Port tallying vehicle monitoring management method and system

Abstract

The invention provides a port tallying vehicle monitoring management method and system, and relates to the technical field of port automation management, and the method comprises the steps: collecting the multi-dimensional tallying feature data of a port tallying vehicle through an Internet of Things sensing network, and carrying out the preprocessing; extracting a multi-dimensional tallying feature vector, and constructing a dynamic weighted directed graph model in combination with the port traffic situation; planning an optimal tallying driving path for the vehicle based on the dynamic weighted directed graph model, generating an optimal tallying operation plan, and pushing the optimal tallying operation plan to the vehicle-mounted intelligent terminal; when the vehicle executes the plan, monitoring vehicle position posture change and cargo state change through a multi-sensor fusion technology, and when an abnormal event is monitored, triggering a grading early warning mechanism and adjusting the plan; and after the vehicle completes the plan, the operation process data is verified based on the block chain distributed account book, and a digital twin report is generated and synchronized to the monitoring end, so that efficient, safe and intelligent monitoring management of the port tallying vehicle can be realized, and the operation efficiency and safety of the port are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of port automation management, and in particular to a port tally vehicle monitoring and management method and system. Background Art

[0002] With the rapid development of technologies such as the Internet of Things, big data, and artificial intelligence, port automation and intelligence have become the key to improving port operational efficiency and competitiveness.

[0003] In the traditional port tallying process, the monitoring and management of tallying vehicles mainly rely on manual operations and simple information technology. There are problems such as low tallying efficiency, low data accuracy, insufficient real-time monitoring capabilities and low transparency of the tallying process. It cannot meet the requirements of modern ports for efficiency and transparency of tallying operations.

[0004] Therefore, it is necessary to provide a port tally vehicle monitoring and management method and system to solve the above technical problems. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides a port tallying vehicle monitoring and management method and system, which is used to solve the problems that traditional port tallying vehicle monitoring and management methods mainly rely on manual operations and simple information means, resulting in low tallying efficiency, low data accuracy, insufficient real-time monitoring capabilities and low transparency of the tallying process.

[0006] The present invention provides a port tally vehicle monitoring and management method, the monitoring and management method comprising: The multi-dimensional tally feature data of port tally vehicles is collected in real time through the port Internet of Things sensing network and synchronously transmitted to the edge computing node for preprocessing, wherein the multi-dimensional tally feature data includes the electronic tag information of the port tally vehicles, the vehicle's three-dimensional point cloud data, and the cargo spectral feature data; Extracting a multidimensional tally feature vector corresponding to the pre-processed multidimensional tally feature data, and constructing a dynamic weighted directed graph model in combination with the real-time traffic situation of the port, wherein the multidimensional tally feature vector includes the vehicle identity characteristics, cargo status characteristics, and tallying operation priority of the port tally vehicle; Based on the dynamic weighted directed graph model, an optimal tallying driving path with time and space constraints is planned for the port tallying vehicle, and an optimal tallying operation plan is generated and pushed to the on-board intelligent terminal via 5G vehicle networking technology; When the port tallying vehicle executes the optimal tallying operation plan, it monitors the vehicle position and posture change data and cargo status change data in real time through multi-sensor fusion technology. When an abnormal event is detected, a hierarchical early warning mechanism is triggered and the optimal tallying operation plan is automatically adjusted; After the port tallying vehicle completes the optimal tallying operation plan, it verifies the integrity and consistency of the tallying operation process data based on the blockchain distributed ledger, generates a digital twin tallying report, and synchronizes it to the monitoring end through the quantum security channel.

[0007] Preferably, the real-time collection of multi-dimensional tally feature data of port tally vehicles through the port Internet of Things sensing network specifically includes: Dynamically calculating the attention weight coefficient of the sensor data in the port IoT perception network based on a multi-head attention mechanism, and introducing a time decay factor to adjust the attention weight coefficient; Based on the adjusted attention weight coefficient, the sensor data is weighted and summed to obtain the fused port IoT perception data, i.e., the multi-dimensional tally feature data: Where, represents the fused port IoT perception data, i.e., multi-dimensional tally feature data; B represents the total amount of sensor data involved in the fusion in the port IoT perception network; represents the attention weight coefficient of the bth sensor data in the port IoT perception network; represents the bth sensor data in the port IoT perception network; represents the b-th sensor data after being processed by the multi-head attention mechanism; represents the time decay term, which is used to adjust the attention weight coefficient of the sensor data according to the difference between the acquisition time of the sensor data and the current time; represents the time decay factor, ; represents the acquisition time of the bth sensor data; t represents the current time; Represent the query matrix, key matrix, and value matrix of the b-th sensor data respectively; Represents the bond matrix The transpose of Represents the bond matrix Dimension; Softmax( ) represents the activation function.

[0008] Preferably, the dynamic weighted directed graph model includes port traffic nodes and port traffic connecting sections; Based on the dynamic weighted directed graph model, a path planning objective function and constraints are set, and the path planning objective function is solved to determine the optimal tally driving path. The expressions of the path planning objective function and constraints are as follows: Where i and j represent the port traffic nodes in the dynamic weighted directed graph model; ij represents the port traffic connection segment from port traffic node i to port traffic node j in the dynamic weighted directed graph model; A represents the set of all port traffic connection segments in the dynamic weighted directed graph model; represents the generalized cost function about the current time t; represents the decision variable for road segment selection, Indicates the selection of the port transportation connection section from port transportation node i to port transportation node j, Indicates that the port transportation connection section from port transportation node i to port transportation node j is not selected. Indicates the selection of the port transportation connection section from port transportation node j to port transportation node i, Indicates that the port transportation connection section from port transportation node j to port transportation node i is not selected; represents the path planning balance coefficient; L represents the set of all port traffic nodes in the dynamic weighted directed graph model; represents the weight coefficient of port transportation node i; represents the deviation value associated with port traffic node i; They represent the starting port transportation node and the ending port transportation node of the optimal tally driving route respectively; They represent the time when the port tally vehicle arrives at the port traffic node i and j respectively; represents the communication time of the port tally vehicle from port transportation node i to port transportation node j, which depends on the decision variable .

[0009] Preferably, for the port traffic connection section ij, the traffic congestion index predicted by integrating the spatiotemporal graph convolutional network is and dynamic reward values ​​generated by reinforcement learning , we get the generalized cost function for time t : Where, Represents the fusion weight coefficient, which is used to adjust the traffic congestion index and dynamic reward value In the generalized cost function The proportion of .

[0010] Preferably, the triggering steps of the hierarchical early warning mechanism are as follows: Adopt DS evidence theory to fuse multi-source anomaly evidence and calculate anomaly index , that is, summarizing the trust distribution of each evidence source on the abnormal event and deducting the cross-conflict trust, the abnormality index The calculation formula is as follows: Where r represents the total number of multi-source anomaly evidence involved in the fusion, that is, the total number of evidence sources; A represents the abnormal event; represents the complement of abnormal event A, i.e., non-abnormal events; k and l represent the index of the evidence source, , , ; represents the trust distribution of the k-th evidence source to the abnormal event A, that is, the degree of trust that the k-th evidence source believes that the abnormal event A has occurred; Indicates the lth source of evidence for non-abnormal events The trust distribution of the lth evidence source is that it is not an abnormal event the level of trust that occurs; Set two levels of warning thresholds, including the first level warning threshold and the second level warning threshold ; According to the abnormality index The comparison result with the two-level warning threshold triggers the corresponding warning level: .

