Port cargo intelligent scheduling system and method based on big data analysis

Abstract

The application discloses a port cargo intelligent scheduling system and method based on big data analysis, and relates to the technical field of port cargo scheduling data processing.The system comprises an environment collection and evaluation module, a cargo intelligent scheduling distributed computing node first comparative analysis calling module and a cargo intelligent scheduling distributed computing node second comparative analysis calling module.The application forms an intelligent closed-loop optimization system of "environment-equipment-computing power" through cargo environment monitoring optimization, equipment operation efficiency optimization and computing resource dynamic allocation, realizes all-round efficiency improvement of port operation from physical infrastructure to digital decision system through Internet of Things sensing layer data collection, digital twin modeling and edge cloud computing, and solves the problem of insufficient accuracy of the port cargo intelligent scheduling system in responding to the distributed scheduling environment under extreme weather in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of port cargo dispatching data processing technology, and in particular to a port cargo intelligent dispatching system and method based on big data analysis. Background Art

[0002] With the rapid development of international trade, the throughput of ports, as logistics hubs, continues to climb. Traditional manual dispatching methods, plagued by information asymmetry and delayed decision-making, are inefficient and unable to meet demand. The maturation of technologies such as big data, the Internet of Things, and artificial intelligence (AI) has provided new insights for port dispatching. Optimizing resource allocation through real-time data analysis and intelligent algorithms has become an inevitable choice for industry upgrades. With the deep integration of technologies and the advancement of automation, future port dispatching will deeply integrate big data, cloud computing, the Internet of Things, 5G, and AI technologies, achieving millisecond-level data response and full-process automation. Intelligent dispatching systems will connect ports with inland transportation networks such as railways and highways, enabling cross-regional collaboration.

[0003] The existing intelligent port cargo dispatching system, based on big data analysis, is implemented through the following technologies: Machine learning is used to predict cargo throughput and vessel arrival times, optimizing resource allocation. The LSTM time series model excels in demand forecasting, and operations research and reinforcement learning are combined to generate optimal loading and unloading sequences and routing. Sensors, RFID, and other technologies are used to collect real-time cargo and equipment status data, supporting full-process tracking.

[0004] For example, the patent application with publication number CN119151403A discloses a port management method, system and equipment based on a digital intelligent algorithm, which includes: collecting cargo information, ship information, and vehicle information and preprocessing them; making an intelligent berth plan based on the preprocessed ship information to obtain berth information, and then making an intelligent loading plan based on the preprocessed cargo information, preprocessed vehicle information and berth information to obtain loading information; collecting port storage information, and making an intelligent storage plan based on the loading information combined with the port storage information to obtain storage information; making an intelligent location selection based on the berth information, loading information and storage information to obtain location selection parameters and location selection tasks, and automatically sending boxes based on the location selection parameters and location selection tasks to realize real-time port scheduling.

[0005] For example, the invention patent with announcement number CN116542488B discloses an artificial intelligence-based port scheduling method and system, which includes: calculating the navigation parameters of each ship according to the nature of the cargo, ship tonnage and ship type; sorting each ship in descending order according to the navigation parameters to obtain a navigation sequence; constructing a ship scheduling model and a berth allocation model; constructing the objective function of the ship scheduling model based on the total scheduling time, total waiting time and time value loss, and training the ship scheduling model with the goal of minimizing the objective function of the ship scheduling model; calculating the scheduling time of each ship through the ship scheduling model according to the navigation sequence; constructing the objective function of the berth allocation model based on the total time in port, and training the berth allocation model with the goal of minimizing the objective function of the berth allocation model; and allocating berths to each ship through the ship scheduling model.

[0006] However, in the process of implementing the technical solutions of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the existing technology, due to the multi-level risks faced by the port intelligent scheduling system under extreme weather conditions, including the influence of multiple factors such as the data perception layer, the algorithm decision layer, and the execution control layer, the risk of dynamic scheduling and multimodal transport suspension is greatly increased, the scheduling response time is greatly delayed, and the timeliness of the response to climate risk challenges is weak. There is a problem that the port cargo intelligent scheduling system lacks the accuracy of adapting to the distributed scheduling environment under extreme weather conditions. Summary of the Invention

[0008] The embodiments of the present application solve the problem in the prior art that the intelligent port cargo scheduling system is insufficiently responsive to distributed scheduling environments under extreme weather conditions by providing a port cargo intelligent scheduling system and method based on big data analysis, thereby improving the accuracy of the intelligent port cargo scheduling system's responsiveness to distributed scheduling environments under extreme weather conditions.

[0009] An embodiment of the present application provides a port cargo intelligent scheduling system based on big data analysis, including: an environmental collection and evaluation module, a first comparative analysis and calling module of a cargo intelligent scheduling distributed computing node, and a second comparative analysis and calling module of a cargo intelligent scheduling distributed computing node; wherein, the environmental collection and evaluation module is used to evaluate and analyze the environmental parameters of port cargo, and perform comparative analysis and adjustment according to the port cargo environmental parameters; the first comparative analysis and calling module of the cargo intelligent scheduling distributed computing node is used to evaluate and analyze the fluctuation parameters of the port scheduling equipment, and perform comparative analysis and adjustment according to the fluctuation parameters of the port scheduling equipment; the second comparative analysis and calling module of the cargo intelligent scheduling distributed computing node is used to perform comprehensive evaluation and analysis of the port scheduling distributed computing node equipment, and perform comparative analysis and adjustment according to the comprehensive evaluation and analysis results.

[0010] Furthermore, comparative analysis and adjustment are performed based on the port cargo environmental parameters, specifically including: collecting and obtaining the port environmental wind speed through the wind speed sensor, collecting and obtaining the maximum value of the port environmental seawater level through the water level sensor, and collecting and obtaining the maximum value of rainfall per unit time through the rainfall sensor; if the maximum value of the port environmental seawater level is less than the port environmental seawater level maximum value threshold and the maximum value of rainfall per unit time is less than the unit time maximum value threshold and the port environmental wind speed is less than the port environmental wind speed threshold, then a sudden change assessment of the port cargo intelligent scheduling environmental assessment is performed; if the maximum value of the port environmental seawater level is equal to or greater than the port environmental seawater level maximum value threshold or the maximum value of rainfall per unit time is equal to or greater than the unit time maximum value threshold or the port environmental wind speed is equal to or greater than the port environmental wind speed threshold, then an early warning is issued and relevant personnel are notified to increase the port cargo intelligent scheduling early warning level to the highest level.

[0011] Furthermore, a mutation assessment of the port cargo intelligent dispatching environment assessment is conducted, specifically including: obtaining the maximum value of the port environment salt fog concentration through the laser scattering method salt fog online monitoring instrument; extracting the historical average value of the port environment seawater level, the historical average value of the port environment salt fog concentration, the correction factor of the port environment sea wind speed for the port environment seawater level, the historical average value of the port environment rainfall, the first dispatching proportion factor of the port cargo environment characteristic change, the second dispatching proportion factor of the port cargo environment characteristic change and the third dispatching proportion factor of the port cargo environment characteristic change from the port cargo intelligent dispatching environment assessment database; performing a ratio analysis on the maximum value of the port environment seawater level and the historical average value of the port environment seawater level, correcting the port environment seawater level correction factor by the port environment sea wind speed, and then correcting the port environment seawater level correction factor by the port cargo environment characteristic change. The first scheduling proportion factor is corrected to obtain the first component of the port cargo intelligent scheduling environment assessment mutation; the maximum value of the port environment salt spray concentration and the historical average value of the port environment salt spray concentration are analyzed, and the second component of the port cargo intelligent scheduling environment assessment mutation is corrected by the second scheduling proportion factor of the port cargo environment characteristic change to obtain the second component of the port cargo intelligent scheduling environment assessment mutation; the maximum value of the port environment rainfall and the historical average value of the port environment rainfall are analyzed, and the third component of the port cargo environment characteristic change is corrected to obtain the third component of the port cargo intelligent scheduling environment assessment mutation; the first component of the port cargo intelligent scheduling environment assessment mutation, the second component of the port cargo intelligent scheduling environment assessment mutation and the third component of the port cargo intelligent scheduling environment assessment mutation are coupled to analyze the port cargo intelligent scheduling environment assessment mutation value.

