An intelligent marine shipping document reminder system and method

Abstract

The present invention relates to the technical field of data processing, and specifically to an intelligent maritime shipping document reminder system and method. It adjusts storage resources according to data access frequency, analyzes the task relevance and processing capabilities in the reminder path, optimizes the path, evaluates the task execution status, progress and delay risks, monitors data traffic changes, identifies risk nodes, adjusts the node path and task priority, and plans the task scheduling order. In the present invention, by dynamically adjusting data storage resources, it ensures that high-frequency data is preferentially stored in fast devices, effectively reducing storage latency and resource waste. The real-time optimization of the reminder path performs intelligent scheduling according to task priorities and node processing capabilities, avoiding task delays and backlogs. Through data traffic monitoring and fluctuation analysis, risk nodes are timely identified and path adjustments are made, effectively ensuring the smooth execution of tasks.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to an intelligent sea shipping document reminder system and method. Background Art

[0002] The technical field of data processing involves the collection, storage, processing, and analysis of various types of data. The core contents include data acquisition and transmission, data storage and management, data processing and analysis methods, and data visualization. With the development of information technology, data processing technology has been widely applied in various industries, especially in the fields of logistics, finance, and healthcare, playing an important role in data processing and management. It mainly involves how to process a large amount of complex data through reasonable technical means to improve system efficiency, reduce human intervention, increase the automation level, and then optimize decision support and business processes.

[0003] Among them, an intelligent sea shipping document reminder system refers to a system that optimizes the problems existing in the reminder work of sea shipping documents by using information technology means. It mainly includes data management and transmission in the process of reminding sea shipping documents, and uses automated tools to improve the reminder efficiency. Specifically, by collecting sea shipping document information and docking with relevant systems, automatic comparison and tracking of document information are realized, and reminder notices are sent based on set rules to ensure accurate information transmission and timely completion of the reminder work. Using data storage and management technology, it supports multi-party data sharing and real-time update to ensure the efficiency and timeliness of the reminder process, and then improves the overall management level of sea shipping logistics.

[0004] In the prior art, the management of storage resources is mostly static and cannot be adjusted in real time according to changes in data types, access frequencies, and node loads, resulting in unreasonable storage of high-frequency data and inability to guarantee access speed and storage efficiency. The execution of the reminder path also lacks flexible adjustment according to task priorities and progress, easily causing task progress delays and inability to respond to actual demand changes in a timely manner, resulting in low reminder work efficiency. In addition, the existing system has weak monitoring and risk prevention and control capabilities for traffic changes and cannot identify and respond to traffic fluctuations or excessive node loads in a timely manner, thus affecting the stability of the system and the smooth completion of task execution. These deficiencies limit the flexibility and efficiency of the system in handling complex tasks. Especially in the face of large-scale data or changing environments, delays and resource waste cannot be effectively avoided, affecting the overall management effect of sea shipping document reminders. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies existing in the prior art and propose an intelligent sea shipping document reminder system and method.

[0006] To achieve the above object, the present invention adopts the following technical solutions: An intelligent maritime bill of lading reminder system, the system includes:

[0007] The data storage management module obtains the types, storage requirements and access frequencies of maritime bill of lading data, divides each type of data into high-frequency data and low-frequency data according to the access frequency, and generates a data storage allocation result;

[0008] The reminder path optimization module calls the data access frequencies and storage capacities of each storage node in the data storage allocation result, analyzes the task relevance and processing capabilities between each node in the reminder path, and generates an optimized reminder path;

[0009] The dynamic scheduling module extracts the task execution status, progress information and delay risk of each node in the optimized reminder path, compares the processing capabilities of the tasks with the current processing progress, and generates a reminder path execution result;

[0010] The data flow monitoring module, based on the reminder path execution result, monitors the data flow information of the execution path nodes, analyzes the change trend of the node data flow, and reallocates the execution paths of the risk nodes to generate a traffic fluctuation monitoring result;

[0011] The feedback adjustment module, based on the traffic fluctuation monitoring result, analyzes the traffic fluctuation amplitude and path load of the current path nodes, dynamically adjusts the task execution priorities of the path nodes, plans the task scheduling order according to the priorities, and generates an adjusted reminder task report.

[0012] As a further solution of the present invention, the data storage allocation result includes high-frequency data storage nodes, low-frequency data storage nodes, storage capacity allocation, and node load allocation; the optimized reminder path includes task priority adjustment, task processing capacity matching, analysis of relevance between path nodes, and evaluation of matching between storage capacity and processing capacity; the reminder path execution result includes task execution status, task progress, delay risk, ship navigation status, and port scheduling status; the traffic fluctuation monitoring result includes data flow change trend, traffic fluctuation of risk nodes, nodes with traffic fluctuation amplitude exceeding the limit, and node path adjustment suggestions; the adjusted reminder task report includes task priority adjustment results, task progress evaluation, node processing capacity evaluation, node delay situation, and optimization of task scheduling order.

[0013] As a further solution of the present invention, the data storage management module includes:

[0014] The data classification sub-module obtains the types of maritime bill of lading data and storage requirements, analyzes and compares the access frequencies of each type of data, divides each type of data into high-frequency data and low-frequency data according to the access frequency, and generates a data classification result;

[0015] Based on the data classification result, the data storage allocation sub-module evaluates the load situation and storage capacity of each storage node, and combines the data storage requirements. Using the formula:

[0016] ;

[0017] Performing operations to obtain the storage allocation result, and combining the node load, storage capacity, and data access frequency difference to generate a preliminary storage allocation result;

[0018] Wherein, represents the storage capacity of the SSD node, represents the storage capacity of the HDD node, and respectively represent the access frequencies of high-frequency and low-frequency data, and respectively represent the load conditions of the SSD node and the HDD node, represents the storage requirement, represents the storage allocation result;

[0019] The storage allocation adjustment sub-module monitors the real-time change of the storage node load according to the preliminary storage allocation result, analyzes the difference between the current storage capacity and the requirement, performs dynamic adjustment of the storage allocation, and generates the data storage allocation result.

