An AI-based intelligent customs clearance method and system for digital ports

Abstract

The present invention provides a digital port intelligent customs clearance method and system based on artificial intelligence. The method includes collecting multi-source data, generating a customs declaration data stream through standardized processing by a data base; predicting risks using federated learning and generating an inspection plan through reinforcement learning; verifying the consistency of documents in combination with blockchain, and verifying the document logic through OCR and a knowledge graph; dynamically adjusting the cargo distribution strategy, and simulating emergency responses through digital twins, and finally generating a full-process visual supervision view. The present invention can improve the customs clearance efficiency, risk prevention and control capabilities and emergency response levels.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent ports and customs supervision. More specifically, the present invention relates to a digital port intelligent customs method and system based on artificial intelligence. Background Art

[0002] In traditional port customs management, the customs department mainly relies on manual review of customs clearance documents, on-site inspection of goods, and manual scheduling of supervision site resources to complete customs operations. This mode is not only inefficient and vulnerable to human factors, but also difficult to achieve accurate risk identification and efficient resource allocation in the face of a large number of goods and complex customs clearance processes. With the rapid development of international trade, the port business volume has been continuously increasing, and the traditional customs management mode has been difficult to meet the requirements of efficient customs clearance and accurate supervision. In recent years, with the development of information technology, some ports have begun to try to introduce information systems to assist customs management. For example, the electronic declaration system is used to achieve the electronic submission of documents, and the video monitoring system is used to remotely monitor the supervision site. However, most of these systems operate in isolation, lacking data integration and sharing, and unable to form a complete supervision chain. For example, although the electronic declaration system improves the efficiency of document submission, during the document review process, it is still necessary to manually check the consistency between the customs declaration form and the logistics documents, which is prone to errors; although the video monitoring system can view the situation of the supervision site in real time, it cannot automatically identify and warn of abnormal events and cannot be effectively linked with business links such as inspection and control.

[0003] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: First, the comprehensiveness and real-time nature of data collection are insufficient, unable to meet the requirements of accurate supervision; second, there is a lack of effective risk prediction and intelligent decision-making mechanisms, making it difficult to dynamically optimize complex and changing port operations; third, the data sharing and collaboration capabilities between systems are weak, resulting in an incomplete supervision chain and unable to achieve full-process visualization and efficient emergency response. Summary of the Invention

[0004] The present invention provides a digital port intelligent customs method and system based on artificial intelligence.

[0005] In the first aspect of the present invention, a digital port intelligent customs method based on artificial intelligence is provided, including:

[0006] S1. Real-time collect multi-source heterogeneous data through the logistics public information platform, where the multi-source heterogeneous data includes electronic scanned copies of customs clearance documents, real-time container positioning data, supervision site environment monitoring data, and checkpoint passing records;

[0007] S2. Standardize multi-source heterogeneous data based on the intelligent port data base, generate structured customs declaration data streams and distribute them to business systems; construct a risk prediction model using the federated learning framework to analyze the correlation characteristics of the compliance of goods HS codes and abnormal events in supervision sites;

[0008] S3. Based on the real-time operating status of the supervision site management system, dynamically generate inspection and control plans through reinforcement learning algorithms; call blockchain smart contracts to verify the consistency of customs declaration forms and logistics documents;

[0009] S4. Use the OCR engine to parse the content of attached documents, and verify the logical contradiction items of the documents in combination with the knowledge graph database; dynamically adjust the goods distribution strategy according to the real-time load data of the supervision site management system;

[0010] S5. Simulate emergencies in the supervision site through digital twin technology to generate emergency response plans; generate a full-process visual supervision view on the intelligent port data base.

[0011] Further, in S1:

[0012] The supervision site environmental monitoring data includes temperature and humidity, smoke concentration, and vibration sensing data;

[0013] When the residence time of the container exceeds the preset residence time threshold, trigger a detention warning and record it in the abnormal event library.

[0014] Further, the construction of the intelligent port data base in S2 includes:

[0015] Deploy a data cleaning engine using a distributed architecture to eliminate moiré patterns and scanning distortions in document images;

[0016] Align RFID positioning data and video stream data at the millisecond level through a spatio-temporal fusion algorithm;

[0017] Establish a data lineage tracking mechanism to record the data transfer paths between business subsystems.

[0018] Further, the state space of the reinforcement learning algorithm in S3 is defined as:

[0019] Warehouse utilization rate:

[0020]

[0021] Where is the number of occupied storage locations, is the total number of storage locations in the supervision site;

[0022] Equipment health:

[0023]

[0024] Among them, is the historical failure duration of the device, is the cumulative operation duration of the device;

[0025] Channel waiting queue:

[0026]

[0027] Among them, is the waiting duration of goods in the i-th channel, is the preset tolerance threshold, is the channel priority weight coefficient.

[0028] Furthermore, the said S4 includes:

[0029] Optimizing the storage location allocation through an integer programming model, and the objective function is the weighted sum of minimizing the warehousing cost and maximizing the access efficiency;

[0030] When the passing flow at the checkpoint exceeds the set throughput threshold, automatically enable the standby inspection channel and supplement the AGV handling equipment.

[0031] Furthermore, the said S5 includes the following sub-steps:

[0032] S51. Construct a three-dimensional digital model of the supervision site, and import the real-time goods distribution, equipment status and environmental parameters;

[0033] S52. Inject the preset types of unexpected events, including equipment failures, goods detention and abnormal customs clearance behaviors;

[0034] S53. Evaluate the impact of the events on the customs clearance efficiency based on the Monte Carlo simulation, and generate an emergency instruction set with priority ranking;

[0035] S54. Synchronize the emergency instructions to the supervision site management system and the logistics public information platform through the data bus.

[0036] Furthermore, the preset types of unexpected events include stacker downtime, network delay exceeding the limit and temperature control system failure;

[0037] The emergency instruction set includes the emergency allocation plan for storage locations, the instruction to enable standby equipment and the customs clearance process downgrading strategy.

[0038] Furthermore, the said S5 includes the following sub-steps:

[0039] S51. Integrate the customs declaration data, site operation data and risk interception records to construct a multi-dimensional analysis data set;

[0040] S52. Generate a three-dimensional heat map of the supervision site, and dynamically mark the areas where the goods density exceeds the limit and the equipment alarm points;

[0041] S53. Display the full - link tracking trajectory of goods from declaration, inspection to release based on spatio - temporal indexing technology;

[0042] S54. Output an intelligent analysis report containing key performance indicators (KPIs) and synchronize it to the customs supervision platform.

[0043] Furthermore, the key performance indicators include:

[0044] Compliance rate of customs clearance timeliness:

[0045]

[0046] wherein, is the number of goods released on time, is the total number of declared goods;

[0047] Accuracy rate of risk interception:

[0048]

[0049] wherein, is the number of high - risk goods correctly intercepted, is the total number of goods intercepted by the system.