[0012] Furthermore, the specific process of comparative analysis and adjustment based on the port cargo environmental parameters is as follows: if the port cargo intelligent scheduling environment assessment mutation value is less than the port cargo intelligent scheduling environment assessment mutation threshold, no adjustment is made; if the port cargo intelligent scheduling environment assessment mutation value is equal to or greater than the port cargo intelligent scheduling environment assessment mutation threshold, the port cargo intelligent scheduling environment assessment mutation value is coupled with the real-time centralized computing power load utilization rate of the port cargo intelligent scheduling resource center for analysis to obtain the port cargo intelligent scheduling resource center coupling value, which is used to quantify the risk level of relative scarcity of the schedulable resources of the port cargo intelligent scheduling resource center. If the port cargo intelligent scheduling resource center coupling value is less than the port cargo intelligent scheduling resource center coupling threshold, a notification is issued to relevant personnel and automated distributed resource scheduling is temporarily not started. If the port cargo intelligent scheduling resource center coupling value is equal to or greater than the port cargo intelligent scheduling resource center coupling threshold, distributed resource scheduling is started.

[0013] Furthermore, the fluctuation parameters of the port dispatching equipment are evaluated and analyzed, specifically including: collecting the network delay and the network bit error rate of the port distributed dispatching equipment through the built-in tools of the port distributed dispatching equipment; directly extracting the network delay threshold of the port distributed dispatching equipment, the network bit error rate threshold of the port distributed dispatching equipment, the average failure rate threshold of the port distributed dispatching equipment, the first dispatching proportion factor of the port equipment dispatching characteristic change, the second dispatching proportion factor of the port equipment dispatching characteristic change, the third dispatching proportion factor of the port equipment dispatching characteristic change and the correction factor of the salt spray corrosion of the terminal interface of the port distributed dispatching equipment for the usage time from the port cargo intelligent dispatching environment assessment database; performing a ratio analysis of the network delay of the port distributed dispatching equipment and the network delay threshold of the port distributed dispatching equipment, and correcting it through the first dispatching proportion factor of the port equipment dispatching characteristic change to obtain the port distributed dispatching equipment. The first component of the performance evaluation fluctuation of the port distributed scheduling equipment is analyzed; the network bit error rate of the port distributed scheduling equipment and the network bit error rate threshold of the port distributed scheduling equipment are analyzed in proportion, and the second scheduling proportion factor of the port equipment scheduling feature change is corrected to obtain the second component of the performance evaluation fluctuation of the port distributed scheduling equipment; the average failure rate of the port distributed scheduling equipment and the average failure rate threshold of the port distributed scheduling equipment are analyzed in proportion, and the third scheduling proportion factor of the port equipment scheduling feature change is corrected to obtain the third component of the performance evaluation fluctuation of the port distributed scheduling equipment; the first component of the performance evaluation fluctuation of the port distributed scheduling equipment, the second component of the performance evaluation fluctuation of the port distributed scheduling equipment and the third component of the performance evaluation fluctuation of the port distributed scheduling equipment are coupled analyzed, and then the correction factor of the salt spray corrosion of the terminal interface of the port distributed scheduling equipment for the service life is corrected to obtain the sudden change value of the performance evaluation fluctuation of the port distributed scheduling equipment.

[0014] Furthermore, comparative analysis and adjustment are performed based on the fluctuation parameters of the port dispatching equipment, specifically including: if the port distributed dispatching equipment performance evaluation fluctuation mutation value is less than the port distributed dispatching equipment performance evaluation fluctuation mutation threshold, then the corresponding port distributed dispatching equipment is recorded and recorded as the first node of the port distributed dispatching equipment to be adjusted; if the port distributed dispatching equipment performance evaluation fluctuation mutation value is equal to or greater than the port distributed dispatching equipment performance evaluation fluctuation mutation threshold, then the corresponding port distributed dispatching equipment is recorded and recorded as the second node of the port distributed dispatching equipment to be adjusted, and emergency anti-corrosion and dehumidification treatment is performed, and the port distributed dispatching equipment performance evaluation fluctuation mutation values ​​corresponding to the second node of the port distributed dispatching equipment to be adjusted are arranged in descending order, and the corresponding computing tasks of the arranged second nodes of the port distributed dispatching equipment to be adjusted are gradually migrated to the first node of the port distributed dispatching equipment to be adjusted in descending order.

[0015] Furthermore, a comprehensive evaluation and analysis of the port dispatching distributed computing node equipment is conducted, specifically including: directly extracting the first comprehensive scheduling weight factor of the distributed computing power resources of the port cargo intelligent dispatching equipment, the second comprehensive scheduling weight factor of the distributed computing power resources of the port cargo intelligent dispatching equipment, and the correction factor of the local temperature rise rate of the port distributed dispatching equipment for the comprehensive risk value of the distributed computing power resources of the port cargo intelligent dispatching equipment from the port cargo intelligent dispatching environment assessment database; coupling the mutation value of the port cargo intelligent dispatching environment assessment with the first comprehensive scheduling weight factor of the distributed computing power resources of the port cargo intelligent dispatching equipment to obtain the correction factor of the distributed computing power resources of the port cargo intelligent dispatching equipment. The first component of the comprehensive risk of the source; the fluctuation mutation value of the performance evaluation of the port distributed scheduling equipment is coupled analyzed by the second comprehensive scheduling proportion factor of the distributed computing power resources of the port cargo intelligent scheduling equipment to obtain the second component of the comprehensive risk of the distributed computing power resources of the port cargo intelligent scheduling equipment; the first component of the comprehensive risk of the distributed computing power resources of the port cargo intelligent scheduling equipment is coupled analyzed with the second component of the comprehensive risk of the distributed computing power resources of the port cargo intelligent scheduling equipment, and then the correction factor of the comprehensive risk value of the distributed computing power resources of the port cargo intelligent scheduling equipment is corrected by the local temperature rise rate of the port distributed scheduling equipment to obtain the comprehensive risk value of the distributed computing power resources of the port cargo intelligent scheduling equipment.

[0016] Further, according to the comparative analysis adjustment of the comprehensive evaluation analysis result, specifically: if the port cargo intelligent scheduling equipment distributed computing resource comprehensive risk value is less than the port cargo intelligent scheduling equipment distributed computing resource comprehensive risk threshold, then arrange the port distributed scheduling equipment to be adjusted second node in descending order, and gradually migrate the corresponding computing task to the port distributed scheduling equipment to be adjusted first node, and according to the descending order, the corresponding port cargo intelligent scheduling equipment is recorded as a secondary high-power node according to the predefined percentage, and a predefined percentage of the algorithm resource is reserved for the secondary high-power node.

[0017] Further, according to the comparative analysis adjustment of the comprehensive evaluation analysis result, it also includes: if the port cargo intelligent scheduling equipment distributed computing resource comprehensive risk value is equal to or greater than the port cargo intelligent scheduling equipment distributed computing resource comprehensive risk threshold, then the cooling fan speed of the corresponding port cargo intelligent scheduling equipment is increased and the liquid cooling system of the corresponding port cargo intelligent scheduling equipment is started, and the port cargo intelligent scheduling equipment is arranged according to the descending order of the port cargo intelligent scheduling equipment distributed computing resource comprehensive risk value, and the port cargo intelligent scheduling equipment within the predefined percentage is recorded as a high-power node according to the predefined percentage, and the wireless link of the port cargo intelligent scheduling equipment is switched to a fiber special network, and the algorithm task is stopped.

[0018] The embodiment of the application provides a port cargo intelligent scheduling method based on big data analysis, which is characterized by the following specific steps: evaluating and analyzing the port cargo environment parameters, and adjusting the comparison analysis according to the port cargo environment parameters; evaluating and analyzing the port scheduling device fluctuation parameters, and adjusting the comparison analysis according to the port scheduling device fluctuation parameters; and evaluating and analyzing the port scheduling distributed computing node device, and adjusting the comparison analysis according to the comprehensive evaluation analysis result.

[0019] The one or more technical solutions provided in the embodiment of the application have at least the following technical effects or advantages:

[0020] 1、The application optimizes the cargo environment monitoring, the equipment operation efficiency and the dynamic allocation of computing resources, which form an intelligent closed-loop optimization system of "environment-equipment-computing power", realizes the all-round efficiency improvement of port operation from physical infrastructure to digital decision system through Internet of Things sensing layer data acquisition, digital twin modeling and edge cloud computing, and solves the problem of insufficient accuracy of the port cargo intelligent scheduling system in responding to the distributed scheduling environment adaptation under extreme weather in the prior art.

[0021] 2. Comparative analysis and adjustments are conducted based on the fluctuation parameters of port dispatching equipment. The maintenance cycle is dynamically adjusted through the salt spray corrosion correction factor. When the salt spray concentration surges during the typhoon season, the energy consumption of task migration is reduced, and the nitrogen spray system reduces corrosion loss. This enables the port to have the intelligent capabilities of environmental self-adaptation, risk self-management, and resource self-allocation, thereby improving the environmental adaptability of the port's intelligent cargo dispatching system.

