performs a data consistency verification by dividing the queried declared value by the total weight of the processing plant in the initial weight data to obtain the unit value. This unit value is then compared with the dynamic range consisting of the average historical unit value of this product type plus or minus two standard deviations. If the calculated unit value falls outside this range, an alert log is generated. Finally, the system aggregates the product type, AEO certification level, historical error rate, and declared value information obtained from the query and calculation into a dataset to obtain the batch-related business information.

[0020] Based on batch-related business information, the system calls a predefined data archive template. This template is a structured data format with fields including batch number, timestamp, process identifier, weighing data, equipment ID, equipment accuracy level, calibration status, ambient temperature, ambient humidity, product type, exporter AEO certification level, historical error rate, and declared value. Subsequently, the system populates each data item from the static batch-related business information, such as the AEO certification level being "Advanced Certified Enterprise" and the historical error rate being 1.0%, into each dynamic record in the initial weighing and environmental data, using the batch number as the association key, completing the initial data integration. Next, the system sorts all integrated data records in ascending order according to the timestamp field, ensuring that all operations and events are arranged in chronological order. On the sorted dataset, the system processes each record individually, classifying each record based on the weighing equipment ID information within it. Each data point is assigned a clear stage identifier. For example, data originating from ID "P-Scale-01" is marked as "factory exit", data originating from "W-Scale-01" is marked as "warehouse entry", and data originating from "C-Scale-01" is marked as "container loading". This achieves one-to-one marking and association of data items corresponding to each weighing stage. After association, the system performs a final logical check to check whether the timestamps of the records corresponding to each stage identifier follow the business logic order that "factory exit" is earlier than "warehouse entry" and "warehouse entry" is earlier than "container loading". If the timestamp order is found to be disordered, such as warehouse entry time being earlier than factory exit time, a "logical timing error" warning flag is placed in the batch file, and all processed data is organized into a complete data set arranged in batches and internally in chronological order, forming a comprehensive data file to be processed.

[0021] The steps to obtain the predicted theoretical weight between nodes are as follows: Based on the comprehensive data archive to be processed, the product types marked in the archive are extracted, and the environmental temperature and humidity data are obtained from the corresponding product type records. The environmental temperature and humidity data are matched and associated one by one according to the time points corresponding to the weighing data to form the association information between product types and corresponding environmental temperature and humidity. Based on the correlation information between product type and corresponding environmental temperature and humidity, the standard for water evaporation rate corresponding to agricultural product type is called. According to the combination of environmental temperature and humidity values, the theoretical water evaporation of agricultural product batches in each time period is calculated one by one. The theoretical water evaporation rate is determined by the ratio between the theoretical water evaporation and the actual weight of adjacent time periods, thus forming the theoretical water evaporation rate. Based on the theoretical moisture evaporation rate, the weight data recorded on the factory's weighbridge slip is used as the initial weight benchmark. The weighing time of the factory's weighbridge slip and the planned arrival time of the next logistics node are called to calculate the total moisture loss value corresponding to the theoretical moisture evaporation rate during the period. The total moisture loss value is then subtracted from the initial weight benchmark to obtain the theoretical weight prediction value between nodes.

[0022] Specifically, based on the comprehensive data archive to be processed, the system first parses and extracts the "product type" field marked on each record in the archive, such as "Grade 1 clean garlic, size 5.5 cm". Then, from the same archive, the system extracts the corresponding environmental temperature and humidity data for each unique weighing event timestamp. To accurately align low-frequency environmental data (e.g., once per minute) with high-frequency weighing data (e.g., once per second), the system uses a time nearest neighbor matching algorithm for each weighing data point. The system searches for timestamps in the environmental temperature and humidity dataset. And find the absolute value of the time difference. The smallest record is used, and a maximum matching tolerance window is set. This window is based on an empirical value, such as 90 seconds. If the minimum time difference exceeds this window, the environmental information of that weighing data point will be marked as missing and filled using linear interpolation of the preceding and following valid points. The linear interpolation calculation method is as follows: if... If environmental data is missing at a given location, then find the two nearest valid environmental data points before and after it. and The interpolation result is By repeating this matching or interpolation process on all weighing data points in the archive, each weighing record is assigned a corresponding ambient temperature and humidity value, forming information linking product type with corresponding ambient temperature and humidity.

[0023] Based on the correlation information between product type and corresponding environmental temperature and humidity, the system first calls upon the internally stored standard database of agricultural product moisture evaporation rates, according to product type information, such as "Grade 1 Clean Garlic". This database is derived from empirical model parameters fitted by long-term weighing experiments on various agricultural products under different environments (temperature range 5℃ to 35℃, humidity range 40% to 95%). Specifically, the system calls the calculation function of the Penman-Monteith model, iterates through all weighing data points, and sets adjacent time points (e.g., ...) and Using the ambient temperature and humidity data as a set of inputs, calculate the value for this time period. The theoretical evaporation rate is calculated by dividing the theoretical evaporation rate of the time period by the actual weight value at the beginning of that time period. By repeating this calculation over all adjacent time periods, a set of theoretical water evaporation rate data arranged in time series is formed, thus creating the theoretical water evaporation rate.