[0050] In the second aspect of the present invention, a digital port intelligent customs system based on artificial intelligence is provided, including:

[0051] A multi - source data acquisition module, which is used to collect multi - source heterogeneous data in real time through a logistics public information platform, including electronic scanned copies of customs clearance documents, real - time container positioning data, regulatory site environmental monitoring data, and checkpoint access records;

[0052] A data pre - processing module, which is used to perform standardized processing on multi - source heterogeneous data based on the intelligent port data base, generate a structured customs declaration data stream and distribute it to the business system;

[0053] A risk prediction module, which is used to construct a risk prediction model using a federated learning framework and analyze the correlation characteristics of the compliance of goods HS codes and abnormal events in regulatory sites;

[0054] A strengthened control decision - making module, which is used to dynamically generate an inspection and control plan based on the real - time operation status of the regulatory site management system through a reinforcement learning algorithm;

[0055] A consistency verification module, which is used to call a blockchain smart contract to verify the consistency between the customs declaration form and the logistics documents;

[0056] A multi - modal document parsing module, which is used to parse the content of attached documents using an OCR engine and verify the logical contradiction items of the documents in combination with a knowledge graph database;

[0057] A resource scheduling module, which is used to dynamically adjust the goods distribution strategy according to the real-time load data of the supervision site management system;

[0058] A digital twin module, which is used to simulate emergencies in the supervision site through digital twin technology and generate emergency response plans;

[0059] A visual supervision module, which is used to generate a full-process visual supervision view on the intelligent port data base.

[0060] The above embodiments of the present invention have at least the following beneficial effects: Through the real-time collection and standardized processing of multi-source heterogeneous data, combined with the risk prediction model constructed by the federated learning framework, the present invention can effectively analyze the correlation characteristics of the compliance of goods HS codes and abnormal events in the supervision site, so as to achieve accurate risk identification and early warning. At the same time, using the reinforcement learning algorithm to dynamically generate inspection and control plans, it can flexibly adjust the inspection strategy according to the real-time operation status of the supervision site, optimize the allocation of inspection resources, and improve the inspection efficiency and accuracy. In addition, by verifying the consistency of the customs declaration form and the logistics documents through blockchain smart contracts, and using the OCR engine combined with the knowledge graph database to verify the logical contradiction items of the documents, the automation level and accuracy of document review can be further improved, and the workload and error rate of manual review can be reduced.

[0061] The present invention can also dynamically adjust the goods distribution strategy according to the real-time load data of the supervision site, optimize the goods location allocation through the integer programming model, and achieve the minimization of warehousing costs and the maximization of storage and retrieval efficiency. At the same time, by simulating emergencies in the supervision site with the help of digital twin technology and generating emergency response plans, it can effectively improve the emergency handling ability of the port and ensure the stable operation of the customs clearance process. Finally, a full-process visual supervision view is generated on the intelligent port data base, integrating customs declaration data, site operation data and risk interception records, constructing a multi-dimensional analysis data set, realizing the full-link tracking of goods from declaration, inspection to release, providing scientific and intuitive decision-making support for customs supervision, and improving the overall efficiency and intelligent level of port customs affairs management. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] By referring to the accompanying drawings and reading the following detailed description, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become easily understood. In the drawings, several embodiments of the present invention are shown in an exemplary rather than restrictive manner, wherein:

[0063] Figure 1 It is a schematic flowchart of a digital port intelligent customs method based on artificial intelligence provided by an embodiment of the present invention;

[0064] Figure 2Schematic diagram of the structure of the intelligent customs clearance system for digital ports based on artificial intelligence provided by an embodiment of the present invention. Detailed implementation manners

[0065] The principles and spirit of the present invention will be described below with reference to several exemplary implementation manners. It should be understood that these implementation manners are provided only to enable those skilled in the art to better understand and then implement the present invention, and do not limit the scope of the present invention in any way. On the contrary, these implementation manners are provided to make the present invention more thorough and complete, and to be able to fully convey the scope of the present invention to those skilled in the art.

[0066] Those skilled in the art know that the implementation manners of the present invention can be realized as a system, a device, an equipment, a method or a computer program product. Therefore, the present invention can be specifically realized in the following forms, namely: completely hardware, completely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0067] It should be noted that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction and does not have any limiting meaning.

[0068] Next, refer to Figure 1 , Figure 1 Schematic diagram of the process of the intelligent customs clearance method for digital ports based on artificial intelligence provided by an embodiment of the present invention. As Figure 1 shown, an intelligent customs clearance method 100 for digital ports based on artificial intelligence includes:

[0069] S1. Real-time collect multi-source heterogeneous data through the logistics public information platform, where the multi-source heterogeneous data includes electronic scanned copies of customs clearance documents, real-time container positioning data, regulatory site environment monitoring data, and checkpoint access records;

[0070] S2. Perform standardized processing on the multi-source heterogeneous data based on the intelligent port data base, generate structured customs declaration data streams and distribute them to the business systems; build a risk prediction model using the federated learning framework to analyze the correlation characteristics of the compliance of the HS codes of goods and abnormal events in the regulatory sites;

[0071] S3. Dynamically generate inspection and control plans through the reinforcement learning algorithm based on the real-time operating status of the regulatory site management system; call the blockchain smart contract to verify the consistency between the customs declaration form and the logistics documents;

[0072] S4. Use the OCR engine to parse the content of the attached documents, and verify the logical contradiction items in combination with the knowledge graph database; dynamically adjust the goods distribution strategy according to the real-time load data of the regulatory site management system;

[0073] S5. Simulate emergencies in supervised places through digital twin technology to generate emergency response plans; generate a full-process visual supervision view on the data base of the intelligent port.

[0074] It should be noted that this method integrates the electronic scanned copies of customs clearance documents (including digital copies of paper documents such as customs declarations and bills of lading), real-time container positioning data (location information of transportation tools obtained through GPS / Beidou positioning devices), environmental monitoring data of supervised places (physical environment parameters collected by Internet of Things devices such as temperature and humidity sensors and smoke detectors), and access records at checkpoints (including customs clearance gate data such as license plate recognition and cargo types) through the logistics public information platform. The data base of the intelligent port uses a distributed storage architecture to clean and standardize multi-source data, eliminate image distortion, and establish a data lineage tracking mechanism. Under the premise of protecting enterprise privacy, the federated learning framework collaboratively trains a risk prediction model through cross-institutional data, and focuses on identifying the correlation features between abnormal HS code (customs commodity classification code) declarations and equipment failures in supervised places.

[0075] Specifically, the logistics public information platform collects data through edge computing gateways deployed at nodes such as ports and free trade zones. Among them, the sampling frequency of container positioning data is set to 1 time per second, and the positioning accuracy is controlled within ±0.5 meters. The temperature and humidity sensors in the environmental monitoring data use the RS485 bus protocol to transmit data, with a measurement range covering -20°C to 60°C and an accuracy of ±0.5°C. The standardization process of the data base of the intelligent port includes an image preprocessing sub-module, which uses a de-moire algorithm based on deep learning (using a U-Net network structure) and a document correction algorithm (applying the Hough transform to detect edges). The participants in the federated learning framework include the data center of the General Administration of Customs, the port operation system, and logistics enterprise nodes, and the Paillier homomorphic encryption technology is used to ensure the security of gradient parameter transmission.

[0076] More specifically, in the data collection stage, when the container residence time exceeds the preset threshold (such as staying in the yard for more than 72 hours), the system automatically triggers an alarm and generates a detention event record, and this threshold can be dynamically adjusted according to different cargo types (such as setting it to 24 hours for cold chain goods). During the image preprocessing process, an adaptive binarization algorithm (OTSU algorithm) is used for the scanned documents to improve the OCR recognition accuracy. When training the federated learning model, each participant locally deploys a ResNet-34 network to extract the HS code declaration document features, and the central server aggregates the gradient update amounts output by each node. The model convergence criterion is set to that the change rate of the training loss value is less than 0.1% for 3 consecutive rounds of training.