[0022] 3. Comparative analysis and adjustments are conducted based on the comprehensive assessment and analysis results. Task migration is triggered based on the computing power risk value, and computing power resources are reserved to cope with sudden demands, ensuring zero interruption of high-priority tasks. The emergency response speed is improved, and automatic startup heat dissipation is enhanced, chip junction temperature is reduced, and the probability of equipment frequency reduction is reduced. The secondary high-level node pre-allocation mechanism improves the emergency task processing capability, thereby realizing the closed loop of "real-time risk perception-dynamic resource adaptation-continuous performance optimization", which reduces the comprehensive failure rate of port dispatch distributed computing node equipment in extreme weather and improves energy efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A schematic diagram of the structure of the port cargo intelligent scheduling system based on big data analysis provided in an embodiment of the present application.

[0024] Figure 2 A schematic diagram of the process of comparative analysis and adjustment based on port cargo environmental parameters provided in an embodiment of the present application.

[0025] Figure 3 A schematic diagram of a process for comparative analysis and adjustment based on fluctuation parameters of port dispatching equipment provided in an embodiment of the present application.

[0026] Figure 4 A flowchart for comprehensive evaluation and analysis of distributed computing node equipment for port scheduling provided in an embodiment of the present application.

[0027] Figure 5 A schematic flow chart of the intelligent port cargo scheduling method based on big data analysis provided in an embodiment of the present application. DETAILED DESCRIPTION

[0028] The embodiments of the present application provide a port cargo intelligent dispatching system and method based on big data analysis, thereby solving the problem in the prior art that the port cargo intelligent dispatching system has insufficient accuracy in adapting to the distributed dispatching environment under extreme weather conditions. Through cargo environment monitoring optimization, equipment operation efficiency optimization and dynamic allocation of computing resources, the three constitute an intelligent closed-loop optimization system of "environment-equipment-computing power". Through data collection at the IoT perception layer, digital twin modeling and edge cloud computing, the all-round efficiency improvement of port operations from physical infrastructure to digital decision-making systems is achieved.

[0029] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0030] like Figure 1 As shown, it is a structural diagram of the port cargo intelligent scheduling system based on big data analysis provided in an embodiment of the present application. The port cargo intelligent scheduling system based on big data analysis provided in an embodiment of the present application includes an environmental acquisition and evaluation module, a first comparative analysis and calling module of the cargo intelligent scheduling distributed computing node, and a second comparative analysis and calling module of the cargo intelligent scheduling distributed computing node; wherein, the environmental acquisition and evaluation module is used to evaluate and analyze the port cargo environmental parameters, and perform comparative analysis and adjustment according to the port cargo environmental parameters; the first comparative analysis and calling module of the cargo intelligent scheduling distributed computing node is used to evaluate and analyze the fluctuation parameters of the port scheduling equipment, and perform comparative analysis and adjustment according to the fluctuation parameters of the port scheduling equipment; the second comparative analysis and calling module of the cargo intelligent scheduling distributed computing node is used to perform comprehensive evaluation and analysis on the port scheduling distributed computing node equipment, and perform comparative analysis and adjustment according to the comprehensive evaluation and analysis results.

[0031] Furthermore, comparative analysis and adjustment are performed based on the port cargo environmental parameters, specifically including: collecting and obtaining the port environmental wind speed through the wind speed sensor, collecting and obtaining the maximum value of the port environmental seawater level through the water level sensor, and collecting and obtaining the maximum value of rainfall per unit time through the rainfall sensor; if the maximum value of the port environmental seawater level is less than the port environmental seawater level maximum value threshold and the maximum value of rainfall per unit time is less than the unit time maximum value threshold and the port environmental wind speed is less than the port environmental wind speed threshold, then a sudden change assessment of the port cargo intelligent scheduling environmental assessment is performed; if the maximum value of the port environmental seawater level is equal to or greater than the port environmental seawater level maximum value threshold or the maximum value of rainfall per unit time is equal to or greater than the unit time maximum value threshold or the port environmental wind speed is equal to or greater than the port environmental wind speed threshold, then an early warning is issued and relevant personnel are notified to increase the port cargo intelligent scheduling early warning level to the highest level.

[0032] In this embodiment, port ambient wind speed is collected and determined using wind speed sensors. For example, ultrasonic wind speed sensors without mechanical transmission structures are deployed atop fixed structures in the port area whose vertical height exceeds a preset threshold, including but not limited to shoreside monitoring towers, the top platforms of large loading and unloading equipment, and meteorological observation facilities. These ultrasonic wind speed sensors are coupled to edge computing nodes via industrial-grade communication gateways. These edge computing nodes have built-in real-time data processing algorithms, and their response delays meet the preset timing requirements of the port operation safety control system.

[0033] The maximum value of the seawater level in the port environment is collected and obtained through the water level sensor. For example, the water level monitoring module adopts an electromagnetic wave reflection type water level sensor device, and its installation location includes the port breakwater structure.

[0034] Rainfall sensors are used to collect and obtain the maximum rainfall value per unit time. For example, a mechanical bucket metering device is installed at the elevation benchmark point of the yard area to detect the water accumulation risk parameters in the solid bulk cargo storage area. A composite monitoring terminal is set at the key node of the drainage network. The composite monitoring terminal integrates a flow detection unit and a liquid level sensor unit. Its output signal forms a closed-loop control circuit with the drainage pump station control system. An expandable communication module is set in the container storage area. The module is connected to multiple rainfall detection terminals through a standard industrial bus protocol to form a grid monitoring array.

[0035] Furthermore, a mutation assessment of the port cargo intelligent dispatching environment assessment is conducted, specifically including: obtaining the maximum value of the port environment salt fog concentration through the laser scattering method salt fog online monitoring instrument; extracting the historical average value of the port environment seawater level, the historical average value of the port environment salt fog concentration, the correction factor of the port environment sea wind speed for the port environment seawater level, the historical average value of the port environment rainfall, the first dispatching proportion factor of the port cargo environment characteristic change, the second dispatching proportion factor of the port cargo environment characteristic change and the third dispatching proportion factor of the port cargo environment characteristic change from the port cargo intelligent dispatching environment assessment database; performing a ratio analysis on the maximum value of the port environment seawater level and the historical average value of the port environment seawater level, correcting the port environment seawater level correction factor by the port environment sea wind speed, and then correcting the port environment seawater level correction factor by the port cargo environment characteristic change. The first scheduling proportion factor is corrected to obtain the first component of the port cargo intelligent scheduling environment assessment mutation; the maximum value of the port environment salt spray concentration and the historical average value of the port environment salt spray concentration are analyzed, and the second component of the port cargo intelligent scheduling environment assessment mutation is corrected by the second scheduling proportion factor of the port cargo environment characteristic change to obtain the second component of the port cargo intelligent scheduling environment assessment mutation; the maximum value of the port environment rainfall and the historical average value of the port environment rainfall are analyzed, and the third component of the port cargo environment characteristic change is corrected to obtain the third component of the port cargo intelligent scheduling environment assessment mutation; the first component of the port cargo intelligent scheduling environment assessment mutation, the second component of the port cargo intelligent scheduling environment assessment mutation and the third component of the port cargo intelligent scheduling environment assessment mutation are coupled to analyze the port cargo intelligent scheduling environment assessment mutation value.

[0036] In this embodiment, the distributed port cargo intelligent dispatching environment monitoring points are numbered. Indicates the number of distributed port cargo intelligent scheduling environment monitoring points, , Indicates the total number of distributed port cargo intelligent scheduling environment monitoring points.

[0037] Indicates the The port cargo intelligent scheduling environment assessment mutation value of each distributed port cargo intelligent scheduling environment monitoring point is used to quantify the mutation degree of the environment in which the software and hardware equipment required for port cargo intelligent scheduling are located.

[0038] ;

[0039] ;

[0040] Indicates the The maximum value of the port environment seawater level is collected by the water level sensor at the distributed port cargo intelligent scheduling environment monitoring point.

[0041] It represents the historical average of the seawater level in the port environment, which is extracted from the port cargo intelligent scheduling environmental assessment database.

[0042] Continuous sea breezes will generate tangential stress on the surface of the seawater, resulting in the transfer of momentum of the water body in the direction of the wind field. When the wind direction forms a specific angle with the coastline, the surface seawater is transported and accumulated on the shore under the continuous action of onshore winds, causing the actual water level to be significantly higher than the theoretical value predicted by the tidal model. The wave breaking caused by high-frequency gusts will form a climbing effect in the nearshore area, further raising the instantaneous water level. At the same time, when a typhoon or a strong cyclone passes, the synergistic effect of sea breeze speed and sudden drop in air pressure may trigger a storm surge. At this time, the water level correction factor needs to be dynamically amplified to reflect the nonlinear effect of the wind field and air pressure coupling on the water level.

