Digital port intelligent customs method and system based on artificial intelligence

Abstract

The invention provides a digital port intelligent customs affair method and system based on artificial intelligence, and the method comprises the steps: collecting multi-source data, and carrying out the standardization processing of a data base, and generating customs declaration data flow; predicting the risk by using federal learning, and reinforcing learning to generate a checking scheme; verifying document consistency in combination with a block chain, and verifying document logic through OCR and a knowledge graph; and dynamically adjusting a cargo distribution strategy, performing digital twinborn simulation emergency response, and finally generating a full-process visual supervision view. The customs clearance efficiency, the risk prevention and control capability and the emergency response level can be improved.

Description

Technical Field

[0001] The present invention relates to the field of smart ports and customs supervision technology, and more specifically, to an artificial intelligence-based digital port smart customs method and system. Background Art

[0002] In traditional customs management at ports, customs departments mainly rely on manual review of customs clearance documents, on-site inspection of goods, and manual dispatch of resources at regulatory sites to complete customs operations. This model is not only inefficient and easily affected by human factors, but also difficult to achieve accurate risk identification and efficient resource allocation when faced with massive amounts of goods and complex customs clearance processes. With the rapid development of international trade, the volume of port business continues to grow, and the traditional customs management model has been unable to meet the needs of efficient customs clearance and precise supervision. In recent years, with the development of information technology, some ports have begun to try to introduce information systems to assist customs management, such as electronic submission of documents through electronic customs declaration systems, and remote monitoring of regulatory sites using video surveillance systems. However, most of these systems operate in isolation, lack data integration and sharing, and cannot form a complete regulatory chain. For example, although the electronic customs declaration system has improved the efficiency of document submission, during the document review process, it is still necessary to manually check the consistency between the customs declaration and the logistics documents, which is prone to errors; although the video surveillance system can view the situation in the supervision site in real time, it cannot automatically identify and warn of abnormal events, and cannot effectively link 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 to meet the needs of precise supervision; second, there is a lack of effective risk prediction and intelligent decision-making mechanisms, making it difficult to dynamically optimize complex and changeable port operations; third, the data sharing and coordination capabilities between systems are weak, resulting in an incomplete supervision chain and the inability 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 a first aspect of the present invention, a digital port smart customs method based on artificial intelligence is provided, comprising: 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; 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.

[0006] Furthermore, in said 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.

[0007] Furthermore, the construction of the smart port data base in S2 includes: A distributed architecture is used to deploy a data cleaning engine to eliminate moiré patterns and scanning distortion in document images; The RFID positioning data and the video stream data are aligned at the millisecond level through the spatiotemporal fusion algorithm; Establish a data lineage tracking mechanism to record the data flow path between business subsystems.

[0008] Furthermore, the state space of the reinforcement learning algorithm in S3 is defined as: Warehouse utilization rate: in, is the number of currently occupied cargo spaces, The total number of cargo spaces in the regulated venue; Equipment health: in, is the historical fault duration of the device, The accumulated running time of the equipment; Channel waiting queue: in, is the waiting time of goods in channel i, is the preset tolerance threshold, is the channel priority weight coefficient.

[0009] Further, 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.

[0010] Furthermore, the S5 comprises the following sub-steps: S51. Build a three-dimensional digital model of the supervision site and import real-time cargo distribution, equipment status and environmental parameters; S52. Inject preset emergency event types, including equipment failure, cargo detention and abnormal customs clearance behavior; 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.

[0011] Furthermore, the preset emergency event types include forklift shutdown, network delay exceeding the limit and temperature control system failure; The emergency instruction set includes an emergency cargo space allocation plan, spare equipment activation instructions and customs clearance process downgrade strategy.

[0012] Furthermore, the S5 comprises the following sub-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, 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 (KPI) and synchronize it to the customs supervision platform.

[0013] Furthermore, the key performance indicators include: Customs clearance efficiency compliance rate: in, The quantity of goods released on time, is the total declared quantity of goods; Risk interception accuracy: in, To determine the number of high-risk shipments that were correctly intercepted, The total number of goods intercepted by the system.

[0014] In a second aspect of the present invention, there is provided a digital port smart customs system based on artificial intelligence, comprising: 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 positioning data of containers, 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; 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; Multimodal document parsing module, which uses the OCR engine to parse the contents of the attached documents and verifies logical contradictions in 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.

[0015] According to the above-mentioned embodiments of the present invention, at least the following beneficial effects are achieved: the present invention can effectively analyze the compliance of goods HS codes and the correlation characteristics of abnormal events in regulatory places through real-time collection and standardized processing of multi-source heterogeneous data, combined with the risk prediction model constructed by the federated learning framework, so as to achieve accurate risk identification and early warning. At the same time, the inspection and control plan is dynamically generated by the reinforcement learning algorithm, which can flexibly adjust the inspection strategy according to the real-time operating status of the regulatory place, optimize the inspection resource allocation, and improve the inspection efficiency and accuracy. In addition, by verifying the consistency of the customs declaration and logistics documents through the blockchain smart contract, and using the OCR engine combined with the knowledge graph database to verify the logical contradictions of the documents, the automation level and accuracy of the document review can be further improved, and the workload and error rate of manual review can be reduced.