[0020] As a further solution of the present invention, the reminder path optimization module includes:

[0021] The data access analysis sub-module calls the data access frequency and storage capacity of each storage node according to the data storage allocation result, combines the reminder task priority and progress, analyzes the node task relevance and data processing requirements, calculates and obtains the data access matching degree of each node, and generates the data access matching degree;

[0022] The node processing capacity evaluation sub-module evaluates the processing capacity of each storage node based on the reminder task priority and progress, analyzes the storage capacity and load condition of the node, calculates the matching degree between the node processing capacity and the task requirements, and uses the formula:

[0023] ;

[0024] Performing operations to obtain the matching degree between the node processing capacity and the task requirements, and combining the storage capacity, load condition, and task requirements to generate the node processing capacity evaluation result;

[0025] Wherein, represents the matching degree between the node processing capacity and the task requirements, represents the storage capacity of the node, represents the node load rate, Represents the processing time requirement of the task, represents the priority of the task;

[0026] The reminder path optimization sub-module optimizes the nodes in the reminder path according to the data access matching degree and the node processing ability evaluation result, analyzes the task correlation and processing ability matching situation between nodes, adjusts the storage resources and task allocation of the nodes in the path, and generates an optimized reminder path.

[0027] As a further solution of the present invention, the dynamic scheduling module includes:

[0028] The task status extraction sub-module extracts the task execution status, progress information and delay risk of each node in the optimized reminder path, obtains the current execution progress of each task, monitors the progress of the node task, and obtains the task execution processing status;

[0029] The processing ability and progress comparison sub-module compares the processing ability of the task with the current progress according to the task execution processing status, evaluates whether each node can complete the task on time, calculates the gap between the processing ability and the progress of the node, and uses the formula:

[0030] ;

[0031] Performs operations to obtain the gap between the processing ability and the progress of each node, and generates a task progress matching result;

[0032] Wherein, represents the gap between the processing ability and the progress, represents the storage capacity of the node, represents the load rate of the node, represents the processing time requirement of the task, represents the progress that the task has currently completed, represents the total progress of the task;

[0033] The path adjustment and execution sub-module makes real-time adjustments to the task path of each node according to the task progress matching result, and combines the ship navigation status and port scheduling status during the reminder process of maritime shipping documents. Based on the processing ability and task progress of each node, it re-plans the task path and executes it, and generates a reminder path execution result.

[0034] As a further solution of the present invention, the data flow monitoring module includes:

[0035] The data collection sub-module monitors the data flow information of each node in the reminder path execution result at different time periods, collects traffic information in real time based on the task processing ability and delay risk assessment, and generates real-time data traffic information;

[0036] The data analysis sub-module analyzes the data traffic change trend of each node according to the real-time data traffic information, and uses the formula:

[0037] ;

[0038] Perform operations to obtain the traffic fluctuation percentage, compare it with the preset fluctuation threshold, screen out the risk nodes with fluctuations exceeding the threshold, and generate a traffic fluctuation trend analysis report;

[0039] Among them, represents the traffic fluctuation percentage, represents the node data traffic at the current moment, represents the node data traffic at the previous moment, represents the average processing delay of the node, represents the total amount of tasks, represents the length of the time period;

[0040] The path adjustment sub-module re-plans the execution path of the risk nodes according to the traffic fluctuation trend analysis report, analyzes the traffic fluctuations of each risk node, optimizes the execution order between nodes, and generates a traffic fluctuation monitoring result.

[0041] As a further solution of the present invention, the feedback adjustment module includes:

[0042] The traffic monitoring sub-module detects the traffic fluctuation amplitude and trend of the current path nodes according to the traffic fluctuation monitoring result, obtains the traffic difference and volatility of each path node, and evaluates the traffic fluctuation amplitude of the path nodes to generate a path traffic evaluation result;

[0043] The path load analysis sub-module analyzes the load conditions of the path nodes based on the path traffic evaluation result, calculates the load index of each node, compares the relationship between the load and the traffic fluctuation amplitude, and screens out the path nodes with heavy loads, obtains the load ratio of the path nodes, and uses the formula:

[0044] ;

[0045] Perform operations to obtain the load index of each node and generate path node load data;

[0046] Among them, is the load index of the th node, is the traffic fluctuation amplitude value of the th node, is the node traffic, is the average value of the traffic, is the total number of nodes;

[0047] The task scheduling optimization sub-module analyzes the task execution priorities of the current path nodes according to the path node load data and the path traffic evaluation results, calculates the adjustment requirements for the task scheduling order, adjusts the node task scheduling order, determines whether it is necessary to re-plan the tasks, and generates an adjusted reminder task report.

[0048] An intelligent maritime document reminder method, which is executed based on the above intelligent maritime document reminder system, includes the following steps:

[0049] S1: Obtain the data types, storage requirements, and access frequencies of maritime documents, divide the data into high-frequency data and low-frequency data according to the access frequencies, store the high-frequency data in SSD nodes, store the low-frequency data in HDD nodes, monitor the node loads and storage capacities in real time, dynamically adjust the data storage allocation according to the current load conditions, and generate a storage allocation result;

[0050] S2: Call the data access frequencies and storage capacities in the storage allocation result, analyze the task relevance and processing capabilities of each node, combine the priorities and progress of the reminder tasks, evaluate the matching degree of the storage and processing capabilities of the nodes in the path, and generate an optimized reminder path;

[0051] S3: According to the task execution status and progress information of each node in the optimized reminder path, combine the ship navigation status and port scheduling status, evaluate the task processing capabilities and current progress in real time, judge the delay risks, and adjust the task execution path based on the evaluation results to generate a reminder path execution result;

[0052] S4: Based on the reminder path execution result, monitor the real-time data traffic of the path nodes, analyze the data traffic change trend, screen the risk nodes with traffic fluctuations exceeding the fluctuation threshold, re-adjust the execution paths of the risk nodes, and generate a traffic fluctuation monitoring result;

[0053] S5: Based on the traffic fluctuation monitoring result, analyze the traffic fluctuation amplitude and path load of the current path nodes, dynamically adjust the task execution priorities of the path nodes, evaluate the task progress, node processing capabilities, and delay situations of each node, judge whether it is necessary to adjust the task scheduling order, and generate an adjusted reminder task report.