[0043] Indicates the The correction factor for the port environmental sea wind speed to the port environmental seawater level at each distributed port cargo intelligent dispatching environmental monitoring point is calculated. The value range is 0 to 1. This correction factor is extracted from the port cargo intelligent dispatching environmental assessment database. A specific example of this is as follows: ultrasonic wind speed and direction meters are deployed in highly exposed areas of the port (such as lighthouses and elevated monitoring towers), ensuring coverage of the main waterway and loading and unloading areas, and avoiding obstruction by buildings. Real-time water level data is obtained from port tide monitoring stations and combined with historical averages (such as chart depth datums). Wind speed and tide data are timestamp-aligned to eliminate outliers (such as sudden changes caused by sensor failure). Missing data is filled using an interpolation algorithm (such as cubic splines). The fitting coefficient is determined by fitting historical data using the least squares method, with cross-validation required to avoid overfitting. The data is divided into a training set (70%) and a test set (30%). A mapping relationship is obtained between the real-time wind speed value and the corresponding correction factor for the port environmental sea wind speed to the port environmental seawater level. Inputting the real-time wind speed value yields the corresponding correction factor for the port environmental sea wind speed to the port environmental seawater level.

[0044] Indicates the The highest salt fog concentration in the port environment at each distributed port cargo intelligent dispatching environmental monitoring point is collected using a laser scattering salt fog online monitor. This monitor measures the concentration of salt particles in the air using the laser scattering principle and, combined with environmental parameters such as temperature and humidity, outputs the salt fog content in real time. The laser scattering salt fog online monitor can be mounted on a wall or on a guide rail, making it suitable for confined spaces and easy to integrate with monitoring systems. It is suitable for coastal and offshore environments with high salt fog levels and provides long-term stable operation.

[0045] It represents the historical average value of salt spray concentration in the port environment, which is extracted from the port cargo intelligent scheduling environmental assessment database.

[0046] Indicates the The maximum value of port environmental rainfall at each distributed port cargo intelligent scheduling environmental monitoring point is collected through the mechanical tipping bucket metering device in the example above.

[0047] It represents the historical average rainfall in the port environment, which is extracted from the port cargo intelligent scheduling environmental assessment database.

[0048] The first scheduling weighting factor representing the change in port cargo environmental characteristics is directly extracted from the port cargo intelligent scheduling environmental assessment database.

[0049] The second scheduling weighting factor representing the change in port cargo environment characteristics is directly extracted from the port cargo intelligent scheduling environment assessment database.

[0050] The third scheduling weighting factor representing the change in port cargo environment characteristics is directly extracted from the port cargo intelligent scheduling environmental assessment database.

[0051] Sudden changes in ambient temperature, such as unusual fluctuations in temperature within a 24-hour period exceeding the historical average (e.g., cold snaps or heat waves), can lead to increased seawater density and volume contraction, potentially causing abnormal tidal levels. Extremely high temperatures accelerate evaporation, indirectly affecting water level equilibrium. Sudden temperature changes (e.g., the passage of a cold front) are often accompanied by heavy rainfall or rainstorms, directly impacting the load on port drainage systems. High temperatures accelerate seawater evaporation, increasing salt spray concentrations. Low temperatures inhibit evaporation but may indirectly affect salt spray distribution by altering humidity.

[0052] An example of constructing a mapping relationship between ambient temperature and corresponding weights is as follows: High-precision temperature sensors are deployed in key port areas (such as docks and warehouses) to collect temperature data in real time. Historical temperature fluctuation events (such as cold waves and heat waves) and their corresponding port operational data are extracted. Outliers (such as sudden changes caused by sensor failures) are eliminated, and missing data is filled using the moving average method. Temperatures are normalized and the temperature change rate is calculated. Dynamic mapping of temperature fluctuations to three scheduling weights is performed, and the frequency of sudden changes in the three evaluation dimensions under different temperature fluctuation ranges is calculated. Mapping relationships are constructed for the contributions of water level, salt spray, and rainfall within different ranges. Mapping relationships are then generated between ambient temperature and the corresponding first, second, and third scheduling weights for changes in port cargo environmental characteristics. Inputting the ambient temperature yields the corresponding first, second, and third scheduling weights for changes in port cargo environmental characteristics.

[0053] Furthermore, the specific process of comparative analysis and adjustment based on port cargo environmental parameters is as follows: if the port cargo intelligent scheduling environment assessment mutation value is less than the port cargo intelligent scheduling environment assessment mutation threshold, no adjustment is made; if the port cargo intelligent scheduling environment assessment mutation value is equal to or greater than the port cargo intelligent scheduling environment assessment mutation threshold, the port cargo intelligent scheduling environment assessment mutation value is multiplied by the real-time centralized computing power load utilization rate of the port cargo intelligent scheduling resource center to obtain the port cargo intelligent scheduling resource center coupling value, which is used to quantify the risk level of relative scarcity of the schedulable resources of the port cargo intelligent scheduling resource center. If the port cargo intelligent scheduling resource center coupling value is less than the port cargo intelligent scheduling resource center coupling threshold, a notification is issued to relevant personnel and automated distributed resource scheduling is temporarily not started. If the port cargo intelligent scheduling resource center coupling value is equal to or greater than the port cargo intelligent scheduling resource center coupling threshold, distributed resource scheduling is started.

[0054] In this embodiment, if Figure 2 As shown, it is a schematic diagram of the process of comparative analysis and adjustment based on port cargo environmental parameters provided in an embodiment of the present application.

[0055] The purpose of sending notifications to relevant personnel and temporarily not starting automatic distributed resource scheduling is to allow relevant personnel to determine whether manual adjustment of distributed resource scheduling is required.

[0056] In a distributed port cargo intelligent dispatching system, determining whether to dispatch distributed computing resources (edge ​​computing) requires real-time assessment of environmental mutation values, combined with system load and business needs. The following is the specific judgment logic, technical steps, and system adjustment plan:

[0057] Status monitoring: Real-time monitoring of edge node computing power (CPU / GPU utilization), storage, and network status.

[0058] Task segmentation and distribution: Task segmentation: split centralized tasks into subtasks (such as water level prediction, salt spray diffusion simulation, and path planning).

[0059] Distribution rules: Priority tasks: Highly real-time tasks (such as typhoon path prediction) are assigned to the nearest edge node. Data-intensive tasks, such as salt spray concentration analysis, are assigned to nodes with sufficient storage resources.

[0060] Edge node task execution: Salt fog diffusion simulation: Based on temperature fluctuation data, a Gaussian diffusion model is run on the edge node to simulate the specific salt fog diffusion situation. A diffusion dynamics model is constructed based on the Navier-Stokes equations to simulate the effects of wind speed and temperature on salt fog migration. A DQN model is trained, and a reward function (obstacle avoidance, low energy consumption, and short latency) is defined to generate a globally optimal path. Transport vehicles are automatically dispatched to follow this globally optimal path.

[0061] An example of an adjustment plan for the port cargo intelligent scheduling system invoked by distributed resource scheduling is as follows: Task priority reallocation: When the temperature rises by more than 30 degrees Celsius, salt spray anti-corrosion operations are prioritized to the highest level, and lower-priority tasks (such as inventory counts) are suspended. When the temperature drops by more than 30 degrees Celsius, the frequency of antifreeze spray equipment dispatch is tripled.

[0062] Automation equipment linkage:

[0063] Edge nodes directly control the deceleration of the gantry crane at the terminal (for example, when the wind speed is greater than 15m / s) and the locking of the container stacker (for example, when the salt spray concentration is greater than 100μg / m³).

[0064] Energy Management:

[0065] Edge computing nodes dynamically adjust power consumption based on task load (such as entering low-power mode when idle).

[0066] Incremental learning: Edge nodes regularly upload local data to the cloud to update global model parameters.

[0067] The above solution can effectively alleviate the bottleneck of centralized computing power and improve the real-time performance and environmental adaptability of the port intelligent scheduling system.