[0024] Based on the theoretical moisture evaporation rate, the system first extracts the weighbridge record marked "processing plant outgoing" from the comprehensive data archive to be processed, sets the weight data in this record (e.g., 20,500 kg) as the initial weight baseline value, and sets the corresponding weighing time (e.g., 14:00 on October 26, 2023) as the start time. Next, the system retrieves the transportation plan associated with this batch from the logistics execution system or ERP to obtain the planned arrival time of the next logistics node (e.g., warehouse) (e.g., 08:00 on October 27, 2023). The system then calls a third-party commercial meteorological service API, inputting the origin and destination geographical coordinates of the transportation route and the time period it traverses, to obtain the hourly temperature and humidity forecasts for that time period. Subsequently, the system divides the entire transportation duration (18 hours in this example) into 18 one-hour time steps. For each time step, the system uses the hourly predicted temperature and humidity data and the moisture evaporation rate model established in the previous steps, using the weight at the end of the previous time step as a baseline, to iteratively calculate the moisture loss value for the current time step. The specific calculation process is as follows: The hourly loss value equals the theoretical water evaporation rate for that hour multiplied by the [number of hours]. The predicted weight at the end of the hour is based on the initial weight baseline value of the factory. The total moisture loss value during the entire transportation period is obtained by accumulating the moisture loss values ​​calculated over all 18 time steps. For example, the total loss is calculated to be 35.8 kg. Finally, the system subtracts the calculated total moisture loss value (20500 kg - 35.8 kg = 20464.2 kg) from the initial weight baseline value to obtain the theoretical weight prediction value between nodes.

[0025] The steps to obtain the optimal estimated declared weight are as follows: Extract the theoretical weight prediction values ​​between nodes, the weighing data of the warehouse weighbridge, and the corresponding timestamp information. Pair the theoretical weighing data with the actual weighing data one by one by comparing the timestamps. Then, call up the accuracy level and calibration status of the measuring equipment corresponding to each pair of data, retrieve and standardize them into relative uncertainty values, and obtain a ternary combination set of theoretical weighing value, actual weighing value, and uncertainty value. Based on the ternary combination set of theoretical weighing value, actual weighing value, and uncertainty value, the optimal estimated declared weight is calculated using the following formula: ; in, This indicates the optimal estimated declared weight. This indicates the total number of valid weighing data points. Indicates the first The weighting factor for each data point, Indicates the first Weighing data from the weighbridges for each warehouse entry point. Indicates the first Theoretical weight prediction values ​​between nodes Indicates the first The uncertainty value of each paired data point The sensitivity coefficient for theoretical conformity. This is the uncertainty sensitivity coefficient; Based on the optimal estimated declared weight, and combined with all the weighing point numbers paired in the dataset and the list of actual weighing data participating in the fusion, the corrected weighing basis is summarized to obtain the optimal estimated declared weight set.

[0026] Specifically, the system extracts the theoretical weight prediction values ​​between nodes generated in the preceding steps. For example, it predicts the total weight upon warehouse entry to be 20464.2 kg and its corresponding planned arrival timestamp. Simultaneously, it retrieves all weighing data records from the comprehensive data archive to be processed, all of which are tagged as "warehouse entry weighbridge." Each record contains the actual weighing value and a weighing timestamp. The system sets a time matching window of ±2 hours, centered on the planned arrival timestamp, and iterates through all warehouse entry weighbridge weighing data, filtering out all records whose timestamps fall within this window. These records are then marked as associated with the theoretical prediction value. For each successfully associated actual weighing record, the system further extracts the corresponding weighing device ID from the record and queries the comprehensive data archive to be processed again based on this ID to retrieve the accuracy class text of the weighing device, such as "OIML". When the system displays "OIML Level III" and its current calibration status text, such as "Valid", it initiates a standardized conversion procedure. This procedure has a built-in two-dimensional mapping table used to quantify the two textual descriptions of accuracy level and calibration status into a unified relative uncertainty value. The specific conversion rules are as follows: First, a basic uncertainty is assigned according to the accuracy level. For example, "OIML Level III" corresponds to a basic uncertainty of 0.001, and "OIML Level III" corresponds to a basic uncertainty of 0.002. Then, a multiplicative adjustment factor is applied according to the calibration status. This factor is set as follows: if the calibration status is "Valid", the factor is 1.0; if it is "Expired", the factor is 1.5; and if it is "Faulty", the factor is 3.0. The final relative uncertainty value is the product of the basic uncertainty and the adjustment factor. For example, for a device with "OIML Level III" and a calibration status of "Valid", its relative uncertainty value is calculated as 0.001 multiplied by 1.0, which equals 0.001. By repeating this process for all successfully paired data, a ternary combination set consisting of the theoretical weighing value, the actual weighing value, and the uncertainty value is obtained.