[0054] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0055] In the present invention, by dynamically adjusting data storage resources, it is ensured that high-frequency data is preferentially stored in fast devices, effectively reducing storage latency and resource waste. The real-time optimization of the reminder path performs intelligent scheduling based on task priorities and node processing capabilities, avoiding task delays and backlogs. Through data traffic monitoring and fluctuation analysis, risk nodes are timely identified and path adjustments are made, effectively ensuring the smooth execution of tasks. The overall process realizes automation and intelligence, improving system efficiency and adaptability, and making the reminder work more efficient and stable. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 is the system flow chart of the present invention;

[0057] Figure 2 is the flowchart for obtaining the data storage management module of the present invention;

[0058] Figure 3 is the flowchart for obtaining the reminder path optimization module of the present invention;

[0059] Figure 4 is the flowchart for obtaining the dynamic scheduling module of the present invention;

[0060] Figure 5 is the flowchart for obtaining the data flow monitoring module of the present invention;

[0061] Figure 6 is the flowchart for obtaining the feedback adjustment module of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0062] The following describes the technical solutions in the present invention with reference to the accompanying drawings.

[0063] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as an "example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of the word "example" is intended to present concepts in a specific manner. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two.

[0064] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same. "(of)", "corresponding", and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same.

[0065] In the embodiments of the present invention, sometimes subscripts such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meanings they express are the same.

[0066] To make the technical problems, technical solutions, and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0067] Please refer to Figure 1 , the present invention provides a technical solution: an intelligent maritime bill of lading reminder system, the system includes:

[0068] The data storage management module obtains the types, storage requirements, and access frequencies of maritime bill of lading data, divides each type of data into high-frequency data and low-frequency data according to the access frequency, stores the high-frequency data in the SSD node, stores the low-frequency data in the HDD node, and adjusts the storage allocation in real time according to the load conditions and storage capacities of each node to generate a data storage allocation result;

[0069] The reminder path optimization module calls the data access frequencies and storage capacities of each storage node in the data storage allocation result, refers to the priorities and progress of the reminder tasks, analyzes the task relevance and processing capabilities between each node in the reminder path, evaluates the matching degree of the storage capabilities and processing capabilities in the reminder path, and generates an optimized reminder path;

[0070] The dynamic scheduling module extracts the task execution status, progress information, and delay risks of each node in the optimized reminder path, compares the processing capabilities of the tasks with the current processing progress, and adjusts the task path in real time in combination with the ship navigation status and port scheduling status during the maritime bill of lading reminder process to generate a reminder path execution result;

[0071] The data flow monitoring module, based on the reminder path execution result, monitors the data flow information of the execution path nodes, analyzes the change trend of the node data flow, screens the risk nodes whose flow fluctuations exceed the fluctuation threshold, reallocates the execution paths of the risk nodes, and generates a flow fluctuation monitoring result;

[0072] The feedback adjustment module, based on the flow fluctuation monitoring result, analyzes the flow fluctuation amplitude and path load of the current path nodes, dynamically adjusts the task execution priorities of the path nodes, evaluates the task progress, node processing capabilities, and delay conditions of each node after adjustment, determines whether to plan the task scheduling order, and generates an adjusted reminder task report.

[0073] The data storage allocation results include high-frequency data storage nodes, low-frequency data storage nodes, storage capacity allocation, and node load allocation; the optimized reminder path includes task priority adjustment, task processing capacity matching, correlation analysis between path nodes, and evaluation of the matching of storage capacity and processing capacity; the execution results of the reminder path include task execution status, task progress, delay risk, ship navigation status, and port scheduling status; the flow fluctuation monitoring results include data flow change trend, flow fluctuation of risk nodes, nodes with excessive flow fluctuation amplitude, and suggestions for node path adjustment; the adjusted reminder task report includes the results of task priority adjustment, task progress evaluation, node processing capacity evaluation, node delay situation, and optimization of task scheduling order.

[0074] Please refer to Figure 2 , the data storage management module includes:

[0075] The data classification sub-module obtains the types of maritime shipping documents data and storage requirements, analyzes and compares the access frequencies of each type of data, divides each type of data into high-frequency data and low-frequency data according to the access frequency, and generates a data classification result;

[0076] According to the obtained types of maritime shipping documents data and storage requirements, combined with the access frequencies of each type of data, detailed classification processing is carried out. Suppose there are two types of data: type A and type B. Type A data is frequently accessed and belongs to high-frequency data, while type B data is less frequently accessed and belongs to low-frequency data. In practical applications, type A data may be real-time tracked cargo information, which is frequently accessed, while type B data may be historical records, which are only occasionally queried. After quantitatively analyzing the access frequencies of each type of data, the high-frequency data is classified and stored in SSD nodes, and the low-frequency data is stored in HDD nodes. In actual operation, when obtaining the access frequency, the access times of each data point can be analyzed through the log, the access frequency of each type of data can be calculated, and it can be compared with the storage capacity and reading performance of SSD and HDD nodes, and finally the optimal matching of data classification and storage is achieved.