[0068] Furthermore, the fluctuation parameters of the port dispatching equipment are evaluated and analyzed, specifically including: collecting the network delay and the network bit error rate of the port distributed dispatching equipment through the built-in tools of the port distributed dispatching equipment; directly extracting the network delay threshold of the port distributed dispatching equipment, the network bit error rate threshold of the port distributed dispatching equipment, the average failure rate threshold of the port distributed dispatching equipment, the first dispatching proportion factor of the port equipment dispatching characteristic change, the second dispatching proportion factor of the port equipment dispatching characteristic change, the third dispatching proportion factor of the port equipment dispatching characteristic change and the correction factor of the salt spray corrosion of the terminal interface of the port distributed dispatching equipment for the usage time from the port cargo intelligent dispatching environment assessment database; performing a ratio analysis of the network delay of the port distributed dispatching equipment and the network delay threshold of the port distributed dispatching equipment, and correcting it through the first dispatching proportion factor of the port equipment dispatching characteristic change to obtain the port distributed dispatching equipment. The first component of the performance evaluation fluctuation of the port distributed scheduling equipment is analyzed; the network bit error rate of the port distributed scheduling equipment and the network bit error rate threshold of the port distributed scheduling equipment are analyzed in proportion, and the second scheduling proportion factor of the port equipment scheduling feature change is corrected to obtain the second component of the performance evaluation fluctuation of the port distributed scheduling equipment; the average failure rate of the port distributed scheduling equipment and the average failure rate threshold of the port distributed scheduling equipment are analyzed in proportion, and the third scheduling proportion factor of the port equipment scheduling feature change is corrected to obtain the third component of the performance evaluation fluctuation of the port distributed scheduling equipment; the first component of the performance evaluation fluctuation of the port distributed scheduling equipment, the second component of the performance evaluation fluctuation of the port distributed scheduling equipment and the third component of the performance evaluation fluctuation of the port distributed scheduling equipment are coupled analyzed, and then the correction factor of the salt spray corrosion of the terminal interface of the port distributed scheduling equipment for the service life is corrected to obtain the sudden change value of the performance evaluation fluctuation of the port distributed scheduling equipment.

[0069] In this embodiment, the collected device parameters are specifically for the distributed computing power scheduling device called to start the distributed resource scheduling mentioned above.

[0070] Number the monitoring points of distributed port dispatching equipment. Indicates the number of monitoring points of distributed port dispatching equipment, , Indicates the total number of monitoring points of distributed port scheduling equipment.

[0071] Indicates the The first distributed port cargo intelligent dispatching environmental monitoring point The port distributed scheduling equipment performance evaluation fluctuation mutation value of each distributed port scheduling equipment monitoring point is used to quantify the mutation degree of the environment inside the software and hardware equipment required for port cargo intelligent scheduling.

[0072] The distributed port cargo intelligent scheduling environment monitoring point corresponds to at least one distributed port scheduling device monitoring point. In actual deployment, a plurality of distributed port scheduling device monitoring points are arranged in the specific distributed port scheduling device, and at least one distributed port cargo intelligent scheduling environment monitoring point is arranged outside the distributed port scheduling device.

[0073] ;

[0074] ;

[0075] The network probe built in the port distributed scheduling device analyzes the real-time packet capture to obtain the port distributed scheduling device network delay and the port distributed scheduling device network error rate.

[0076] represents the port distributed scheduling device network delay of the i-th distributed port scheduling device monitoring point of the j-th distributed port cargo intelligent scheduling environment monitoring point. represents the port distributed scheduling device network delay threshold value, which is directly extracted from the port cargo intelligent scheduling environment evaluation database.

[0077] represents the port distributed scheduling device network error rate of the i-th distributed port scheduling device monitoring point of the j-th distributed port cargo intelligent scheduling environment monitoring point.

[0078]

[0079] represents the port distributed scheduling device network error rate threshold value, which is directly extracted from the port cargo intelligent scheduling environment evaluation database.

[0080] The historical operation data of the corresponding device includes the operation time of the device, the time of fault occurrence, and the type of fault, etc. These data can be obtained through device maintenance records, fault logs or device monitoring systems. According to the collected data, the operation time between each fault is calculated. This can be calculated by the difference between the time points of fault occurrence. Add up the operation time between all faults, and then divide by the number of faults to get the average fault interval time, and get the average failure rate of the device.

[0081] represents the average failure rate of the i-th distributed port scheduling device monitoring point of the j-th distributed port cargo intelligent scheduling environment monitoring point. represents the average failure rate of the i-th distributed port scheduling device monitoring point of the j-th distributed port cargo intelligent scheduling environment monitoring point.​​​​​

[0082] It represents the average failure rate threshold of the port's distributed scheduling equipment, which is directly extracted from the port cargo intelligent scheduling environment assessment database.

[0083] The basic rate of salt spray corrosion of the terminal interface of the port distributed dispatching equipment (unit: μm / year) is measured using the electrochemical corrosion rate probe built into the port distributed dispatching equipment (such as Rohrback Cosasco).

[0084] Deployment location: Device interface panel (such as next to the RJ45 / USB port) or power distribution cabinet grounding busbar: Monitors grounding system corrosion risks.

[0085] Indicates the The first distributed port cargo intelligent dispatching environmental monitoring point The correction factor of salt spray corrosion on the terminal interface of the port distributed dispatching equipment for the usage time of each distributed port dispatching equipment monitoring point is extracted from the port cargo intelligent dispatching environment assessment database.

[0086] The correction factor for salt spray corrosion on the service life of distributed port dispatching equipment terminal interfaces is a dynamic parameter used to quantify the impact of the corrosion rate of distributed port dispatching equipment terminal interfaces in salt spray environments on the equipment's service life. Its core purpose is to predict the extent of equipment interface life degradation by real-time monitoring of environmental corrosion intensity, equipment material properties, and historical data models.

[0087] when When it is greater than 1: it means that the current corrosion rate exceeds the baseline value, the equipment life is accelerating, and the maintenance cycle needs to be shortened or the task load needs to be reduced. When it is less than 1: it means that the corrosion rate is lower than expected and the equipment life can be appropriately extended.

[0088] Salt spray concentration: A laser scattering sensor is used to monitor the chloride ion concentration in the air (unit: micrograms / cubic meter), which is positively correlated with the corrosion rate.

[0089] Temperature and humidity: High temperature and high humidity environments will significantly accelerate corrosion, and real-time data collection is required through temperature and humidity sensors. For example, in an environment with a temperature greater than 40°C and a humidity greater than 80%, the interface corrosion rate is accelerated by 10%.

[0090] An example of calculating the correction factor for salt spray corrosion at the terminal interface of distributed port dispatching equipment for service life is as follows:

[0091] Benchmark corrosion rate: Measured by accelerated aging tests under standard laboratory conditions (salt spray concentration 50 μg / m3, temperature 25°C, humidity 60%).

[0092] Material corrosion resistance coefficient: Different materials have different corrosion resistance (for example, the corrosion resistance of a gold-plated interface is three times that of a tin-plated interface).

[0093] Environmental correction coefficient: A joint mapping model of the effects of temperature, humidity and salt spray concentration on corrosion rate based on historical data fitting is constructed to obtain a joint mapping model of the correction factor of salt spray concentration, temperature and humidity with the corresponding correction factor of salt spray corrosion of the terminal interface of port distributed scheduling equipment for the usage time. The real-time salt spray concentration, temperature and humidity are input to obtain the correction factor of salt spray corrosion of the terminal interface of port distributed scheduling equipment for the usage time.

[0094] The first scheduling weight factor representing the change in port equipment scheduling characteristics is directly extracted from the port cargo intelligent scheduling environment assessment database.

[0095] The second scheduling weighting factor, which represents the change in port equipment scheduling characteristics, is directly extracted from the port cargo intelligent scheduling environment assessment database.

[0096] The third scheduling weighting factor, which represents the change in port equipment scheduling characteristics, is directly extracted from the port cargo intelligent scheduling environment assessment database.

[0097] Relative humidity is another natural factor in the port environment that can significantly impact the network latency, bit error rate, and average failure rate of distributed dispatching equipment. Heat dissipation suppression: High humidity reduces the air's thermal conductivity, leading to decreased equipment heat dissipation capacity and increased chip temperatures, which in turn causes performance throttling. Electrical performance degradation: Moisture penetrates electronic components, increasing leakage current and signal noise, affecting communication stability. Accelerated material corrosion: High humidity, combined with environmental factors such as salt spray, accelerates metal oxidation and electrochemical corrosion, shortening equipment life.

[0098] Increased humidity reduces heat sink efficiency. Processing latency increases by approximately 5% for every 10°C increase in chip junction temperature. When ambient humidity rises from 50% to 90%, FPGA task processing latency increases by 20% to 35%. Constant humidity experiments have shown that for every 10% increase in humidity, device heat dissipation efficiency decreases by 2% to 5% and chip temperature increases by 3 to 8°C.