[0027] formula: The advantage of this formula lies in its replacement of traditional subjective judgments or simple averaging; its primary impact is reflected in the denominator. This item utilizes the objective uncertainty of the equipment itself. The weights are adjusted to reduce the weight of data obtained from low-precision or outdated equipment, thereby suppressing the propagation of equipment errors. The second effect is reflected in the exponential function term. It compares the predicted values ​​of the physical model. Compared with actual measured value The consistency of the two is used to adjust the weights. When the deviation between the two is large, the weight will decrease exponentially. This effectively weakens the interference of abnormal measurement values ​​caused by sudden physical events (such as accidental spills or moisture absorption) on the final result. It can dynamically evaluate the "credibility" of each data point and give higher weights to more reliable data that is more in line with physical laws. This represents the total number of valid weighing data points, indicating the total number of times the same batch of agricultural products is independently weighed during the warehouse receiving process. For example, if a container of goods consists of 3 pallets, and each pallet is weighed separately on a weighbridge upon arrival at the warehouse, then... The value of is 3.

[0028] Indicates the first The weighting factor for the nth data point is a dimensionless numerical value ranging from 0 to 1. It quantifies the weighting factor for the nth data point. Measurement data Calculate the final optimal estimated declared weight Contribution or credibility at the time The value is defined by the second part of the formula, and it is also subject to the uncertainty of the measuring device. The combined effect of the deviation between measured and theoretically predicted values ​​results in a value close to 1. The value signifies the corresponding measurement data. The source is reliable and highly consistent with theoretical predictions, therefore it dominates in the final weight calculation; conversely, a value close to 0... The value indicates that the data point has significant uncertainty or anomalies, and its impact in the final calculation will be significantly weakened.

[0029] Indicates the first The weighing data of the first warehouse receiving weighbridge, i.e., the first... The actual weight value measured independently, in kilograms (kg), is directly derived from the ternary combination set. This data represents the raw reading output by the weighbridge equipment after weighing the goods (e.g., a single pallet). In this example, a batch of goods consisting of 3 pallets is processed, resulting in 3 independent weighing data points. By querying the ternary combination set, the actual weighing data for these three pallets are obtained as follows: kg, kg, kg.

[0030] Indicates the first The theoretical weight prediction between nodes, in kilograms (kg), is the theoretical weight that a batch of goods should have when it arrives at the warehouse, predicted based on a moisture evaporation model. This value is obtained from the calculation results of previous steps. For multiple weighings within the same batch (e.g., weighing by pallet), theoretically, the initial weight of each pallet is the same, and the temperature and humidity changes experienced should also be consistent. Therefore, the theoretical prediction value... For all Yes, it is the same; it equals the total predicted theoretical weight between nodes divided by the total number of valid weighing data points. For example, the total weight predicted by the preceding steps is 20464.2 kg, and there are a total of 3 pallets ( Then, the theoretical weight prediction for each pallet is: kg.

[0031] Indicates the first The uncertainty value of the paired data is a dimensionless relative value that quantifies the uncertainty of the first pair of data. The overall uncertainty of the weighing equipment used in this measurement is derived from the standardization conversion based on the equipment's accuracy class and calibration status in the previous step. In this example, it is assumed that all three pallets are weighed by the same well-functioning and calibrated Class III weighbridge. According to the aforementioned standardization conversion rules, its uncertainty value is 0.001. Therefore, we have .

[0032] This is the theoretical compliance sensitivity coefficient, a dimensionless positive number used to adjust the degree of influence of the deviation between the measured value and the theoretical value on the weight. The larger the value, the more sensitive the weight is to deviations; that is, even small deviations can cause the weight to drop sharply. This coefficient is set based on regression analysis of historical declaration data. Specifically, the method involves collecting data from at least 100 completed export transactions. , Given the data pairs and the final reimbursement weight, an objective function is set: minimizing the root mean square error between the historical best estimated declared weight and the reimbursement weight. A grid search method is used to iterate within the interval [10, 1000] with a step size of 10 to find the value that minimizes the objective function. Values, for example, found through testing, in Historical predictions are most accurate at this time, therefore, in this example, we set... .

[0033] The uncertainty sensitivity coefficient is a dimensionless positive number used to amplify equipment uncertainties. Regarding the impact on weights, the goal of setting this coefficient is to ensure that when... When the value reaches a level generally considered unreliable within the industry (e.g., 0.5%), its weight contribution decreases significantly. Its value is determined by conducting comparative experiments on weighing instruments of different accuracy levels, recording the deviation distribution between their measured values ​​and standard weight values, and establishing... A statistical relationship model between the variance of measurement error and the uncertainty term is established, and a threshold is set for the weight decay of 20% when the uncertainty term causes the corresponding... The value is the industry's high-risk threshold, and the inverse solution is obtained through this relationship. Values, for example, are set through experimental data analysis, when... When the value is 0.01 (1%), the weight is halved, i.e. Solving for Therefore, in this example, we set... .

[0034] Calculation process: In this example, the batch of goods consists of 3 pallets, therefore The relevant parameter values ​​are: , ; Data point 1: , , ; Data point 2: , , ; Data point 3: , , ; First, calculate the weighting factor for each data point. : for : ; ; ; for : ; ; ; for : ; ; ; Next, calculate the optimal estimated declared weight. : ; ; ; Finally, calculate the optimal estimated declared weight for the entire shipment, which is the optimal weight per pallet multiplied by the number of pallets. : Overall optimal estimated declared weight kg.