[0077] The state space constructed by the reinforcement learning algorithm includes the warehouse utilization rate index (the percentage of the current occupied storage locations to the total number of storage locations), the equipment health index (the complement of the historical failure duration of the equipment to the total operation duration), and the channel waiting queue index (the weighted difference between the waiting duration of goods at each inspection channel and the preset threshold). The blockchain smart contract is deployed on the Hyperledger Fabric consortium blockchain, and the consistency verification is achieved by comparing the hash value of the customs declaration form (generated using the SHA-256 algorithm) with the hash value of the electronic deposit certificate of the logistics documents. The digital twin system constructs a three-dimensional visual simulation model by importing real-time goods distribution data (including attributes such as storage location coordinates and goods weight) and physical environment parameters (temperature field, equipment vibration spectrum, etc.).

[0078] Specifically, in the calculation of the warehouse utilization rate, the total number of storage locations is dynamically updated according to the physical layout of the supervision site, and the denominator value is automatically expanded when new shelves are added. The statistics of the failure duration in the equipment health index includes planned maintenance time, but a weight coefficient of 2 is assigned to sudden failures. The priority weight coefficient α_i in the channel waiting queue formula is set differently according to the channel type: 1.2 for the manual inspection channel, 1.0 for the CT machine inspection channel, and 0.8 for the automatic sorting channel. The blockchain node deployment adopts a multi-level architecture, with the customs supervision node as the Orderer node, and the port operator and logistics enterprises as Peer nodes. The consensus mechanism uses the Kafka sorting service. The goods distribution data in the digital twin model is updated in real time through RFID readers, and the position refresh frequency is 1 time per minute.

[0079] Preferably, in the design of the state space of the reinforcement learning, when the warehouse utilization rate exceeds 85%, the state space expansion mechanism is automatically triggered to add the shelf load-bearing distribution dimension parameter. When the blockchain smart contract is executed, a warning mechanism for the double-color ball is generated for the abnormal situation of the failed hash value comparison (the red ball indicates the lack of documents, and the blue ball indicates data tampering), and the manual review process is started. The emergency injection module of the digital twin system presets 18 standard event templates, including scenarios such as the stacker crane stopping (setting the hydraulic pressure <5MPa for 30 seconds) and network delay exceeding the limit (the time delay >500ms for 5 minutes), and the simulation step size is set to a virtual time acceleration ratio of 1:60.

[0080] The visual supervision view integrates multi-dimensional data sources to generate a 3D heat map, and uses the HSV color space mapping rule (red indicates that the cargo density > 3 tons / square meter, and blue indicates < 1 ton / square meter) to dynamically label the status of the storage area. The whole-link tracking trajectory realizes the whole-process traceability of the goods from declaration to release through spatio-temporal indexing technology (based on GeoHash coding and time-series database), and the time resolution is accurate to the second level. The passing-time compliance rate index in the intelligent analysis report sets different assessment criteria (≤ 24 hours for Class A goods, ≤ 72 hours for Class B goods), and automatically generates improvement suggestions (such as optimizing the control ratio of high-risk goods).

[0081] Specifically, the rendering of the 3D heat map adopts WebGL technology, which supports multi-level zooming (from the overall view of the whole field to the detailed drawing of a single shelf). The spatio-temporal index database uses PostGIS extension to store geographical information, and the accuracy of the timestamp field reaches the millisecond level. When calculating the passing time, force majeure factors (such as during a red weather warning) are excluded, and the statistical period of the compliance rate can be switched according to daily / weekly / monthly dimensions. In the risk interception accuracy rate index, the determination of "correct interception" needs to be manually reviewed and confirmed, and the system presets a 7-day dispute appeal period.

[0082] More specifically, a human-computer interaction function is set in the visual view, and the supervisor can view the details of the goods through gesture operations (such as pinch zooming, sliding and rotating). The spatio-temporal trajectory playback function supports setting the playback speed (1x - 10x) and key frame markers (such as the start of inspection, the time point of abnormal alarm). When generating the intelligent analysis report, natural language generation technology (fine-tuned based on the GPT-3 model) is used to automatically write the executive summary, highlighting the abnormal indicators with KPI fluctuations exceeding ±5%. For indicators that have not met the standard for 3 consecutive statistical periods, the system automatically triggers a root cause analysis process (using the random forest algorithm to identify key influencing factors).

[0083] In some embodiments, in S1:

[0084] The environmental monitoring data of the supervision site includes temperature and humidity, smoke concentration and vibration sensing data;

[0085] When the residence time of the container exceeds the preset residence time threshold, a detention alarm is triggered and recorded in the abnormal event library.

[0086] For the temperature and humidity collection of environmental monitoring data in supervision sites, SHT35 digital sensors with distributed deployment are used to collect data every 30 seconds and upload it to the central processing unit through the LoRaWAN protocol. For smoke concentration monitoring, a laser scattering detector is used, and the detection accuracy reaches 0.1% obs / m. When the concentration value exceeds 15% LEL, a first-level fire alarm warning is triggered. Vibration sensing data is obtained through a three-axis acceleration sensor, and the sampling frequency is set to 200 Hz. The determination standard for abnormal vibration events is that the Z-axis acceleration exceeds 2.5 g within three consecutive sampling periods and the spectral energy is concentrated in the 5 - 50 Hz range. The container residence time threshold setting module dynamically adjusts the reference value according to the cargo attributes: 72 hours for general cargo, compressed to 24 hours for dangerous goods, and set to 48 hours for cold chain goods. The detention determination logic includes a dual verification mechanism, which not only meets the continuous stay limit but also excludes the normal loading and unloading operation periods. The abnormal event library is stored in a time series database structure, and the recorded fields include the container number, detention start and end timestamps, associated shipper enterprise code, and disposal status mark.

[0087] In some embodiments, the construction of the intelligent port data base in S2 includes:

[0088] Deploy a data cleaning engine using a distributed architecture to eliminate moiré patterns and scanning distortions in document images;

[0089] Align RFID positioning data and video stream data at the millisecond level through a spatio-temporal fusion algorithm;

[0090] Establish a data lineage tracking mechanism to record the data flow paths between business subsystems.

[0091] The data cleaning engine deployed with a distributed architecture is built using a Hadoop cluster, with 10 computing nodes set up to process document images in parallel. Moiré pattern elimination uses an improved U-Net deep learning model. The input layer receives 600 dpi scanned images, and after extracting features through five convolutional layers, a filtered image is output. The scanning distortion correction module integrates a perspective transformation algorithm. After detecting the positioning marks at the four corners of the document, a correction matrix is generated, and the distortion rate is controlled within a 0.3 pixel error range. The cleaned document data is stored in an encrypted PNG format, and the metadata includes the cleaning timestamp and quality score, which are distributed to downstream systems through a Kafka message queue.

[0092] The spatio-temporal fusion algorithm realizes the time alignment between the RFID reader and the camera through the hardware clock synchronization unit, and uses the PTP precision time protocol to compress the time error between devices to ±2ms. The spatial alignment module converts the two-dimensional coordinates of RFID into three-dimensional spatial coordinates of the video stream, deploys AprilTag positioning markers at the edge of the shelf as reference points, and the spatial matching error does not exceed 5cm. The data processing pipeline sets a double-buffer mechanism, performs data alignment operations every 200ms, and compensates for missing RFID signals using cubic spline interpolation.