[0099] When moisture reduces the insulation resistance of a device's communication module from 1 GΩ to 100 MΩ, the signal-to-noise ratio drops by 3 to 6 dB. Condensation on the endfaces of optical fiber connectors increases optical return loss by 0.2 to 0.8 dB, significantly increasing the bit error rate. When humidity exceeds 80%, the optical fiber bit error rate increases by an order of magnitude.

[0100] High humidity accelerates electrochemical migration, causing dendrite shorts in the device's communication module. When humidity exceeds 70%, the copper ion migration rate increases exponentially. The lifespan of aluminum electrolytic capacitors in an environment with 90% humidity is shortened by 60% to 80% compared to 50% humidity.

[0101] The following is an example of how to extract and calculate the scheduling weight factor:

[0102] Environmental simulation: Simulate a humidity environment of 30% to 95% in a constant humidity chamber and record device performance data.

[0103] Key tests: Use an infrared thermal imager to monitor chip temperature changes, an LCR meter to measure PCB insulation resistance, and a HALT test to evaluate failure rates.

[0104] Parameter calibration: Multiple regression analysis was used to determine the sensitivity coefficients of humidity to delay, bit error rate, and failure rate. The three types of scheduling weighting factors were normalized to a range of 0 to 1, ensuring that the sum of the comprehensive weights was 1. A mapping relationship was constructed between the ambient humidity at the distributed port scheduling equipment monitoring point and the corresponding first scheduling weighting factor, second scheduling weighting factor, and third scheduling weighting factor of the port equipment scheduling characteristic change. The real-time ambient humidity of the distributed port scheduling equipment monitoring point was input to obtain the corresponding first scheduling weighting factor, second scheduling weighting factor, and third scheduling weighting factor of the port equipment scheduling characteristic change.

[0105] Furthermore, comparative analysis and adjustment are performed based on the fluctuation parameters of the port dispatching equipment, specifically including: if the port distributed dispatching equipment performance evaluation fluctuation mutation value is less than the port distributed dispatching equipment performance evaluation fluctuation mutation threshold, then the corresponding port distributed dispatching equipment is recorded and recorded as the first node of the port distributed dispatching equipment to be adjusted; if the port distributed dispatching equipment performance evaluation fluctuation mutation value is equal to or greater than the port distributed dispatching equipment performance evaluation fluctuation mutation threshold, then the corresponding port distributed dispatching equipment is recorded and recorded as the second node of the port distributed dispatching equipment to be adjusted, and emergency anti-corrosion and dehumidification treatment is performed, and the port distributed dispatching equipment performance evaluation fluctuation mutation values ​​corresponding to the second node of the port distributed dispatching equipment to be adjusted are arranged in descending order, and the corresponding computing tasks of the arranged second nodes of the port distributed dispatching equipment to be adjusted are gradually migrated to the first node of the port distributed dispatching equipment to be adjusted in descending order.

[0106] In this embodiment, as shown in Figure 3 FIG. 1 is a flowchart of a process for adjusting a port scheduling device according to fluctuation parameters for comparative analysis provided by the embodiment of the present application.

[0107] Performing emergency treatment for corrosion and dehumidification, for example, starting a high-pressure nitrogen corrosion spray device, triggering the corrosion spray every ten minutes, and starting a dehumidifier to maintain humidity less than 60%.

[0108] The second node to be adjusted of the arranged port distributed scheduling device is arranged in descending order, and the corresponding calculation task is gradually migrated to the first node to be adjusted of the port distributed scheduling device, that is, the calculation task of the high-load node is migrated to the low-load or low-environmental-risk node. The task migration is realized through a 5G TSN network (the end-to-end delay is less than 10 ms). Based on the task-to-node matching model of the Hungarian algorithm, the migration cost (delay + energy consumption) is minimized.

[0109] Further, the port scheduling distributed computing node device is comprehensively evaluated and analyzed, specifically including: directly extracting the port cargo intelligent scheduling device distributed computing resource comprehensive first scheduling proportion factor, the port cargo intelligent scheduling device distributed computing resource comprehensive second scheduling proportion factor, and the port distributed scheduling device local temperature rise rate correction factor for the port cargo intelligent scheduling device distributed computing resource comprehensive risk value from the port cargo intelligent scheduling environment evaluation database; coupling analysis of the port cargo intelligent scheduling environment evaluation mutation value through the port cargo intelligent scheduling device distributed computing resource comprehensive first scheduling proportion factor to obtain the port cargo intelligent scheduling device distributed computing resource comprehensive risk first component; coupling analysis of the port distributed scheduling device performance evaluation fluctuation mutation value through the port cargo intelligent scheduling device distributed computing resource comprehensive second scheduling proportion factor to obtain the port cargo intelligent scheduling device distributed computing resource comprehensive risk second component; coupling analysis of the port cargo intelligent scheduling device distributed computing resource comprehensive risk first component and the port cargo intelligent scheduling device distributed computing resource comprehensive risk second component, and then correcting through the port distributed scheduling device local temperature rise rate correction factor for the port cargo intelligent scheduling device distributed computing resource comprehensive risk value to obtain the port cargo intelligent scheduling device distributed computing resource comprehensive risk value.

[0110] In this embodiment, as shown in Figure 4 FIG. 1 is a flowchart of a process for adjusting a port scheduling device according to fluctuation parameters for comparative analysis provided by the embodiment of the present application. representing the number of distributed port cargo intelligent scheduling environment monitoring points, , Indicates the total number of distributed port cargo intelligent scheduling environment monitoring points.

[0111] ;

[0112] ;

[0113] Indicates the The first distributed port cargo intelligent dispatching environmental monitoring point The comprehensive risk value of distributed computing resources of port cargo intelligent scheduling equipment at each distributed port scheduling equipment monitoring point.

[0114] Indicates the The mutation value of the port cargo intelligent scheduling environment assessment of the distributed port cargo intelligent scheduling environment monitoring points.

[0115] Indicates the The first distributed port cargo intelligent dispatching environmental monitoring point The fluctuation mutation value of the performance evaluation of distributed port scheduling equipment at each distributed port scheduling equipment monitoring point.

[0116] Indicates the The first distributed port cargo intelligent dispatching environmental monitoring point The correction factor of the local temperature rise rate of the port distributed dispatching equipment at each distributed port dispatching equipment monitoring point for the comprehensive risk value of the distributed computing power resources of the port cargo intelligent dispatching equipment is obtained. The correction factor of the salt spray corrosion of the terminal interface of the port distributed dispatching equipment for the usage time is extracted from the port cargo intelligent dispatching environmental assessment database. The mapping relationship between the local temperature rise rate of the port distributed dispatching equipment and the correction factor of the local temperature rise rate of the port distributed dispatching equipment for the comprehensive risk value of the distributed computing power resources of the port cargo intelligent dispatching equipment is obtained through the actual port historical data. The real-time local temperature rise rate of the port distributed dispatching equipment is input to obtain the correction factor of the local temperature rise rate of the port distributed dispatching equipment for the comprehensive risk value of the distributed computing power resources of the port cargo intelligent dispatching equipment.

[0117] The local temperature rise rate, the rate of temperature increase at a device or environmental monitoring point per unit time, reflects an imbalance between heat accumulation and heat dissipation. High temperature gradients cause local airflow disturbances, resulting in abnormal wind speed sensor data. The local temperature rise rate accelerates the evaporation of surrounding seawater, increasing nearshore salt fog concentrations. Hardware throttling: When the local temperature rise rate exceeds the corresponding threshold, it triggers emergency CPU throttling, leading to a mismatch in the PCB's thermal expansion coefficient and an increased risk of solder joint cracking. High temperature rise accelerates wavelength drift in optical modules, significantly increasing the fiber's bit error rate. The coupling effect of the local temperature rise rate and other factors: the combination of high temperature rise and salt fog increases the corrosion rate. Interaction with humidity: High temperature rise causes condensation to accumulate inside the device, increasing the probability of short circuits. Interaction with network load: When computing power load is high, the local temperature rise rate is linearly positively correlated with the load. It represents the comprehensive first scheduling weight factor of the distributed computing resources of the port cargo intelligent scheduling equipment, which is directly extracted from the port cargo intelligent scheduling environment assessment database. This represents the secondary scheduling weighting factor for the distributed computing resources of intelligent port cargo dispatching equipment. This weighting factor is directly extracted from the intelligent port cargo dispatching environmental assessment database. This weighting factor is used to correct for changes in the current load and temperature of monitoring equipment, quantify the risk of sudden failures, and facilitate the triggering of load diversion mechanisms.

[0118] The first comprehensive scheduling weight factor of the distributed computing resources of the port cargo intelligent scheduling equipment is used to combine historical load data with the salt spray corrosion environment to predict the equipment life attenuation. Long-term high load will accelerate salt spray corrosion and hardware fatigue.