[0077] Based on the data classification result, the data storage allocation sub-module evaluates the load situation and storage capacity of each storage node, and combines the data storage requirements to use the formula:

[0078] ;

[0079] Performs operations to obtain the storage allocation result, and generates a preliminary storage allocation result in combination with node load, storage capacity, and data access frequency differences;

[0080] Among them, represents the storage capacity of the SSD node, represents the storage capacity of the HDD node, and respectively represent the access frequencies of high-frequency and low-frequency data, and respectively represent the load conditions of SSD nodes and HDD nodes, represents the storage requirement, represents the storage allocation result;

[0081] Assume: The storage capacity of the SSD node ( ): 500 GB, the storage capacity of the HDD node ( ): 2 TB (i.e., 2000 GB), the data storage requirement ( ): 1.4 TB (i.e., 1400 GB), the access frequency of high-frequency data ( ): 80 times / hour, the access frequency of low-frequency data ( ): 10 times / hour, the load of the SSD node ( ): 75%, the load of the HDD node ( ): 60%;

[0082] Calculation process:

[0083] Calculate the difference in access frequencies of high-frequency and low-frequency data:

[0084] ;

[0085] Calculate the first term of the storage allocation formula:

[0086] ;

[0087] Calculate the load difference term:

[0088] ;

[0089] ;

[0090] Calculate the storage allocation result:

[0091] ;

[0092] Analysis of the calculation results:

[0093] The finally obtained storage allocation result , the result shows that the adjustment index of the storage allocation is relatively high, indicating that there are relatively large load differences or storage capacity imbalances in the current storage allocation scheme, and optimization is required.

[0094] The storage allocation adjustment sub-module monitors the real-time changes in the load of storage nodes according to the preliminary result of storage allocation, analyzes the difference between the current storage capacity and demand, performs dynamic adjustment of storage allocation, and generates the data storage allocation result;

[0095] Continuously monitor the storage capacity and load status of each node, and perform optimization and adjustment of storage allocation through real-time data updates. For example, assume that the current storage utilization rate of the SSD node is 85%, while that of the HDD node is 60%. At this time, it can be found that the load of the SSD node is too high, which may affect the system performance. Therefore, perform dynamic adjustment to migrate some high-frequency data (such as type A data) from the SSD to the HDD node to balance the load. During this process, by continuously monitoring the storage capacity and load status of the system and reallocating data based on dynamic changes, ensure the stable operation of the entire storage environment, and finally generate an adjusted data storage allocation plan. This process dynamically adjusts the distribution of data between the SSD and HDD nodes by calculating the storage capacity, load, and data access frequency of each node, and finally ensures that the load of each node is maintained within a reasonable range.

[0096] Please refer to Figure 3 , the reminder path optimization module includes:

[0097] According to the data storage allocation results, the data access analysis sub-module calls the data access frequency and storage capacity of each storage node, combines the reminder task priority and progress, analyzes the node task correlation and data processing requirements, calculates and obtains the data access matching degree of each node, and generates the data access matching degree;

[0098] Suppose there are multiple tasks, where task A has a higher priority and task B has a lower priority. Each task is processed on different storage nodes, and the loads and storage capacities of the nodes are different. Therefore, it is necessary to determine whether each node can efficiently process the task through evaluation. In this task, first, it is necessary to obtain the storage capacity and load rate of each node. Taking node X as an example, its storage capacity is 500GB, and the current load rate is 60%; the storage capacity of node Y is 1TB, and the load rate is 40%. Based on these data, calculate the processing ability of each node. The load rate of the node has a direct impact on its processing ability. A node with a high load may slow down when processing tasks, thereby affecting the overall task completion speed. Therefore, analyzing the matching degree between the processing ability of the node and the task requirements is the key, and generating the matching degree between the node processing ability and the task for subsequent path optimization and task allocation.

[0099] Based on the priority and progress of the reminder task, the node processing ability evaluation sub-module evaluates the processing ability of each storage node, analyzes the storage ability and load status of the node, calculates the matching degree between the node processing ability and the task requirements, and uses the formula:

[0100] ;

[0101] Calculate the matching degree between the node processing capacity and the task requirements, and generate the node processing capacity evaluation result by combining the storage capacity, load status, and task requirements;

[0102] Among them, represents the matching degree between the node processing capacity and the task requirements, represents the storage capacity of the node, represents the load rate of the node, represents the processing time requirement of the task, represents the priority of the task;

[0103] Suppose there are two nodes and , and two tasks and , and the data is as follows:

[0104] Node X: storage capacity , load rate ;

[0105] Node Y: storage capacity , load rate ;

[0106] The processing time requirements and priorities of Task A and Task B are as follows:

[0107] Task A: processing time requirement , priority ;

[0108] Task B: processing time requirement , priority ;

[0109] Calculate the matching degree of Node X:

[0110] Substitute the known values into the formula:

[0111] ;

[0112] Calculate each part:

[0113] ;

[0114] ;

[0115] ;

[0116] Substitute these calculated values:

[0117] ;

[0118] The results show that node X can handle task A, with a matching degree value of 7.61, indicating that node X can complete the processing of task A under high load, but with relatively low efficiency. Therefore, when allocating tasks, it may be necessary to prioritize allocating tasks to nodes with lower load to improve the overall processing efficiency.

[0119] Calculate the matching degree of node Y:

[0120] Substitute the known values into the formula:

[0121] ;

[0122] Calculate each part: ;

[0123] ;

[0124] ;

[0125] Substitute these calculated values:

[0126] ;

[0127] The results show that node Y can efficiently handle task B, with a matching degree value of 12, indicating that node Y can process task B more quickly under low load and large storage capacity. Compared with node X, node Y has stronger processing ability. Therefore, task B should be preferentially allocated to node Y.

