A combined fleet intelligent cargo tracking method and system

Abstract

The application provides a kind of combination ship team intelligent goods tracking method and system, it is related to shipping transport technical field, the method includes: step 1, on the barge and auxiliary function ship of combination ship team, deployment goods state monitoring device, real-time acquisition physical state parameter and position information of goods, wherein the monitoring device includes first target detection point and second target detection point;Step 2, the physical state parameter of goods, position information, temperature data of first target detection point and position offset data of second target detection point are integrated and analyzed by ship team central control platform, to generate target correction value.The application realizes combination ship team goods whole process accurate tracking, efficient management and risk prevention and control by real-time monitoring, intelligent analysis, collaborative response and dynamic optimization.

Description

Technical Field

[0001] The present invention relates to the field of shipping and transportation technology, and in particular to an intelligent cargo tracking method and system for a combined fleet. Background Art

[0002] In traditional shipping, cargo tracking relies primarily on manual inspections and decentralized, local monitoring systems, which presents inefficiencies and safety issues. Specifically, cargo status monitoring in traditional fleet transport relies heavily on basic sensors and periodic manual checks, making it impossible to dynamically collect physical parameters (such as temperature and humidity) and location information in real time.

[0003] For example, in the transport of dangerous goods, the lack of immediate warning mechanisms for temperature anomalies or positional deviations results in delayed emergency responses to incidents such as leaks and fires, posing significant safety risks. Furthermore, existing fleet management systems operate independently, with weak data collaboration capabilities between barges, main propulsion vessels, and auxiliary vessels. This makes it difficult to share information such as cargo loading and unloading progress and navigation status in real time, resulting in extended port stays and low transport efficiency. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a combined fleet intelligent cargo tracking method and system to achieve all-weather monitoring of cargo status, risk quantification assessment and closed-loop optimization of transportation strategy.

[0005] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0006] In a first aspect, a method for intelligent cargo tracking of a combined fleet is provided, the method comprising:

[0007] Step 1: Deploy cargo status monitoring devices on barges and auxiliary vessels of the combined fleet to collect physical status parameters and location information of the cargo in real time, wherein the monitoring devices include a first target detection point and a second target detection point;

[0008] Step 2: The fleet central control platform integrates and analyzes the cargo's physical state parameters, location information, temperature data of the first target detection point, and position offset data of the second target detection point to generate a target correction value.

[0009] Step 3: Generate a cargo tracking report based on the target correction value, trigger collaborative operation instructions according to preset rules, and monitor the execution status of the collaborative operation instructions in real time;

[0010] Step 4: When the target correction value is greater than or equal to a preset threshold, the fleet central control platform sends an emergency response instruction to the auxiliary function ship and the main propulsion ship via the collaborative communication network. The emergency response instruction may include adjusting the fleet's navigation speed, correcting the route, or initiating an emergency response mechanism, and obtaining the execution result of the emergency response instruction;

[0011] Step 5: Based on the execution results of the emergency response instructions and the target correction values, they are shared in real time with external ports or logistics platforms via the fleet communication link, forming monitoring data for the entire transportation chain and a basis for dynamic scheduling;

[0012] In step 6, the fleet central control platform analyzes the execution effect of the emergency response instructions and the risk change trend of the target correction value based on the shared monitoring data and dynamic scheduling basis, and generates an optimization plan for the cargo loading strategy.

[0013] Furthermore, the fleet central control platform integrates and analyzes the physical state parameters, location information, temperature data of the first target detection point, and position offset data of the second target detection point of the cargo to generate a target correction value, including:

[0014] The cargo status monitoring device collects physical status parameters, location information, temperature data of the first target detection point, and position offset data of the second target detection point of the cargo, and performs preprocessing, including data normalization, noise filtering, and outlier correction, to obtain processed data;

[0015] Based on the temperature data of the first target detection point in the preprocessed data, the cargo temperature anomaly coefficient is calculated. The temperature anomaly coefficient is the deviation ratio between the current temperature and the preset safe temperature range. Based on the position offset data of the second target detection point in the preprocessed data, the cargo position offset coefficient is calculated. The position offset coefficient is the ratio of the real-time deviation distance between the current position of the cargo and the preset path to the maximum allowable deviation.

[0016] Based on the cargo type and transportation priority data, the temperature anomaly coefficient and the position offset coefficient are classified and judged to obtain the classification judgment result;

[0017] According to the classification determination result, a target correction value is generated, where the target correction value is a comprehensive risk index of the temperature anomaly coefficient and the position offset coefficient.