[0011] Preferably, the automatic adjustment process of the optimal tallying operation plan is as follows: Set a plan adjustment objective function and constraints, solve the plan adjustment objective function, and obtain the adjusted optimal tallying operation plan. The expressions of the plan adjustment objective function and constraints are as follows: Where M represents the total number of tally operations; N represents the total number of resources performing tally operations; represents the cost of the mth tallying operation performed by the nth resource; represents the tally operation allocation decision vector, Indicates that the mth tally operation is assigned to the nth resource, Indicates that the mth tally operation is not assigned to the nth resource; The weight coefficient representing the adjustment deviation of the optimal tallying operation plan is used to balance the degree of deviation between the actual tallying operation allocation cost and the target tallying operation allocation cost; F represents the total number of adjustment stages; represents the adjustment weight coefficient of the mth tallying operation, which is used to distinguish the importance of different tallying operations in the adjustment process of the optimal tallying operation plan; represents the actual completion time of the mth tallying operation; represents the target completion time of the mth tallying operation; represents the processing time of the mth tallying operation; Indicates the capacity limit of the nth resource, that is, the maximum tally operation processing time carried by the nth resource; They represent the time nodes of the f+1th and fth adjustment stages respectively; Represents the indicator vector. When the mth tally operation is processed in the fth adjustment stage, , when the mth tallying operation is not processed in the fth adjustment stage, .

[0012] Preferably, after the port tallying vehicle completes the optimal tallying operation plan, the integrity and consistency of the tallying operation process data are verified based on the blockchain distributed ledger, and a digital twin tallying report is generated and synchronized to the monitoring end through a quantum secure channel, specifically including: After the port tallying vehicle completes the optimal tallying operation plan, the tallying operation process data of the port tallying vehicle is automatically collected, including cargo loading and unloading records, tallying operation time series, vehicle position coordinate sets, and equipment interaction logs, and an original tallying operation data set is generated after standardization processing; Calculate the unique hash value of the original tally operation dataset using the SM3 hash algorithm, and bind it with the original tally operation dataset to form the tally operation dataset to be verified; Pushing the to-be-verified tallying operation dataset to a blockchain distributed node, the blockchain distributed node calls an improved practical Byzantine fault-tolerant consensus algorithm to perform multi-node cross-validation on the to-be-verified tallying operation dataset, and verifying the integrity and consistency of the tallying operation process data by comparing the chain continuity of the tallying operation time series and the consistency of the unique hash value. After the verification is passed, a blockchain evidence certificate containing the digital signature of the blockchain distributed node is generated and bound to the tallying operation process data; Based on the tallying operation process data including the blockchain evidence, construct the digital twin tallying report including the vehicle entity three-dimensional model, the tallying operation process time sequence chain, the cargo attribute parameter set and the physical-virtual mapping rules; Calling the quantum key distribution protocol to generate a one-time session key, encrypting the digital twin tally report, and pushing it to the monitoring end through the quantum secure channel; After the monitoring end uses the paired key to decrypt the encrypted digital twin tally report, it verifies the authenticity of the unique hash value and the blockchain evidence certificate. After the verification is passed, it triggers the report reception confirmation signal and updates the local database.

[0013] A port tally vehicle monitoring and management system, the monitoring and management system comprising: A tally data acquisition module is used to collect multi-dimensional tally feature data of port tally vehicles in real time through the port Internet of Things perception network, and synchronously transmit it to the edge computing node for preprocessing, wherein the multi-dimensional tally feature data includes the electronic tag information of the port tally vehicles, the vehicle's three-dimensional point cloud data, and the cargo spectral feature data; a directed graph model construction module, configured to extract a multidimensional tally feature vector corresponding to the preprocessed multidimensional tally feature data, and construct a dynamic weighted directed graph model based on the real-time port traffic situation, wherein the multidimensional tally feature vector includes the vehicle identity characteristics, cargo status characteristics, and tallying operation priority of the port tally vehicle; A tallying route planning module is used to plan an optimal tallying route with time and space constraints for the port tallying vehicle based on the dynamic weighted directed graph model, generate an optimal tallying operation plan, and push it to the on-board intelligent terminal via 5G vehicle networking technology; An early warning mechanism triggering module is used to monitor vehicle position and posture change data and cargo status change data in real time through multi-sensor fusion technology when the port tallying vehicle executes the optimal tallying operation plan. When an abnormal event is detected, a hierarchical early warning mechanism is triggered and the optimal tallying operation plan is automatically adjusted; The tallying report generation module is used to verify the integrity and consistency of the tallying operation process data based on the blockchain distributed ledger after the port tallying vehicle completes the optimal tallying operation plan, generate a digital twin tallying report, and synchronize it to the monitoring end through the quantum secure channel.

[0014] An electronic device includes a memory and a processor, wherein the memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes the steps of a port tally vehicle monitoring and management method as described above.

[0015] A readable storage medium stores a computer program, which, when executed by a processor, is used to implement the steps of a port tally vehicle monitoring and management method as described above.

[0016] Compared with related technologies, the port tally vehicle monitoring and management method and system provided by the present invention has the following beneficial effects: The present invention collects multidimensional tally feature data of port tally vehicles in real time through the port Internet of Things sensing network and synchronously transmits it to the edge computing node for preprocessing, wherein the multidimensional tally feature data includes the electronic tag information of the port tally vehicles, the vehicle three-dimensional point cloud data and the cargo spectral feature data; extracts the multidimensional tally feature vector corresponding to the preprocessed multidimensional tally feature data, and constructs a dynamic weighted directed graph model in combination with the real-time traffic situation of the port, wherein the multidimensional tally feature vector includes the vehicle identity characteristics, cargo status characteristics and tallying operation priority of the port tally vehicles; based on the dynamic weighted directed graph model, plans the optimal tally driving path with time and space constraints for the port tally vehicles, and generates The optimal tallying operation plan is pushed to the on-board intelligent terminal through 5G vehicle networking technology; when the port tallying vehicle executes the optimal tallying operation plan, the vehicle position and posture change data and cargo status change data are monitored in real time through multi-sensor fusion technology. When an abnormal event is detected, the hierarchical early warning mechanism is triggered and the optimal tallying operation plan is automatically adjusted; after the port tallying vehicle completes the optimal tallying operation plan, the integrity and consistency of the tallying operation process data are verified based on the blockchain distributed ledger, and a digital twin tallying report is generated and synchronized to the monitoring end through a quantum secure channel, thereby realizing efficient, safe and intelligent monitoring and management of port tallying vehicles, significantly improving the operational efficiency and safety of the port.

[0017] The present invention collects multi-dimensional tallying feature data in real time through the port Internet of Things sensing network, including electronic tag information, vehicle three-dimensional point cloud data and cargo spectral feature data, and uses edge computing nodes for preprocessing, which greatly improves the efficiency and accuracy of data processing; then, it can be combined with the real-time traffic situation of the port to construct a dynamic weighted directed graph model to plan the optimal tallying driving path containing time and space constraints for the port tallying vehicles. By solving the path planning objective function, the model ensures that the tallying vehicles complete the operation in the shortest time, thereby improving the port tallying efficiency; when the port tallying vehicles execute the optimal tallying operation plan, the present invention monitors the vehicle position and posture change data and the cargo status change data in real time through multi-sensor fusion technology. According to the system, once an abnormal event is detected, the hierarchical early warning mechanism is immediately triggered, and the optimal tallying operation plan is automatically adjusted, effectively avoiding potential safety risks; in addition, after the port tallying vehicle completes the optimal tallying operation plan, the integrity and consistency of the tallying operation process data can be verified based on the blockchain distributed ledger, and a digital twin tallying report can be generated, thereby ensuring the data's immutability and traceability, providing reliable data support for port management; finally, the present invention can synchronize the digital twin tallying report to the monitoring end through a quantum secure channel, and use the quantum key distribution protocol to generate a one-time session key for encryption, thereby ensuring the security of the data transmission process and preventing data leakage and illegal access. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flowchart of a method for monitoring and managing port tally vehicles provided by an embodiment of the present invention; Figure 2 A system block diagram of a port tally vehicle monitoring and management system provided by an embodiment of the present invention; Figure 3 A schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings 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 making creative efforts shall fall within the scope of protection of the present invention.