[0128] The reminder path optimization sub-module optimizes the nodes in the reminder path according to the data access matching degree and the evaluation results of node processing capabilities, analyzes the task correlation and processing capability matching situation between nodes, adjusts the storage resources and task allocation of the nodes in the path, and generates an optimized reminder path;

[0129] Suppose the reminder path involves multiple nodes, where node X and node Y handle task A and task B. Node X has weak processing ability, small storage capacity, but high load; while node Y has strong processing ability and can handle tasks efficiently. Based on the evaluation results of node processing capabilities, it is necessary to re-allocate task A and task B. In this process, task A is preferentially allocated to node Y, while task B is allocated to node X. In this way, it can ensure that tasks can be efficiently processed according to priorities and node capabilities. The optimized path not only improves the task completion efficiency but also reduces the burden on high-load nodes. Through the optimized path, a more efficient reminder path can be obtained.

[0130] Please refer to Figure 4 , the dynamic scheduling module includes:

[0131] The task status extraction sub-module extracts the task execution status, progress information, and delay risk of each node in the optimized reminder path, obtains the current execution progress of each task, monitors the progress of node tasks, and obtains the task execution processing status;

[0132] Suppose the task progress of node A is 70% and that of node B is 40%. There is a certain lag in ship scheduling, which will have a certain impact on the task progress. Therefore, it is necessary to monitor the task progress, delay risk, and scheduling status of nodes in real time to ensure the smooth completion of tasks between nodes. In practice, by monitoring the scheduling status of ports and the navigation status of ships, the execution data of each node can be collected in real time to evaluate the delay risk of tasks and provide data support for subsequent optimization. The task execution status and delay risk assessment can provide a reference for subsequent task path optimization.

[0133] The processing capacity and progress comparison sub-module compares the processing capacity of the task with the current progress according to the task execution processing status, evaluates whether each node can complete the task on time, calculates the gap between the processing capacity and progress of the node, and uses the formula:

[0134] ;

[0135] Calculate the gap between the processing capacity and progress of each node through the operation to generate the task progress matching result;

[0136] Among them, represents the gap between the processing capacity and progress, represents the storage capacity of the node, represents the load rate of the node, represents the processing time requirement of the task, represents the progress that has been completed for the task currently, represents the total progress of the task;

[0137] Suppose the task processing capacity of node A is 300 documents per hour, and that of task B is 200 documents per hour. The current progress of task A is 70%, and that of task B is 40%. The total progress of task A and task B . Compare the progress information of the task with the processing capacity of the node, calculate the remaining processing time of each node, and calculate according to the formula in the processing capacity and progress comparison sub-module.

[0138] Calculate the remaining task volume of task A:

[0139] ;

[0140] The remaining time of node A:

[0141] ;

[0142] Substitute into the formula: Assume the load rate of node A , the processing time requirement of task A :

[0143] ;

[0144] Calculate each part:

[0145] , , ;

[0146] Substitute the values into the formula:

[0147] ;

[0148] The results show that there is a certain gap between the processing capacity of node A and the progress of task A. The progress matching degree is 0.68, indicating that although node A can process task A, due to its high load, the completion time of the remaining tasks may be long. This gap indicates that the processing efficiency of node A is low and appropriate optimization or task allocation adjustment is needed.

[0149] Calculate the remaining task volume of task B:

[0150] ;

[0151] The remaining time of node B:

[0152] ;

[0153] Substitute into the formula: Assume the load rate of node B , the processing time requirement of task B :

[0154] ;

[0155] Calculate each part:

[0156] , , ;

[0157] Substitute the values into the formula:

[0158] ;

[0159] The results show that the progress matching degree between the processing capacity of node B and the progress of task B is relatively high, and the progress matching degree is 1.19, indicating that the processing capacity of node B is relatively well-matched with the progress of task B, and the task can be completed efficiently. This result shows that node B can complete task B relatively quickly, and the progress gap is small.

[0160] The path adjustment and execution submodule adjusts the task path of each node in real time according to the task progress matching results, combined with the ship's navigation status and port scheduling status during the maritime document urging process, and replans and executes the task path according to the processing capacity and task progress of each node to generate the urging path execution results;

[0161] Assuming that the current navigation status of the ship will cause the processing progress of task A to lag behind, and the processing capacity of the port is limited, it is necessary to consider how to readjust the task path. Specifically, if the task progress of node A lags behind, the remaining documents can be assigned to node B, or the task processing order of node A can be adjusted to reduce the risk of task delays. For example, if node A is expected to be unable to process the remaining tasks within 1 hour, part of the tasks can be assigned to node B in advance, or the processing order can be adjusted when the ship approaches the port. This is implemented by real-time monitoring of factors such as the ship's navigation status and port scheduling status, and ultimately generates an optimized order execution path execution result.

[0162] See also Figure 5 , the data flow monitoring module includes:

[0163] The data collection submodule monitors the data flow information of each node in the execution results of the order reminder path in different time periods, collects flow information in real time based on task processing capacity and delay risk assessment, and generates real-time data flow information;

[0164] Assume that there are two nodes, node A and node B, and the task processing capacity of node A is 300 documents / hour, and that of node B is 250 documents / hour. The system needs to monitor and record the data flow of nodes A and B in real time. Taking node A as an example, assuming that its current task progress is 70%, and the task progress of node B is 40%, at this time, it is necessary to monitor the data flow of each node according to the real-time data flow of the node. For example, the real-time data flow of node A is 300 documents / hour, and the real-time data flow of node B is 250 documents / hour. At this time, the system will collect the flow data of each node in real time, including the percentage of node task completion, the total amount of tasks, etc., and generate the real-time data flow information of each node as the data basis for subsequent analysis. Based on these real-time collected data, the system can analyze the task flow trend of each node to help further determine whether the node task is proceeding according to the scheduled progress.