[0119] Construct the relationship between the utilization rate of distributed computing power of port cargo intelligent scheduling equipment and the first comprehensive scheduling proportion factor of distributed computing power resources of port cargo intelligent scheduling equipment and the second comprehensive scheduling proportion factor of distributed computing power resources of port cargo intelligent scheduling equipment, and obtain a mapping model of the utilization rate of distributed computing power of port cargo intelligent scheduling equipment and the first comprehensive scheduling proportion factor of distributed computing power resources of port cargo intelligent scheduling equipment and the second comprehensive scheduling proportion factor of distributed computing power resources of port cargo intelligent scheduling equipment. Input the utilization rate of distributed computing power of port cargo intelligent scheduling equipment to obtain the corresponding first comprehensive scheduling proportion factor of distributed computing power resources of port cargo intelligent scheduling equipment and the second comprehensive scheduling proportion factor of distributed computing power resources of port cargo intelligent scheduling equipment.

[0120] Furthermore, comparative analysis and adjustment are conducted based on the comprehensive evaluation and analysis results, specifically including: if the comprehensive risk value of the distributed computing power resources of the port cargo intelligent scheduling equipment is less than the comprehensive risk threshold of the distributed computing power resources of the port cargo intelligent scheduling equipment, then the second node to be adjusted of the port distributed scheduling equipment after arrangement will be arranged in descending order, and the corresponding computing tasks will be gradually migrated to the first node to be adjusted of the port distributed scheduling equipment, and the corresponding port cargo intelligent scheduling equipment after the descending order will be recorded as a secondary high-level computing power node according to a predefined percentage, and computing power resources of a predefined computing power percentage will be reserved for the secondary high-level computing power node.

[0121] In this embodiment, when the system comprehensive risk value is lower than the preset threshold, it indicates that the current operating environment is relatively stable, but preventive measures still need to be taken to maintain system efficiency and security.

[0122] Intelligent task scheduling: Through the Kubernetes cluster management system, we monitor the computing resource usage of each edge node (such as CPU and GPU utilization) in real time. If the resource utilization of a node exceeds 80%, some tasks will be automatically migrated to idle nodes to ensure overall load balancing.

[0123] Reserve a predefined percentage of computing power resources for secondary high-power nodes. For example, reserve 10% of computing power resources in advance to deal with sudden tasks such as typhoon warning data processing, to avoid response delays caused by resource crowding.

[0124] Furthermore, comparative analysis and adjustment are carried out based on the comprehensive evaluation and analysis results, and also include: if the comprehensive risk value of the distributed computing power resources of the port cargo intelligent dispatching equipment is equal to or greater than the comprehensive risk threshold of the distributed computing power resources of the port cargo intelligent dispatching equipment, then the speed of the cooling fan of the corresponding port cargo intelligent dispatching equipment is increased and the liquid cooling system of the corresponding port cargo intelligent dispatching equipment is started, and the port cargo intelligent dispatching equipment is arranged in descending order according to the comprehensive risk value of the distributed computing power resources of the port cargo intelligent dispatching equipment, and the port cargo intelligent dispatching equipment within the corresponding predefined percentage is recorded as a high computing power node according to the predefined percentage, and the wireless link of the port cargo intelligent dispatching equipment is switched to the fiber optic private network, and the computing power task is stopped from being allocated to it.

[0125] In this embodiment, the speed of the cooling fan of the corresponding port cargo intelligent dispatching equipment is increased and the liquid cooling system of the corresponding port cargo intelligent dispatching equipment is started. For example, the speed of the cooling fan is increased in advance and the liquid cooling system is started at the same time to forcibly limit the maximum power consumption of the equipment to 70% of the nominal value.

[0126] This prevents chips from triggering frequency reduction protection due to overheating. If this occurs, the distributed computing power resources of the corresponding port cargo intelligent dispatching equipment will be significantly reduced, and further use of distributed computing power resources will no longer be able to meet timeliness requirements. The corresponding port cargo intelligent dispatching equipment will be marked as high-computing power nodes according to a predefined percentage, and computing power tasks will be stopped from being assigned to them. Nodes will be ranked according to performance fluctuation values, and the nodes in the top predefined percentage will be marked as "high-risk nodes," and no new tasks will be assigned to them. If a node's failure rate surges due to excessive salt spray concentration, the system will automatically freeze its task queue. The wireless link used for communication will be immediately switched to a dedicated fiber-optic network, and forward error correction coding will be enabled to reduce the bit error rate.

[0127] Through the above-mentioned hierarchical adjustment strategy, the system provides highly reliable and adaptive intelligent scheduling guarantees for port operations through the deep integration of environmental perception, real-time decision-making and multi-dimensional control.

[0128] like Figure 5 FIG. 1 is a flow chart of a method for intelligently dispatching port cargo based on big data analysis according to an embodiment of the present application. The method for intelligently dispatching port cargo based on big data analysis according to an embodiment of the present application is characterized in that the specific steps are as follows:

[0129] Evaluate and analyze port cargo environmental parameters, and make comparative analysis and adjustments based on these parameters;

[0130] Evaluate and analyze the fluctuation parameters of port dispatching equipment, and make comparative analysis and adjustments based on the fluctuation parameters of port dispatching equipment;

[0131] Conduct comprehensive evaluation and analysis on the distributed computing node equipment for port scheduling, and make comparative analysis and adjustments based on the results of the comprehensive evaluation and analysis.

[0132] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CDROM, optical storage, etc.) containing computer-usable program code.

[0133] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the one or more flowcharts and / or blocks

[0134] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the one or more flowcharts and / or blocks

[0135] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the one or more flowcharts and / or blocks

[0136] While the preferred embodiments of the application have been described, additional variations and modifications can be employed by those skilled in the art. Therefore, the appended claims intend to cover all such modifications and variations as fall within the true spirit and scope of the present application. Further, the appended claims can be construed to cover all alternatives falling within the equivalent range of the claims.

[0137] It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application cover the modifications and variations of this application provided they come within the scope of the appended claims and their equivalents.

Claims

1. The intelligent port cargo dispatching system based on big data analysis is characterized by: It includes an environmental collection and evaluation module, a first comparative analysis and call module of a cargo intelligent scheduling distributed computing node, and a second comparative analysis and call module of a cargo intelligent scheduling distributed computing node; The environmental collection and evaluation module is used to evaluate and analyze the environmental parameters of port cargo, and to make comparative analysis and adjustments based on the environmental parameters of port cargo; The first comparative analysis calling module of the cargo intelligent scheduling distributed computing node is used to evaluate and analyze the fluctuation parameters of the port scheduling equipment, and perform comparative analysis and adjustment based on the fluctuation parameters of the port scheduling equipment; The evaluation and analysis of the fluctuation parameters of port dispatching equipment specifically includes: The network delay and bit error rate of the port distributed scheduling equipment are collected through the built-in tools of the port distributed scheduling equipment; Directly extract from the port cargo intelligent scheduling environment assessment database the port distributed scheduling equipment network delay threshold, port distributed scheduling equipment network bit error rate threshold, port distributed scheduling equipment average failure rate threshold, port equipment scheduling feature change first scheduling ratio factor, port equipment scheduling feature change second scheduling ratio factor, port equipment scheduling feature change third scheduling ratio factor and port distributed scheduling equipment terminal interface salt spray corrosion correction factor for service life; The proportion of the port distributed scheduling equipment network delay and the port distributed scheduling equipment network delay threshold is analyzed, and the first scheduling weight factor of the port equipment scheduling characteristic change is corrected to obtain the first component of the port distributed scheduling equipment performance evaluation fluctuation; The network bit error rate of the port distributed scheduling equipment and the network bit error rate threshold of the port distributed scheduling equipment are analyzed, and the second scheduling weight factor of the port equipment scheduling characteristic change is corrected to obtain the second component of the port distributed scheduling equipment performance evaluation fluctuation; The average failure rate of port distributed scheduling equipment and the average failure rate threshold of port distributed scheduling equipment are analyzed, and the third component of the performance evaluation fluctuation of port distributed scheduling equipment is obtained by correcting the third scheduling weighting factor of the port equipment scheduling characteristics change; The first component, second component and third component of the performance evaluation fluctuation of the port distributed scheduling equipment are coupled and analyzed. The correction factor of the salt spray corrosion of the terminal interface of the port distributed scheduling equipment for the service life is then used to correct the sudden change value of the performance evaluation fluctuation of the port distributed scheduling equipment. The comparative analysis and adjustment based on the fluctuation parameters of the port dispatching equipment specifically includes: If the port distributed scheduling equipment performance evaluation fluctuation mutation value is less than the port distributed scheduling equipment performance evaluation fluctuation mutation threshold, the corresponding port distributed scheduling equipment is recorded as the first node of the port distributed scheduling equipment to be adjusted; If the fluctuation mutation value of the performance evaluation of the port distributed scheduling equipment is equal to or greater than the fluctuation mutation threshold of the performance evaluation of the port distributed scheduling equipment, the corresponding port distributed scheduling equipment is recorded as the second node of the port distributed scheduling equipment to be adjusted and emergency anti-corrosion and dehumidification treatment is performed, and the fluctuation mutation values ​​of the performance evaluation of the port distributed scheduling equipment corresponding to the second node of the port distributed scheduling equipment to be adjusted are arranged in descending order, and the corresponding computing tasks of the second nodes of the port distributed scheduling equipment to be adjusted are gradually migrated to the first node of the port distributed scheduling equipment to be adjusted in descending order. The second comparative analysis calling module of the cargo intelligent scheduling distributed computing node is used to perform comprehensive evaluation and analysis on the port scheduling distributed computing node equipment, and perform comparative analysis and adjustment based on the comprehensive evaluation and analysis results.