[0020] like Figure 1 FIG. 1 is a flow chart of a method for monitoring and managing port tally vehicles provided by an embodiment of the present invention. Figure 1 The execution subject of the method shown may be a software and / or hardware device. The execution subject of the present application may include but is not limited to at least one of the following: user equipment, network equipment, etc. Among them, user equipment may include but is not limited to computers, smart phones, personal digital assistants (PDAs) and the electronic devices mentioned above. Network equipment may include but is not limited to a single network server, a server group consisting of multiple network servers, or a cloud based on cloud computing consisting of a large number of computers or network servers, wherein cloud computing is a type of distributed computing, a super virtual computer composed of a group of loosely coupled computers. This embodiment does not limit this. It includes steps S1 to S5, as follows: S1, collecting multi-dimensional tally feature data of port tally vehicles in real time through the port Internet of Things sensing network, and synchronously transmitting it to the edge computing node for preprocessing, wherein the multi-dimensional tally feature data includes the electronic tag information of the port tally vehicles, the vehicle's three-dimensional point cloud data, and the cargo spectral feature data; The port IoT perception network refers to a distributed perception system deployed throughout the port. Composed of multiple sensor types, it collects multidimensional feature data, such as electronic tag information from tally vehicles, three-dimensional point cloud data, and cargo spectral signature data, in real time, providing data support for tally vehicle monitoring and management. Multidimensional tally feature data refers to a multidimensional data set reflecting the status of port tally vehicles and operations. Edge computing nodes, computing units deployed near the port IoT perception network and equipped with localized data processing capabilities, can perform pre-processing operations such as cleaning, noise reduction, and format conversion on the collected multidimensional tally feature data, reducing data transmission volume, alleviating cloud computing pressure, and improving system response speed.

[0021] Electronic tag information refers to structured data such as identity identification and ownership information stored in the radio frequency tags carried by vehicles or cargo, which is used to quickly identify the identity of the subject; vehicle three-dimensional point cloud data refers to the set of three-dimensional spatial coordinates of the vehicle generated by lidar scanning, which can accurately characterize the vehicle's shape, size and spatial position; cargo spectral characteristic data refers to the wavelength distribution information of light reflected from the surface of the cargo collected by a spectrometer, which is used to analyze the physical properties of the cargo such as material and composition.

[0022] In practical applications, a port-wide IoT sensing network can be deployed to capture multi-dimensional features of tally vehicles in real time. This information includes the identity and ownership data carried by the vehicle's electronic tag, the vehicle's three-dimensional point cloud coordinates generated by LiDAR scanning, and the cargo surface reflectance spectral characteristics collected by a spectrometer. This collected multi-dimensional tally feature data can then be synchronously transmitted to edge computing nodes for pre-processing operations such as data cleaning, noise reduction, and format standardization. Furthermore, the localized processing capabilities of edge computing can be leveraged to reduce data transmission pressure and improve system response efficiency.

[0023] S2, extracting a multidimensional tally feature vector corresponding to the pre-processed multidimensional tally feature data, and constructing a dynamic weighted directed graph model based on the real-time traffic situation of the port, wherein the multidimensional tally feature vector includes the vehicle identity characteristics, cargo status characteristics, and tallying operation priority of the port tally vehicle; It can be understood that the multidimensional tally feature vector refers to a high-dimensional numerical vector extracted from the pre-processed multidimensional tally feature data. The real-time traffic situation of the port includes dynamic traffic information such as road traffic conditions within the port, vehicle density, and congestion level in the operation area.

[0024] Vehicle identity characteristics refer to the set of features used to uniquely identify a vehicle, such as license plate numbers and equipment numbers. Cargo status characteristics refer to characteristic parameters that reflect the loading status, integrity, quantity, etc. of cargo. Tally operation priority refers to the operation sorting indicators determined based on cargo urgency and port scheduling rules.

[0025] Furthermore, a multidimensional tallying feature vector representing core information can be extracted from the preprocessed multidimensional tallying feature data. This vector contains identity features that uniquely identify vehicles, status features reflecting the loading status and integrity of cargo, and priority features determined by the urgency of the operation. Then, a dynamic weighted directed graph model can be constructed based on the real-time port traffic situation. This model uses port traffic nodes as vertices. Port traffic nodes can be tallying operation areas, intersections, etc., and the connecting sections between port traffic nodes as directed edges. The edge weights are dynamically adjusted based on real-time traffic conditions and tallying operation requirements, providing a quantitative analysis framework for subsequent path planning.

[0026] S3, based on the dynamic weighted directed graph model, planning an optimal tallying driving path with time and space constraints for the port tallying vehicle, generating an optimal tallying operation plan, and pushing it to the on-board intelligent terminal via 5G vehicle networking technology; Among them, 5G vehicle networking technology refers to wireless communication technology between vehicles and infrastructure based on the fifth generation of mobile communication technology. It supports low-latency, highly reliable data transmission and is used to realize real-time information exchange between vehicles and dispatch centers and other vehicles. On-board intelligent terminals refer to embedded intelligent devices installed in tallying vehicles, which have data reception, display, positioning and short-range communication functions, and are used to receive operation plans and feedback vehicle status. The optimal tallying driving path refers to the driving path planned for port tallying vehicles in the port tallying scenario based on a dynamic weighted directed graph model, which meets time and space constraints and has the lowest generalized cost. The optimal tallying operation plan refers to an efficient and feasible operation execution plan formulated for port tallying vehicles based on the optimal tallying driving path, which is a comprehensive plan for the entire tallying process.

[0027] In practical applications, the constructed dynamic weighted directed graph model can be used to solve the path planning objective function, minimize the generalized cost that integrates the traffic congestion index and the dynamic reward value, and plan the optimal driving path for the tally vehicle that meets temporal and spatial constraints. These temporal and spatial constraints include both time constraints such as operation time windows and road section access time limits, as well as spatial constraints such as road width limits and no-entry zones. The generated optimal tally operation plan can then be pushed to the on-board intelligent terminal in real time via 5G vehicle networking technology, ensuring that the vehicle receives the accurate operation execution plan in a timely manner.

[0028] S4, when the port tallying vehicle executes the optimal tallying operation plan, the vehicle position and posture change data and the cargo status change data are monitored in real time through multi-sensor fusion technology. When an abnormal event is detected, a hierarchical early warning mechanism is triggered and the optimal tallying operation plan is automatically adjusted; It can be understood that multi-sensor fusion technology refers to the fusion of multi-source sensor data carried by port tallying vehicles through algorithms such as Kalman filtering and DS evidence theory. This technology can combine the advantages of different sensors, offset the errors of individual sensors, and improve the accuracy and robustness of vehicle position, posture, and cargo status monitoring, ensuring reliable monitoring results in complex port environments. Vehicle position and posture change data refers to a dynamic data set used to reflect the real-time motion status of port tallying vehicles. It includes the vehicle's spatial position coordinates, such as latitude and longitude, and three-dimensional coordinates, as well as posture parameters such as heading, pitch, and roll. These are used to determine whether the vehicle is following the planned path and whether there is any deviation or abnormal movement. Cargo status change data refers to a dynamic data set used to characterize the real-time status changes of cargo during the tallying process. It includes cargo loading position deviation, integrity (such as damage, quantity changes, and surface characteristics). This data is used to determine whether cargo is in normal condition during transshipment and loading and unloading. Abnormal events refer to unexpected deviations from normal operating conditions during the execution of port tallying vehicles' planned operations. These include, but are not limited to, vehicle deviations from the planned route, cargo damage or abnormal location, vehicle breakdowns, and increased traffic congestion. Furthermore, these abnormal events can seriously impact the efficiency or safety of tallying operations. A graded warning mechanism is a multi-level response and warning system based on the severity of the abnormal event.