[0016] The present invention can also dynamically adjust the cargo distribution strategy according to the real-time load data of the supervision site, optimize the cargo space allocation through the integer programming model, and realize the minimization of storage costs and the maximization of access efficiency. At the same time, by simulating emergencies in supervision sites with the help of digital twin technology and generating emergency response plans, the emergency response capabilities of ports can be effectively improved and the stable operation of customs clearance procedures can be guaranteed. Finally, a full-process visual supervision view is generated on the smart port data base, integrating customs declaration data, site operation data and risk interception records, and constructing a multidimensional analysis data set to achieve 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 intelligence level of port customs management. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, in which: Figure 1 A flowchart of a digital port smart customs method based on artificial intelligence provided by an embodiment of the present invention; Figure 2 A schematic diagram of the structure of an artificial intelligence-based digital port smart customs system provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0018] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0019] Those skilled in the art know that the embodiments of the present invention can be implemented as a system, device, apparatus, method or computer program product. Therefore, the present invention can be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0020] 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.

[0021] Reference below Figure 1 , Figure 1 The following is a flow chart of a digital port smart customs management method based on artificial intelligence provided by an embodiment of the present invention. Figure 1As shown, a digital port smart customs method 100 based on artificial intelligence includes: 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; 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.

[0022] It should be noted that this method integrates electronic scans of customs documents (including digital copies of paper documents such as customs declarations and bills of lading), real-time container positioning data (transportation tool location information obtained through GPS / Beidou positioning devices), regulatory site environmental monitoring data (covering physical environmental parameters collected by IoT devices such as temperature and humidity sensors and smoke detectors) and checkpoint pass records (including license plate recognition, cargo type and other customs clearance gate data) through the logistics public information platform. The smart port data base 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 corporate privacy, the federated learning framework uses cross-institutional data to collaboratively train risk prediction models, focusing on identifying the correlation characteristics between HS code (customs commodity classification code) declaration anomalies and regulatory site equipment failures.

[0023] Specifically, the logistics public information platform collects data through edge computing gateways deployed at ports, bonded areas and other nodes. The sampling frequency of container positioning data is set to once per second, and the positioning accuracy is controlled at ±0.5 meters. The temperature and humidity sensors in the environmental monitoring data use the RS485 bus protocol to transmit data. The measurement range covers -20°C to 60°C, with an accuracy of ±0.5°C. The standardized processing of the smart port data base includes an image preprocessing submodule, which uses a deep learning-based de-moiré algorithm (using the U-Net network structure) and a document correction algorithm (using the Hough transform to detect edges). Participants in the federated learning framework include the General Administration of Customs Data Center, the port operation system and the logistics enterprise nodes, and the Paillier homomorphic encryption technology is used to ensure the security of gradient parameter transmission.

[0024] More specifically, during the data collection phase, when the container stay 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. The threshold can be dynamically adjusted according to different cargo types (such as 24 hours for cold chain cargo). During image preprocessing, an adaptive binarization algorithm (OTSU algorithm) is used for document scans to improve OCR recognition accuracy. When training the federated learning model, each participant locally deploys a ResNet-34 network to extract HS code declaration document features, and the central server aggregates the gradient updates output by each node. The model convergence standard is set to a loss value change rate of less than 0.1% for three consecutive rounds of training.

[0025] The state space constructed by the reinforcement learning algorithm includes warehouse utilization index (the percentage of the current number of occupied cargo spaces to the total number of cargo spaces), equipment health index (the complement of the historical equipment failure time and the total operating time) and channel waiting queue index (the weighted difference between the cargo waiting time of each inspection channel and the preset threshold). The blockchain smart contract is deployed on the Hyperledger Fabric consortium chain, and consistency verification is achieved by comparing the hash value of the customs declaration (generated using the SHA-256 algorithm) with the hash value of the electronic storage of logistics documents. The digital twin system constructs a three-dimensional visual simulation model by importing real-time cargo distribution data (including cargo space coordinates, cargo weight and other attributes) and physical environment parameters (temperature field, equipment vibration spectrum, etc.).

[0026] Specifically, the total number of cargo spaces in the calculation of warehouse utilization 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 fault duration statistics in the equipment health index include planned maintenance time, but a 2-fold weight coefficient 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: the manual inspection channel is set to 1.2, the CT machine inspection channel is set to 1.0, and the automatic sorting channel is set to 0.8. The blockchain node deployment adopts a multi-level architecture, with the customs supervision node as the Orderer node, the port operator and the logistics company as the Peer node, and the consensus mechanism uses the Kafka sorting service. The cargo distribution data in the digital twin model is updated in real time through the RFID reader, and the location refresh frequency is once per minute.

[0027] Preferably, in the state space design of reinforcement learning, when the storage utilization rate exceeds 85%, the state space expansion mechanism is automatically triggered to increase the shelf load distribution dimension parameters. When the blockchain smart contract is executed, a double-color ball mechanism warning is generated for abnormal situations where the hash value comparison fails (the red ball indicates that the document is missing, 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 types of standard event templates, including forklift shutdown (setting hydraulic pressure <5MPa for 30 seconds), network delay limit (delay >500ms for 5 minutes) and other scenarios, and the simulation step is set to a virtual time acceleration ratio of 1:60.