2. The port cargo intelligent dispatching system based on big data analysis as claimed in claim 1 is characterized in that: The comparative analysis and adjustment based on the port cargo environmental parameters specifically include: The wind speed sensor is used to collect and obtain the port environment wind speed, the water level sensor is used to collect and obtain the maximum value of the port environment seawater level, and the rainfall sensor is used to collect and obtain the maximum value of rainfall per unit time; If the maximum value of the port environment seawater level is less than the maximum value threshold of the port environment seawater level and the maximum value of rainfall per unit time is less than the maximum value threshold of rainfall per unit time and the port environment wind speed is less than the port environment wind speed threshold, then a sudden change assessment of the port cargo intelligent scheduling environment assessment will be carried out; if the maximum value of the port environment seawater level is equal to or greater than the maximum value threshold of the port environment seawater level or the maximum value of rainfall per unit time is equal to or greater than the maximum value threshold of rainfall per unit time or the port environment wind speed is equal to or greater than the port environment wind speed threshold, then an early warning will be issued and relevant personnel will be notified to increase the port cargo intelligent scheduling early warning level to the highest level.

3. The port cargo intelligent dispatching system based on big data analysis as claimed in claim 2 is characterized in that: The above mentioned assessment is to conduct a sudden change assessment of the port cargo intelligent dispatching environment, specifically including: The maximum value of salt spray concentration in the port environment was obtained by collecting data using a laser scattering salt spray online monitor; Extracted from the port cargo intelligent scheduling environmental assessment database are the historical average of the port environment seawater level, the historical average of the port environment salt spray concentration, the correction factor of the port environment wind speed for the port environment seawater level, the historical average of the port environment rainfall, the first scheduling weighting factor of the port cargo environment characteristic change, the second scheduling weighting factor of the port cargo environment characteristic change, and the third scheduling weighting factor of the port cargo environment characteristic change; The maximum value of the port environment seawater level and the historical average value of the port environment seawater level are analyzed, and the port environment seawater level correction factor is corrected by the port environment wind speed. Then, the correction is made by the first scheduling weight factor of the change in the port cargo environment characteristics to obtain the first component of the sudden change in the port cargo intelligent scheduling environment assessment. The maximum value of the port environment salt spray concentration and the historical average value of the port environment salt spray concentration are analyzed, and the second scheduling weight factor of the change of the port cargo environment characteristics is corrected to obtain the second component of the port cargo intelligent scheduling environment assessment mutation; The maximum value of port environmental rainfall and the historical average value of port environmental rainfall are analyzed, and the third component of the mutation of the port cargo intelligent scheduling environment assessment is obtained by correcting the third scheduling weighting factor of the port cargo environmental characteristics change; The mutation value of the port cargo intelligent scheduling environment assessment is obtained by coupling analysis of the first component of the port cargo intelligent scheduling environment assessment mutation, the second component of the port cargo intelligent scheduling environment assessment mutation and the third component of the port cargo intelligent scheduling environment assessment mutation.

4. The port cargo intelligent dispatching system based on big data analysis as claimed in claim 3 is characterized in that: The comparative analysis and adjustment based on the port cargo environmental parameters specifically includes: If the mutation value of the port cargo intelligent scheduling environment assessment is less than the mutation threshold of the port cargo intelligent scheduling environment assessment, no adjustment will be made; If the port cargo intelligent scheduling environment assessment mutation value is equal to or greater than the port cargo intelligent scheduling environment assessment mutation threshold, the port cargo intelligent scheduling environment assessment mutation value and the real-time centralized computing power load utilization rate of the port cargo intelligent scheduling resource center are coupled and analyzed to obtain the port cargo intelligent scheduling resource center coupling value. The port cargo intelligent scheduling resource center coupling value is used to quantify the risk level of the relative scarcity of the schedulable resources of the port cargo intelligent scheduling resource center. If the port cargo intelligent scheduling resource center coupling value is less than the port cargo intelligent scheduling resource center coupling threshold, a notification is issued to relevant personnel and the automated distributed resource scheduling is temporarily not started. If the port cargo intelligent scheduling resource center coupling value is equal to or greater than the port cargo intelligent scheduling resource center coupling threshold, the distributed resource scheduling is started.

5. The port cargo intelligent dispatching system based on big data analysis as claimed in claim 4 is characterized in that: The comprehensive evaluation and analysis of the port dispatch distributed computing node equipment specifically includes: Directly extract from the port cargo intelligent scheduling environmental assessment database the first comprehensive scheduling weight factor of the distributed computing power resources of the port cargo intelligent scheduling equipment, the second comprehensive scheduling weight factor of the distributed computing power resources of the port cargo intelligent scheduling equipment, and the correction factor of the local temperature rise rate of the port distributed scheduling equipment for the comprehensive risk value of the distributed computing power resources of the port cargo intelligent scheduling equipment; The mutation value of the port cargo intelligent scheduling environment assessment is coupled with the first comprehensive scheduling weight factor of the distributed computing power resources of the port cargo intelligent scheduling equipment to obtain the first comprehensive risk component of the distributed computing power resources of the port cargo intelligent scheduling equipment; The fluctuation mutation value of the performance evaluation of the port distributed scheduling equipment is coupled with the second comprehensive scheduling weight factor of the distributed computing power resources of the port cargo intelligent scheduling equipment to obtain the second comprehensive risk component of the distributed computing power resources of the port cargo intelligent scheduling equipment; The first component of the comprehensive risk of distributed computing power resources of port cargo intelligent scheduling equipment is coupled with the second component of the comprehensive risk of distributed computing power resources of port cargo intelligent scheduling equipment. Then, the correction factor of the comprehensive risk value of distributed computing power resources of port cargo intelligent scheduling equipment is corrected by the local temperature rise rate of port distributed scheduling equipment to obtain the comprehensive risk value of distributed computing power resources of port cargo intelligent scheduling equipment.

6. The port cargo intelligent dispatching system based on big data analysis as claimed in claim 5 is characterized in that: The comparative analysis and adjustment based on the comprehensive evaluation and analysis results specifically include: If the comprehensive risk value of the distributed computing power resources of the port cargo intelligent scheduling equipment is equal to or greater than the comprehensive risk threshold of the distributed computing power resources of the port cargo intelligent scheduling equipment, the speed of the cooling fan of the corresponding port cargo intelligent scheduling equipment will be increased and the liquid cooling system of the corresponding port cargo intelligent scheduling equipment will be started. The port cargo intelligent scheduling equipment will be sorted in descending order according to the comprehensive risk value of the distributed computing power resources of the port cargo intelligent scheduling equipment. According to the predefined percentage, the corresponding port cargo intelligent scheduling equipment within the predefined percentage will be recorded as a high-order computing power node, the wireless link of the port cargo intelligent scheduling equipment will be switched to the fiber-optic private network, and the computing power task will be stopped from being allocated to it.

7. A method for intelligent dispatching of port cargo based on big data analysis, applied to the intelligent dispatching system for port cargo based on big data analysis according to any one of claims 1 to 6, characterized in that: The specific steps are: Evaluate and analyze port cargo environmental parameters, and make comparative analysis and adjustments based on these parameters; Evaluate and analyze the fluctuation parameters of port dispatching equipment, and make comparative analysis and adjustments based on the fluctuation parameters of port dispatching equipment; Conduct comprehensive evaluation and analysis on the distributed computing node equipment for port scheduling, and make comparative analysis and adjustments based on the results of the comprehensive evaluation and analysis.

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