[0029] As port tallying vehicles execute their optimal tallying operations, multi-sensor fusion technology can be used to monitor the dynamic information of the vehicle and cargo in real time. This technology integrates data collected by multiple onboard sensors, including but not limited to visual information captured by cameras, attitude parameters recorded by gyroscopes, position coordinates acquired by GPS, 3D point clouds generated by lidar, and spectral characteristics of cargo detected by spectrometers. This data is then fused and processed using algorithms such as Kalman filtering and DS evidence theory, effectively offsetting measurement errors from individual sensors and improving monitoring robustness and accuracy.

[0030] Among them, the real-time monitored vehicle position and posture change data covers the vehicle's real-time spatial coordinates, heading angle, pitch angle, roll angle and other dynamic parameters, which are used to accurately determine whether the vehicle is traveling according to the planned path, whether there is any deviation or abnormal movement; the cargo status change data includes the cargo loading position offset, integrity, quantity changes and surface spectral feature variations and other information, which are used to grasp the status of the cargo during transshipment and loading and unloading in real time.

[0031] When an abnormal event is detected, such as a vehicle deviating from the preset route, cargo being shifted or damaged, or equipment malfunctioning, a graded warning mechanism is immediately triggered. Based on the DS evidence theory, this mechanism integrates multiple sources of abnormal evidence and calculates an abnormality index to quantify the severity of the abnormality. By summarizing the confidence assigned to the abnormal event by each evidence source and deducting the cross-conflicting confidence, the abnormality index is compared with the preset two-level warning threshold to determine the corresponding warning level.

[0032] When an alert is triggered, the optimal tallying operation plan adjustment process is automatically initiated. This process is achieved by setting a plan adjustment objective function and constraints. The plan adjustment objective function focuses on minimizing job allocation costs and adjustment deviations, while taking into account the adjustment priorities of different jobs. The constraints ensure that each job is assigned to only one resource, that resource load does not exceed capacity limits, and that the time nodes of the adjustment phase are consistent and orderly. By solving the plan adjustment objective function that meets the constraints, an adjusted operation plan can be generated, ensuring that the tallying task can be completed efficiently and safely even under abnormal circumstances, achieving dynamic adaptation and closed-loop control of the operation process.

[0033] S5: After the port tallying vehicle completes the optimal tallying operation plan, it verifies the integrity and consistency of the tallying operation process data based on the blockchain distributed ledger, generates a digital twin tallying report, and synchronizes it to the monitoring end through the quantum secure channel.

[0034] It should be noted that the blockchain distributed ledger refers to a decentralized data storage system deployed in port tallying scenarios. It maintains a chain record of tallying process data through distributed nodes. Its core characteristics are immutability and traceability, ensuring the integrity and consistency of tallying process data, and providing a trusted foundation for subsequent data auditing and tracing. The digital twin tallying report refers to a virtual digital mirror report constructed based on the tallying process data stored on the blockchain. It is a precise mapping of the physical tallying process in the virtual space. It includes a three-dimensional model of the vehicle entity, a time sequence chain of the tallying process, a set of cargo attribute parameters, and physical-virtual mapping rules. Through spatiotemporal consistency verification, it ensures the temporal synchronization and spatial matching between the virtual model and the physical operation process. It can be used for operation review, visual management, and multi-party collaborative verification, intuitively presenting the details of the entire tallying process. A quantum secure channel (QSC) is an encrypted communication link built on the quantum key distribution protocol. It is used to ensure the security of digital twin tally report transmission. It encrypts the report by generating a one-time session key and exploits the quantum properties of single-photon polarization state key transmission. This theoretically protects against quantum computing attacks, ensuring that the report cannot be stolen or tampered with during transmission, and providing unconditionally secure communication for cross-subject data synchronization. The monitoring terminal, typically deployed in a port dispatch center or relevant management department, is the terminal system that receives and processes digital twin tally reports. It is the core node for visual monitoring and data archiving throughout the tally operation.

[0035] After a tallying vehicle completes its operation, it automatically aggregates all operational data from the entire process, including cargo loading and unloading records, time series, location coordinates, and equipment interaction logs, for verification via a blockchain distributed ledger. Leveraging a distributed node consensus mechanism, the blockchain performs multi-node cross-verification of the chain continuity and hash value consistency of the tallying process data, ensuring its integrity and consistency. Upon successful verification, a blockchain-backed certificate with node signatures is generated. Based on this trusted data, a digital twin tallying report is constructed, including the vehicle's 3D model, the operation timeline, the cargo parameter set, and physical-virtual mapping rules. This report is a precise virtual mirror of the physical operation process. Finally, the report is encrypted and transmitted to the monitoring terminal via a quantum secure channel. The monitoring terminal decrypts the report and verifies the authenticity of the hash value and the blockchain-backed certificate, completing data synchronization and updating the local database, forming a complete tallying management closed loop.

[0036] The real-time collection of multi-dimensional tally feature data of port tally vehicles through the port Internet of Things sensing network specifically includes: Dynamically calculating the attention weight coefficient of the sensor data in the port IoT perception network based on a multi-head attention mechanism, and introducing a time decay factor to adjust the attention weight coefficient; Based on the adjusted attention weight coefficient, the sensor data is weighted and summed to obtain the fused port IoT perception data, i.e., the multi-dimensional tally feature data: Where, represents the fused port IoT perception data, i.e., multi-dimensional tally feature data; B represents the total amount of sensor data involved in the fusion in the port IoT perception network; represents the attention weight coefficient of the bth sensor data in the port IoT perception network; represents the bth sensor data in the port IoT perception network; represents the b-th sensor data after being processed by the multi-head attention mechanism; represents the time decay term, which is used to adjust the attention weight coefficient of the sensor data according to the difference between the acquisition time of the sensor data and the current time; represents the time decay factor, ; represents the acquisition time of the bth sensor data; t represents the current time; Represent the query matrix, key matrix, and value matrix of the b-th sensor data respectively; Represents the bond matrix The transpose of Represents the bond matrix Dimension; Softmax( ) represents the activation function.

[0037] In practical applications, the attention weight coefficients of each sensor data in the port IoT perception network can first be dynamically calculated based on the multi-head attention mechanism. During this process, a corresponding query matrix, key matrix, and value matrix need to be constructed for each sensor data. Through matrix operations and combined with the Softmax activation function, the sensor data processed by the attention mechanism is generated. This quantifies the importance of different sensor data in the current scenario and forms the initial attention weight coefficient.

[0038] Secondly, to optimize the timeliness of the weight coefficients, a time decay factor can be introduced to adjust the weight coefficients. The time decay factor ranges from 0.1 to 0.5. This adjustment is achieved by constructing a time decay term. This decay term is calculated based on the difference between the acquisition time of each sensor data and the current time. The larger the difference, the more significant the weight decay of the corresponding sensor data, thereby reducing the impact of outdated data on the fusion results.

[0039] After dynamic calculation and time-attenuation adjustment of the weight coefficients, a weighted summation operation can be performed on the sensor data based on the adjusted attention weight coefficients. This process integrates the raw data collected by different sensors, ultimately generating fused port IoT perception data—the required multidimensional tally feature data. This fused data comprehensively considers the importance differences of different sensors and the timeliness of the data. It can more accurately and comprehensively reflect the actual status of port tally vehicles and cargo, providing a high-quality data foundation for subsequent feature extraction and model building.

[0040] The dynamic weighted directed graph model includes port traffic nodes and port traffic connecting sections; Based on the dynamic weighted directed graph model, a path planning objective function and constraints are set, and the path planning objective function is solved to determine the optimal tally driving path. The expressions of the path planning objective function and constraints are as follows: Where i and j represent the port traffic nodes in the dynamic weighted directed graph model; ij represents the port traffic connection segment from port traffic node i to port traffic node j in the dynamic weighted directed graph model; A represents the set of all port traffic connection segments in the dynamic weighted directed graph model; represents the generalized cost function about the current time t; represents the decision variable for road section selection, Indicates the selection of the port transportation connection section from port transportation node i to port transportation node j, Indicates that the port transportation connection section from port transportation node i to port transportation node j is not selected. Indicates the selection of the port transportation connection section from port transportation node j to port transportation node i, Indicates that the port transportation connection section from port transportation node j to port transportation node i is not selected; represents the path planning balance coefficient; L represents the set of all port traffic nodes in the dynamic weighted directed graph model; represents the weight coefficient of port transportation node i; represents the deviation value associated with port traffic node i; They represent the starting port transportation node and the ending port transportation node of the optimal tally driving route respectively; They represent the time when the port tally vehicle arrives at the port traffic node i and j respectively; represents the communication time of the port tally vehicle from port transportation node i to port transportation node j, which depends on the decision variable .