[0035] The results indicate that the optimal estimated declared weight of the entire shipment, obtained through weighted fusion calculation, is 20455.98 kg. This value comprehensively considers the minor differences among the three measurements, the reliability of the measuring equipment itself, and the degree of deviation from the physical model's prediction. Among these, the second measurement (6815.0 kg) has the largest deviation from the theoretical prediction, and its weight is the highest. It is slightly lower than the other two times, so its contribution to the final average is also reduced accordingly.

[0036] Based on the optimal estimated declared weight calculated in the previous step, for example, 20455.98 kg, the system will initiate a weighing basis summary procedure. This procedure first creates a structured data record to store complete traceability information for this weight calculation. The top of this record is the core result, namely the final determined optimal estimated declared weight of 20455.98 kg. Next, the system will query and retrieve detailed information for all data points involved in this fusion calculation, including the unique number of each weighing point, such as "WH-S01," and a list of all actual weighing data associated with that point, such as pallet 1 weighing 6820.0 kg, pallet 2 weighing 6815.0 kg, and pallet 3 weighing 6822.0 kg. These original measurements will be listed one by one. Below the original data, the system will attach the theoretical basis and process parameters used for the calculation, including the referenced inter-node theoretical weight prediction value, such as 6821.4 kg per pallet, the specific uncertainty value used in the calculation process, such as 0.001, and the dynamically adjusted weighting factor, such as... , , The system also generates a comparative analysis summary, which clearly shows the original total measured weight, i.e., the sum of all actual weighing data, which is 20457.0 kg, the theoretically predicted total weight, which is 20464.2 kg, and the final corrected optimal estimated declared weight, which is 20455.98 kg. It also calculates the difference between the correction amount and the original measured value, for example, -1.02 kg. By organizing all the above information, including the final result, the original data list, process parameters, and the comparative summary, a complete and auditable corrected weighing basis is formed. Finally, this structured data record containing all traceability information is output as a whole to obtain the set of optimal estimated declared weights.

[0037] The steps to obtain a multi-dimensional risk set are as follows: Based on the comprehensive data archive to be processed, the values ​​of the exporter's AEO certification level field and the historical error rate percentage are analyzed. According to the unified credit rating mapping table, the values ​​of the exporter's AEO certification level field and the historical error rate percentage are converted into standard credit scores and normalized to generate normalized exporter rating scores. Finally, a multi-dimensional risk set is obtained.

[0038] Specifically, based on the comprehensive data archive to be processed, the system first parses and extracts the exporter's AEO certification level field value, such as "Advanced Certified Enterprise," and the historical error rate percentage, such as 1.0%. Then, the system calls a built-in unified credit rating mapping table. This table is constructed based on the intensity of customs management measures for enterprises with different AEO certification levels and historical data analysis. It maps the textual AEO certification level to a basic credit score; for example, "Advanced Certified Enterprise" is mapped to 90 points, and "General Certified Enterprise" is mapped to 70 points. The "dishonest enterprise" is mapped to 40 points. Subsequently, the system uses historical error rates to adjust this basic credit score. The logic is that the lower the error rate, the higher the credit score, but the impact is non-linear. The system uses a correction function: when the error rate is below 0.5%, no points are deducted; when the error rate is between 0.5% and 2.0%, 1 point is deducted for every 0.1% increase in the error rate; and when the error rate exceeds 2.0%, 2 points are deducted for every 0.1% increase in the error rate. For example, for a historical error rate of 1.0%, the deduction value is (1.0-0.5) / 0.1. 1 = 5 points, therefore, the corrected standard credit score is 90 - 5 = 85 points. After obtaining the standard credit score, the system performs max-min normalization on it, converting it into a value between 0 and 1. The normalization formula is: ,in and These are the minimum and maximum scores of all possible scores in the mapping table, for example, 40 and 100 respectively. Therefore, the normalized exporter rating is divided into... Finally, the system uses this normalized exporter rating score as a core risk dimension, and stores it together with other risk dimensions to be calculated later (product sensitivity score, regional risk score, etc.) into a temporary structured data object, thus summarizing a multi-dimensional risk set.

[0039] The steps for obtaining the intervention index are as follows: Based on the normalized exporter rating score from the multi-dimensional risk set, the sensitivity level corresponding to the product HS code and the historical inspection risk level of the destination port are extracted and converted into a risk standard score in the range of 0 to 1. After normalization, these scores are recorded as product sensitivity score and regional risk score. The commodity name, category, quantity and currency fields in the invoice, packing list and contract are analyzed, and the content consistency ratio is compared according to the field dimension and converted into a normalized score to obtain the document consistency score. Based on normalized exporter rating scores, product sensitivity scores, regional risk scores, document consistency scores, and declared cargo value and system average price, the inspection intervention index is calculated using the following formula: ; in, This indicates the need to check the intervention index. The normalized exporter rating score, For the normalized product sensitivity score, For the normalized regional risk score, For the normalized document consistency score, This is the declared value of the current batch. The average price of similar products recorded. These are adjustment coefficients for product sensitivity and regional risk, respectively. The price deviation sensitivity coefficient, This is the credit adjustment coefficient. This is the penalty index for document consistency.

