Intelligent block chain data traceability method and system based on intelligent port

Abstract

The invention discloses an intelligent block chain data traceability method and system based on an intelligent port, and relates to the technical field of container management, and the system comprises a data collection module, an identification analysis module, a management optimization module, a data management module and a broadcast module. The data acquisition module is responsible for acquiring entrance and exit condition information, path video information and container number information, processing the acquired image data through the preprocessing unit, and transmitting the processed data information to the identification analysis module; all key information circulation paths, container state values and container numbers of the containers are broadcasted to a block chain network through a broadcast module to ensure the authenticity of data, the circulation history of the containers is recorded by using a Merkle Tree structure, and the data consistency between nodes is ensured by using a Raft consensus mechanism. And the data change can generate a timestamp, and the timestamp is packaged and stored in the block chain, so that the operation record can be traced and cannot be tampered.

Description

Technical Field

[0001] The present invention relates to the technical field of container management, and specifically to a blockchain data intelligent tracing method and system based on a smart port. Background Art

[0002] With the rapid development of global trade, containers, as the main carrier of cargo transportation, have become an indispensable core link in international logistics. The traditional container management method faces the problems of data opacity and low management efficiency. The circulation, storage, transportation and security monitoring of containers at ports still rely on manual operation and paper records. Traditional port management systems adopt a centralized management model, where container information is stored in a single local database. This centralized data storage results in information blockage and inability to spread. In addition, if the central server is attacked or fails, container information may be tampered with or lost, thus affecting the traceability of the entire logistics. In view of the above technical defects, a solution is now proposed. Summary of the invention

[0003] In view of the shortcomings of the existing technology, the present invention provides a blockchain data intelligent tracing method and system based on a smart port.

[0004] To achieve the above objectives, the present invention is implemented through the following technical solutions: a blockchain data intelligent traceability method and system based on smart ports, including: a data acquisition module, an identification and analysis module, a management optimization module, a data management module and a broadcast module; The data acquisition module is responsible for collecting information including import and export information, route video information and box number information, and processes the collected image data through the pre-processing unit, and transmits the processed data information to the recognition and analysis module; The recognition and analysis module uses Faster R-CNN to detect the path video information and container number information, and uses Tesseract OCR to extract the container number information. The recognition and analysis module uses triangulation to calculate the three-dimensional coordinates of the container, and combines GPS data to convert it into global coordinates as the location information of the container. It records the flow history and generates a timestamp and container number, and the location information is packaged and transmitted to the management optimization module; The management optimization module manages containers through container numbers and location information, calculates the container status value F according to the container status calculation method, and classifies the containers into low risk, medium risk and high risk. Low risk containers are transported according to standard warehousing, medium risk containers are allocated to the secondary storage area, and high risk containers are placed in the hazardous goods storage area. The circulation path information, timestamp and container number are transmitted to the data management module; The data management module divides the flow path information, timestamp and container number into multiple shards, and uses DHT to allocate shards based on data type and timestamp. Each shard is stored, verified and processed by an independent node cluster. The node assigns tasks through the Round Robin algorithm and then transmits the tasks to the broadcast module. When querying, the shard where the data is located is identified through Consistent Hashing, and the query request is sent to the target node through RPC. The query result is processed by MapReduce and returned to the user; The broadcast module receives tasks and broadcasts them to the blockchain network, adopts the Raft consensus mechanism to ensure data synchronization, and uses smart contracts to update the status.

[0005] The data acquisition module includes a video acquisition unit and a preprocessing unit. The video acquisition unit includes a remote camera and a near-end camera for real-time collection of container image data, and combines infrared temperature detection and humidity sensors to collect environmental parameters. The remote camera is used to collect the import and export conditions and path video information of the container, and the near-end camera is used to shoot the container number information of the container. Finally, all video data information is transmitted to the preprocessing unit; The preprocessing unit is responsible for processing the collected image data, converting the video data into H.265 encoding format, using the Non-LocalMeans denoising algorithm to remove noise in the data, and using the Gamma transform to enhance the brightness and contrast of images in low-light environments. The video data processing module uses FFmpeg to extract key frames and transmits the processed key frame video data to the recognition and analysis module.