[0028] The visual supervision view integrates multi-dimensional data sources to generate a three-dimensional heat map, and uses the HSV color space mapping rule (red indicates cargo density >3 tons / square meter, blue indicates <1 ton / square meter) to dynamically mark the storage area status. The full-link tracking trajectory uses spatiotemporal indexing technology (based on GeoHash encoding and time series database) to trace the entire process from declaration to release of goods, with a time resolution accurate to seconds. The customs clearance timeliness compliance rate indicator in the intelligent analysis report sets differentiated assessment standards (Class A cargo ≤24 hours, Class B cargo ≤72 hours), and automatically generates improvement suggestions (such as optimizing the control ratio of high-risk cargo).

[0029] Specifically, the rendering of the three-dimensional heat map uses WebGL technology, which supports multi-level zooming (from the overall overview to the detailed map of a single shelf). The spatiotemporal index database uses PostGIS extension to store geographic information, and the timestamp field accuracy reaches milliseconds. Force majeure factors (such as red weather warnings) are excluded when calculating the customs clearance time, and the statistical period of the compliance rate can be switched by day / week / month. The "correct interception" judgment in the risk interception accuracy indicator needs to be manually reviewed and confirmed, and the system presets a dispute appeal period of 7 working days.

[0030] More specifically, the human-computer interaction function is set in the visual view, and supervisors can view the details of the goods through gestures (such as pinch-to-zoom, slide-to-rotate). The spatiotemporal trajectory playback function supports setting the playback speed (1x-10x) and key frame markings (such as the start of inspection and the time of abnormal alarm). When the intelligent analysis report is generated, natural language generation technology (based on GPT-3 model fine-tuning) is used to automatically write the executive summary, focusing on abnormal indicators with KPI fluctuations exceeding ±5%. For indicators that have not met the standards for three consecutive statistical cycles, the system automatically triggers the root cause analysis process (using the random forest algorithm to identify key influencing factors).

[0031] In some embodiments, 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.

[0032] The temperature and humidity data of the environmental monitoring data of the supervision site are collected by distributed SHT35 digital sensors, which collect data every 30 seconds and upload them to the central processing unit through the LoRaWAN protocol. The smoke concentration is monitored by a laser scattering detector with a detection accuracy of 0.1% obs / m. When the concentration value exceeds 15%LEL, a first-level fire alarm is triggered. The vibration sensor data is obtained by a three-axis acceleration sensor, and the sampling frequency is set to 200Hz. The abnormal vibration event judgment standard is that the Z-axis acceleration exceeds 2.5g in three consecutive sampling cycles and the spectrum energy is concentrated in the 5-50Hz range. The container residence time threshold setting module dynamically adjusts the benchmark value according to the cargo attributes: ordinary cargo is set to 72 hours, dangerous goods are compressed to 24 hours, and cold chain cargo is set to 48 hours. The detention judgment logic includes a double verification mechanism, which not only meets the continuous residence limit, but also excludes the normal loading and unloading operation period. The abnormal event library is stored in a time series database structure, and the record fields include the container number, detention start and end timestamps, the associated cargo owner enterprise code and the disposal status mark.

[0033] In some embodiments, the construction of the smart port data base in S2 includes: A distributed architecture is used to deploy a data cleaning engine to eliminate moiré patterns and scanning distortion in document images; The RFID positioning data and the video stream data are aligned at the millisecond level through the spatiotemporal fusion algorithm; Establish a data lineage tracking mechanism to record the data flow path between business subsystems.

[0034] The data cleaning engine deployed in a distributed architecture is built using a Hadoop cluster, with 10 computing nodes set up to process document images in parallel. Moire removal uses an improved U-Net deep learning model. The input layer receives a 600dpi scanned image, extracts features through five convolutional layers, and outputs a filtered image. The scan distortion correction module integrates a perspective transformation algorithm, generates a correction matrix after detecting the four corner positioning marks of the document, and controls the distortion rate 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 is distributed to downstream systems through the Kafka message queue.

[0035] The time-space fusion algorithm uses a hardware clock synchronization unit to achieve time alignment between the RFID reader and the camera, and uses the PTP precision time protocol to compress the time error between devices to ±2ms. The spatial alignment module converts the RFID two-dimensional coordinates into the three-dimensional spatial coordinates of the video stream, and deploys the AprilTag positioning mark on the edge of the shelf as a reference point. The spatial matching error does not exceed 5cm. The data processing pipeline sets up a double buffer mechanism, performs a data alignment operation every 200ms, and uses cubic spline interpolation to compensate for missing RFID signals.

[0036] The data lineage tracking mechanism adds a unique traceability code to each data packet. The traceability code consists of a data source identifier (3 letters) + a timestamp (13 digits) + a hash value (6 hexadecimal digits). The tracking log record field contains the data producer's IP address, processing node number, and flow time node, which are stored in the Elasticsearch cluster for full-text retrieval. The lineage visualization module generates a directed acyclic graph. The node size reflects the time consumption of data processing, and the thickness of the edge represents the amount of data transmission. It supports drilling into the upstream and downstream association paths of any node.

[0037] In some embodiments, the state space of the reinforcement learning algorithm in S3 is defined as: Warehouse utilization rate: in, is the number of currently occupied cargo spaces, The total number of cargo spaces in the regulated venue; Equipment health: in, is the historical fault duration of the device, The accumulated running time of the equipment; Channel waiting queue: in, is the waiting time of goods in channel i, is the preset tolerance threshold, is the channel priority weight coefficient.