[0093] The data lineage tracing mechanism attaches a unique traceability code to each data packet. The traceability code consists of a data source identifier (3 letters) + timestamp (13 digits) + hash value (6 hexadecimal digits). The tracing log record fields include the IP address of the data producer, the processing node number, and the transfer time node, and are stored in the Elasticsearch cluster for full-text retrieval. The lineage visualization module generates a directed acyclic graph, where the node size reflects the data processing time consumption, and the thickness of the edge represents the data transmission volume, supporting drilling down the upstream and downstream association paths of any node.

[0094] In some embodiments, the state space of the reinforcement learning algorithm in S3 is defined as:

[0095] Warehouse utilization rate:

[0096]

[0097] Where, is the current number of occupied storage locations, is the total number of storage locations in the supervised area;

[0098] Equipment health:

[0099]

[0100] Where, is the historical failure duration of the equipment, is the cumulative operating duration of the equipment;

[0101] Channel waiting queue:

[0102]

[0103] Where, is the waiting duration of goods in the i-th channel, is the preset tolerance threshold, is the channel priority weight coefficient.

[0104] The state space definition of the reinforcement learning algorithm includes three core dimensions: the utilization efficiency of the storage space, the health level of equipment operation, and the degree of cargo backlog in the passage. The warehousing utilization rate index reflects the tightness of warehousing resources by statistically calculating the proportion of the currently occupied storage locations to the total number of storage locations in the supervised area in real time. The equipment health index is calculated based on the complement of the historical failure duration of the equipment to the total operation duration, and is used to quantify the reliability of equipment operation. The passage waiting queue index characterizes the congestion status of the customs clearance process by weighted calculation of the difference between the actual waiting duration of goods at each inspection passage and the preset tolerance threshold.

[0105] Specifically, the total number of storage locations is dynamically updated according to the actual layout of the supervised area. When new shelves are added, the system automatically expands the denominator value. The statistics of equipment failure duration includes planned maintenance periods, but a weight coefficient of 2 is added to sudden failure events. The passage priority weight coefficient is set differently according to the inspection type: 1.2 for manual inspection passages, 1.0 for CT machine intelligent inspection passages, and 0.8 for automatic sorting passages. The preset tolerance threshold is dynamically adjusted according to historical customs clearance efficiency data, and the benchmark value is set to 1.3 times the average processing duration of similar goods.

[0106] More specifically, when the warehousing utilization rate exceeds 85%, the state space expansion mechanism is automatically triggered, and a shelf load-bearing distribution dimension parameter is added to the original index. In the calculation of equipment health, a fatigue coefficient is automatically added to equipment that has been continuously operating for more than 200 hours, increasing the decline rate of its health index by 30%. The sliding window algorithm is used for monitoring the passage waiting queue. The priority weights of each passage are recalculated every 5 minutes. When the weight increase of a certain passage exceeds 10% for three consecutive cycles, the passage resource allocation optimization process is triggered.

[0107] The statistics of equipment failure duration includes three typical failure modes: sensor signal interruption, abnormal vibration of mechanical components, and communication timeout of the control system. The historical operation data is stored using a circular buffer structure, and the equipment operation logs for the most recent 30 days are retained for health calculation. The weight coefficient adjustment mechanism for the passage waiting queue introduces a time decay factor, automatically increasing the weight priority of backlogged goods that have not been processed for more than 24 hours.

[0108] Specifically, the determination condition for sensor signal interruption is set to 10 consecutive heartbeat packet losses, and the abnormal mechanical vibration threshold is set to an acceleration sensor reading exceeding 4g for more than 5 seconds. The circular buffer is set with a dual storage area structure. The current operation area stores real-time data, and the historical analysis area retains the compressed data after feature extraction. The time decay factor is designed as an exponential function, with the weight increasing by 5% per hour of timeout, and the maximum cumulative increase not exceeding 50% of the original weight.

[0109] Preferably, an independent weight calculation rule is set for the special cargo channels in the cold chain supervision site. When the ambient temperature deviates from the set value by more than ±2°C, the weight of this channel is automatically increased to 1.5 times the reference value. When calculating the equipment health index, a lower limit threshold of 60% is set for key inspection equipment (such as X-ray machines and radiation detectors). When the value is lower than this, a preventive maintenance work order is forcibly triggered. The channel resource allocation optimization process includes two parallel strategies: dynamically opening standby inspection workstations and adjusting the AGV handling path planning.

[0110] The real-time update mechanism of the state space parameters adopts a dual mode of event-driven and periodic polling. When a change in the cargo position status or an equipment alarm event occurs, it immediately triggers a local parameter update, and at the same time, all status indicators are fully refreshed every 15 minutes. The parameter persistent storage uses a time series database, which supports historical state backtracking and comparative analysis at an hourly granularity. The abnormal state recovery mechanism is configured with an automatic rollback function. When the parameter mutation amplitude exceeds 3 times the standard deviation, the previous valid state is retained.

[0111] Specifically, the event-driven update sets two levels of trigger conditions: ordinary cargo position change events are batch processed with a 5-second delay, and key equipment alarm events are processed immediately in real time. The time series database adopts a sharded storage strategy. The current running data is stored in the SSD high-speed storage area, and the historical data three days ago is transferred to the HDD large-capacity storage area. The automatic rollback mechanism retains the last 10 valid state snapshots, and the rollback operation requires double verification to confirm data consistency.

[0112] More specifically, a dedicated state monitoring agent is deployed in the cold chain storage area, and its parameter sampling frequency is increased to 2 times that of the regular area. The time series database establishes an index structure partitioned by shelves, which supports quickly retrieving the historical state evolution trend of a specified area. During the abnormal state recovery process, the system synchronously generates an event analysis report, marking the time node of the parameter mutation, the associated equipment, and the possible causes, providing data support for subsequent optimization.

[0113] In some embodiments, the S4 includes:

[0114] Optimize the cargo location allocation through an integer programming model, and the objective function is the weighted sum of minimizing the warehousing cost and maximizing the access efficiency;

[0115] When the passing flow at the checkpoint exceeds the set throughput threshold, automatically enable the standby inspection channel and supplement the AGV handling equipment.

[0116] The optimization of storage location allocation uses a mixed-integer programming model to construct the objective function, comprehensively considering three core constraints: the frequency of goods access and storage, the load-bearing limit of the shelves, and the distance of the transportation path. The calculation of warehousing costs includes site occupancy fees and equipment depreciation fees. The access efficiency index quantifies the working cycle time of the AGV handling equipment and the goods turnover rate. When the real-time traffic monitoring value at the checkpoint exceeds the preset throughput threshold, the system automatically executes the channel switching protocol and activates the standby inspection resource pool.

[0117] Specifically, the integer programming model sets the double-objective optimization weight coefficient. The weighted ratio of warehousing cost to access efficiency is default set to 6:4, allowing managers to dynamically adjust the cost weight in the range of 0.5 - 0.8. The statistics of the frequency of goods access and storage are based on the historical data of the past 7 days. The load-bearing threshold of the shelves is set to range from 500 kg to 2 tons according to the shelf type. The throughput threshold is dynamically calculated according to the number of physical channels at the checkpoint. The benchmark value is (the number of channels × the standard processing volume per hour × 0.8). When the real-time traffic exceeds the limit for 15 consecutive minutes, the response mechanism is triggered.

[0118] More specifically, the A* algorithm can be used to optimize the shortest distance for the AGV handling path planning, while restricting the maximum turning angle not to exceed 45 degrees. When the standby inspection channel is enabled, the electronic fence guiding system is synchronously switched, and the LED indicator strip is switched from green to the yellow warning state. After the supplementary AGV equipment is activated from the standby area, the path planning module automatically generates an avoidance route and updates the task scheduling queue to ensure that the new equipment arrives at the designated operation area within 3 minutes.