[0040] Specifically, based on the normalized exporter rating score already calculated from the multi-dimensional risk set, the system continues to extract HS codes related to the current batch of products from the pending comprehensive data archive, such as "07032010," and destination port codes, such as "USLAX." The system first queries an internally maintained product sensitivity level database. This database, maintained by a team of experts with at least 10 years of customs clearance experience, classifies all common HS codes into three levels: "high," "medium," and "low," based on factors such as customs supervision priorities, domestic and international policies and regulations, product value, and the presence of intellectual property risks. For example, "07032010" (garlic) is marked as a "medium" sensitivity level. The system converts these textual levels into risk standard scores ranging from 0 to 1, with the conversion rule being: "high" corresponds to 0.8, "medium" to 0.5, and "low" to 0.2. Therefore, the current product sensitivity score is 0.5. Next, the system queries another destination port historical inspection risk database, which compiles past... Within 36 months, the inspection rate is the ratio of the number of shipments inspected to the total number of shipments exported to each port. Based on this inspection rate, the port risk level is also divided into three levels: "high," "medium," and "low." For example, historical data shows that the average inspection rate of the port "USLAX" is 8%, which is relatively high among all ports, thus it is rated as a "high" risk level. The system converts this into a risk standard score of 0.8, resulting in a regional risk score. Subsequently, the system uses Optical Character Recognition (OCR) technology to parse electronic invoices, packing lists, and contract documents, extracting key fields including commodity name, category, quantity, and currency. It then performs pairwise comparisons of the same field content in these three documents, calculating the proportion of completely matching fields out of the total number of compared fields, to obtain the document consistency ratio. For example, if three out of four fields are completely matching, the consistency ratio is 75%. This ratio is directly used as the normalized document consistency score, which is 0.75, resulting in the document consistency score.

[0041] formula: The advantage of the formula lies in the fact that the numerator of the formula... and By employing addition and multiplication structures, the three core risk sources—product, region, and price—are integrated, reflecting the cumulative effect of risks. The denominator... This introduces exporter credit as a key risk mitigation factor. Better credit results in a larger denominator and a lower overall risk index, reflecting the customs' facilitation management approach for trustworthy enterprises. The final index penalty item... An exponential penalty is imposed on discrepancies in documentation, highlighting the extreme importance of documentation accuracy, because even if other risks are low, obvious errors in documentation can easily trigger audits. The normalized exporter rating score is a dimensionless value in the range [0, 1] that quantifies the exporter's creditworthiness. This value is calculated by previous steps based on the AEO certification level and historical error rate. A high score represents high creditworthiness and is a key factor in reducing inspection risk. In this example, this value is extracted from the multi-dimensional risk set and is 0.75.

[0042] The normalized product sensitivity score is a dimensionless value in the range [0, 1] that reflects the inherent risk level of the exported product. This value is derived from the previous steps by querying the internal database based on the product's HS code and converting it. A high score indicates that the product belongs to a sensitive category that is under the key supervision of customs. For example, in this example, this value is extracted from the multi-dimensional risk set, and the sensitivity score of garlic is rated as 0.5.

[0043] The normalized regional risk score is a dimensionless value in the range [0, 1] that represents the historical inspection risk of the destination port. This value is derived from the previous steps by querying the historical inspection rate database based on the destination port code. A high score indicates that the destination is a high-risk region or port. For example, in this example, this value is extracted from the multi-dimensional risk set, and the risk score of the destination port USLAX is rated as 0.8.

[0044] The normalized document consistency score is a dimensionless value in the range [0, 1] that measures the degree of consistency of key document information such as invoices, packing lists, and contracts. This value is obtained by comparing the matching rate of key fields of the documents in the previous steps. 1 indicates complete consistency. The lower the score, the greater the document difference and the higher the risk. For example, in this example, this value is extracted from the multi-dimensional risk set and is 0.75.

[0045] This is the declared value of the current batch, in US dollars (USD). This is the total value of the goods filled in on this export declaration form. This data is extracted directly from the pending comprehensive data archive. For example, the declared value of garlic in this batch is US$35,000.

[0046] The average price of similar products recorded is expressed in US dollars (USD). This is the average delivered value of exported goods within a similar time period (e.g., the past three months) for products with the same HS code as the currently declared product, as recorded in the system database. This average price is calculated statistically from historical declaration records of similar products in the database. The calculation method is to divide the total value by the total weight and then multiply by the weight of the current batch. For example, if the historical average price of similar garlic recorded by the system is US$1.70 / kg, and the current batch weight is 20455.98 kg, then the system average price is... Dollar.