[0018] Furthermore, based on the target correction value, a cargo tracking report is generated, and collaborative operation instructions are triggered according to preset rules. At the same time, the execution status of the collaborative operation instructions is monitored in real time, including:

[0019] Based on the numerical range of the target correction value, the cargo status risk level is divided and a cargo tracking report is generated based on the risk level. The cargo tracking report includes the cargo temperature anomaly detection results and historical temperature change trends, cargo position offset and real-time deviation analysis from the preset position, target correction value risk level assessment and recommended action priority;

[0020] According to the preset rules, the corresponding cargo status risk level is matched, and the coordinated operation instructions of the energy storage device are triggered. The coordinated operation instructions include adjusting the charge and discharge rate of the energy storage device, switching the load power supply mode, or starting the backup power supply access mechanism;

[0021] The collaborative operation instructions are issued to the corresponding barges, main propulsion ships and auxiliary function ships through the fleet central control platform, and the execution status of the collaborative operation instructions is monitored in real time.

[0022] Furthermore, based on the execution results of emergency response instructions and target correction values, they are shared in real time with external ports or logistics platforms through fleet communication links, forming monitoring data and dynamic scheduling basis for the entire transportation chain, including:

[0023] Obtain the execution results of the demand response instruction, including the charge and discharge status adjustment records of the energy storage device, the load switching response time, and the backup power supply access effect;

[0024] The execution results are linked to the target correction values ​​to form a data set containing timestamps, correction value risk levels, and execution effects.

[0025] The data set is transmitted to an external port or logistics platform in real time through the energy management link, and dynamically matched with the real-time load data and energy scheduling plan of the power grid platform to obtain the matching results;

[0026] Based on the matching results, a dynamic control basis for the energy supply and demand link is generated, which includes the current power grid supply and demand balance status, energy storage equipment operation efficiency evaluation and user demand priority adaptability analysis.

[0027] Furthermore, the data set is transmitted in real time to an external port or logistics platform through the energy management link, and dynamically matched with the real-time load data and energy scheduling plan of the power grid platform to obtain matching results, including:

[0028] After receiving the dataset, the external port or logistics platform dynamically matches the dataset with the current port operation plan, real-time waterway status data, and logistics scheduling needs. That is, the port cargo reception priority is adjusted according to the corrected value risk level; the dynamic allocation of waterway resources is optimized based on the execution effect of the emergency response instructions; based on the risk change trend of the target corrected value, the potential risk areas of the transportation task are predicted to obtain matching results.

[0029] Furthermore, based on shared monitoring data and dynamic scheduling, the fleet central control platform analyzes the execution effect of emergency response instructions and the risk trend of target correction values, and generates cargo loading strategy optimization solutions, including:

[0030] Conduct a multi-dimensional analysis of the shared dynamic control basis, including the execution efficiency of demand response instructions, the risk trend of target correction values, and the matching degree between the charging and discharging efficiency of energy storage equipment and grid load fluctuations, to obtain analysis results;

[0031] Based on the analysis results, an energy storage strategy optimization plan is generated, including adjusting the charging and discharging plans of energy storage equipment, optimizing the load power supply mode switching logic, and dynamically configuring the access threshold of the backup power supply.

[0032] Furthermore, the first target detection point is a cargo temperature detection unit, which is used to monitor the temperature change of the cargo in real time; the second target detection point is a cargo position offset detection unit, which is used to monitor the offset of the cargo from the preset position in real time.

[0033] In a second aspect, a combined fleet intelligent cargo tracking system includes:

[0034] A data acquisition module is used to deploy cargo status monitoring devices on the barges and auxiliary function ships of the combined fleet to collect physical status parameters and location information of the cargo in real time, wherein the monitoring device includes a first target detection point and a second target detection point;

[0035] The data processing and analysis module is used by the fleet central control platform to integrate and analyze the physical state parameters, location information, temperature data of the first target detection point, and position offset data of the second target detection point of the cargo to generate a target correction value;

[0036] The instruction trigger module is used to generate cargo tracking reports based on the target correction value, trigger collaborative operation instructions according to preset rules, and monitor the execution status of collaborative operation instructions in real time;

[0037] An emergency response module is used to send emergency response instructions from the fleet central control platform to the auxiliary function ships and main propulsion ships through the collaborative communication network when the target correction value is greater than or equal to the preset threshold. The emergency response instructions may include adjusting the fleet's navigation speed, correcting the route, or initiating an emergency processing mechanism, and obtaining the execution results of the emergency response instructions;

[0038] The data sharing module is used to share the execution results and target correction values ​​of emergency response instructions with external ports or logistics platforms in real time through fleet communication links, forming monitoring data and dynamic scheduling basis for the entire transportation chain;

[0039] The solution optimization module is used by the fleet central control platform to analyze the execution effect of emergency response instructions and the risk change trend of target correction values ​​based on shared monitoring data and dynamic scheduling basis, and generate cargo loading strategy optimization solutions.

[0040] According to a third aspect, a computing device includes:

[0041] one or more processors;

[0042] The storage device is used to store one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method.