[0119] The dynamic adjustment strategy includes a two-level response mechanism: the first-level response enables the standby inspection channels in adjacent areas, and the second-level response activates the mobile inspection workstations across regions. The AGV supplementary strategy performs an optimal sorting according to the equipment idle rate and the battery endurance status, and preferentially schedules the equipment that has completed the current task and has a battery power higher than 80%. The data synchronization mechanism during the channel switching process ensures business continuity, and the historical inspection records are migrated to the operation terminal of the new channel in real time.

[0120] Specifically, the mobile inspection workstation is configured with a liftable operation platform and a foldable radiation detector, and the deployment time is controlled within 5 minutes. The AGV optimal algorithm sets an equipment status scoring matrix, including three dimensions: task completion rate, fault history record, and real-time positioning accuracy. The data migration uses dual-channel redundant transmission. The main channel uses a 5G private network, and the standby channel enables the LoRaWAN Internet of Things protocol.

[0121] Preferably, in the scenario of a dedicated cold-chain goods passage, the standby passage pre-cooling system is started 30 minutes in advance to ensure that the temperature meets the standard. When supplementing AGV equipment, the insulated box carriers are automatically loaded, and the path planning avoids high-temperature areas and shortens the open-air driving distance. The mobile inspection workstation is equipped with an environmental parameter self-checking function. When the temperature fluctuates by more than ±1°C or the humidity exceeds the standard, the operation permission is automatically locked until the parameters are restored. When the traffic flow is continuously lower than the threshold of 85% for two hours, the standby resources are gradually released and the original configuration is restored. The resource release process adopts a progressive strategy, reducing the load of the standby passage by 25% per hour, and synchronously recovering the AGV equipment to the charging standby area. The resource configuration status dashboard displays the heat map of the equipment utilization rate in each area in real time, and simulates the implementation effect of the adjustment plan through the digital twin system.

[0122] Specifically, the adaptive mechanism sets two levels of fallback thresholds: when the traffic drops to 90% of the threshold, it enters the observation period, and when the trigger threshold of 85% is reached, the resource release program is started. The AGV recovery path is optimized using the greedy algorithm, and the return tasks of multiple devices are merged to reduce the empty driving mileage. The digital twin simulator loads real-time physical parameters to build a virtual scene, and performs Monte Carlo simulation verification on the resource configuration plan before executing the actual adjustment.

[0123] During the AGV recovery process, the system preferentially retains the composite devices with multitasking capabilities as mobile resources. The digital twin system sets three pressure test scenarios: peak traffic impact test, sudden equipment failure test, and network delay test, to verify the robustness of the resource configuration plan. The heat map rendering uses the HSV color space conversion technology to convert the device movement trajectory into a dynamic optical flow visualization effect.

[0124] In some embodiments, S5 includes the following sub-steps:

[0125] S51. Construct a three-dimensional digital model of the supervision site, and import the real-time cargo distribution, equipment status, and environmental parameters;

[0126] S52. Inject preset emergency event types, including equipment failures, cargo delays, and abnormal customs clearance behaviors;

[0127] S53. Evaluate the impact of the event on the customs clearance efficiency based on Monte Carlo simulation, and generate an emergency instruction set with priority ranking;

[0128] S54. Synchronize the emergency instructions to the supervision site management system and the logistics public information platform through the data bus.

[0129] The three-dimensional digital model is constructed using the BIM building information model and laser point cloud scanning fusion technology. The real-time imported cargo distribution data includes RFID coordinate positioning information and cargo weight attributes. The equipment status data collects the hydraulic pressure value of the stacker, the battery temperature of the AGV, and the power fluctuation curve of the X-ray machine through the OPC UA protocol. The environmental parameters integrate the real-time readings of temperature and humidity sensors, PM2.5 detectors, and noise decibel meters. The model update mechanism sets double trigger conditions: it is updated immediately when the cargo position changes by more than 0.5 meters, and is fully refreshed every 30 seconds under normal conditions.

[0130] Specifically, the laser point cloud scanning accuracy reaches the ±2mm level, and the constructed three-dimensional grid model includes the shelf structural mechanics parameters and ground bearing data. The layout spacing of RFID readers is 15 meters, and the positioning error is controlled within 0.3 meters. A differential strategy is set for the equipment data collection frequency: key inspection equipment is collected once per second, and ordinary handling equipment is collected once every 5 seconds. The abnormal thresholds of environmental parameters are set as temperature fluctuation ±3°C, humidity deviation ±15%RH, and noise sudden increase of 10dB for 10 seconds.

[0131] More specifically, when constructing the model in the cold chain supervision area, the thermal map of the shelf surface temperature distribution and the refrigerant pipeline pressure data are additionally imported. A shelf load-bearing safety factor calculation module is preset in the BIM model, which automatically highlights and warns when the cargo weight distribution exceeds 90% of the design load. The coordinate alignment between the three-dimensional model and the physical station is corrected by the Beidou positioning base station, with a plane error less than 5cm and an elevation error less than 3cm.

[0132] The preset emergency event types include three categories: equipment failure, cargo detention, and abnormal customs clearance. When injecting equipment failure events, the operating parameter thresholds of specific equipment are associated: for the stacker shutdown event, the trigger condition is set as the hydraulic pressure <5MPa for 30 seconds, and for the network delay event, the definition is that the delay >500ms lasts for 5 minutes. The cargo detention event is configured with three levels of severity, corresponding to the simulation scenarios of the detention area accounting for 30%, 50%, and 70% of the channel cross-section respectively. The abnormal customs clearance behavior template includes three typical modes: document forgery, cargo-certificate inconsistency, and smuggling dangerous goods.

[0133] Specifically, the event injection interface provides a parameterized configuration panel, where the event duration (10 - 120 minutes), influence radius (5 meters - 50 meters), and chain reaction probability (0 - 100%) can be adjusted. When simulating the stacker failure, the vibration spectrum distortion data of the virtual equipment is generated synchronously, and the amplitude increase range is set to be adjustable from 30% to 200%. The cargo detention scenario generator supports a polygon drawing tool, which automatically calculates the minimum circumscribed rectangle of the detention area and the list of affected channels.

[0134] Preferably, in the simulation of dangerous goods stowaway incidents, the system automatically associates with the X-ray machine image recognition model and injects a data packet of dangerous goods feature images that complies with the STCW standard. When a network latency event is triggered, the digital twin system synchronously simulates derivative effects such as video surveillance screen freezing and sensor data packet loss. The abnormal customs clearance behavior template library supports custom expansion and allows the import of typical feature parameters of historical seizure cases.

[0135] The Monte Carlo simulation can set 5,000 random samplings, and the compression ratio of each simulation running time is 1:60 (1 hour of physical time to complete 24 hours of virtual deduction). The impact assessment model calculates the customs clearance efficiency decline index, and the formula includes three dimensions: the weighted value of cargo delay duration, equipment idle rate, and personnel deployment cost. The emergency instruction set generator uses a hierarchical sorting algorithm to arrange the disposal plans in descending order of comprehensive benefit value, and the top three plans are marked with execution priority identifiers.

[0136] Specifically, the simulation parameter setting interface includes an event propagation coefficient adjustment slider (0.1 - 2.0), a resource scheduling delay time setting (0 - 30 minutes), and a personnel response error range setting (±20%). When calculating the customs clearance efficiency decline index, the weight of the cold chain cargo delay duration is set to 1.5 times that of ordinary cargo. The hierarchical sorting algorithm introduces a Pareto optimal solution screening mechanism to ensure the best balance between disposal timeliness and resource consumption for the top three plans.