[0047] These are adjustment coefficients for product sensitivity and regional risk, respectively. These two dimensionless coefficients are used to adjust the weight of product and regional risks in the overall risk calculation. Their values ​​are set based on periodic risk analysis reports and expert assessments. For example, when customs issues a notice to strengthen supervision of a certain type of commodity (such as agricultural products) or goods destined for a certain region (such as North America), the coefficients are adjusted accordingly. or The value is typically set to a baseline of 1.0, and can fluctuate between 0.5 and 2.0 depending on the risk warning level. In this example, it is set to... , .

[0048] The price deviation sensitivity coefficient is a dimensionless coefficient used to adjust the impact of the deviation between the declared value and the average market price on the risk index. Its value is determined through statistical analysis of price deviations in historical inspection cases. The goal is to ensure that when the price deviation exceeds a significant threshold (e.g., 20%), the risk index rises significantly. It is typically set between 2.0 and 5.0. For commodities with large price fluctuations, this coefficient can be appropriately lowered. In this example, it is set to... .

[0049] The credit adjustment coefficient is a dimensionless coefficient used to adjust the risk reduction effect of exporter credit. High-credit companies should enjoy a more significant risk reduction. This coefficient is set to match the preferential measures of the Customs AEO system. It is obtained through regression analysis of the differences in inspection rates between a large number of advanced certified companies and general certified companies. Its value is typically between 1.0 and 3.0. In this example, it is set as follows: .

[0050] The document consistency penalty index is a dimensionless coefficient used to amplify the risks associated with document inconsistencies. Since document inconsistencies are a direct trigger for customs inspections, this coefficient is typically set relatively high to reflect its "veto power." Analysis of cases where inspections are caused by document discrepancies reveals that even with low other risks, document errors can lead to a sharp increase in inspection rates. Therefore, setting a high penalty index is reasonable, and its value is usually between 3.0 and 8.0. In this example, we set... .

[0051] Calculation process: Substitute the above parameter values ​​into the formula: , , , , , , , , , , .

[0052] The calculation process is as follows: ; ; ; ; ; ; The results indicate that the inspection intervention index for this batch of goods is 3.4156. This value represents a relatively high level of overall risk, mainly due to the combined effect of a high regional risk score and an exponential penalty for document consistency. Although the exporter has good credit and the price deviation is not large, these positive factors are not enough to completely offset the impact of the risk items. This index will be used to compare with the preset risk threshold to trigger corresponding risk response measures.

[0053] The steps to obtain the basic customs declaration document information are as follows: Based on the optimal estimated declared weight and the pending comprehensive data archive, the corresponding batch of customs declaration templates in the pending comprehensive data archive is called. According to the field mapping relationship of the customs declaration template, the optimal estimated declared weight value is filled into the weight field of the customs declaration template to form the initial weight-filled customs declaration. Based on the initial weight-filled customs declaration, the system sequentially calls up the factory's outgoing weighbridge weight, warehouse's incoming weighbridge weight, and container loading dynamic weighing data from the comprehensive data archive to be processed. It then compares the numerical differences between the weight data at each node, records the weighing node, weight difference, and adjustment method corresponding to the numerical differences, and compiles them into the basic customs declaration content.

[0054] Specifically, based on the optimal estimated declared weight of 20455.98 kg calculated in the previous steps, and the integrated data archive to be processed, the system first automatically loads a standard electronic customs declaration template from the system's built-in template library according to the batch number in the archive. This template is an XML or JSON file that pre-defines the field structure of all required fields in the customs declaration. Next, the system queries a pre-configured field mapping table, which explicitly defines the correspondence between data items in the integrated data archive to be processed and fields in the customs declaration template. For example, the "Exporter Name" field in the archive corresponds to the "Shipping Unit" field in the customs declaration template. The "HS Code" field in the file corresponds to the "Commodity Number" field in the template. Based on this mapping relationship, the system automatically fills all relevant information in the file, except for weight, such as exporter information, product information, contract number, port of destination, etc., into the corresponding positions in the customs declaration template. Subsequently, the system fills the core optimal estimated declared weight value, i.e., 20455.98, into the weight fields named "Gross Weight (kg)" and "Net Weight (kg)" in the customs declaration template. Since agricultural products usually do not distinguish between gross and net weight, the same value is filled in for both fields. After the automatic filling of all fields is completed, an initial weight-filled customs declaration is formed with complete data but not yet finalized.

[0055] Based on the final declared weight of 20455.98 kg included in the initial weight-filled customs declaration, the system initiates a weight discrepancy tracing and explanation generation procedure. This procedure first retrieves all data records marked with weighing stages from the comprehensive data archive to be processed, sequentially according to time. This includes the sum of "factory exit weighbridge weight" (e.g., 20500 kg), "warehouse entry weighbridge weight" (e.g., 20457.0 kg), and the sum of "container loading dynamic weighing data" (e.g., 20455.0 kg). Using the factory exit weighbridge weight as a benchmark, the system calculates the difference between the weight of subsequent nodes and the weight of the previous node. For example, the difference between the warehouse entry weighbridge weight and the factory exit weighbridge weight is 20457.0 - 20500 = -43.0 kg, and the difference between the container loading dynamic weighing data and the warehouse entry weighbridge weight is 20455.0 - 20457.0 = For each calculated weight difference of -2.0 kg, the system records the starting and ending weighing nodes, the specific weight difference, and automatically associates a preset adjustment method description. This description is based on the physical process that produces the difference. For example, for a difference of -43.0 kg, the system will label the adjustment method as "fusion calculation based on the prediction of travel loss and measurement uncertainty of temperature and humidity model". For a difference of -2.0 kg, it will label it as "systematic deviation correction between different measuring devices". Finally, the system packages these records together, including the customs declaration form itself with the initial weight, and a detailed explanatory document listing the weight at each node, the weight difference, and the scientific explanation and adjustment method for the difference, to form the basic customs declaration document content.