[0043] In a fourth aspect, a computer-readable storage medium stores a program, which implements the method when executed by a processor.

[0044] The above solution of the present invention includes at least the following beneficial effects:

[0045] By deploying monitoring devices on barges and auxiliary vessels, the fleet's central control platform can collect real-time data on the cargo's physical state parameters, location information, temperature, and position deviation. This allows the fleet's central control platform to accurately monitor the cargo's real-time status. For example, this can promptly detect abnormal temperature increases or position deviations, ensuring safe transportation. Target correction values ​​are generated by integrating and analyzing various data, taking into account multiple aspects of the cargo's status. Risk levels are assigned based on these target correction values, providing a scientific and quantitative standard for cargo risk assessment. This allows the fleet's central control platform to take appropriate measures based on different risk levels, thereby improving the scientific and effective nature of risk management.

[0046] Based on the target correction value, collaborative operation instructions are triggered, enabling the main propulsion ship, barge, and auxiliary function ship to work together. For example, in operations such as adjusting the charge and discharge rate of energy storage equipment and switching the load power supply mode, each ship acts in coordination according to the instructions, improving the overall operational efficiency of the fleet and ensuring the stability of the cargo during transportation. When the target correction value reaches the preset threshold, emergency response instructions are quickly sent, including adjusting the navigation speed, correcting the route, and activating the emergency response mechanism. In the face of emergencies, timely responses can be made to reduce cargo losses and ship safety risks. For example, when abnormal cargo temperature may cause danger, the route is promptly adjusted to a port with the necessary conditions for handling.

[0047] The execution results and target revisions of emergency response instructions are shared in real time with external ports or logistics platforms, enabling monitoring and dynamic scheduling of the entire transport chain. Ports can use this information to pre-arrange loading and unloading operations, and logistics platforms can optimize transport plans, improving the efficiency and coordination of the entire transport chain. Based on shared monitoring data and dynamic scheduling, emergency response effectiveness and risk trends are analyzed to generate optimized cargo loading strategies. By continuously summarizing experience, cargo loading can be optimized, improving transportation safety and efficiency, and reducing transportation costs. For example, the loading location and method of cargo on barges can be adjusted based on the risk profile of the cargo during transportation. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1The present invention provides a flow chart of a method for intelligent cargo tracking in a combined fleet.

[0049] Figure 2 Schematic diagram of a combined fleet intelligent cargo tracking system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0051] like Figure 1 As shown, an embodiment of the present invention provides a combined fleet intelligent cargo tracking method, the method comprising the following steps:

[0052] Step 1: Deploy cargo status monitoring devices on barges and auxiliary vessels of the combined fleet to collect physical status parameters and location information of the cargo in real time, wherein the monitoring devices include a first target detection point and a second target detection point;

[0053] Step 2: The fleet central control platform integrates and analyzes the cargo's physical state parameters, location information, temperature data of the first target detection point, and position offset data of the second target detection point to generate a target correction value.

[0054] Step 3: Generate a cargo tracking report based on the target correction value, trigger collaborative operation instructions according to preset rules, and monitor the execution status of the collaborative operation instructions in real time;

[0055] Step 4: When the target correction value is greater than or equal to a preset threshold, the fleet central control platform sends an emergency response instruction to the auxiliary function ship and the main propulsion ship via the collaborative communication network. The emergency response instruction may include adjusting the fleet's navigation speed, correcting the route, or initiating an emergency response mechanism, and obtaining the execution result of the emergency response instruction;

[0056] Step 5: Based on the execution results of the emergency response instructions and the target correction values, they are shared in real time with external ports or logistics platforms via the fleet communication link, forming monitoring data for the entire transportation chain and a basis for dynamic scheduling;

[0057] In step 6, the fleet central control platform analyzes the execution effect of the emergency response instructions and the risk change trend of the target correction value based on the shared monitoring data and dynamic scheduling basis, and generates an optimization plan for the cargo loading strategy.

[0058] In an embodiment of the present invention, by deploying cargo status monitoring devices at multiple detection points on barges and auxiliary vessels, multi-dimensional data such as temperature and position offset can be collected in real time. This addresses the issues of inadequate cargo status monitoring and delayed position tracking in traditional technologies, enabling dynamic and accurate monitoring of the physical condition and location of cargo (e.g., timely detection of abnormal temperatures of refrigerated cargo and position offsets of dangerous goods), providing real-time data support for safe cargo transportation. The fleet's central control platform integrates multi-source data and generates target correction values. By quantitatively analyzing cargo status risks (e.g., exceeding temperature limits or position offsets), it shifts fleet management from experience-driven to data-driven. For example, combining temperature and position offset data can accurately assess the risk level of cargo during transportation, avoiding decision-making errors caused by isolated data.