[0165] The data analysis submodule analyzes the data flow change trend of each node based on the real-time data flow information, using the formula:

[0166] ;

[0167] Calculate and obtain the traffic fluctuation percentage, compare it with the preset fluctuation threshold, filter out the risk nodes whose fluctuation exceeds the threshold, and generate a traffic fluctuation trend analysis report;

[0168] in, Represents the flow fluctuation percentage, Represents the node data traffic at the current moment, Represents the node data traffic at the previous moment, Represents the average processing delay of the node, Represents the total amount of tasks, Represents the length of the time period;

[0169] According to the monitored node data traffic, combined with the preset traffic fluctuation threshold, the traffic change trend of each node is analyzed to screen out risk nodes whose traffic fluctuation exceeds the threshold. Assuming that the traffic of node A suddenly jumps from 300 documents / hour to 600 documents / hour, and this fluctuation continues to exceed the set threshold by 10% (for example, the threshold is 30 documents / hour), then node A will be marked as a risk node, and the traffic fluctuation percentage of node A will be calculated;

[0170] We will use actual data for calculation. Assume that the traffic of node A is 300 documents / hour ( ) jumped to 600 documents / hour ( ), the average processing delay of the node is The total amount of tasks is , the time period is ,So: , , , ;

[0171] Calculate the sum of the absolute differences in flow changes:

[0172] ;

[0173] Calculate the sum of squared flow rates:

[0174] ;

[0175] Calculate the delay adjustment factor:

[0176] ;

[0177] Substitute into the formula to calculate the flow fluctuation percentage:

[0178] ;

[0179] According to the calculation results, the percentage of traffic fluctuation at Node A is 0.4471 or 44.71%, indicating that the traffic fluctuation at Node A is very large, exceeding the preset fluctuation threshold. It is necessary to mark it as a risk node and adjust the path;

[0180] The results show that the percentage of traffic fluctuation at Node A is much higher than the set threshold, indicating that the traffic change at Node A is too drastic, which may bring unstable factors to task execution. Therefore, Node A will be marked as a risk node, and its task allocation and execution path will be re-evaluated.

[0181] The path adjustment sub-module re-plans the execution path of the risk node according to the traffic fluctuation trend analysis report, analyzes the traffic fluctuation of each risk node, optimizes the execution order between nodes, and generates the traffic fluctuation monitoring results;

[0182] After screening out the risk nodes, the path adjustment sub-module will combine the processing capacity, latency, and total task volume of each node to adjust the execution path of the nodes with large data traffic fluctuations, avoiding task delays caused by excessive node load. Specifically, when it is found that the traffic fluctuation of Node A is abnormal and exceeds the set threshold, the system will transfer some task traffic to Node B with a lighter load through task reallocation to ensure that the data traffic of each node is maintained within a stable range. For example, if the task volume of Node A is 1000 documents and its traffic fluctuation exceeds the threshold, the system may transfer the task of 500 documents to Node B, keeping the load of Node A within a reasonable range and ensuring that the task is not affected by real-time calculation of the processing capacity of Node B. After adjustment, the system generates traffic fluctuation monitoring results to ensure the smooth execution of the entire process task path.

[0183] Please refer to Figure 6 The feedback adjustment module includes:

[0184] The traffic monitoring sub-module detects the traffic fluctuation amplitude and trend of the current path nodes according to the traffic fluctuation monitoring results, obtains the traffic difference and volatility of each path node, and evaluates the traffic fluctuation amplitude of the path nodes to generate the path traffic evaluation results;

[0185] Data collection is performed on each path node to record its traffic fluctuations, especially the traffic differences between each path node. The traffic difference can be obtained by comparing the input and output traffic of the node with the historical average traffic. Specifically, assume that the traffic of path node A is 50 at a certain moment, while the historical average traffic of this node is 40. Then the traffic difference of this node is 50 - 40 = 10. Next, by comparing the fluctuation amplitudes of each node, data analysis is carried out to further quantify the traffic fluctuation amplitude of each node, which can reflect the current traffic change trend of the path node. Through the traffic fluctuation amplitude, the traffic pressure of the node can be predicted, providing a basis for subsequent path load analysis. In practical applications, if it is detected that the traffic fluctuation amplitude of node A is relatively large, it means that there is a high traffic fluctuation at this node, and it may be necessary to adjust the task execution strategy or allocate resources preferentially to cope with the impact of potential traffic fluctuations on task execution. The generated traffic fluctuation amplitude value is the quantification result of the traffic fluctuation situation of this node.

[0186] Based on the path traffic evaluation results, the path load analysis sub-module analyzes the load conditions of path nodes, calculates the load index of each node, compares the relationship between the load and the traffic fluctuation amplitude, and screens out the path nodes with heavy loads, obtains the load ratio of the path nodes, and uses the formula:

[0187] ;

[0188] Through calculation, the load index of each node is obtained, and path node load data is generated;

[0189] Among them, is the load index of the th node, is the traffic fluctuation amplitude value of the th node, is the node traffic, is the average value of the traffic, is the total number of nodes;

[0190] Suppose the traffic fluctuation amplitudes of path nodes A, B, and C are 10, 8, and 12 respectively, and the processing capabilities of the nodes are 5, 4, and 6 respectively, and the total number of nodes is 3. First, the average value of the traffic needs to be calculated. The average value of the traffic fluctuation amplitude is:

[0191] ; Calculate the load index of each node:

[0192] For node A, the traffic fluctuation amplitude is 10 and the processing capacity is 5:

[0193] ;

[0194] For node B, the traffic fluctuation range is 8 and the processing capacity is 4:

[0195] ;

[0196] For node C, the traffic fluctuation range is 12 and the processing capacity is 6:

[0197] ;

[0198] The results show that the load index of node C is the highest, which means that the load pressure of this node is relatively large, and it may be necessary to prioritize task processing or optimize task scheduling. While the load index of node B is the lowest, indicating that its load pressure is relatively light and the scheduling priority may be lower. By calculating the load index, it can help determine which nodes have heavy loads and need to optimize the scheduling tasks. The load index of the generated path nodes provides a quantitative basis for subsequent task scheduling.