[0056] The steps to obtain the intelligent export customs declaration document combination are as follows: Based on the basic customs declaration documents and the initial weight-filled customs declaration, the inspection intervention index is compared with the preset low-risk threshold and high-risk threshold one by one. If the inspection intervention index is less than the low-risk threshold, no additional explanation is added. If the inspection intervention index is between the low-risk threshold and the high-risk threshold, a product usage description and production flow chart are added. If the inspection intervention index is greater than or equal to the high-risk threshold, a risk self-assessment result description is added, thus forming an intelligent export customs declaration document combination.

[0057] Specifically, based on the basic customs declaration documents and the initial weight-filled customs declarations included therein, the system extracts the inspection intervention index calculated in the previous steps, for example, 3.4156, and compares it with two preset risk thresholds. These two thresholds are determined based on statistical analysis of over 5,000 historical declarations and the experience of customs brokers. The specific setting method is to sort all historical inspection intervention indices, take the 30th percentile value as the low-risk threshold (e.g., 1.5), and take the 80th percentile value as the high-risk threshold (e.g., 3.0). These two thresholds are dynamically updated quarterly based on the latest customs inspection data. The system performs a comparison judgment, first comparing the inspection intervention index 3.4156 with the low-risk threshold 1.5, because 3.4156 is significantly higher. Since the value is 1.5, it does not meet the condition of being less than the low-risk threshold. Next, 3.4156 is compared with the high-risk threshold of 3.0. Because 3.4156 is greater than 3.0, it meets the condition of being greater than or equal to the high-risk threshold. Based on this judgment, the system triggers a high-risk response strategy, automatically generating a risk self-assessment result explanation. This explanation document calls a template from the company's standard document library and dynamically fills in the content, including a detailed explanation of the calculation process for the declared weight, a comparison of all original weighbridge data and theoretical weight predictions between nodes, an explanation of the rationality of the price setting, and supporting materials such as the manufacturer's qualification certificate and product quality inspection report. Finally, the system attaches this generated risk self-assessment result explanation as an appendix, electronically binding it with the basic customs declaration documents to form an intelligent export customs declaration document combination.

[0058] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. An intelligent declaration document generation method for agricultural product export, characterized in that, The method comprises the following steps: Collecting the weighing data of the target agricultural product batch at the processing plant delivery scale, warehouse ground scale and container loading link, extracting the AEO certification level of the exporter, historical error rate and declared value corresponding to the batch, and establishing a comprehensive data file to be processed; Based on the comprehensive data file to be processed, the theoretical moisture evaporation rate is calculated, the theoretical weight of the next logistics node is predicted according to the moisture evaporation rate and the weight and time point of the processing plant delivery scale, the theoretical weight prediction value between nodes is obtained, and the optimal estimated declared weight is generated based on the theoretical weight prediction value between nodes and the weighing data of the warehouse ground scale; Based on the comprehensive data file to be processed, the AEO certification level and historical error rate of the exporter are extracted to generate the exporter rating score, the product HS code sensitivity is extracted to generate the product sensitivity score, the destination port risk coefficient is extracted to generate the regional risk score, the multi-dimensional risk set is obtained, and the deviation value of the declared value and the average price in the system is calculated. The consistency of the invoice, packing list and contract data is compared to obtain the document consistency score, and the document consistency score and the multi-dimensional risk set are operated to obtain the inspection intervention index; Based on the optimal estimated declared weight and the comprehensive data file to be processed, the optimal estimated declared weight is filled into the weight field of the customs declaration form, and the basic customs declaration form content is compiled. The inspection intervention index is compared with the set low risk threshold and high risk threshold to determine whether to add the product use description, production process diagram or risk self-assessment result to the basic customs declaration form content according to the determination result, and an intelligent export customs declaration form combination is generated.

2. The method for generating an intelligent declaration document for agricultural product export according to claim 1, characterized in that, The acquisition step of the comprehensive data file to be processed is: The weighing data of the target agricultural product batch is collected through the weighing equipment at the processing plant, warehouse and container loading link, the weighing equipment precision grade and current calibration state information corresponding to the weighing data are obtained from the weighing equipment label or calibration record, and the environmental temperature and humidity data in the environment at each link are collected in real time through the temperature and humidity sensor to obtain the initial weighing and environmental data; Based on the initial weighing and environmental data, the product type information of the corresponding batch is retrieved from the business system with the agricultural product batch number as the associated field, and the AEO certification level of the exporter of the batch, the historical error rate of the export declaration and the declared value information are queried to obtain the batch associated business information; Based on the batch associated business information, the initial weighing and environmental data and the batch associated business information are integrated into a unified data file template, each weighing link corresponding data item is marked and associated in sequence according to time, and a comprehensive data file to be processed is formed.