[0059] Based on the target correction value, coordinated operation instructions and emergency response instructions are triggered to achieve real-time scheduling of internal fleet resources (such as adjusting sailing speed and correcting routes) and emergency handling (such as activating the dangerous goods isolation mechanism). Compared with the shortcomings of traditional technologies such as slow manual intervention and delayed response, this reduces the risk of cargo damage and ship accidents. By sharing data with external ports and logistics platforms in real time, a closed-loop management of the entire link "cargo status-fleet operation-port scheduling" is formed. For example, ports can obtain cargo location and loading and unloading requirements in advance, optimize berth arrangements and loading and unloading equipment scheduling, shorten the ship's stay in port, and improve overall logistics efficiency. Based on historical data and emergency response analysis, loading strategy optimization plans are generated, and loading plans can be dynamically adjusted for different cargo types (such as bulk cargo, containers, dangerous goods) and transportation scenarios (such as shallow navigation and severe sea conditions).

[0060] In a preferred embodiment of the present invention, in step 1, cargo status monitoring devices are deployed on the barges and auxiliary vessels of the combined fleet to collect physical status parameters and location information of the cargo in real time. The monitoring devices include a first target detection point and a second target detection point; the first target detection point is a cargo temperature detection unit for monitoring cargo temperature changes in real time; the second target detection point is a cargo position offset detection unit for monitoring the offset of the cargo from a preset position in real time, which may include:

[0061] In the embodiment of the present invention, the collection of the cargo temperature detection unit (first target detection point) is specifically:

[0062] The deployment strategy of temperature sensors will be different for different types of goods. For large and uniform block goods, in addition to placing sensors at the center and edges of the goods, additional sensors will be added at some key symmetrical points based on the geometric shape of the goods to ensure that the temperature distribution inside the goods can be fully monitored. For example, when transporting large metal castings, due to their heat dissipation and heating characteristics, sensors must be installed in different thickness areas and corners of the castings. For goods that are transported by refrigeration or heating, the temperature gradient in the area close to the refrigeration or heating equipment is large, so sensors must be installed at different distances from the air outlet and return air outlet of the equipment, such as 10 cm and 30 cm from the air outlet and 20 cm from the return air outlet, etc., to accurately monitor temperature changes.

[0063] The data collection interval isn't fixed, but rather adjusted based on the characteristics of the goods. For fresh goods that are extremely sensitive to temperature, such as certain high-end seafood or rare flowers, the data collection interval may be shortened to every 15 seconds in order to promptly capture even the slightest temperature change. This is because these goods can be affected by even the slightest temperature fluctuation, and shortening the data collection interval ensures that any temperature anomalies can be detected and addressed immediately. For relatively stable goods at room temperature, such as ordinary daily necessities, the data collection interval can be appropriately extended to 2-3 minutes. This meets the basic monitoring needs for the goods' temperature status without increasing the burden on the equipment or the pressure on data processing due to overly frequent data collection.

[0064] After a temperature sensor converts the sensed temperature into an electrical signal, the signal must first be amplified. Because the electrical signal output by the sensor is typically weak and susceptible to interference, an amplifier amplifies the signal to a level suitable for subsequent processing. During the amplification process, an appropriate amplification factor is selected to ensure that the signal clearly reflects temperature changes without introducing excessive noise due to over-amplification. Next, filtering is performed, using a low-pass filter to remove high-frequency noise from the signal. This noise may originate from the ship's electrical equipment or external electromagnetic interference. The filtered signal is then converted to a digital signal by an analog-to-digital converter chip. This digital signal represents the temperature data in binary code, facilitating subsequent transmission and storage. During this conversion process, the appropriate quantization accuracy, such as 8, 12, or 16 bits, must be determined. Higher accuracy results in higher resolution of temperature changes, but this also increases the amount of data required.

[0065] The collection of cargo position deviation detection unit (second target detection point) is specifically:

[0066] Even when stationary, a ship at sea is subject to swaying due to factors such as waves and currents. The operation of the ship's own equipment can also cause minor vibrations, all of which can affect the cargo's position. Measuring cargo coordinates multiple times can account for these random variations. For example, 5-10 measurements can ensure data comprehensiveness to a certain extent. The interval between each measurement should be appropriately set, typically 1-2 minutes. This avoids overloading the equipment with frequent measurements while effectively capturing subtle changes in the cargo's position. Outliers may appear in the measurement data due to external interference, such as electromagnetic interference from nearby ships or temporary malfunctions of the measurement equipment. A common method for identifying outliers is to set a reasonable data range. Assuming that cargo coordinates normally fluctuate within a certain range, such as ±5 meters for the horizontal and vertical coordinates (this range is determined based on the actual ship's stability and measurement accuracy), data points outside this range may be identified as outliers. Outliers are removed from the dataset to ensure the accuracy of subsequent calculations. After removing outliers, the remaining measurement data is statistically analyzed to calculate the average. For the horizontal axis, the abscissas of all remaining measurement data are summed and divided by the number of remaining data points to obtain the average. The same process is repeated for the vertical axis. This average comprehensively reflects the stable position of the cargo when the ship is stationary and, as a preset position coordinate, effectively reduces initial errors.