[0006] The recognition and analysis module uses Faster R-CNN to perform target detection, cuts out the area containing the container, uses Tesseract OCR to perform character recognition on the cut container area to extract the container number information, and uses CRNN combined with CTC Loss technology to optimize recognition of blurred and deformed characters; the recognition and analysis module uses triangulation to calculate the three-dimensional coordinates of the container, and combines GPS data to convert them into global coordinates as the location information of the container; the recognition and analysis module generates a timestamp and the container number and location coordinate information, which are packaged and transmitted to the management optimization module.

[0007] The management optimization module calculates the container status value F according to the formula. The specific formula is as follows: Among them, T is the temperature and humidity parameter value; D is the container depreciation percentage value, which is obtained based on the analysis of the usage level of the container surface; R is the cargo danger level value; W1 is the weight coefficient of the temperature and humidity parameter value, W2 is the weight coefficient of the container depreciation percentage value, and W3 is the weight coefficient of the cargo danger level value. The weight coefficient can be adjusted according to actual application to optimize the container management strategy.

[0008] The management optimization module evaluates the status of the container by the F value, and classifies it into low risk, medium risk and high risk. The management optimization module sets a first threshold and a second threshold, wherein the second threshold is greater than the first threshold. When the F value of a container is less than the first threshold, the container is marked as low risk. When the F value of the container is greater than or equal to the first threshold and less than the second threshold, the container is marked as medium risk. When the F value of the container is greater than the second threshold, the container is marked as high risk. The low-risk containers are dispatched to regular storage areas and circulated through ordinary transportation channels, medium-risk containers are deployed to secondary storage areas, and high-risk containers are arranged to be stored in dangerous goods storage areas. Special vehicles are used for transportation, and the circulation path information, timestamp and container number are transmitted to the data management module.

[0009] The data management module is responsible for storing video data and blockchain information. The data management module divides the video and contract data into multiple shards. The shard division is based on the data type, geographic location and timestamp. Specifically, the distributed hash table DHT is used for shard allocation. The hash_function method is used to map data to different shards. Each shard is stored, verified and processed by an independent node cluster. The nodes are assigned tasks through the load balancing algorithm Round Robin. Each node only stores data related to its own shard, and then transmits the task to the broadcast module. The blockchain system uses the routing mechanism Consistent Hashing to identify the shard where the query data is located, and sends the query request to the node of the corresponding shard through RPC. The node executes the query operation, and the query result is processed through the MapReduce method and then returned to the user. The shards are coordinated through the cross-shard communication mechanism Cross-ShardTransactions, so that the data of different shards can be updated and exchanged synchronously. Cross-shard data synchronization uses Kafka as the message queue for real-time update and consistency guarantee.

[0010] The broadcast module receives tasks and broadcasts them to the blockchain network. It uses the Raft consensus mechanism to achieve state synchronization and improve data consistency in a distributed environment. The Merkle Tree structure is used to record data change history. The smart contract Solidity is used to automatically update the container status. The Kafka batch processing mechanism is used for large-scale data synchronization.

[0011] The specific method of the blockchain data intelligent traceability method based on the smart port is as follows: S1, the data acquisition module collects import and export information, route video and box number information, and transmits them to the recognition and analysis module after preprocessing; S2, the recognition and analysis module performs target detection, extracts container number information, calculates three-dimensional coordinates and converts them into global coordinates, and generates timestamp packaged data for transmission to the management optimization module; S3, the management optimization module classifies the containers for storage and transportation based on the container numbers, generates the circulation path information, timestamp and container number, and transmits them to the data management module; S4, the data management module divides the flow path information, timestamp and container number information into slices and assigns tasks, and then transmits the tasks to the broadcast module, identifies the slice where the query data is located through the routing mechanism, executes the query and returns the results to the user; S5. The broadcast module receives the task and broadcasts it to the blockchain network, adopts the consensus mechanism to achieve status synchronization, and uses smart contracts to update the container status.