3. The method for generating an intelligent declaration document for agricultural product export according to claim 1, characterized in that, The acquisition step of the theoretical weight prediction value between nodes is: Based on the comprehensive data file to be processed, the product type marked in the file is extracted, and the environmental temperature and humidity data are obtained from the corresponding product type record. The environmental temperature and humidity data are matched and associated one by one according to the time point corresponding to the weighing data to form the product type and corresponding environmental temperature and humidity associated information; Based on the product category and the corresponding environmental temperature and humidity correlation information, the water evaporation rate standard corresponding to the agricultural product category is called, and the theoretical water evaporation amount of the agricultural product batch in each time period is calculated according to the numerical combination of the environmental temperature and humidity. The ratio between the theoretical water evaporation amount and the actual weight of the adjacent time period is determined to determine the theoretical water evaporation rate, and the theoretical water evaporation rate is formed. Based on the theoretical water evaporation rate, the weight data recorded in the factory shipping scale record is taken as the initial weight reference value, the weighing time point of the factory shipping scale and the planned arrival time point of the next logistics node are called, the total water loss value corresponding to the theoretical water evaporation rate during the calculation period is calculated, and the total water loss value is deducted from the initial weight reference value to obtain the theoretical weight prediction value between nodes.

4. The method for generating an intelligent declaration document for agricultural product export according to claim 1, characterized in that, The acquisition step of the optimal estimated declared weight is: The node theoretical weight prediction value, the weighing data of the warehouse entry ground scale and the corresponding timestamp information are extracted, the theoretical weighing data and the actual weighing data are paired one by one through timestamp comparison, and the accuracy level and calibration state of the measurement equipment corresponding to each group of paired data are called to retrieve and standardize the relative uncertainty value, thereby obtaining a three-element combination set of theoretical weighing value, actual weighing value and uncertainty value; Based on the three-element combination set of theoretical weighing value, actual weighing value and uncertainty value, the optimal estimated declared weight is calculated; According to the optimal estimated declared weight, combined with all the paired weighing point numbers in the data set and the actual weighing data involved in the fusion, the corrected weighing basis is summarized to obtain the optimal estimated declared weight set.

5. The method for generating an intelligent declaration document for agricultural product export according to claim 1, characterized in that, The acquisition step of the multi-dimensional risk set is: Based on the to-be-processed comprehensive data file, the exporter AEO certification level field value and the historical error rate percentage are parsed, the exporter AEO certification level field value and the historical error rate percentage are converted into standard credit scores according to a unified credit rating mapping table and normalized to generate normalized exporter rating scores, and the multi-dimensional risk set is obtained by summarizing.

6. The method for generating an export declaration for agricultural products according to claim 1, wherein, The acquisition step of the inspection intervention index is: According to the normalized exporter rating score in the multi-dimensional risk set, the product HS code corresponding sensitivity level and the historical inspection risk level of the destination port are extracted, converted into risk standard scores in the interval of 0 to 1, normalized respectively as product sensitivity score and regional risk score, and the commodity name, product category, quantity and currency field in the invoice, packing list and contract are parsed, the content consistency ratio is compared according to the field dimension, and the normalized score value is obtained to obtain the single certificate consistency score; Based on the normalized exporter rating score, product sensitivity score, regional risk score, single certificate consistency score, declared value and system average price, the inspection intervention index is calculated.

7. The method for generating an intelligent declaration document for agricultural product export according to claim 1, characterized in that, The acquisition step of the basic customs declaration document content is: Based on the optimal estimated declared weight and the to-be-processed comprehensive data file, the customs declaration form template corresponding to the batch in the to-be-processed comprehensive data file is called, the optimal estimated declared weight value is filled into the weight field in the customs declaration form template according to the field mapping relationship of the customs declaration form template, and the initial weight filled customs declaration form is formed. Based on the initial weight filled customs declaration, the factory shipping scale weight, the warehouse storage scale weight and the container loading dynamic weighing data in the to-be-processed comprehensive data file are called one by one, the numerical differences between the weight data of each node are compared in turn, the weighing nodes corresponding to the numerical differences, the weight differences and the adjustment methods are recorded, and the basic customs declaration content is assembled.

8. The method for generating an export declaration for agricultural products according to claim 1, wherein, The obtaining step of the intelligent export customs declaration document combination is: Based on the basic customs declaration content and the initial weight filled customs declaration, the inspection intervention index is compared with the preset low risk threshold and high risk threshold respectively, if the inspection intervention index is less than the low risk threshold, no additional explanation content is added, if the inspection intervention index is between the low risk threshold and the high risk threshold, the product use explanation and the production flow chart are added, if the inspection intervention index is greater than or equal to the high risk threshold, the risk self-evaluation result explanation is added, and the intelligent export customs declaration document combination is formed.

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