[0038] The state space definition of the reinforcement learning algorithm includes three core dimensions: storage space utilization efficiency, equipment operation health level, and channel cargo backlog. The storage utilization index reflects the degree of storage resource tension by real-time statistics of the proportion of the current number of occupied cargo spaces to the total number of cargo spaces in the supervision area. The equipment health index is calculated based on the complement of the equipment's historical failure time to the total operation time, and is used to quantify the reliability of equipment operation. The channel waiting queue index represents the congestion of the customs clearance process by weighted calculation of the difference between the actual waiting time of cargo in each inspection channel and the preset tolerance threshold.

[0039] Specifically, the total number of cargo spaces is dynamically updated according to the actual layout of the supervision site, and the system automatically expands the denominator value when new shelves are added. The equipment failure duration statistics include planned maintenance periods, but the weight coefficient for sudden failure events is doubled. The channel priority weight coefficient is set differently according to the inspection type: the manual inspection channel is set to 1.2, the CT machine intelligent inspection channel is set to 1.0, and the automatic sorting channel is set to 0.8. 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 time of similar goods.

[0040] More specifically, when the warehouse utilization rate exceeds 85%, the state space expansion mechanism is automatically triggered, and the shelf load distribution dimension parameters are added on the basis of the original indicators. In the equipment health calculation, the fatigue coefficient is automatically added to the equipment that has been running continuously for more than 200 hours, so that its health index decreases by 30%. The channel waiting queue monitoring adopts a sliding window algorithm, and the priority weight of each channel is recalculated every 5 minutes. When the weight increase of a channel exceeds 10% for 3 consecutive cycles, the channel resource configuration optimization process is triggered.

[0041] The statistics of equipment failure duration include three typical failure modes: sensor signal interruption, abnormal vibration of mechanical parts, and control system communication timeout. The historical operation data storage adopts a ring buffer structure, retaining the equipment operation logs of the last 30 days for health calculation. The weight coefficient adjustment mechanism of the channel waiting queue introduces a time decay factor, which automatically increases the weight priority of the backlog of goods that have not been processed for more than 24 hours.

[0042] Specifically, the sensor signal interruption judgment condition is set to 10 consecutive heartbeat packet losses, and the mechanical vibration abnormality threshold is set to the acceleration sensor reading exceeding 4g for more than 5 seconds. The ring buffer is set up 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 according to the exponential function, and the weight increases by 5% for every hour of overtime, and the maximum cumulative increase does not exceed 50% of the original weight.

[0043] Preferably, an independent weight calculation rule is set for the special cargo channel in the cold chain supervision site. When the ambient temperature deviates from the set value by more than ±2°C, the weight of the channel is automatically increased to 1.5 times the reference value. When calculating the equipment health index, a health lower limit threshold of 60% is set for key inspection equipment (such as X-ray machines and radiation detectors). If it is lower than this value, a preventive maintenance work order is forcibly triggered. The channel resource configuration optimization process includes two parallel strategies: dynamically opening spare inspection stations and adjusting AGV handling path planning.

[0044] The real-time update mechanism of state space parameters adopts event-driven and periodic polling dual modes. When a change in the cargo position status or an equipment alarm event occurs, a local parameter update is triggered immediately, and all status indicators are fully refreshed every 15 minutes. The parameter persistent storage adopts a time series database, which supports historical status backtracking and comparative analysis at hourly granularity. The abnormal status recovery mechanism is configured with an automatic rollback function, and the previous valid state is retained when the parameter mutation amplitude exceeds 3 times the standard deviation.

[0045] Specifically, event-driven updates set two levels of trigger conditions: ordinary cargo location change events are delayed for 5 seconds for batch processing, and critical equipment alarm events are processed immediately in real time. The time series database adopts a sharding storage strategy, with current running data stored in the SSD high-speed storage area and historical data from three days ago 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.

[0046] More specifically, a dedicated status monitoring agent is deployed in the cold chain storage area, and its parameter sampling frequency is increased to twice that of the conventional area. The time series database establishes an index structure partitioned by shelf to support rapid retrieval of historical status evolution trends in a specified area. During the abnormal status recovery process, the system simultaneously generates an event analysis report, marking the time node of the parameter mutation, related equipment and possible causes, to provide data support for subsequent optimization.

[0047] In some embodiments, 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.

[0048] The optimization of cargo space allocation uses a mixed integer programming model to construct the objective function, taking into account the three core constraints of cargo access frequency, shelf load limit, and transportation path distance. The storage cost calculation includes site occupancy fees and equipment depreciation fees, and the access efficiency index quantifies the working cycle time and cargo turnover rate of AGV handling equipment. When the real-time traffic flow monitoring value of the checkpoint exceeds the preset throughput threshold, the system automatically executes the channel switching protocol and activates the backup inspection resource pool.

[0049] Specifically, the integer programming model sets the weight coefficient of dual-objective optimization. The weighted ratio of storage cost and access efficiency is set to 6:4 by default, allowing managers to dynamically adjust the cost weight in the range of 0.5-0.8. The frequency statistics of goods access are based on the historical data of the past 7 days, and the shelf load threshold is set to 500kg to 2 tons according to the shelf type. The throughput threshold is dynamically calculated based on the number of physical channels at the card gate, and the benchmark value is (number of channels × standard processing volume per hour × 0.8). When the real-time flow exceeds the limit for 15 consecutive minutes, the response mechanism is triggered.