[0012] The present invention provides a blockchain data intelligent traceability method and system based on smart ports. Compared with the prior art, it has the following beneficial effects: The present invention uses a broadcast module to broadcast all key information flow paths, container status values ​​and container numbers of containers to the blockchain network to ensure data authenticity, uses a Merkle Tree structure to record the flow history of containers, and adopts a Raft consensus mechanism to ensure data consistency between each node. Data changes will generate a timestamp and be packaged and stored in the blockchain to ensure that operation records can be traced and cannot be tampered with. The present invention collects container data through cameras and sensors, obtains the status, container number and location of the container in real time, uses a remote camera to clearly capture the image and flow path of the container, and uses a near-end camera with a long focal length and high magnification zoom function to collect the container number information of the container. By replacing the positioning device with a camera, the present invention is compatible with traditional old containers, thereby eliminating the need to install hardware positioning devices on traditional containers, and reducing the use and operation costs of enterprises. The present invention extracts the container number information of the container through the identification and analysis module, calculates the three-dimensional coordinates and global position of the container by using the triangulation method, classifies the container by risk according to the F value, and formulates targeted transportation strategies according to different risk levels to dynamically optimize the circulation path of the container, reasonably regulates the storage location and transportation path of the container, thereby avoiding traffic congestion and transportation bottlenecks in the port, and improving the circulation efficiency and management level of the port. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a schematic diagram of the principle framework of the present invention; Figure 2 Schematic diagram of the working process of the system of the present invention. DETAILED DESCRIPTION

[0014] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0015] See also Figure 1 ,This application provides a blockchain data intelligent traceability method and system based on smart ports, including: data acquisition module, identification and analysis module, management optimization module, data management module and broadcast module; The data acquisition module is responsible for collecting information including import and export information, route video information and box number information, and processes the collected image data through the pre-processing unit, and transmits the processed data information to the recognition and analysis module; The recognition and analysis module uses Faster R-CNN to detect the path video information and container number information, and uses Tesseract OCR to extract the container number information. The recognition and analysis module uses triangulation to calculate the three-dimensional coordinates of the container, and combines GPS data to convert it into global coordinates as the location information of the container. It records the flow history and generates a timestamp and container number, and the location information is packaged and transmitted to the management optimization module; The management optimization module manages containers through container numbers and location information, calculates the container status value F according to the container status calculation method, and classifies the containers into low risk, medium risk and high risk. Low risk containers are transported according to standard warehousing, medium risk containers are allocated to the secondary storage area, and high risk containers are placed in the hazardous goods storage area. The circulation path information, timestamp and container number are transmitted to the data management module; The data management module divides the flow path information, timestamp and container number into multiple shards, and uses DHT to allocate shards based on data type and timestamp. Each shard is stored, verified and processed by an independent node cluster. The node assigns tasks through the Round Robin algorithm and then transmits the tasks to the broadcast module. When querying, the shard where the data is located is identified through Consistent Hashing, and the query request is sent to the target node through RPC. The query result is processed by MapReduce and returned to the user; The broadcast module receives tasks and broadcasts them to the blockchain network, adopts the Raft consensus mechanism to ensure data synchronization, and uses smart contracts to update the status.

[0016] The data acquisition module includes a video acquisition unit and a preprocessing unit. The video acquisition unit includes a remote camera and a near-end camera for real-time collection of container image data, and combines infrared temperature detection and humidity sensors to collect environmental parameters. The remote camera is used to collect the import and export conditions and path video information of the container, and the near-end camera is used to shoot the container number information of the container. Finally, all video data information is transmitted to the preprocessing unit; The preprocessing unit is responsible for processing the collected image data, converting the video data into H.265 encoding format, using the Non-LocalMeans denoising algorithm to remove noise in the data, and using the Gamma transform to enhance the brightness and contrast of images in low-light environments. The video data processing module uses FFmpeg to extract key frames and transmits the processed key frame video data to the recognition and analysis module.

[0017] The preprocessing unit is responsible for processing the collected image data, converting the video data into H.265 encoding format, using the Non-LocalMeans denoising algorithm to remove noise in the data, and using the Gamma transform to enhance the brightness and contrast of images in low-light environments. The video data processing module uses FFmpeg to extract key frames. Specifically, the -vf select=eq(n\,mod(n,5)) command is used to extract key frames every 5 frames to reduce redundancy, and the processed video data is transmitted to the recognition and analysis module.