[0137] More specifically, in the customs clearance scenario of fresh goods, the simulation module automatically enables an accelerated decay model, and the loss coefficient of the cargo value over time is set to decrease by 2% per hour. When calculating the benefit value of the emergency disposal plan, those with a plan implementation cost exceeding the budget limit by 50% are automatically downgraded. The priority identifier uses a three-color warning system: the red plan needs to be started within 15 minutes, the yellow plan is allowed to be executed within 2 hours, and the green plan is used as a long-term optimization suggestion.

[0138] The data bus uses a distributed message system based on Apache Kafka and sets a dual-channel redundant transmission mechanism. The instruction synchronization process includes a format conversion module that converts the JSON instruction set output by the three-dimensional simulation environment into an XML format recognizable by the target system. The consistency check module compares the device status snapshots of the digital twin system and the physical system, and when the deviation exceeds 5%, it triggers an alarm and starts the instruction rollback process.

[0139] Specifically, the message system configures 3 replication factors to ensure data reliability, and the size limit of a single message packet is 1MB. The format conversion template library pre-sets 8 types of protocol converters such as customs declaration standards and port TOS system interfaces. The consistency check frequency is set to be executed once every 5 seconds after the instruction is issued, for three check cycles. When the rollback process is started, the standby instruction channel is automatically activated and the previous valid instruction set is resent.

[0140] More specifically, during the transmission of emergency instructions, digital timestamps and quantum encryption signatures are added to critical control instructions, and the anti-tampering level reaches the GB / T 39786-2021 standard. The physical site receiving end is configured with an intelligent parser to automatically identify the instruction type and distribute it to the corresponding subsystems: the cargo location allocation instruction is sent to the WMS system, the equipment start / stop instruction is routed to the PLC controller, and the process change instruction is synchronized to the customs clearance business engine.

[0141] The emergency instruction set includes a cargo location emergency allocation plan, a standby equipment activation instruction, and a customs clearance process downgrading strategy.

[0142] In some embodiments, S5 includes the following sub-steps:

[0143] S51. Integrate customs declaration data, site operation data, and risk interception records to construct a multi-dimensional analysis data set;

[0144] S52. Generate a three-dimensional heat map of the supervision site, dynamically mark the areas where the cargo density exceeds the limit and the equipment alarm points;

[0145] S53. Based on the spatio-temporal indexing technology, display the full-link tracking trajectory of the goods from declaration, inspection to release;

[0146] S54. Output an intelligent analysis report containing key performance indicators (KPIs) and synchronize it to the customs supervision platform.

[0147] In S51, the multi-dimensional analysis data set integration module docks with the customs single window, the WMS warehousing system, and the risk interception database through ETL tools, and sets a 72-hour rolling time window to collect the latest business data. The customs declaration data fields include the unified social credit code of the enterprise, the first 8 digits of the commodity HS code, and the floating rate of the declared value. The site operation data integrates the operation frequency of the stacker, the average handling speed of the AGV, and the opening rate of the inspection channels. The risk interception record association module establishes a two-way index between the interception event and the original declaration document, and the extraction of abnormal cargo characteristics includes the packaging breakage rate, the abnormal points in the X-ray imaging, and the result code of the unpacking inspection.

[0148] Specifically, the ETL cleaning rule sets the field integrity verification threshold at 95%, and automatically fills the missing declared value field with the average value of the last three declarations of similar commodities. The parsing of the first 8 digits of the HS code uses the standard library of the General Administration of Customs, and the unrecognized codes are marked as pending manual review status. The risk event index establishes a three-level association relationship: the first level associates with the original declaration document number, the second level associates with the container code, and the third level associates with the credit rating of the consignor enterprise.

[0149] More specifically, in the scenario of cold-chain goods, the dataset additionally integrates temperature control record curves and OBD operation data of cold-chain vehicles. The abnormal X-ray points are marked using Bounding Box coordinate positioning, with an accuracy reaching 0.1% of the image resolution. The data rolling window sets a double-buffer mechanism to ensure that data import does not affect the performance of the real-time business system.

[0150] In S52, the three-dimensional heat map rendering engine adopts the HSV color space mapping rule. The calculation of cargo density is based on the weight-bearing index per unit cargo location area, and the density overrun threshold is set differently according to cargo categories: 3 tons per square meter for general goods, 1.5 tons per square meter for dangerous goods, and 2.2 tons per square meter for cold-chain goods. The equipment alarm point marking system integrates an audible and visual prompt function. When the mouse hovers over the alarm area of the heat map, a comparison curve of real-time equipment parameters and historical fault statistics will automatically pop up.

[0151] Specifically, the update frequency of the heat map is synchronized with the warehouse management system, refreshing the global view every 30 seconds, and the local key areas can be manually switched to the mode of refreshing once per second. The alarm color coding rules are set as follows: red indicates that the equipment has been shut down for more than 1 hour, yellow indicates that the parameter deviates more than 30% from the normal value, and purple represents a network communication interruption. The comparison curve shows the dynamic relationship between the current value and the average value in the past 24 hours and the peak value in this week.

[0152] Preferably, an independent layer is set for the cross-border e-commerce supervision area, and the cargo density threshold is lowered to 70% of the normal value. The equipment alarm is linked to the emergency response plan library, and three alternative disposal plans can be directly viewed by clicking on the alarm point. The heat map perspective control supports the access of VR devices to achieve immersive spatial situation inspection.

[0153] In S53, the full-link tracking trajectory adopts spatio-temporal cube visualization technology. The X-axis represents the longitude of the geographical coordinate, the Y-axis is the latitude, and the Z-axis maps the time dimension. The cargo movement path is smoothed by a B-spline curve, and the specific operation type and time consumption are marked at the inspection and stay nodes. The trajectory playback function supports speed control and event marking, and can quickly locate the efficiency bottleneck points in the customs clearance process.

[0154] Specifically, the spatio-temporal index database uses GeoHash coding to convert geographical coordinates into 32-bit strings, and the accuracy of the timestamp field reaches the millisecond level. The control point interval of the B-spline curve is set to twice the cargo position update period to ensure the balance between path smoothness and data authenticity. The details pop-up window of the inspection node shows the customs officer number, equipment number, and operation compliance score.

[0155] More specifically, the fresh goods trajectory adds a temperature change trend overlay, and uses a color gradient bar to represent the whole-process temperature control compliance situation. The efficiency bottleneck analysis module automatically identifies the nodes with timeout stays, and generates optimization suggestions when the time-consuming of a single link exceeds 200% of the average processing duration of similar goods. When the trajectory data is compressed and stored, the key path points are retained, and the compression rate can reach 15% of the original data volume.

[0156] The S54 intelligent analysis report generator is built with a dynamic template engine. The statistics of the number of goods released on time in the KPI indicators exclude force majeure periods, and the total declaration quantity includes the declaration records that have been withdrawn but have had data interactions. The correct interception determination requires a three-level review process, and the total interception quantity statistics cover both system automatic interception and manual confirmation interception cases. After being encrypted by the national secret SM2 algorithm, the report synchronization mechanism is transmitted to the supervision platform through the customs private network.

[0157] Specifically, the force majeure period list is docked with the meteorological warning data interface to automatically mark the declaration data during the red warning period. The dynamic template automatically adjusts the content depth according to the reading object: the customs officer version focuses on operation details, and the management version highlights trend analysis. The three-level review process sets a 48-hour processing time limit, and the interception cases that have not been reviewed overdue are temporarily not included in the accuracy rate statistics.