[0067] The fusion of GPS and IMU data specifically includes:

[0068] When a ship is at sea, satellite signals are susceptible to interference from various factors. In ports, tall buildings can block satellite signals. In areas with complex terrain, such as canyons, signals can reflect off mountains, creating multipath effects, leading to brief GPS signal loss or significant errors. The IMU measures the ship's acceleration and angular velocity, providing a basis for correcting GPS data. When the IMU detects a ship turning, the angular velocity and duration of the turn determine the angle of the turn. For example, if a ship turns at a constant angular velocity for a period of time, the integral of the angular velocity over that period can be used to calculate the turning angle. This turning angle is then used to correct the cargo position measured by GPS. Because the cargo's position relative to the satellite changes when the ship turns, failing to make this correction will result in deviations in the cargo position measurement.

[0069] In open waters, GPS signals are strong and positioning accuracy is high. In this case, GPS data is assigned a higher weight, for example, 0.8, while IMU data is weighted 0.2. This is because in open waters, satellite signals are less susceptible to interference, allowing GPS to provide more accurate position information. However, when a ship enters areas prone to satellite signal obstruction, such as harbors or canyons, GPS signal quality degrades, and the weight of IMU data is increased accordingly—for example, to 0.6, while the weight of GPS data is reduced to 0.4. By dynamically adjusting the weights, data can be more effectively integrated based on the reliability of the two devices in different environments, improving the accuracy of cargo position measurement. Over short distances, the Earth can be roughly considered a plane, and ignoring the effect of Earth's curvature on cargo position offset calculations will not result in significant errors. However, in long-distance transport, especially transoceanic transport, the impact of Earth's curvature cannot be ignored. The Earth is a near sphere, and as the ship's voyage distance increases, the calculated displacement based on plane coordinates will deviate from the actual displacement. For example, in transoceanic transportation, ships travel thousands of kilometers. If the curvature of the earth is not taken into account, the calculated straight-line offset distance between the cargo and the preset position may be smaller or larger than the actual distance, affecting the accurate judgment of the cargo location.

[0070] To more accurately calculate the resulting displacement, the coordinate difference needs to be corrected. This approach uses methods from geodesy, taking into account factors such as the Earth's radius of curvature. Based on the longitude and latitude of the vessel's voyage, the approximate radius of curvature of the Earth in that region is determined. The coordinate difference is then appropriately stretched or compressed based on this radius of curvature. For example, in high-latitude regions, the effect of Earth's curvature on the coordinate difference differs from that in low-latitude regions. Therefore, adjustments must be made based on specific geographic information to obtain a coordinate difference that better reflects actual conditions, allowing for accurate calculation of the linear offset distance between the cargo and the preset location.

[0071] Determining the offset direction in combination with the ship's heading includes:

[0072] During a ship's voyage, the cargo's offset direction is affected by changes in the ship's heading. When the ship turns left, even if the cargo's actual offset in a particular direction remains unchanged, the offset direction relative to the ship's heading will change. For example, if the cargo was originally positioned a certain angle to the left of the ship's front, if the ship turns left, the cargo's offset direction will appear further to the left based on the ship's new heading. This is because the ship's heading changes the orientation of the reference coordinate system. When calculating the offset direction, in addition to using the tangent function to calculate a preliminary offset direction based on the coordinate difference, a comprehensive assessment is also required in conjunction with the ship's real-time heading information. After obtaining the ship's real-time heading data, the offset direction calculated using trigonometric functions is superimposed or adjusted with the ship's real-time heading. If the ship's heading is 0° (assuming the bow is pointing in the direction of 0°), and the cargo offset direction calculated by trigonometric function is 30°, then the actual offset direction is 30°; but if the ship turns 20° to the left at this time, the heading becomes 340°, then the exact offset direction of the cargo relative to the preset position needs to be adjusted by adding the previously calculated 30° to the ship's new heading of 340° to obtain a more accurate offset direction to accurately reflect the actual offset direction of the cargo in space.

[0073] In a preferred embodiment of the present invention, in step 2, the fleet central control platform integrates and analyzes the physical state parameters, location information, temperature data of the first target detection point, and position offset data of the second target detection point of the cargo to generate a target correction value, which may include:

[0074] Step 200: The cargo status monitoring device collects physical status parameters, location information, temperature data of a first target detection point, and position offset data of a second target detection point of the cargo, and performs preprocessing, including data normalization, noise filtering, and outlier correction, to obtain processed data.