[0018] It not only relies on the information of neighboring pixels, but also considers the similarity of global pixels to effectively reduce noise; The recognition and analysis module uses Faster R-CNN for target detection, cuts out the area containing the container, uses Tesseract OCR to perform character recognition on the cut container area to extract the container number information, and uses CRNN combined with CTC Loss technology to optimize recognition of blurred and deformed characters; the recognition and analysis module uses triangulation to calculate the three-dimensional coordinates of the container, and combines GPS data to convert it into global coordinates as the location information of the container; the recognition and analysis module generates a timestamp and the container number and location coordinate information, which are packaged and transmitted to the management optimization module.

[0019] Faster R-CNN is a deep learning-based target detection algorithm that can detect objects quickly and accurately. Its core is the combination of Region Proposal Network (RPN) and Convolutional Neural Network (CNN). In the recognition and analysis module, Faster R-CNN is used to detect containers, locate container regions, and prepare for further OCR processing. Triangulation is a method of calculating the three-dimensional position of an unknown point using two known points and their relative angles. In the recognition and analysis module, the three-dimensional coordinates of the container are calculated using triangulation and converted into global coordinates in combination with GPS data. The management optimization module calculates the container status value F according to the formula. The specific formula is as follows: Among them, T is the temperature and humidity parameter value; D is the container depreciation percentage value, which is obtained based on the analysis of the usage level of the container surface; R is the cargo danger level value; W1 is the weight coefficient of the temperature and humidity parameter value, W2 is the weight coefficient of the container depreciation percentage value, and W3 is the weight coefficient of the cargo danger level value. The weight coefficient can be adjusted according to actual application to optimize the container management strategy.

[0020] The temperature and humidity parameter value T and the cargo danger level value R are obtained by connecting to the local management system.

[0021] To better illustrate, we can assign values ​​to these variables and perform operations; for example: Assume that the temperature and humidity parameters are T=0.85T = 0.85T=0.85; The depreciation percentage of the container is D = 0.20D = 0.20D = 0.20; The danger level of the cargo is R=0.75R = 0.75R=0.75; Weight coefficients W1=0.4, W2=0.3, W3=0.3; Then, the calculation of the status value F of the container is as follows: F = (0.4 0.75) + (0.3 0.20) + (0.3 0.85) Calculation result: F = 0.30 + 0.06 + 0.255 = 0.615.

[0022] The management optimization module evaluates the status of the container through the F value, classifies it according to low risk, medium risk, and high risk. The management optimization module sets a first threshold and a second threshold, where the second threshold is greater than the first threshold. When the F value of a certain container is less than the first threshold, the container is marked as low risk. When the F value of the container is greater than or equal to the first threshold and less than the second threshold, the container is marked as medium risk. When the F value of the container is greater than the second threshold, the container is marked as high risk; Low-risk containers are scheduled to the regular storage area and circulated through the ordinary transportation channel. Medium-risk containers are allocated to the secondary storage area. High-risk containers are arranged in the dangerous goods storage area during storage and special vehicles are used during transportation. The circulation path information, timestamp, and container number are transmitted to the data management module; Set a specific classification standard for the status value F of the container; such a classification method is based on the status value F to determine the risk level of the container; Risk level classification of the container status value F: Low risk (F ≤ 0.4); Medium risk (0.4 < F ≤ 0.7); High risk (F > 0.7); According to the calculated container status value F = 0.615, the assumed risk level of the container is medium risk.