[0041] It should be noted that the core components of the dynamic weighted directed graph model are port traffic nodes and port traffic connection sections. Among them, port traffic nodes represent key locations such as operation areas and intersections within the port, and port traffic connection sections represent the passable paths between port traffic nodes. The port traffic connection sections are directional to reflect the unidirectional constraint of vehicle traffic.

[0042] Then, based on the dynamic weighted directed graph model, it is necessary to set the path planning objective function and constraints, and determine the optimal tally driving path by solving the path planning objective function.

[0043] The path planning objective function is centered around minimizing the total cost, which consists of two parts: the sum of the generalized costs of all selected sections. The generalized cost function is a function of the current time, reflecting the travel cost of a section at a specific moment; and the product of the path planning balance coefficient and the weighted sum of all node-related deviation values. Node deviation values ​​are weighted by the node weight coefficient. The path planning balance coefficient is used to adjust the proportion of section costs and node deviations in the total cost. Port traffic connection sections are selected using a section selection decision variable. If the section selection decision variable is 1, the corresponding port traffic connection section is selected; if the section selection decision variable is 0, the port traffic connection section is not selected. This clarifies the composition of the optimal tally route.

[0044] The constraints are twofold: first, a flow balance constraint. For the starting node of the optimal tally route, the difference between the sum of the segment selection decision variables flowing out of the node and the sum of the segment selection decision variables flowing into the node is 1; for the ending node, this difference is -1; and for all other nodes, this difference is 0. This ensures that the optimal tally route starts from the starting point and eventually reaches the end point without redundant loops. Second, a timing constraint. The time it takes for a vehicle to arrive at a subsequent node must be no less than the sum of the time it takes to arrive at the previous node and the travel time between the two nodes. The travel time depends on the segment selection decision variables to ensure the temporal consistency and feasibility of the optimal tally route.

[0045] By solving the path planning objective function that satisfies the above constraints, the optimal tallying driving path with the minimum generalized cost and conforming to the spatiotemporal logic can be determined, providing efficient traffic guidance for tallying vehicles.

[0046] For the port traffic connection section ij, the traffic congestion index predicted by integrating the spatiotemporal graph convolutional network is and dynamic reward values ​​generated by reinforcement learning , we get the generalized cost function for time t : Where, Represents the fusion weight coefficient, which is used to adjust the traffic congestion index and dynamic reward value In the generalized cost function The proportion of .

[0047] For port traffic link ij, a generalized cost function at time t is constructed by integrating two core indicators. One indicator is the traffic congestion index predicted by a spatiotemporal graph convolutional network, which reflects the degree of traffic congestion on port traffic link ij at time t. The other indicator is a dynamic reward value generated by reinforcement learning, which is used to quantify the adaptability and efficiency benefits of port traffic link ij in the current operating scenario.

[0048] To balance the contributions of these two indicators in the generalized cost function, a fusion weight coefficient can be introduced. This coefficient dynamically adjusts the ratio of the traffic congestion index to the dynamic reward value, allowing the generalized cost function to both reflect the real-time traffic pressure of the road section and adapt to the dynamic needs of port tallying operations. The range of values ​​for the fusion weight coefficient ensures a reasonable combination of the two indicators, neither over-relying on congestion conditions at the expense of operational efficiency nor ignoring actual traffic conditions in pursuit of rewards. The resulting generalized cost function accurately quantifies the comprehensive travel cost of road section ij at time t, providing a scientific cost assessment basis for planning optimal tallying routes.

[0049] The triggering steps of the hierarchical early warning mechanism are as follows: Adopt DS evidence theory to fuse multi-source anomaly evidence and calculate anomaly index , that is, summarizing the trust distribution of each evidence source on the abnormal event and deducting the cross-conflict trust, the abnormality index The calculation formula is as follows: Where r represents the total number of multi-source anomaly evidence involved in the fusion, that is, the total number of evidence sources; A represents the abnormal event; represents the complement of abnormal event A, i.e., non-abnormal events; k and l represent the index of the evidence source, , , ; represents the trust distribution of the k-th evidence source to the abnormal event A, that is, the degree of trust that the k-th evidence source believes that the abnormal event A has occurred; Indicates the lth source of evidence for non-abnormal events The trust distribution of the lth evidence source is that it is not an abnormal event the level of trust that occurs; Set two levels of warning thresholds, including the first level warning threshold and the second level warning threshold ; According to the abnormality index The comparison result with the two-level warning threshold triggers the corresponding warning level: .

[0050] It is understandable that we can first fuse the multi-source abnormal evidence based on the DS evidence theory and calculate the abnormality index. Specifically, we need to summarize the trust distribution of all evidence sources for abnormal events, and at the same time deduct the cross-conflict trust between different evidence sources, that is, the product of the trust of a certain evidence source for abnormal events and the trust of another evidence source for non-abnormal events, so as to obtain the abnormality index used to quantify the severity of abnormal events. Among them, the total number of evidence sources involved in the fusion is r, k and l represent the indexes of different evidence sources and are different from each other; abnormal events are recorded as A, and its complement is non-abnormal events. The k-th evidence source's trust distribution for abnormal event A reflects the degree of trust that the evidence source believes that abnormal event A has occurred, while the l-th evidence source's trust distribution for non-abnormal events reflects the degree of trust that the non-abnormal event has occurred.

[0051] Then, two levels of warning thresholds can be preset, namely the first level warning threshold and the second level warning threshold, to define abnormal events of different degrees.

[0052] Finally, the corresponding warning level can be determined based on the comparison results of the calculated abnormality index and the two-level warning threshold. When the abnormality index is less than the first-level warning threshold, the triggered warning level is 1, which is a mild warning; when the abnormality index is greater than or equal to the first-level warning threshold and less than the second-level warning threshold, the triggered warning level is 2, which is a moderate warning; when the abnormality index is greater than or equal to the second-level warning threshold, the triggered warning level is 3, which is a severe warning. Through this grading method, the corresponding warning level can be triggered according to the severity of the abnormal event, and different warning levels correspond to different disposal measures, thereby achieving accurate and graded response to abnormal events and reducing the risk impact of abnormal events.

[0053] The automatic adjustment process of the optimal tallying operation plan is as follows: Set a plan adjustment objective function and constraints, solve the plan adjustment objective function, and obtain the adjusted optimal tallying operation plan. The expressions of the plan adjustment objective function and constraints are as follows: Where M represents the total number of tally operations; N represents the total number of resources performing tally operations; represents the cost of the mth tallying operation performed by the nth resource; represents the tally operation allocation decision vector, Indicates that the mth tally operation is assigned to the nth resource, Indicates that the mth tally operation is not assigned to the nth resource; The weight coefficient representing the adjustment deviation of the optimal tallying operation plan is used to balance the degree of deviation between the actual tallying operation allocation cost and the target tallying operation allocation cost; F represents the total number of adjustment stages; represents the adjustment weight coefficient of the mth tallying operation, which is used to distinguish the importance of different tallying operations in the adjustment process of the optimal tallying operation plan; represents the actual completion time of the mth tallying operation; represents the target completion time of the mth tallying operation; represents the processing time of the mth tallying operation; Indicates the capacity limit of the nth resource, that is, the maximum tally operation processing time carried by the nth resource; They represent the time nodes of the f+1th and fth adjustment stages respectively; Represents the indicator vector. When the mth tally operation is processed in the fth adjustment stage, , when the mth tallying operation is not processed in the fth adjustment stage, .