[0075] Step 201: Calculate the cargo temperature anomaly coefficient based on the temperature data of the first target detection point in the preprocessed data. The temperature anomaly coefficient is the deviation ratio between the current temperature and the preset safe temperature range. Calculate the cargo position offset coefficient based on the position offset data of the second target detection point in the preprocessed data. The position offset coefficient is the ratio of the real-time deviation distance between the current position of the cargo and the preset path to the maximum allowable deviation.

[0076] Step 202: Classify and determine the temperature anomaly coefficient and the position offset coefficient based on the cargo type and transportation priority data to obtain a classification determination result;

[0077] Step 203: Generate a target correction value based on the classification determination result. The target correction value is a comprehensive risk index of the temperature anomaly coefficient and the position offset coefficient.

[0078] In an embodiment of the present invention, various sensors in the cargo status monitoring device work continuously. For example, the temperature sensor collects temperature data of the first target detection point, the position sensor obtains the location information of the cargo and the position offset data of the second target detection point, and other sensors for detecting physical state parameters (such as humidity sensors, pressure sensors, etc., if configured) collect corresponding data. These sensors collect data at a set sampling frequency (for example, once per second or once per minute, depending on actual needs). For different types of data collected, their value ranges are determined respectively. For example, for temperature data, assuming that the historical temperature range collected is -30°C to 80°C, if it is to be normalized to the 0-1 range, for the currently collected temperature value T, by calculating , mapping the temperature value to the range of 0-1. Other data such as position offset data are also normalized according to their own maximum and minimum values ​​using a similar method.

[0079] Thresholds are set to identify noise. For example, for temperature data, if the temperature fluctuation within a short period exceeds the normal fluctuation range (for example, if the temperature fluctuation does not exceed 2°C per 10 minutes under normal circumstances, a threshold of 5°C / 10 minutes is set), the data point may be considered noise. For suspected noise data points, the trend of adjacent data points is considered. If the trend does not match, the data point is removed or corrected. For example, a reasonable value for the point is estimated by averaging the previous and subsequent data points, and the original noise data is replaced. Outliers are identified based on the data distribution pattern of normal cargo conditions. For example, for position offset data, if the offset exceeds the range of cargo displacement under normal ship navigation (pre-set based on factors such as ship type and navigation environment, for example, the maximum allowable offset of cargo relative to the ship in calm seas is 5 meters), the data point is considered an outlier. Statistical methods are used to correct outliers, such as using historical data from similar ships under similar shipping conditions to identify the normal data point that most closely resembles the current outlier.

[0080] Step 201: Calculate the cargo temperature anomaly coefficient and position offset coefficient:

[0081] Clearly define the preset safe temperature range of the goods. For example, if the preset safe temperature range of a certain goods is 10℃-20℃, if the current collected temperature is T, when calculating the temperature anomaly coefficient, first determine the relationship between T and the safe range. If T is higher than 20℃, the temperature anomaly coefficient = ; If T is lower than 10℃, the temperature anomaly coefficient = Through such calculation, the deviation ratio between the current temperature and the preset safe temperature range is obtained.

[0082] Calculate the cargo position deviation coefficient specifically:

[0083] Determine the preset route and maximum allowable deviation of the cargo. Assume that the preset route of the cargo is a planned route from port A to port B. In a certain section of the route, the maximum allowable deviation is determined to be 30 meters from the preset route based on factors such as ship performance and channel conditions. Obtain the real-time deviation distance D between the current position of the cargo and the preset route. The cargo position deviation coefficient = , that is, the ratio of the real-time deviation distance between the current location of the goods and the preset path to the maximum allowable deviation is obtained.

[0084] Step 202: Classification and determination based on cargo type and transportation priority data specifically includes:

[0085] Data on cargo type (e.g., dangerous goods, perishable goods, general cargo) and transport priority (e.g., urgent, general, low-priority) is collected. This data is updated during transport based on actual conditions. Different assessment rules are developed for different cargo types and transport priorities. For example, dangerous goods may be classified as high-risk even if the temperature anomaly coefficient or position shift coefficient is only slightly exceeded. For general cargo, high-risk is only considered when both coefficients reach a certain level (e.g., the temperature anomaly coefficient exceeds 0.5 and the position shift coefficient exceeds 0.6). Urgent cargo is more sensitive to the position shift coefficient; any position shift coefficient exceeding 0.4 may result in a medium-risk or higher risk assessment. Low-priority cargo, on the other hand, has a more relaxed risk assessment criteria. Based on these rules, a comprehensive assessment of the temperature anomaly coefficient and position shift coefficient is performed to generate a classification result, which can be categorized as high, medium, or low risk.