[0050] More specifically, the AGV handling path planning can use the A* algorithm to optimize the shortest distance, while constraining the maximum turning angle to no more than 45 degrees. When the standby inspection channel is enabled, the electronic fence guidance system is switched synchronously, and the LED indicator light switches from green to yellow warning status. After the additional 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 additional equipment arrives at the designated work area within 3 minutes.

[0051] The dynamic adjustment strategy includes a two-level response mechanism: the first-level response enables the backup inspection channel in the adjacent area, and the second-level response activates the mobile inspection workstation across the area. The AGV supplementation strategy prioritizes the equipment according to the equipment idle rate and battery life status, and prioritizes the equipment that has completed the current task and has a battery level above 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.

[0052] Specifically, the mobile inspection workstation is equipped with a liftable operating platform and a foldable radiation detector, and the deployment time is controlled within 5 minutes. The AGV optimization algorithm sets the equipment status scoring matrix, which includes three dimensions: task completion rate, fault history record, and real-time positioning accuracy. Data migration uses dual-channel redundant transmission, the main channel uses 5G private network, and the backup channel uses LoRaWAN Internet of Things protocol.

[0053] Preferably, in the scenario of a dedicated channel for cold chain goods, the pre-cooling system of the standby channel is started 30 minutes in advance to ensure that the temperature meets the standard. When the AGV equipment is supplemented, the insulated box carrier is 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 operating authority is automatically locked until the parameters are restored. When the traffic flow is lower than the threshold of 85% for two consecutive hours, the standby resources are gradually released and the original configuration is restored. The resource release process adopts a progressive strategy, reducing the standby channel load by 25% every hour, and synchronously recovering the AGV equipment to the charging standby area. The resource configuration status dashboard displays the equipment utilization heat map of each area in real time, and simulates the implementation effect of the adjustment plan through the digital twin system.

[0054] Specifically, the adaptive mechanism sets two levels of fallback thresholds: when the traffic drops to 90% of the threshold, the observation period begins, and when the threshold is triggered at 85%, the resource release program is started. The AGV recovery path is optimized using a greedy algorithm, and the return tasks of multiple devices are merged to reduce idle mileage. The digital twin simulator loads real-time physical parameters to build a virtual scene, and the resource allocation plan is verified by Monte Carlo simulation before actual adjustment.

[0055] During the AGV recycling process, the system gives priority to retaining composite equipment with multi-tasking capabilities as mobile resources. The digital twin system sets up three stress test scenarios: peak traffic impact test, equipment sudden failure test and network delay test to verify the robustness of the resource allocation plan. The heat map rendering uses HSV color space conversion technology to convert the equipment movement trajectory into a dynamic optical flow visualization effect.

[0056] In some embodiments, the S5 includes the following sub-steps: S51. Build a three-dimensional digital model of the supervision site and import real-time cargo distribution, equipment status and environmental parameters; S52. Inject preset emergency event types, including equipment failure, cargo detention and abnormal customs clearance behavior; 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.

[0057] The three-dimensional digital model is constructed using 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 forklift, 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 the temperature and humidity sensor, PM2.5 detector and noise decibel meter. The model update mechanism sets dual trigger conditions: update immediately when the cargo position changes by more than 0.5 meters, and refresh the full amount every 30 seconds under normal conditions.

[0058] Specifically, the laser point cloud scanning accuracy reaches ±2mm level, and the constructed three-dimensional grid model contains the mechanical parameters of the shelf structure and the ground load-bearing data. The RFID reader is arranged at a distance of 15 meters, and the positioning error is controlled within 0.3 meters. The equipment data collection frequency is set with a differentiated strategy: key inspection equipment collects data once per second, and ordinary handling equipment collects data once every 5 seconds. The abnormal threshold of environmental parameters is set to temperature fluctuation ±3℃, humidity deviation ±15%RH, and noise surge of 10dB for 10 seconds.

[0059] More specifically, when constructing the cold chain supervision area model, additional thermal maps of shelf surface temperature distribution and refrigerant pipeline pressure data are imported. A shelf load-bearing safety factor calculation module is pre-set in the BIM model, and an automatic highlight warning is issued when the cargo weight distribution exceeds 90% of the design load. The coordinate alignment of the three-dimensional model and the physical site is corrected using the Beidou positioning base station, with a plane error of less than 5cm and an elevation error of less than 3cm.

[0060] The preset emergency event types include equipment failure, cargo detention, and abnormal customs clearance. When injecting equipment failure events, the operating parameter thresholds of specific equipment are associated: the forklift shutdown event sets the trigger condition of hydraulic pressure <5MPa for 30 seconds, and the network delay event is defined as a delay >500ms for 5 minutes. The cargo detention event is configured with three levels of severity, corresponding to the simulation scenarios where the detention area accounts for 30%, 50%, and 70% of the channel cross-section. The abnormal customs clearance behavior template includes three typical modes: forged documents, inconsistent cargo documents, and smuggling of dangerous goods.

[0061] Specifically, the event injection interface provides a parameterized configuration panel to adjust the event duration (10-120 minutes), impact radius (5 meters-50 meters) and chain reaction probability (0-100%). When simulating a forklift failure, the vibration spectrum distortion data of the virtual device is generated synchronously, and the amplitude increase is set in an adjustable range of 30%-200%. The cargo detention scene generator supports polygon drawing tools to automatically calculate the minimum bounding rectangle of the detention area and the list of affected channels.