[0054] In practice, the plan adjustment objective function consists of two core components. The first is the total cost of tally operation allocation, which is calculated by summing all combinations of tally operations and execution resources. The cost of each combination is the product of the cost of the mth tally operation executed by the nth resource and the corresponding allocation decision vector, thereby quantifying the direct cost of operation allocation. The second is the weighted sum of adjustment deviations. By adjusting the deviation weight coefficient, the degree of deviation between the actual operation allocation cost and the target allocation cost is balanced. At the same time, the adjustment weight coefficient of the mth tally operation is introduced to distinguish the importance of different operations in the adjustment. Combined with the absolute value of the difference between the actual completion time and the target completion time of each operation, a comprehensive consideration of the adjustment deviation is formed.

[0055] Constraints ensure the feasibility of plan adjustments from three dimensions. First, each tallying operation must and can only be assigned to one execution resource. That is, for each operation, the sum of its allocation decision vectors with all resource combinations is 1, ensuring that all operations have a clear execution subject. Second, the total processing time of the operations carried by each resource must not exceed its capacity limit. That is, the sum of the processing time of all operations allocated to the nth resource and the product of the corresponding allocation decision vector must not exceed the maximum carrying time of the resource, ensuring that the resource load is within a reasonable range. Third, the time nodes of each adjustment stage must meet the timing constraints. The time node of the f+1th adjustment stage must not be earlier than the time node of the fth stage and the sum of the total time of all operations processed in this stage. Whether the operation belongs to the fth stage is identified by the indicator vector, thereby ensuring the consistency of the adjustment process in the time dimension.

[0056] By solving the plan adjustment objective function that satisfies the above constraints, the optimal tallying operation plan can be adjusted to take into account cost control, resource adaptability and time rationality, and achieve efficient adaptation of the operation plan in dynamic scenarios.

[0057] After the port tallying vehicle completes the optimal tallying operation plan, the integrity and consistency of the tallying operation process data are verified based on the blockchain distributed ledger, and a digital twin tallying report is generated and synchronized to the monitoring end through the quantum secure channel, specifically including: After the port tallying vehicle completes the optimal tallying operation plan, the tallying operation process data of the port tallying vehicle is automatically collected, including cargo loading and unloading records, tallying operation time series, vehicle position coordinate sets, and equipment interaction logs, and an original tallying operation data set is generated after standardization processing; Calculate the unique hash value of the original tally operation dataset using the SM3 hash algorithm, and bind it with the original tally operation dataset to form the tally operation dataset to be verified; Pushing the to-be-verified tallying operation dataset to a blockchain distributed node, the blockchain distributed node calls an improved practical Byzantine fault-tolerant consensus algorithm to perform multi-node cross-validation on the to-be-verified tallying operation dataset, and verifying the integrity and consistency of the tallying operation process data by comparing the chain continuity of the tallying operation time series and the consistency of the unique hash value. After the verification is passed, a blockchain evidence certificate containing the digital signature of the blockchain distributed node is generated and bound to the tallying operation process data; Based on the tallying operation process data including the blockchain evidence, construct the digital twin tallying report including the vehicle entity three-dimensional model, the tallying operation process time sequence chain, the cargo attribute parameter set and the physical-virtual mapping rules; Calling the quantum key distribution protocol to generate a one-time session key, encrypting the digital twin tally report, and pushing it to the monitoring end through the quantum secure channel; After the monitoring end uses the paired key to decrypt the encrypted digital twin tally report, it verifies the authenticity of the unique hash value and the blockchain evidence certificate. After the verification is passed, it triggers the report reception confirmation signal and updates the local database.

[0058] After completing the optimal tallying operation plan, the port tallying vehicle can first automatically collect the port tallying operation process data of the port tallying vehicle, covering all information such as cargo loading and unloading records, tallying operation time series, vehicle location coordinate sets, and equipment interaction logs. After standardized processing, the original tallying operation data set is formed to ensure unified data format and complete content.

[0059] Subsequently, the SM3 hash algorithm can be used to calculate the unique hash value of the original tallying operation dataset and bind it with the original tallying operation dataset to form the tallying operation dataset to be verified. The uniqueness of the hash value can be used to achieve preliminary verification of subsequent data integrity.

[0060] The tally operation dataset to be verified can then be pushed to the blockchain distributed nodes, which can then invoke the improved practical Byzantine fault-tolerant consensus algorithm to conduct multi-node cross-validation. During this process, each blockchain distributed node can collaboratively verify the received tally operation dataset to be verified based on pre-set consensus rules, focusing on comparing the chain continuity of the tally operation time series, that is, ensuring that the operation data forms a coherent chain in chronological order and has no signs of breakage or tampering. At the same time, the consistency of the unique hash value is verified to ensure that the operation data has not been tampered with.

[0061] By optimizing node communication efficiency and fault tolerance mechanisms, the improved practical Byzantine fault-tolerant consensus algorithm ensures that even if some nodes are abnormal, a majority of nodes still reach consensus, ensuring the reliability of the verification results. After multi-node cross-validation, if the tally operation process data is confirmed to meet the integrity and consistency requirements, the blockchain distributed node will generate a blockchain evidence certificate containing its own digital signature and bind this certificate to the tally operation process data. This ensures data immutability and traceability, providing a trusted foundation for the subsequent generation of digital twin tally reports and data synchronization.

[0062] Based on the tallying operation process data containing blockchain evidence, a digital twin tallying report can be constructed. The report contains the vehicle entity 3D model, the tallying operation process time chain, the cargo attribute parameter set and the physical-virtual mapping rules, thereby achieving accurate mapping between the physical operation process and the virtual model.

[0063] To ensure the transmission security of the digital twin tally report, a one-time session key can be generated using the quantum key distribution protocol. This encrypted digital twin tally report is then pushed to the monitoring terminal via a quantum secure channel. Upon receiving the encrypted report, the monitoring terminal decrypts it using the paired key and verifies the authenticity of the unique hash value and the blockchain-stored certificate. Upon successful verification, a report receipt confirmation signal is triggered and the local database is updated, completing the closed-loop data management of the entire tally operation.

[0064] like Figure 2 FIG. 1 is a system block diagram of a port tally vehicle monitoring and management system provided by an embodiment of the present invention, wherein the monitoring and management system includes: A tally data acquisition module is used to collect multi-dimensional tally feature data of port tally vehicles in real time through the port Internet of Things perception network, and synchronously transmit it to the edge computing node for preprocessing, wherein the multi-dimensional tally feature data includes the electronic tag information of the port tally vehicles, the vehicle's three-dimensional point cloud data, and the cargo spectral feature data; a directed graph model construction module, configured to extract a multidimensional tally feature vector corresponding to the preprocessed multidimensional tally feature data, and construct a dynamic weighted directed graph model based on the real-time port traffic situation, wherein the multidimensional tally feature vector includes the vehicle identity characteristics, cargo status characteristics, and tallying operation priority of the port tally vehicle; A tallying route planning module is used to plan an optimal tallying route with time and space constraints for the port tallying vehicle based on the dynamic weighted directed graph model, generate an optimal tallying operation plan, and push it to the on-board intelligent terminal via 5G vehicle networking technology; An early warning mechanism triggering module is used to monitor vehicle position and posture change data and cargo status change data in real time through multi-sensor fusion technology when the port tallying vehicle executes the optimal tallying operation plan. When an abnormal event is detected, a hierarchical early warning mechanism is triggered and the optimal tallying operation plan is automatically adjusted; The tallying report generation module is used to verify the integrity and consistency of the tallying operation process data based on the blockchain distributed ledger after the port tallying vehicle completes the optimal tallying operation plan, generate a digital twin tallying report, and synchronize it to the monitoring end through the quantum secure channel.