[0023] The smart contract is an automatically executed program in the blockchain that automatically executes the contract terms through predefined conditions; ensures that the status update operation of the container information is transparent and tamper-proof; the broadcast module updates the status information of the container through the smart contract and ensures that all participants see the same status; Low-risk containers will be dispatched to regular storage areas. The management standardization of these areas has no special restrictions. The storage area conditions meet the basic temperature and humidity control requirements and are suitable for the storage of general goods. Medium-risk containers are preferentially allocated to storage areas with better conditions. These areas have better temperature and humidity control facilities and can cope with environmental conditions with minor damage, provide more standard warehouse facilities, and prevent excessive vibration, excessive humidity or low temperature. High-risk containers need to be arranged in specific hazardous goods storage areas. These areas are designed with potential risk factors in mind and are equipped with fire prevention, explosion prevention, leakage prevention and other measures. These storage areas are far away from other types of cargo storage areas and have strict safety protection facilities. Accidents can be effectively handled in the shortest time possible to reduce harm to goods and personnel. Special transport vehicles are used to transport high-risk containers. These vehicles are equipped with additional protective measures and emergency response equipment.

[0024] The data management module is responsible for storing video data and blockchain information. The data management module divides the video and contract data into multiple shards. The shard division is based on the data type, geographic location and timestamp. The distributed hash table DHT is used for shard allocation. The hash_function method is used to map data to different shards. Each shard is stored, verified and processed by an independent node cluster. The nodes are assigned tasks through the load balancing algorithm Round Robin. Each node only stores data related to its own shard, and then transmits the task to the broadcast module. The blockchain system uses the routing mechanism Consistent Hashing to identify the shard where the query data is located, and sends the query request to the node of the corresponding shard through RPC. The node executes the query operation, and the query result is processed through the MapReduce method and then returned to the user. The shards are coordinated through the cross-shard communication mechanism Cross-ShardTransactions, so that the data of different shards can be updated and exchanged synchronously. Cross-shard data synchronization uses Kafka as the message queue for real-time update and consistency guarantee.

[0025] The broadcast module receives tasks and broadcasts them to the blockchain network. It uses the Raft consensus mechanism to achieve state synchronization and improve data consistency in a distributed environment. The Merkle Tree structure is used to record data change history. The smart contract Solidity is used to automatically update the container status. The Kafka batch processing mechanism is used for large-scale data synchronization.

[0026] The specific method of blockchain data intelligent traceability based on smart ports is as follows: S1, the data acquisition module collects import and export information, route video and box number information, and transmits them to the recognition and analysis module after preprocessing; S2, the recognition and analysis module performs target detection, extracts container number information, calculates three-dimensional coordinates and converts them into global coordinates, and generates timestamp packaged data for transmission to the management optimization module; S3, the management optimization module classifies the containers for storage and transportation based on the container numbers, generates the circulation path information, timestamp and container number, and transmits them to the data management module; S4, the data management module divides the flow path information, timestamp and container number information into slices and assigns tasks, and then transmits the tasks to the broadcast module, identifies the slice where the query data is located through the routing mechanism, executes the query and returns the results to the user; S5. The broadcast module receives the task and broadcasts it to the blockchain network, adopts the consensus mechanism to achieve status synchronization, and uses smart contracts to update the container status.

[0027] Furthermore, the present invention broadcasts all key information flow paths, container status values ​​and container numbers of containers to the blockchain network through a broadcast module to ensure the authenticity of the data, uses a Merkle Tree structure to record the flow history of containers, and adopts a Raft consensus mechanism to ensure data consistency between each node. Data changes will generate a timestamp and be packaged and stored in the blockchain to ensure that operation records are traceable and cannot be tampered with.

[0028] Furthermore, the present invention collects container data through cameras and sensors, obtains the status, container number and location of the container in real time, uses a far-end camera to clearly capture the image and flow path of the container, and uses a near-end camera with a long-focus and high-magnification zoom function to collect the container number information of the container. By replacing the positioning device with the camera, it can be compatible with traditional old containers, thereby eliminating the need to install hardware positioning equipment on traditional containers, reducing the company's usage and operating costs.

[0029] Furthermore, the present invention extracts the container number information through the identification and analysis module, calculates the three-dimensional coordinates and global position of the container using the triangulation method, classifies the container by risk based on the F value, and formulates targeted transportation strategies according to different risk levels to dynamically optimize the circulation path of the container, reasonably regulates the storage location and transportation path of the container, thereby avoiding traffic congestion and transportation bottlenecks in the port, and improving the circulation efficiency and management level of the port.