[0062] Preferably, in the simulation of dangerous goods smuggling events, the system automatically associates the X-ray machine image recognition model and injects dangerous goods characteristic image data packets that meet the STCW standard. When a network delay event is triggered, the digital twin system synchronously simulates derivative effects such as video surveillance screen freezes and sensor data packet loss. The abnormal clearance behavior template library supports custom extensions, allowing the import of typical characteristic parameters of historical seizure cases.

[0063] Monte Carlo simulation can set up 5,000 random samplings, and the compression ratio of each simulation run time is 1:60 (the physical time is 1 hour to complete the virtual 24-hour simulation). The impact assessment model calculates the customs clearance efficiency reduction index, and the formula includes three dimensions: the weighted value of the cargo delay time, the equipment idle rate, and the personnel deployment cost. The emergency instruction set generator uses a hierarchical sorting algorithm to sort the disposal plans in descending order according to the comprehensive benefit value, and the top three plans are marked with execution priority.

[0064] 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 reduction index, the delay time weight of cold chain goods is set to 1.5 times that of ordinary goods. The hierarchical sorting algorithm introduces a Pareto optimal solution screening mechanism to ensure that the top three solutions achieve the best balance between disposal timeliness and resource consumption.

[0065] More specifically, in the scenario of fresh goods customs clearance, the simulation module automatically enables the accelerated decay model, and the coefficient of the loss of goods value over time is set to decrease by 2% per hour. When calculating the benefit value of the emergency disposal plan, the implementation cost of the plan exceeds the budget limit by 50%, and the plan is automatically downgraded. The priority identification adopts 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.

[0066] The data bus uses a distributed messaging system based on Apache Kafka and sets up a dual-channel redundant transmission mechanism. The instruction synchronization process includes a format conversion module, which converts the JSON instruction set output by the 3D 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. When the deviation exceeds 5%, an alarm is triggered and the instruction rollback process is started.

[0067] Specifically, the message system is configured with 3 replication factors to ensure data reliability, and the size of a single message packet is limited to 1MB. The format conversion template library presets 8 types of protocol converters such as customs message standards and port TOS system interfaces. The consistency check frequency is set to be executed every 5 seconds after the instruction is issued, and lasts for three verification cycles. When the rollback process is started, the backup instruction channel is automatically activated and the previous valid instruction set is resent.

[0068] More specifically, during the transmission of emergency instructions, key control instructions are added with digital timestamps and quantum encryption signatures, and the tamper-proof level reaches the GB / T 39786-2021 standard. The receiving end of the physical site is equipped with an intelligent parser to automatically identify the instruction type and distribute it to the corresponding subsystem: cargo allocation instructions are sent to the WMS system, equipment start and stop instructions are routed to the PLC controller, and process change instructions are synchronized to the customs clearance business engine.

[0069] The emergency instruction set includes emergency cargo space allocation plan, backup equipment activation instructions and customs clearance process downgrade strategy.

[0070] In some embodiments, the S5 includes the following sub-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, 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 (KPI) and synchronize it to the customs supervision platform.

[0071] The multidimensional analysis data set integration module in S51 connects to the customs single window, WMS warehousing system and risk interception database through ETL tools, and sets a 72-hour rolling time window to collect the latest business data. The customs declaration data field includes 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 forklift, the average handling speed of the AGV and the opening rate of the inspection channel. The risk interception record association module establishes a two-way index between the interception event and the original declaration document, and the abnormal cargo feature extraction includes the packaging damage rate, the abnormal point of the X-ray imaging and the unpacking inspection result code.

[0072] Specifically, the ETL cleaning rule sets a field integrity check threshold of 95%, and automatically fills in the average of the last three declarations of similar commodities for the missing declared value field. The first 8 digits of the HS code are parsed using the General Administration of Customs standard library, and unrecognized codes are marked as pending manual review. The risk event index establishes a three-level association relationship: the first level is associated with the original declaration document number, the second level is associated with the container code, and the third level is associated with the credit rating of the cargo owner.

[0073] More specifically, in the cold chain cargo scenario, the data set additionally integrates the temperature control record curve and the cold chain vehicle OBD operation data. X-ray abnormal point marking uses Bounding Box coordinate positioning with an accuracy of 0.1% of the image resolution. The data scrolling window sets a double buffer mechanism to ensure that data import does not affect the performance of the real-time business system.

[0074] The 3D heat map rendering engine in S52 uses the HSV color space mapping rule. The cargo density calculation is based on the load-bearing weight per unit cargo area. The density overlimit threshold is set differently according to the cargo category: 3 tons / square meter for ordinary cargo, 1.5 tons / square meter for dangerous goods, and 2.2 tons / square meter for cold chain cargo. The equipment alarm point marking system integrates the sound and light prompt function. When the mouse hovers over the heat map alarm area, the real-time parameter comparison curve and historical fault statistics of the equipment will pop up automatically.

[0075] Specifically, the frequency of heat map updates is synchronized with the warehouse management system, refreshing the global view every 30 seconds, and the local key areas can be manually switched to refresh mode every second. Alarm color coding rules are set: red indicates that the equipment is down for more than 1 hour, yellow indicates that the parameter deviates from the normal value by more than 30%, and purple indicates network communication interruption. The comparison curve shows the dynamic relationship between the current value and the average value of the past 24 hours and the peak value of this week.