[0065] Figure 2 The apparatus of the embodiment shown can be used to perform Figure 1 The implementation principles and technical effects of the steps in the method embodiment shown are similar and will not be repeated here.

[0066] An electronic device includes a memory and a processor, wherein the memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes the steps of a port tally vehicle monitoring and management method as described above.

[0067] like Figure 3 FIG. 1 is a schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. The electronic device 30 includes: a processor 31, a memory 32 and a computer program; The memory 32 is used to store the computer program, which may also be a flash memory. The computer program is, for example, an application program or a functional module for implementing the above method.

[0068] The processor 31 is configured to execute the computer program stored in the memory to implement the various steps performed by the device in the above method. For details, please refer to the relevant description in the above method embodiment.

[0069] Optionally, the memory 32 may be independent or integrated with the processor 31 .

[0070] When the memory 32 is a device independent of the processor 31, the device may further include: The bus 33 is used to connect the memory 32 and the processor 31 .

[0071] A readable storage medium stores a computer program, which, when executed by a processor, is used to implement the steps of a port tally vehicle monitoring and management method as described above.

[0072] The readable storage medium may be a computer storage medium or a communication medium. Communication media include any medium that facilitates the transfer of computer programs from one location to another. Computer storage media may be any available medium that can be accessed by a general-purpose or special-purpose computer. For example, a readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium may also be an integral part of the processor. The processor and the readable storage medium may be located in an application-specific integrated circuit (ASIC). In addition, the ASIC may be located in a user device. Of course, the processor and the readable storage medium may also exist as discrete components in a communication device. The readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0073] The present invention also provides a program product, which includes execution instructions stored in a readable storage medium. At least one processor of a device can read the execution instructions from the readable storage medium, and at least one processor executes the execution instructions so that the device implements the methods provided in the various embodiments described above.

[0074] In the embodiments of the above-mentioned devices, it should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASICs), etc. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly executed by a hardware processor or by a combination of hardware and software modules within the processor.

[0075] Through the introduction of the above embodiments, the present invention collects multidimensional tallying feature data of port tallying vehicles in real time through the port Internet of Things sensing network, and synchronously transmits it to the edge computing node for preprocessing, wherein the multidimensional tallying feature data includes the electronic tag information of the port tallying vehicles, the vehicle three-dimensional point cloud data and the cargo spectral feature data; extracts the multidimensional tallying feature vector corresponding to the preprocessed multidimensional tallying feature data, and constructs a dynamic weighted directed graph model in combination with the real-time traffic situation of the port, wherein the multidimensional tallying feature vector includes the vehicle identity characteristics, cargo status characteristics and tallying operation priority of the port tallying vehicle; based on the dynamic weighted directed graph model, plans the optimal tallying travel with time and space constraints for the port tallying vehicles The optimal tallying operation plan is generated by the 5G vehicle networking technology, and is pushed to the on-board intelligent terminal; when the port tallying vehicle executes the optimal tallying operation plan, the vehicle position and posture change data and the cargo status change data are monitored in real time through multi-sensor fusion technology. When an abnormal event is detected, the hierarchical early warning mechanism is triggered and the optimal tallying operation plan is automatically adjusted; after the port tallying vehicle completes the optimal tallying operation plan, the integrity and consistency of the tallying operation process data are verified based on the blockchain distributed ledger, and a digital twin tallying report is generated and synchronized to the monitoring end through a quantum secure channel, thereby realizing efficient, safe and intelligent monitoring and management of port tallying vehicles, significantly improving the operational efficiency and safety of the port.

[0076] The present invention collects multi-dimensional tallying feature data in real time through the port Internet of Things sensing network, including electronic tag information, vehicle three-dimensional point cloud data and cargo spectral feature data, and uses edge computing nodes for preprocessing, which greatly improves the efficiency and accuracy of data processing; then, it can be combined with the real-time traffic situation of the port to construct a dynamic weighted directed graph model to plan the optimal tallying driving path containing time and space constraints for the port tallying vehicles. By solving the path planning objective function, the model ensures that the tallying vehicles complete the operation in the shortest time, thereby improving the port tallying efficiency; when the port tallying vehicles execute the optimal tallying operation plan, the present invention monitors the vehicle position and posture change data and the cargo status change data in real time through multi-sensor fusion technology. According to the system, once an abnormal event is detected, the hierarchical early warning mechanism is immediately triggered, and the optimal tallying operation plan is automatically adjusted, effectively avoiding potential safety risks; in addition, after the port tallying vehicle completes the optimal tallying operation plan, the integrity and consistency of the tallying operation process data can be verified based on the blockchain distributed ledger, and a digital twin tallying report can be generated, thereby ensuring the data's immutability and traceability, providing reliable data support for port management; finally, the present invention can synchronize the digital twin tallying report to the monitoring end through a quantum secure channel, and use the quantum key distribution protocol to generate a one-time session key for encryption, thereby ensuring the security of the data transmission process and preventing data leakage and illegal access.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for monitoring and managing port tally vehicles, characterized in that: The monitoring and management method includes: The multi-dimensional tally feature data of port tally vehicles is collected in real time through the port Internet of Things sensing network and synchronously transmitted to the edge computing node for preprocessing, wherein the multi-dimensional tally feature data includes the electronic tag information of the port tally vehicles, the vehicle's three-dimensional point cloud data, and the cargo spectral feature data; Extracting a multidimensional tally feature vector corresponding to the pre-processed multidimensional tally feature data, and constructing a dynamic weighted directed graph model in combination with the real-time traffic situation of the port, wherein the multidimensional tally feature vector includes the vehicle identity characteristics, cargo status characteristics, and tallying operation priority of the port tally vehicle; Based on the dynamic weighted directed graph model, an optimal tallying driving path with time and space constraints is planned for the port tallying vehicle, and an optimal tallying operation plan is generated and pushed to the on-board intelligent terminal via 5G vehicle networking technology; When the port tallying vehicle executes the optimal tallying operation plan, it monitors the vehicle position and posture change data and cargo status change data in real time through multi-sensor fusion technology. When an abnormal event is detected, a hierarchical early warning mechanism is triggered and the optimal tallying operation plan is automatically adjusted; After the port tallying vehicle completes the optimal tallying operation plan, it verifies the integrity and consistency of the tallying operation process data based on the blockchain distributed ledger, generates a digital twin tallying report, and synchronizes it to the monitoring end through the quantum security channel.

2. A method for monitoring and managing port tally vehicles according to claim 1, characterized in that: The real-time collection of multi-dimensional tally feature data of port tally vehicles through the port Internet of Things sensing network specifically includes: Dynamically calculating the attention weight coefficient of the sensor data in the port IoT perception network based on a multi-head attention mechanism, and introducing a time decay factor to adjust the attention weight coefficient; Based on the adjusted attention weight coefficient, the sensor data is weighted and summed to obtain the fused port IoT perception data, i.e., the multi-dimensional tally feature data: Where, represents the fused port IoT perception data, i.e., multi-dimensional tally feature data; B represents the total amount of sensor data involved in the fusion in the port IoT perception network; represents the attention weight coefficient of the bth sensor data in the port IoT perception network; represents the bth sensor data in the port IoT perception network; represents the b-th sensor data after being processed by the multi-head attention mechanism; represents the time decay term, which is used to adjust the attention weight coefficient of the sensor data according to the difference between the acquisition time of the sensor data and the current time; represents the time decay factor, ; represents the acquisition time of the bth sensor data; t represents the current time; Represent the query matrix, key matrix, and value matrix of the b-th sensor data respectively; Represents the bond matrix The transpose of Represents the bond matrix Dimension; Softmax( ) represents the activation function.