[0199] The task scheduling optimization sub-module analyzes the task execution priorities of the current path nodes according to the path node load data and the path traffic evaluation results, calculates the adjustment requirements for the task scheduling order, adjusts the node task scheduling order, determines whether it is necessary to re-plan the tasks, and generates an adjusted reminder task report;

[0200] Based on the load index and the traffic fluctuation range, the priorities of node tasks are calculated. For nodes A, B, and C, first calculate the relationship between their traffic fluctuation ranges and load indexes, and combine the task processing capabilities of the nodes to obtain the task priorities of each node. Since the load indexes of nodes A, B, and C are the same, but their traffic fluctuation ranges are different, the traffic fluctuation range can be used to determine the priority. In practical applications, assume that the traffic fluctuation range of node A is 10, the traffic fluctuation range of node B is 8, and the traffic fluctuation range of node C is 12. According to these values, it can be determined that the priority of node C is the highest, followed by node A, and finally node B. According to this sorting method, the task scheduling order can be adjusted to prioritize the nodes with larger traffic fluctuation ranges, thereby improving the task execution efficiency. Finally, an adjusted reminder task report is generated, and the report content covers the priority sorting of each node and the task adjustment plan.

[0201] An intelligent method for reminding of maritime shipping documents includes the following steps:

[0202] S1: Obtain the data types, storage requirements, and access frequencies of maritime shipping documents, divide the data into high-frequency data and low-frequency data according to the access frequency, store the high-frequency data in SSD nodes, store the low-frequency data in HDD nodes, monitor the node load and storage capacity in real time, dynamically adjust the data storage allocation according to the current load situation, and generate a storage allocation result;

[0203] S2: Invoke the data access frequency and storage capacity in the storage allocation result, analyze the task relevance and processing capabilities of each node, combine the priority and progress of the reminder task, evaluate the matching degree of the storage and processing capabilities of the nodes in the path, and generate an optimized reminder path;

[0204] S3: According to the task execution status and progress information of each node in the optimized reminder path, combine the ship navigation status and port scheduling status, evaluate the processing capabilities and current progress of the task in real time, judge the delay risk, and adjust the task execution path based on the evaluation result to generate the reminder path execution result;

[0205] S4: Based on the reminder path execution result, monitor the real-time data traffic of the path nodes, analyze the change trend of the data traffic, screen the risk nodes with traffic fluctuations exceeding the fluctuation threshold, readjust the execution path of the risk nodes, and generate the traffic fluctuation monitoring result;

[0206] S5: Based on the traffic fluctuation monitoring result, analyze the traffic fluctuation amplitude and path load of the current path nodes, dynamically adjust the task execution priority of the path nodes, evaluate the task progress, node processing capabilities and delays of each node, and judge whether it is necessary to adjust the task scheduling order to generate an adjusted reminder task report.

[0207] As described above, it is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. An intelligent shipping document reminder system, characterized in that: The system comprises: The data storage management module obtains the type, storage requirements and access frequency of shipping document data, divides each type of data into high-frequency data and low-frequency data according to the access frequency, and generates data storage allocation results; The order urging path optimization module calls the data access frequency and storage capacity of each storage node in the data storage allocation result, analyzes the task correlation and processing capacity between each node in the order urging path, and generates an optimized order urging path; The dynamic scheduling module extracts the task execution status, progress information and delay risk of each node in the optimized order urging path, compares the task processing capacity with the current processing progress, and generates the order urging path execution result; The dynamic scheduling module includes: The task status extraction submodule extracts the task execution status, progress information and delay risk of each node in the optimized order reminder path, obtains the current execution progress of each task, monitors the progress of the node task, and obtains the task execution processing status; The processing capacity and progress comparison submodule compares the processing capacity of the task with the current progress according to the task execution processing status, evaluates whether each node can complete the task on time, and calculates the gap between the processing capacity and progress of the node using the formula: ； The calculation obtains the processing capacity and progress gap of each node and generates the task progress matching result; in, Represents the gap between processing capacity and progress, Represents the storage capacity of the node, Represents the load rate of the node, represents the processing time requirement of the task, Represents the current progress of the task. Represents the total progress of the task; The path adjustment and execution submodule adjusts the task path of each node in real time according to the task progress matching result and the ship navigation status and port scheduling status during the marine document urging process, replans the task path and executes it according to the processing capacity and task progress of each node, and generates the urging path execution result; The data flow monitoring module monitors the data flow information of the execution path nodes based on the execution results of the order reminder path, analyzes the change trend of the node data flow, and reallocates the execution path of the risk node to generate the flow fluctuation monitoring results; The feedback adjustment module analyzes the flow fluctuation amplitude and path load of the current path node based on the flow fluctuation monitoring results, dynamically adjusts the task execution priority of the path node, plans the task scheduling sequence according to the priority, and generates an adjusted order urging task report.

2. The intelligent shipping document reminder system according to claim 1 is characterized by: The data storage allocation results include high-frequency data storage nodes, low-frequency data storage nodes, storage capacity allocation, and node load allocation; the optimized order urging path includes task priority adjustment, task processing capacity matching, correlation analysis between path nodes, and storage capacity and processing capacity matching evaluation; The execution result of the order reminder path includes task execution status, task progress, delay risk, ship navigation status, and port scheduling status; The traffic fluctuation monitoring results include data traffic change trends, risk node traffic fluctuations, nodes with excessive traffic fluctuations, and node path adjustment suggestions; The adjusted order urging task report includes task priority adjustment results, task progress evaluation, node processing capacity evaluation, node delay situation, and task scheduling sequence optimization.