[0076] 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. Equipment alarms are linked to the emergency response plan library, and three sets of alternative response plans can be directly viewed by clicking on the alarm point. The thermal map view control supports VR device access to achieve immersive space situation inspection.

[0077] The S53 full-link tracking trajectory uses space-time cube visualization technology, with the X-axis representing geographic coordinate longitude, the Y-axis representing latitude, and the Z-axis mapping the time dimension. The cargo movement path is smoothed by B-spline curves, and the specific operation type and time consumption are marked at the inspection stop nodes. The trajectory playback function supports speed control and event marking, which can quickly locate efficiency bottlenecks in the customs clearance process.

[0078] Specifically, the spatiotemporal index database uses GeoHash encoding to convert geographic coordinates into 32-bit strings, and the timestamp field has a millisecond accuracy. The B-spline curve control point interval is set to twice the cargo location update cycle to ensure a balance between path smoothness and data authenticity. The pop-up window for checking node details displays the employee number, equipment number, and operation compliance score.

[0079] More specifically, the fresh goods trajectory adds a temperature change trend overlay, and uses a color gradient bar to indicate the temperature control compliance throughout the process. The efficiency bottleneck analysis module automatically identifies the timeout node and generates optimization suggestions when the time taken for a single link exceeds 200% of the average processing time for similar goods. When the trajectory data is compressed and stored, key path points are retained, and the compression rate can reach 15% of the original data volume.

[0080] The S54 intelligent analysis report generator has a built-in dynamic template engine. The KPI indicator excludes the force majeure period in the on-time release of goods statistics, and the total declared quantity includes the declaration records of the cancelled orders but data interaction. The correct interception judgment requires a three-level review process, and the total interception quantity statistics cover both automatic interception by the system and manually confirmed interception cases. The report synchronization mechanism is encrypted using the national secret SM2 algorithm and transmitted to the supervision platform through the customs dedicated network.

[0081] Specifically, the force majeure period list is connected to the meteorological bureau's warning data interface, automatically marking 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 operational details, and the management version highlights trend analysis. The three-level review process sets a 48-hour processing time limit, and interception cases that have not been reviewed within the deadline are not included in the accuracy statistics.

[0082] More specifically, the report appendix includes a corporate credit profile module, which highlights the contribution of AA-rated companies to improving customs clearance efficiency. Data encryption transmission uses quantum random numbers to generate keys, effectively preventing replay attacks. The receiving end of the regulatory platform is equipped with an intelligent parser to automatically extract KPI indicators and store them in the performance appraisal database.

[0083] 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.

[0084] When counting the number of goods released on time in the customs clearance time efficiency compliance rate, the system connects the customs release instruction timestamp and the declaration completion time node, and the on-time standard is set differently according to the type of goods: ordinary goods are based on 24 hours after declaration, cold chain goods are compressed to 12 hours, and dangerous goods are extended to 48 hours. The total number of declared goods includes declaration records that have been withdrawn but have completed data verification, and excludes invalid declarations that are automatically rejected by the system due to format errors. The time window statistical period supports flexible switching by natural day, work week or custom time period, and the delay period caused by abnormal weather is automatically marked and excluded from the calculation.

[0085] Specifically, the timestamp alignment uses the Beidou timing system, and the error is controlled within 50 milliseconds. The time determination of cold chain goods adds a temperature control compliance verification link. If the temperature fluctuation exceeds ±2°C throughout the process, the benchmark time will be automatically extended by 6 hours. When the dangerous goods declaration timeout is counted, if the inspection process requires additional time due to safety inspections, you can apply to extend the benchmark time, but manual approval is required. When the statistical report is generated, the system automatically marks the top five and bottom five companies in the compliance rate ranking.

[0086] In the cross-border e-commerce supervision scenario, the on-time standard is shortened to 8 hours and linked to the real-time delivery data of the logistics platform. The system sets up a red, yellow and green three-color early warning mechanism: red for compliance rates below 80%, yellow for 80-90%, and green for above 90%. For enterprises that are marked red for three consecutive cycles, the customs compliance coaching process is automatically triggered.

[0087] The correct interception judgment in the risk interception accuracy rate needs to go through a three-level review process: the first-level system automatically marks high-risk goods, the second-level manual review of document inconsistencies, and the third-level laboratory test to confirm the violation. The total interception quantity statistics include suspicious items identified by the intelligent image review system, high-risk declarations predicted by the risk model, and abnormal goods found by customs officers during on-site inspections. The definition standards for high-risk goods include terrorism-related controlled items, intellectual property infringement goods, and goods with excessive safety indicators.

[0088] Specifically, the automatic marking stage adopts a multi-model voting mechanism, and interception is triggered when two of the three risk prediction models output high risk. The manual review interface integrates augmented reality technology, and customs officers wearing smart glasses can superimpose the X-ray view of the goods and historical similar cases. Laboratory test results are stored through blockchain, and cases with a test cycle of more than 72 hours are temporarily suspended as pending and not included in the statistics.