3. A method for monitoring and managing port tally vehicles according to claim 1, characterized in that: The dynamic weighted directed graph model includes port traffic nodes and port traffic connecting sections; Based on the dynamic weighted directed graph model, a path planning objective function and constraints are set, and the path planning objective function is solved to determine the optimal tally driving path. The expressions of the path planning objective function and constraints are as follows: Where i and j represent the port traffic nodes in the dynamic weighted directed graph model; ij represents the port traffic connection segment from port traffic node i to port traffic node j in the dynamic weighted directed graph model; A represents the set of all port traffic connection segments in the dynamic weighted directed graph model; represents the generalized cost function about the current time t; represents the decision variable for road segment selection, Indicates the selection of the port transportation connection section from port transportation node i to port transportation node j, Indicates that the port transportation connection section from port transportation node i to port transportation node j is not selected. Indicates the selection of the port transportation connection section from port transportation node j to port transportation node i, Indicates that the port transportation connection section from port transportation node j to port transportation node i is not selected; represents the path planning balance coefficient; L represents the set of all port traffic nodes in the dynamic weighted directed graph model; represents the weight coefficient of port transportation node i; represents the deviation value associated with port traffic node i; They represent the starting port transportation node and the ending port transportation node of the optimal tally driving route respectively; They represent the time when the port tally vehicle arrives at the port traffic node i and j respectively; represents the communication time of the port tally vehicle from port transportation node i to port transportation node j, which depends on the decision variable .

4. A method for monitoring and managing port tally vehicles according to claim 3, characterized in that: For the port traffic connection section ij, the traffic congestion index predicted by integrating the spatiotemporal graph convolutional network is and dynamic reward values ​​generated by reinforcement learning , we get the generalized cost function for time t : Where, Represents the fusion weight coefficient, which is used to adjust the traffic congestion index and dynamic reward value In the generalized cost function The proportion of .

5. A method for monitoring and managing port tally vehicles according to claim 1, characterized in that: The triggering steps of the hierarchical early warning mechanism are as follows: Adopt DS evidence theory to fuse multi-source anomaly evidence and calculate anomaly index , that is, summarizing the trust distribution of each evidence source on the abnormal event and deducting the cross-conflict trust, the abnormality index The calculation formula is as follows: Where r represents the total number of multi-source anomaly evidence involved in the fusion, that is, the total number of evidence sources; A represents the abnormal event; represents the complement of abnormal event A, i.e., non-abnormal events; k and l represent the index of the evidence source, , , ; represents the trust distribution of the k-th evidence source to the abnormal event A, that is, the degree of trust that the k-th evidence source believes that the abnormal event A has occurred; Indicates the lth source of evidence for non-abnormal events The trust distribution of the lth evidence source is that it is not an abnormal event the level of trust that occurs; Set two levels of warning thresholds, including the first level warning threshold and the second level warning threshold ; According to the abnormality index The comparison result with the two-level warning threshold triggers the corresponding warning level: 。 6. A method for monitoring and managing port tally vehicles according to claim 1, characterized in that: The automatic adjustment process of the optimal tallying operation plan is as follows: Set a plan adjustment objective function and constraints, solve the plan adjustment objective function, and obtain the adjusted optimal tallying operation plan. The expressions of the plan adjustment objective function and constraints are as follows: Where M represents the total number of tally operations; N represents the total number of resources performing tally operations; represents the cost of the mth tallying operation performed by the nth resource; represents the tally operation allocation decision vector, Indicates that the mth tally operation is assigned to the nth resource, Indicates that the mth tally operation is not assigned to the nth resource; The weight coefficient representing the adjustment deviation of the optimal tallying operation plan is used to balance the degree of deviation between the actual tallying operation allocation cost and the target tallying operation allocation cost; F represents the total number of adjustment stages; represents the adjustment weight coefficient of the mth tallying operation, which is used to distinguish the importance of different tallying operations in the adjustment process of the optimal tallying operation plan; represents the actual completion time of the mth tallying operation; represents the target completion time of the mth tallying operation; represents the processing time of the mth tallying operation; Indicates the capacity limit of the nth resource, that is, the maximum tally operation processing time carried by the nth resource; They represent the time nodes of the f+1th and fth adjustment stages respectively; Represents the indicator vector. When the mth tally operation is processed in the fth adjustment stage, , when the mth tallying operation is not processed in the fth adjustment stage, .

7. A method for monitoring and managing port tally vehicles according to claim 1, characterized in that: After the port tallying vehicle completes the optimal tallying operation plan, the integrity and consistency of the tallying operation process data are verified based on the blockchain distributed ledger, and a digital twin tallying report is generated and synchronized to the monitoring end through the quantum secure channel, specifically including: After the port tallying vehicle completes the optimal tallying operation plan, the tallying operation process data of the port tallying vehicle is automatically collected, including cargo loading and unloading records, tallying operation time series, vehicle position coordinate sets, and equipment interaction logs, and an original tallying operation data set is generated after standardization processing; Calculate the unique hash value of the original tally operation dataset using the SM3 hash algorithm, and bind it with the original tally operation dataset to form the tally operation dataset to be verified; Pushing the to-be-verified tallying operation dataset to a blockchain distributed node, the blockchain distributed node calls an improved practical Byzantine fault-tolerant consensus algorithm to perform multi-node cross-validation on the to-be-verified tallying operation dataset, and verifying the integrity and consistency of the tallying operation process data by comparing the chain continuity of the tallying operation time series and the consistency of the unique hash value. After the verification is passed, a blockchain evidence certificate containing the digital signature of the blockchain distributed node is generated and bound to the tallying operation process data; Based on the tallying operation process data including the blockchain evidence, construct the digital twin tallying report including the vehicle entity three-dimensional model, the tallying operation process time sequence chain, the cargo attribute parameter set and the physical-virtual mapping rules; Calling the quantum key distribution protocol to generate a one-time session key, encrypting the digital twin tally report, and pushing it to the monitoring end through the quantum secure channel; After the monitoring end uses the paired key to decrypt the encrypted digital twin tally report, it verifies the authenticity of the unique hash value and the blockchain evidence certificate. After the verification is passed, it triggers the report reception confirmation signal and updates the local database.

8. A port tally vehicle monitoring and management system, applied to a port tally vehicle monitoring and management method according to any one of claims 1 to 7, characterized in that: The monitoring and management system includes: A tally data acquisition module is used to collect multi-dimensional tally feature data of port tally vehicles in real time through the port Internet of Things perception network, and synchronously transmit it to the edge computing node for preprocessing, wherein the multi-dimensional tally feature data includes the electronic tag information of the port tally vehicles, the vehicle's three-dimensional point cloud data, and the cargo spectral feature data; a directed graph model construction module, configured to extract a multidimensional tally feature vector corresponding to the preprocessed multidimensional tally feature data, and construct a dynamic weighted directed graph model based on the real-time port traffic situation, wherein the multidimensional tally feature vector includes the vehicle identity characteristics, cargo status characteristics, and tallying operation priority of the port tally vehicle; A tallying route planning module is used to plan an optimal tallying route with time and space constraints for the port tallying vehicle based on the dynamic weighted directed graph model, generate an optimal tallying operation plan, and push it to the on-board intelligent terminal via 5G vehicle networking technology; An early warning mechanism triggering module is used to monitor vehicle position and posture change data and cargo status change data in real time through multi-sensor fusion technology when the port tallying vehicle executes the optimal tallying operation plan. When an abnormal event is detected, a hierarchical early warning mechanism is triggered and the optimal tallying operation plan is automatically adjusted; The tallying report generation module is used to verify the integrity and consistency of the tallying operation process data based on the blockchain distributed ledger after the port tallying vehicle completes the optimal tallying operation plan, generate a digital twin tallying report, and synchronize it to the monitoring end through the quantum secure channel.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor runs the computer program stored in the memory, the processor executes the steps of the port tally vehicle monitoring and management method according to any one of claims 1 to 7.

10. A readable storage medium storing a computer program, wherein: When the computer program is executed by a processor, it is used to implement the steps of a port tally vehicle monitoring and management method according to any one of claims 1 to 7.

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