[0030] Specific workflow: First, the data acquisition module uses a camera to collect container image data in real time, and combines infrared temperature and humidity sensors to monitor environmental parameters. The collected image data is encoded, denoised, and enhanced by the preprocessing unit. After extracting the key frame, it is transmitted to the recognition and analysis module. The recognition and analysis module uses the target detection algorithm Faster R-CNN and OCR technology to extract the container number, and calculates the three-dimensional coordinates and global position of the container through triangulation, and generates timestamp packaged data for transmission to the management optimization module. The management optimization module classifies the container risk according to the container status value F, and implements different transportation strategies according to different risk levels. Low-risk containers use standard warehousing and transportation methods, are dispatched to conventional storage areas and circulated through ordinary transportation channels, medium-risk containers need to be allocated to secondary storage areas first, and high-risk containers take special control measures. They are arranged in dangerous goods storage areas during storage, special vehicles are used during transportation, and the stop time on the way is reduced. The data management module divides the flow path information, timestamp and container number information into slices and assigns tasks, and then transmits the tasks to the broadcast module. The broadcast module receives the tasks and broadcasts them to the blockchain network. The Raft consensus mechanism is used to ensure data consistency, and smart contracts are used to automatically update the container status.

[0031] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0032] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A blockchain data intelligent traceability system based on smart ports, characterized in that: include: Data collection module, identification and analysis module, management optimization module, data management module and broadcast module; The data acquisition module is responsible for collecting information including import and export information, route video information and box number information, and processes the collected image data through the pre-processing unit, and transmits the processed data information to the recognition and analysis module; The recognition and analysis module uses Faster R-CNN to detect the path video information and container number information, and uses Tesseract OCR to extract the container number information. The recognition and analysis module uses triangulation to calculate the three-dimensional coordinates of the container, and combines GPS data to convert it into global coordinates as the location information of the container. It records the flow history and generates a timestamp and container number, and the location information is packaged and transmitted to the management optimization module; The management optimization module manages containers by container numbers and location information, calculates the container status value F according to the container status calculation method, and classifies the containers into low risk, medium risk and high risk. Low risk containers are transported according to standard warehousing, medium risk containers are allocated to the secondary storage area, and high risk containers are placed in the hazardous goods storage area. The circulation path information, timestamp and container number are transmitted to the data management module; The data management module divides the flow path information, timestamp and container number into multiple shards, and uses DHT to allocate shards based on data type and timestamp. Each shard is stored, verified and processed by an independent node cluster. The node assigns tasks through the Round Robin algorithm and then transmits the tasks to the broadcast module. When querying, the shard where the data is located is identified through Consistent Hashing, and the query request is sent to the target node through RPC. The query result is processed by MapReduce and returned to the user; The broadcast module receives tasks and broadcasts them to the blockchain network, adopts the Raft consensus mechanism to ensure data synchronization, and uses smart contracts to update the status.

2. According to the blockchain data intelligent traceability system based on smart ports according to claim 1, it is characterized in that: The data acquisition module includes a video acquisition unit and a preprocessing unit. The video acquisition unit includes a remote camera and a near-end camera for real-time collection of container image data, and combines infrared temperature detection and humidity sensors to collect environmental parameters. The remote camera is used to collect the import and export conditions and path video information of the container, and the near-end camera is used to shoot the container number information of the container. Finally, all video data information is transmitted to the preprocessing unit; The preprocessing unit is responsible for processing the collected image data, converting the video data into H.265 encoding format, using the Non-LocalMeans denoising algorithm to remove noise in the data, and using the Gamma transform to enhance the brightness and contrast of images in low-light environments. The video data processing module uses FFmpeg to extract key frames and transmits the processed key frame video data to the recognition and analysis module.

3. According to the blockchain data intelligent traceability system based on smart ports according to claim 1, it is characterized in that: The recognition and analysis module uses Faster R-CNN to perform target detection, cuts out the area containing the container, uses Tesseract OCR to perform character recognition on the cut container area to extract the container number information, and uses CRNN combined with CTC Loss technology to optimize recognition of blurred and deformed characters; the recognition and analysis module uses triangulation to calculate the three-dimensional coordinates of the container, and combines GPS data to convert them into global coordinates as the location information of the container; the recognition and analysis module generates a timestamp and the container number and location coordinate information, which are packaged and transmitted to the management optimization module.