[0089] Preferably, an independent judgment rule is set for the luxury goods category. When the brand authorization chain verification fails and the declared value is 30% lower than the market price, it is directly judged as high risk. The system establishes a false positive case library, performs feature analysis on the wrong interception cases and feeds back to the model training set. The accuracy report generates a comparative analysis matrix based on the three dimensions of customs area, commodity category, and risk type.

[0090] The KPI calculation engine is equipped with a dynamic calibration mechanism to automatically adjust the statistical parameter benchmark value every month. The customs clearance time benchmark is updated based on the average customs clearance efficiency over the past three months, and the risk interception threshold dynamically fluctuates with the seizure rate. The calibration process introduces a fuzzy logic algorithm. When external policies change or regulatory priorities are adjusted, the system automatically and smoothly transitions to the new parameter system.

[0091] Specifically, the adjustment range of the benchmark value is limited to ±15% to prevent sudden changes in parameters from affecting the adaptability of enterprises. 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 and supports the calculation standard of any historical point in time.

[0092] More specifically, the system sets up a policy sensitivity analysis module. When the country adjusts import and export tariffs or adds control lists, it automatically predicts the KPI fluctuation range and generates a plan. The calibration results are stress-tested through the digital twin system to verify the stability of the new parameters under the impact of 200% declared traffic. The enterprise user end simultaneously pushes parameter change notifications and provides a simulation calculator to estimate the impact value.

[0093] The above-mentioned embodiments of the present invention have the following beneficial effects: the present invention can realize accurate risk identification and early warning by collecting multi-source heterogeneous data in real time and performing standardized processing, and analyze the compliance of goods HS codes and the correlation characteristics of abnormal events in regulatory places in combination with the federated learning framework, so as to provide a scientific basis for customs management. The reinforcement learning algorithm dynamically generates the inspection and control plan, and can flexibly adjust the inspection strategy according to the real-time operation status of the regulatory place, optimize the inspection resource allocation, and improve the inspection efficiency and accuracy. At the same time, calling the blockchain smart contract to verify the consistency of the customs declaration and the logistics documents, and using the OCR engine combined with the knowledge graph database to verify the logical contradictions of the documents can improve the automation level and accuracy of the document review, and reduce the workload and error rate of manual review. In addition, according to the real-time load data of the regulatory place, the cargo distribution strategy is dynamically adjusted, and the cargo space allocation is optimized, which can minimize the storage cost and maximize the access efficiency, and further improve the operational efficiency of the port.

[0094] By building a three-dimensional digital model of the supervision site and injecting preset emergency event types, and evaluating the impact of events on customs clearance efficiency through Monte Carlo simulation, a priority-ordered emergency instruction set can be generated to effectively improve the emergency response capabilities of the port and ensure the stable operation of the customs clearance process. At the same time, by integrating customs declaration data, site operation data and risk interception records, building a multidimensional analysis data set, and generating a full-process visual supervision view, the full-link tracking of goods from declaration, inspection to release can be achieved, 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 to quickly grasp the customs clearance timeliness and risk interception situation, thereby further optimizing the port management strategy and improving the overall efficiency and intelligence level.

[0095] like Figure 2 As shown, in some embodiments, a digital port smart customs system 200 based on artificial intelligence includes: The multi-source data collection module 201 is 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 positioning data of containers, environmental monitoring data of supervision sites, and checkpoint pass records; The data preprocessing module 202 is used to perform standardized processing on multi-source heterogeneous data based on the smart port data base, generate structured customs declaration data streams and distribute them to the business system; The risk prediction module 203 is used to construct a risk prediction model using a federated learning framework to analyze the correlation characteristics of the HS code compliance of goods and abnormal events in the supervision sites; A reinforcement control decision module 204 is 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; The consistency verification module 205 is used to call the blockchain smart contract to verify the consistency between the customs declaration and the logistics document; The multimodal document parsing module 206 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 207, used to dynamically adjust the cargo distribution strategy according to the real-time load data of the supervision site management system; The digital twin module 208 is used to simulate emergencies in regulatory places through digital twin technology and generate emergency response plans; The visual supervision module 209 is used to generate a full-process visual supervision view on the smart port data base.

[0096] It is understandable that the modules recorded in the digital port smart customs system 200 based on artificial intelligence are similar to the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the digital port smart customs method based on artificial intelligence are also applicable to the digital port smart customs system 200 based on artificial intelligence and the modules contained therein, and will not be repeated here.

[0097] The above descriptions are only some preferred embodiments of the present invention and an explanation of the technical principles used. 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 a specific combination of the above technical features, but 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 above features are replaced with (but not limited to) technical features with 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; 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 between customs declarations and logistics documents; S4. Use the OCR engine to parse the contents of the attached documents and verify the logical contradictions of the documents in combination with the knowledge graph database; Dynamically adjust cargo distribution strategies based on real-time load data from the regulatory 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 construction of the smart port data base in S2 includes: A distributed architecture is used to deploy a data cleaning engine to eliminate moiré patterns and scanning distortion in document images; The RFID positioning data and the video stream data are aligned at the millisecond level through the spatiotemporal fusion algorithm; Establish a data lineage tracking mechanism to record the data flow path between business subsystems.

4. 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.

5. 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.

6. 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.

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

8. 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.

9. The method according to claim 8, 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.

10. 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 positioning data of containers, 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; 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; Multimodal document parsing module, which uses the OCR engine to parse the contents of the attached documents and verifies logical contradictions in 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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