[0086] In step 203, different weights are assigned to the temperature anomaly coefficient and the position shift coefficient based on the classification results. For high-risk cargo, where temperature anomalies have a greater impact (e.g., hazardous chemical transportation, where temperature anomalies can cause explosions and other serious consequences), the temperature anomaly coefficient weight is set to 0.7, and the position shift coefficient weight is set to 0.3. For the transportation of precision instruments with extremely high positioning requirements, the position shift coefficient weight may be set to 0.6, and the temperature anomaly coefficient weight to 0.4. For medium- and low-risk cargo, weights are also determined based on their characteristics and actual conditions. The temperature anomaly coefficient is multiplied by its corresponding weight, and the position shift coefficient is multiplied by its corresponding weight. The two results are then added together to obtain the target correction value. For example, for cargo with a temperature anomaly coefficient of 0.4 and a position shift coefficient of 0.3, if the temperature anomaly coefficient weight is 0.6 and the position shift coefficient weight is 0.4, then the target correction value = 0.4 × 0.6 + 0.3 × 0.4.

[0087] Data normalization converts data of varying types and magnitudes to a unified scale, eliminating the impact of differences in data units and magnitudes. Noise filtering removes interference from the data, preventing erroneous analysis and judgments caused by noise. The cargo temperature anomaly coefficient and position offset coefficient quantify the cargo's temperature and positional risks, enabling the fleet's central control platform to intuitively understand the degree of anomalies in these two key areas and facilitate comparative risk analysis across different cargoes. Classification and assessment, based on cargo type and transport priority, fully account for the unique characteristics of different cargoes and transport missions. Different cargoes have varying sensitivities to temperature and positional anomalies, and transport priority also influences the urgency of risk management. This classification and assessment approach enables personalized risk assessment. The results of this classification and assessment enable the rational allocation of transport resources and the implementation of appropriate measures. High-risk cargo and urgent transport missions are prioritized for attention and handling, improving transport safety and efficiency and ensuring that critical cargo and urgent transport missions receive priority.

[0088] The target correction value, a comprehensive risk indicator combining the temperature anomaly coefficient and the position offset coefficient, comprehensively reflects the cargo's risk level. This single indicator enables the fleet's central control platform to quickly understand the overall cargo condition, avoiding misjudgments due to single factors. The target correction value facilitates timely and effective decision-making. For example, the target correction value can be used to determine whether to adjust transportation strategies (such as changing routes or adjusting speeds) or initiate emergency measures (such as cooling and securing dangerous goods). This improves the convenience and scientific nature of transportation management and ensures cargo safety.

[0089] In a preferred embodiment of the present invention, step 3, generating a cargo tracking report based on the target correction value, triggering a coordinated operation instruction according to a preset rule, and monitoring the execution status of the coordinated operation instruction in real time, may include:

[0090] Step 300: Classify the cargo status risk level based on the numerical range of the target correction value, and generate a cargo tracking report based on the risk level. The cargo tracking report includes the cargo temperature anomaly detection results and historical temperature change trends, cargo position offset and real-time deviation analysis from the preset position, target correction value risk level assessment, and recommended action priority.

[0091] Step 301: Matching the corresponding cargo status risk level according to preset rules triggers a coordinated operation instruction for the energy storage device, wherein the coordinated operation instruction includes adjusting the charge and discharge rate of the energy storage device, switching the load power supply mode, or activating a backup power supply access mechanism;

[0092] Step 302: The coordinated operation instructions are sent to the corresponding barges, main propulsion vessels and auxiliary function vessels through the fleet central control platform, and the execution status of the coordinated operation instructions is monitored in real time.

[0093] In an embodiment of the present invention, different target correction value intervals are pre-set to correspond to different risk levels. For example, a target correction value in the range of 0-0.3 is a low risk level, a range of 0.3-0.6 is a medium risk level, and a range of 0.6-1 is a high risk level. After obtaining a specific target correction value, it is compared with these preset intervals to determine the risk level of the cargo status. For the abnormal temperature detection results of the cargo, the current temperature is compared with the preset safety temperature range. If the current temperature exceeds this range, it is determined that a temperature abnormality exists and the abnormality is recorded. When analyzing historical temperature change trends, review the temperature data over a period of time (for example, the past 24 hours or a critical period during the entire transportation process) to observe whether the temperature gradually rises, falls, or fluctuates, so as to present the temperature change trend.

[0094] The cargo position offset is calculated by calculating the distance between the cargo's current position and the preset position. When conducting real-time deviation analysis from the preset position, not only is the offset calculated, but the direction of the cargo's movement and the rationality of this offset within the context of transportation must also be considered. For example, if the cargo is slightly offset toward its destination, and the offset is within the allowable range and consistent with the vessel's navigation status, this may be normal. Conversely, if the offset is in the wrong direction or is excessive, it warrants further attention. A risk assessment is conducted based on the determined risk level, describing the current risk level of the cargo and its potential impact. Recommended actions are prioritized for high-risk levels and require immediate execution; medium-risk actions are prioritized; and low-risk actions can be performed at an appropriate later time. A series of targeted action recommendations are compiled from high to low risk levels. For example, in the event of a high-risk temperature anomaly, immediate adjustment of refrigeration equipment is recommended; in the event of a high-risk position offset, inspection of cargo securing devices is recommended.