4. According to the blockchain data intelligent traceability system based on smart ports according to claim 1, it is characterized in that: The management optimization module calculates the container status value F according to the formula. The specific formula is as follows: Among them, T is the temperature and humidity parameter value; D is the container depreciation percentage value, which is obtained based on the analysis of the usage level of the container surface; R is the cargo danger level value; W1 is the weight coefficient of the temperature and humidity parameter value, W2 is the weight coefficient of the container depreciation percentage value, and W3 is the weight coefficient of the cargo danger level value. The weight coefficient can be adjusted according to actual application to optimize the container management strategy.

5. According to the blockchain data intelligent traceability system based on smart ports according to claim 1, it is characterized in that: The management optimization module evaluates the status of the container by the F value, and classifies it into low risk, medium risk and high risk. The management optimization module sets a first threshold and a second threshold, wherein the second threshold is greater than the first threshold. When the F value of a container is less than the first threshold, the container is marked as low risk. When the F value of the container is greater than or equal to the first threshold and less than the second threshold, the container is marked as medium risk. When the F value of the container is greater than the second threshold, the container is marked as high risk. The low-risk containers are dispatched to regular storage areas and circulated through ordinary transportation channels, medium-risk containers are deployed to secondary storage areas, and high-risk containers are arranged to be stored in dangerous goods storage areas. Special vehicles are used for transportation, and the circulation path information, timestamp and container number are transmitted to the data management module.

6. According to the blockchain data intelligent traceability system based on smart ports according to claim 1, it is characterized in that: The data management module is responsible for storing video data and blockchain information. The data management module divides the video and contract data into multiple shards. The shard division is based on the data type, geographic location and timestamp. Specifically, the distributed hash table DHT is used for shard allocation. The hash_function method is used to map data to different shards. Each shard is stored, verified and processed by an independent node cluster. The nodes are assigned tasks through the load balancing algorithm Round Robin. Each node only stores data related to its own shard, and then transmits the task to the broadcast module. The blockchain system uses the routing mechanism Consistent Hashing to identify the shard where the query data is located, and sends the query request to the node of the corresponding shard through RPC. The node executes the query operation, and the query result is processed through the MapReduce method and then returned to the user. The shards are coordinated through the cross-shard communication mechanism Cross-Shard Transactions, so that the data of different shards can be updated and exchanged synchronously. Kafka is used as the message queue for real-time update and consistency guarantee for cross-shard data synchronization.

7. According to the blockchain data intelligent traceability system based on smart ports according to claim 1, it is characterized in that: The broadcast module receives tasks and broadcasts them to the blockchain network. It uses the Raft consensus mechanism to achieve state synchronization and improve data consistency in a distributed environment. The Merkle Tree structure is used to record data change history. The smart contract Solidity is used to automatically update the container status. The Kafka batch processing mechanism is used for large-scale data synchronization.

8. A blockchain data intelligent traceability method based on a smart port, based on the intelligent traceability system according to any one of claims 1 to 7, characterized in that: The specific method of the blockchain data intelligent traceability method based on the smart port is as follows: S1, the data acquisition module collects import and export information, route video and box number information, and transmits them to the recognition and analysis module after preprocessing; S2, the recognition and analysis module performs target detection, extracts container number information, calculates three-dimensional coordinates and converts them into global coordinates, and generates timestamp packaged data for transmission to the management optimization module; S3, the management optimization module classifies the containers for storage and transportation based on the container numbers, generates the circulation path information, timestamp and container number, and transmits them to the data management module; S4, the data management module divides the flow path information, timestamp and container number information into slices and assigns tasks, and then transmits the tasks to the broadcast module, identifies the slice where the query data is located through the routing mechanism, executes the query and returns the results to the user; S5. The broadcast module receives the task and broadcasts it to the blockchain network, adopts the consensus mechanism to achieve status synchronization, and uses smart contracts to update the container status.

Images