3. The intelligent shipping document reminder system according to claim 1 is characterized by: The data storage management module includes: The data classification submodule obtains the data types and storage requirements of shipping documents, analyzes and compares the access frequency of each type of data, divides each type of data into high-frequency data and low-frequency data according to the access frequency, and generates data classification results; The data storage allocation submodule evaluates the load and storage capacity of each storage node based on the data classification results, and adopts the formula based on the data storage requirements: ； The storage allocation result is obtained by calculation, and the preliminary storage allocation result is generated by combining the node load, storage capacity and data access frequency differences; in, Represents the storage capacity of the SSD node, Represents the storage capacity of the HDD node, and Represent the access frequency of high-frequency and low-frequency data respectively, and Represent the load status of SSD nodes and HDD nodes respectively. Represents storage requirements, Represents the storage allocation result; The storage allocation adjustment submodule monitors the real-time changes of the storage node load according to the preliminary storage allocation result, analyzes the difference between the current storage capacity and the demand, performs dynamic adjustment of storage allocation, and generates data storage allocation results.

4. The intelligent shipping document reminder system according to claim 1 is characterized by: The order urging path optimization module includes: The data access analysis submodule calls the data access frequency and storage capacity of each storage node according to the data storage allocation result, analyzes the node task relevance and data processing requirements in combination with the order urging task priority and progress, calculates and obtains the data access matching degree of each node, and generates the data access matching degree; The node processing capacity evaluation submodule evaluates the processing capacity of each storage node based on the priority and progress of the order urging task, analyzes the storage capacity and load status of the node, and calculates the matching degree between the node processing capacity and the task requirements. The formula is: ； The calculation obtains the matching degree between the node processing capacity and the task requirements, and generates the node processing capacity evaluation result by combining the storage capacity, load status and task requirements; in, Represents the degree of match between the node processing capability and the task requirements. Represents the storage capacity of the node, Represents the load rate of the node, represents the processing time requirement of the task, Represents the priority of the task; The order reminder path optimization submodule optimizes the nodes in the order reminder path according to the data access matching degree and node processing capacity evaluation results, analyzes the task correlation and processing capacity matching between nodes, adjusts the storage resources and task allocation of the nodes in the path, and generates an optimized order reminder path.

5. The intelligent shipping document reminder system according to claim 1 is characterized by: The data flow monitoring module includes: The data collection submodule monitors the data flow information of each node in the execution result of the order urging path in different time periods, collects flow information in real time based on task processing capability and delay risk assessment, and generates real-time data flow information; The data analysis submodule analyzes the data flow change trend of each node according to the real-time data flow information, using the formula: ； Calculate and obtain the traffic fluctuation percentage, compare it with the preset fluctuation threshold, filter out the risk nodes whose fluctuation exceeds the threshold, and generate a traffic fluctuation trend analysis report; in, Represents the flow fluctuation percentage, Represents the node data traffic at the current moment, Represents the node data traffic at the previous moment, represents the average processing delay of the node, Represents the total amount of tasks, Represents the length of the time period; The path adjustment submodule replans the execution path of the risk node according to the traffic fluctuation trend analysis report, analyzes the traffic fluctuation of each risk node, optimizes the execution order between nodes, and generates traffic fluctuation monitoring results.

6. The intelligent shipping document reminder system according to claim 1 is characterized by: The feedback adjustment module comprises: The flow monitoring submodule detects the flow fluctuation amplitude and trend of the current path node according to the flow fluctuation monitoring result, obtains the flow difference and fluctuation rate of each path node, and evaluates the flow fluctuation amplitude of the path node to generate a path flow evaluation result; The path load analysis submodule analyzes the load of the path nodes based on the path flow evaluation results, calculates the load index of each node, compares the relationship between the load and the flow fluctuation amplitude, and selects the path nodes with heavier loads to obtain the load ratio of the path nodes using the formula: ； Obtain the load index of each node through calculation and generate path node load data; in, For the The load index of the nodes, For the The traffic fluctuation amplitude of each node, is the node traffic, is the average flow rate, is the total number of nodes; The task scheduling optimization submodule analyzes the task execution priority of the current path node according to the path node load data and path traffic evaluation results, calculates the adjustment requirements of the task scheduling sequence, adjusts the node task scheduling sequence, determines whether the task needs to be re-planned, and generates an adjusted order urging task report.

7. An intelligent method for urging shipping documents, characterized in that: The intelligent shipping document reminder system according to any one of claims 1 to 6 comprises the following steps: S1: Obtain the data type, storage requirements and access frequency of shipping documents, divide the data into high-frequency data and low-frequency data according to the access frequency, store the high-frequency data in SSD nodes and the low-frequency data in HDD nodes, monitor the node load and storage capacity in real time, dynamically adjust the data storage allocation according to the current load situation, and generate the storage allocation result; S2: calling the data access frequency and storage capacity in the storage allocation result, analyzing the task relevance and processing capacity of each node, combining the priority and progress of the order urging task, evaluating the matching degree of the storage and processing capacity of the nodes in the path, and generating an optimized order urging path; S3: According to the task execution status and progress information of each node in the optimized order urging path, combined with the ship's navigation status and port scheduling status, the task's processing capacity and current progress are evaluated in real time, the delay risk is determined, and the task execution path is adjusted based on the evaluation result to generate the order urging path execution result; S4: Based on the execution result of the order urging path, monitor the real-time data flow of the path nodes, analyze the data flow change trend, screen the risk nodes whose flow fluctuation exceeds the fluctuation threshold, readjust the execution path of the risk nodes, and generate the flow fluctuation monitoring result; S5: Based on the traffic fluctuation monitoring results, analyze the traffic fluctuation amplitude and path load of the current path node, dynamically adjust the path node task execution priority, evaluate the task progress, node processing capacity and delay of each node, determine whether the task scheduling order needs to be adjusted, and generate an adjusted order urging task report.

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