[0158] More specifically, the report appendix includes an enterprise credit portrait module, which highlights the contribution value of the improvement of the customs clearance efficiency for AA-class enterprises. The data encryption transmission uses quantum random numbers to generate keys, effectively preventing replay attacks. The receiving end of the supervision platform is configured with an intelligent parser to automatically extract the KPI indicators and store them in the performance appraisal database.

[0159] The said key performance indicators include the customs clearance timeliness compliance rate and the risk interception accuracy rate:

[0160] The customs clearance timeliness compliance rate is shown in the following calculation formula:

[0161]

[0162] Wherein, is the number of goods released on time, is the total number of declared goods;

[0163] The risk interception accuracy rate is shown in the following calculation formula:

[0164]

[0165] Wherein, is the number of high-risk goods correctly intercepted, is the total number of goods intercepted by the system.

[0166] When counting the number of goods released on time in the customs clearance timeliness compliance rate, the system interfaces with the time stamps of the customs release instructions and the declaration completion time nodes. The on-time standard is set differently according to the type of goods: for general goods, it is based on 24 hours after declaration; for cold-chain goods, it is compressed to 12 hours; for dangerous goods, it is extended to 48 hours. The total number of declared goods includes declaration records that have been withdrawn but have completed data verification, excluding invalid declarations automatically rejected by the system due to format errors. The time window statistical period supports flexible switching according to natural days, working weeks, or custom time periods. Delayed periods caused by abnormal weather are automatically marked and excluded from the calculation.

[0167] Specifically, the time stamp alignment uses the Beidou timing system, with the error controlled within 50 milliseconds. For cold-chain goods, a temperature control compliance verification link is added to the time determination. If the temperature fluctuates by more than ±2°C throughout the process, the benchmark duration is automatically extended by 6 hours. When counting the overtime of dangerous goods declarations, if the inspection process requires additional time due to safety inspections, an extension of the benchmark duration can be applied for, but it requires manual approval. When generating statistical reports, the system automatically marks the top five and bottom five enterprises in the compliance rate ranking.

[0168] In the cross-border e-commerce supervision scenario, the on-time standard is shortened to 8 hours and is associated with the real-time successful delivery data of the logistics platform. The system sets up a red-yellow-green three-color warning mechanism: a compliance rate below 80% is marked red, 80 - 90% is marked yellow, and above 90% is marked green. For enterprises marked red for three consecutive periods, the customs compliance counseling process is automatically triggered.

[0169] For the correct interception determination in the risk interception accuracy rate, a three-level review process is required: at the first level, the system automatically marks high-risk goods; at the second level, manual review is conducted for document contradictions; at the third level, laboratory tests confirm the violation facts. The total number of intercepted goods statistics covers suspicious items identified by the intelligent image recognition system, high-risk declarations predicted by the risk model, and abnormal goods found during the customs officer's on-site inspection. The definition criteria for high-risk goods include terrorist control items, intellectual property infringement goods, and goods with safety indicator exceedances.

[0170] Specifically, a multi-model voting mechanism is adopted in the automatic marking link. When two out of three risk prediction models output high risk, interception is triggered. The manual review interface integrates augmented reality technology. Customs officers wearing smart glasses can overlay and display the X-ray view of the goods and historical similar cases. The laboratory test results are stored on the blockchain. Cases with a test cycle exceeding 72 hours are temporarily suspended as pending status and not included in the statistics.

[0171] Preferably, an independent determination rule is set for luxury goods. When the brand authorization chain verification fails and the declared value is less than 30% of the market price, it is directly determined as high risk. The system establishes a false positive case library, conducts feature analysis on misintercepted cases, and feeds them back to the model training set. The accuracy rate report generates a comparative analysis matrix in three dimensions: customs district, commodity category, and risk type.

[0172] The KPI calculation engine configures a dynamic calibration mechanism to automatically adjust the baseline values of statistical parameters monthly. The baseline duration of customs clearance efficiency is updated dynamically based on the average customs clearance efficiency in the past three months, and the risk interception judgment threshold fluctuates dynamically with the seizure rate. The fuzzy logic algorithm is introduced in the calibration process. When external policies change or regulatory focuses are adjusted, the system automatically makes a smooth transition to the new parameter system.

[0173] Specifically, the adjustment range of the baseline value is limited within ±15% to avoid the impact of parameter mutations on enterprise adaptability. The floating threshold of the seizure rate is set with an elasticity coefficient of 0.5 - 2.0. When the seizure rate of a certain type of commodity exceeds 1.5 for two consecutive weeks, its risk weight is automatically increased. The calibration log records the complete parameter evolution path, supporting the backtracking of calculation criteria at any historical time point.

[0174] More specifically, the system sets up a policy sensitivity analysis module. When the country adjusts the import and export tariff schedule or supplements the control list, it automatically predicts the KPI fluctuation range and generates a pre - plan. The calibration results are subjected to stress tests through the digital twin system to verify the stability of the new parameters under a 200% declaration flow impact. Parameter change notifications are pushed synchronously to the enterprise user side, and a simulation calculator is provided to estimate the impact value.

[0175] The above - mentioned embodiments of the present invention have the following beneficial effects: By collecting multi - source heterogeneous data in real - time and performing standardized processing, and analyzing the correlation characteristics of the HS code compliance of goods and abnormal events in regulatory places in combination with the federated learning framework, the present invention can achieve accurate risk identification and early warning, providing a scientific basis for customs management. The reinforcement learning algorithm dynamically generates inspection and control plans, which can flexibly adjust inspection strategies according to the real - time operation status of regulatory places, optimize the allocation of inspection resources, and improve inspection efficiency and accuracy. At the same time, by invoking blockchain smart contracts to verify the consistency of customs declarations and logistics documents, and using the OCR engine combined with the knowledge graph database to verify the logical contradictory items of documents, the automation level and accuracy of document review can be improved, reducing the workload and error rate of manual review. In addition, by dynamically adjusting the goods distribution strategy according to the real - time load data of regulatory places and optimizing the storage location allocation, the minimization of storage costs and the maximization of storage and retrieval efficiency can be achieved, further improving the operation efficiency of the port.

[0176] Construct a three-dimensional digital model of the supervision site and inject preset types of emergencies. By using Monte Carlo simulation to evaluate the impact of events on the customs clearance efficiency, an emergency instruction set with priority ranking can be generated, effectively improving the emergency response ability of the port and ensuring the stable operation of the customs clearance process. At the same time, integrating customs declaration data, site operation data, and risk interception records, constructing a multi-dimensional analysis data set, and generating a full-process visual supervision view can achieve full-link tracking of goods from declaration, inspection to release, providing scientific and intuitive decision-making support for customs supervision. The output of key performance indicators can reflect the operation status of the port in real time, helping managers quickly grasp the customs clearance timeliness and risk interception situation, so as to further optimize the port management strategy and improve the overall efficiency and intelligent level.

[0177] As Figure 2 shown, a digital port intelligent customs system 200 based on artificial intelligence in some embodiments, the system 200 includes:

[0178] A multi-source data collection module 201, used to collect multi-source heterogeneous data in real time through the logistics public information platform, including electronic scans of customs clearance documents, real-time container positioning data, supervision site environmental monitoring data, and checkpoint access records;

[0179] A data preprocessing module 202, used to perform standardized processing on multi-source heterogeneous data based on the intelligent port data base, generate a structured customs declaration data stream, and distribute it to the business system;

[0180] A risk prediction module 203, used to construct a risk prediction model using the federated learning framework, and analyze the correlation characteristics of the compliance of goods HS codes and abnormal events in the supervision site;

[0181] A strengthened control decision-making module 204, used to dynamically generate an inspection and control plan through a reinforcement learning algorithm based on the real-time operation status of the supervision site management system;

[0182] A consistency verification module 205, used to call the blockchain smart contract to verify the consistency between the customs declaration form and the logistics documents;

[0183] A multi-modal document parsing module 206, used to parse the content of the attached documents using an OCR engine, and verify the logical contradictory items of the documents in combination with the knowledge graph database;

[0184] A resource scheduling module 207, used to dynamically adjust the goods distribution strategy according to the real-time load data of the supervision site management system;

[0185] A digital twin module 208, used to simulate emergencies in the supervision site through digital twin technology and generate an emergency response plan;

[0186] The visualization supervision module 209 is used to generate a full-process visualization supervision view on the intelligent port data base.

[0187] It can be understood that the modules described in the digital port intelligent customs system 200 based on artificial intelligence correspond to the respective steps in the digital port intelligent customs method based on artificial intelligence described in the reference. Figure 1 Therefore, the operations, features, and beneficial effects described above for the digital port intelligent customs method based on artificial intelligence are equally applicable to the digital port intelligent customs system 200 based on artificial intelligence and the modules included therein, and will not be elaborated herein.

[0188] The above description is only some preferred embodiments of the present invention and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the technical solutions formed by mutually replacing the above features with the (but not limited to) technical features having similar functions disclosed in the embodiments of the present invention.

Claims

1. A digital port smart customs method based on artificial intelligence, characterized in that: The following steps are involved: S1. Real-time collection of multi-source heterogeneous data through the logistics public information platform, including electronic scans of customs clearance documents, real-time positioning data of containers, environmental monitoring data of supervision sites and checkpoint pass records; S2. Based on the smart port data base, standardize the multi-source heterogeneous data, generate structured customs declaration data streams and distribute them to the business system; use the federated learning framework to build a risk prediction model to analyze the compliance of the HS code of goods and the correlation characteristics of abnormal events in the supervision site; the standardization processing includes: using a distributed architecture to deploy a data cleaning engine to eliminate moiré and scanning distortion in the document image; aligning RFID positioning data with video stream data at the millisecond level through a spatiotemporal fusion algorithm; establishing a data lineage tracking mechanism to record the data flow path between each business subsystem; S3. Based on the real-time operation status of the supervision site management system, dynamically generate inspection and control plans through reinforcement learning algorithms; call blockchain smart contracts to verify the consistency of customs declarations and logistics documents; S4. Use the OCR engine to analyze the contents of the attached documents and verify the logical contradictions of the documents in combination with the knowledge graph database; dynamically adjust the cargo distribution strategy based on the real-time load data of the supervision site management system; S5. Use digital twin technology to simulate emergencies in regulatory sites and generate emergency response plans; generate a full-process visual regulatory view on the smart port data base.

2. The method according to claim 1, characterized in that: In S1: The environmental monitoring data of the supervision site includes temperature and humidity, smoke concentration and vibration sensor data; When the container stay time exceeds the preset stay time threshold, a detention alarm is triggered and recorded in the abnormal event database.

3. The method according to claim 1, characterized in that The state space of the reinforcement learning algorithm in S3 includes storage utilization, equipment health, and channel waiting queues, specifically: The warehouse utilization rate is calculated as follows: in, is the number of currently occupied cargo spaces, The total number of cargo spaces in the regulated venue; The device health is calculated as follows: in, is the historical fault duration of the device, The accumulated running time of the equipment; The channel waiting queue is calculated as follows: in, is the waiting time of goods in channel i, is the preset tolerance threshold, is the channel priority weight coefficient, is the number of channels.

4. The method according to claim 1, characterized in that The S4 includes: The storage space allocation is optimized through the integer programming model, and the objective function is the weighted sum of minimizing storage cost and maximizing storage and retrieval efficiency; When the traffic volume at the checkpoint exceeds the set throughput threshold, the backup inspection channel and additional AGV handling equipment will be automatically activated.

5. The method according to claim 1, characterized in that The S5 comprises the following steps: S51. Build a three-dimensional digital model of the supervision site and import real-time cargo distribution, equipment status and environmental parameters; S52. Input the preset emergency types, including equipment failure, cargo detention and abnormal customs clearance behavior supervision sites into the three-dimensional digital model of the supervision site; S53. Evaluate the impact of events on customs clearance efficiency based on Monte Carlo simulation and generate a prioritized emergency instruction set; S54. Synchronize emergency instructions to the supervision site management system and logistics public information platform through the data bus.

6. The method according to claim 5, characterized in that The emergency instruction set includes an emergency cargo space allocation plan, spare equipment activation instructions and customs clearance process downgrade strategy.

7. The method according to claim 1, characterized in that The S5 comprises the following steps: S51. Integrate customs declaration data, site operation data and risk interception records to build a multi-dimensional analysis data set; S52. Generate a three-dimensional heat map of the supervision site based on the multi-dimensional analysis data set, and dynamically mark the areas where the cargo density exceeds the limit and the equipment alarm points; S53. Based on spatiotemporal indexing technology, the full-link tracking track of goods from declaration, inspection to release is displayed; S54. Output intelligent analysis report containing key performance indicators and synchronize it to the customs supervision platform.

8. The method according to claim 7, characterized in that The key performance indicators include the customs clearance efficiency compliance rate and risk interception accuracy rate: The customs clearance efficiency compliance rate is shown in the following calculation formula: in, The quantity of goods released on time, is the total declared quantity of goods; The risk interception accuracy is shown in the following calculation formula: in, To determine the number of high-risk shipments that were correctly intercepted, The total number of goods intercepted by the system.

9. A digital port smart customs system based on artificial intelligence, characterized in that: The system comprises: Multi-source data collection module, used to collect multi-source heterogeneous data in real time through the logistics public information platform, including electronic scans of customs clearance documents, real-time container positioning data, environmental monitoring data of supervision sites and checkpoint pass records; The data preprocessing module is used to standardize multi-source heterogeneous data based on the smart port data base, generate structured customs declaration data streams and distribute them to the business system; wherein, the standardization processing includes: using a distributed architecture to deploy a data cleaning engine to eliminate moiré and scanning distortion in document images; using a spatiotemporal fusion algorithm to align RFID positioning data with video stream data at the millisecond level; establishing a data lineage tracking mechanism to record the data flow path between each business subsystem; The risk prediction module is used to build a risk prediction model using a federated learning framework to analyze the compliance of goods HS codes and the correlation characteristics of abnormal events in regulatory sites; Strengthen the deployment decision module, which is used to dynamically generate inspection and deployment plans based on the real-time operating status of the supervision site management system through reinforcement learning algorithms; The consistency verification module is used to call the blockchain smart contract to verify the consistency between the customs declaration and the logistics documents; The multimodal document parsing module is used to parse the contents of the attached documents using the OCR engine and verify the logical contradictions of the documents in combination with the knowledge graph database; Resource scheduling module, used to dynamically adjust the cargo distribution strategy based on the real-time load data of the supervision site management system; Digital twin module, used to simulate emergencies in regulatory sites through digital twin technology and generate emergency response plans; The visual supervision module is used to generate a full-process visual supervision view on the smart port data base.

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