[0095] Step 301 establishes a set of pre-set rules that map different cargo risk levels to coordinated operational instructions for energy storage devices. For example, if the risk level is high, and the cause is temperature anomalies that could impact the operation of critical cargo equipment (such as refrigeration equipment), an instruction to adjust the energy storage device's charge and discharge rates is triggered to ensure a stable power supply for the refrigeration equipment. If the high risk is caused by positional displacement, an instruction to switch the load power supply mode may be triggered to prioritize power for equipment related to cargo securing and repositioning. For medium and low risk levels, corresponding operational instructions are also set based on the specific circumstances. After determining the triggering instruction to adjust the energy storage device's charge and discharge rates, the adjustment range for the charge and discharge rates is determined based on factors such as the current remaining energy storage device charge, device load, and cargo demand. For example, if the energy storage device has sufficient remaining charge and critical cargo equipment urgently needs more power, the charge rate is appropriately increased. Conversely, if the remaining charge is low and some equipment can temporarily reduce power, the discharge rate is reduced. When switching the load power supply mode, the importance and power demand of each load are analyzed to determine which loads should be switched to the backup power source or have their power supply priority adjusted. When starting the backup power supply access mechanism, check the status of the backup power supply (such as power level, equipment integrity, etc.) to ensure that the backup power supply can be connected normally and meet the power needs of critical equipment.

[0096] In step 302, the fleet's central control platform accurately transmits coordinated operation instructions to the corresponding barges, main propulsion vessels, and auxiliary vessels based on the pre-set communication protocol and the connection relationships between the vessels. For example, instructions to adjust the energy storage device's charge and discharge rates are transmitted via the wireless network to the barge or auxiliary vessel responsible for managing the energy storage device; instructions to switch the load power supply mode are transmitted to the vessel involved in the load switching, ensuring that each vessel accurately receives the corresponding instructions. After receiving the instructions, each vessel uses its onboard sensors and monitoring systems to provide feedback on the execution of the instructions. For example, for instructions to adjust the energy storage device's charge and discharge rates, the monitoring system obtains parameters such as the energy storage device's charge and discharge current and voltage in real time to determine whether the charge and discharge rates are adjusted according to the instructions. For instructions to switch the load power supply mode, the monitoring system checks whether the power supply status of each load has been successfully switched. For instructions to activate the backup power supply connection mechanism, the monitoring system confirms whether the backup power supply has been properly activated and connected to the circuit. If any problems are detected during instruction execution, such as charge and discharge rate adjustment failure, load switching anomalies, or the inability to connect the backup power supply, feedback is promptly provided to the fleet's central control platform so that appropriate measures can be taken.

[0097] By categorizing cargo status risk levels, the risk level of cargo is presented in an intuitive hierarchical format, allowing personnel to quickly understand the cargo's safety status. The cargo tracking report details key information such as temperature and location. The report's contents, particularly the recommended action priorities, help personnel clarify appropriate measures in different risk situations, avoid blind actions, improve the accuracy and timeliness of decision-making, and ensure cargo transportation safety. By triggering coordinated operation instructions for energy storage equipment based on risk levels, the operating status of the energy storage equipment can be adjusted in a timely manner, ensuring the stable operation of key cargo-related equipment (such as refrigeration and fixed equipment), and reducing the risk of cargo damage or transportation accidents caused by power problems.

[0098] By adjusting charge and discharge rates, switching load power supply modes, and other operations, energy is rationally allocated, improving energy efficiency and reducing transportation costs while ensuring cargo safety. Collaborative operation instructions are accurately distributed to each vessel, and execution status is monitored in real time to ensure effective execution. Any execution issues are promptly identified and resolved, preventing the impact of inadequate instruction execution on cargo safety and transportation efficiency. Real-time monitoring of execution status enables the fleet's central control platform to monitor the operating status of vessels and the implementation of cargo security measures, enabling dynamic management and control of the entire transportation process and improving the sophistication of transportation management.

[0099] In a preferred embodiment of the present invention, in step 4 above, when the target correction value is greater than or equal to the preset threshold, the fleet central control platform sends an emergency response instruction to the auxiliary function ship and the main propulsion ship via the collaborative communication network; the emergency response instruction includes adjusting the fleet's navigation speed, correcting the route, or initiating an emergency processing mechanism. The execution result of the emergency response instruction may include:

[0100] In an embodiment of the present invention, the cargo status monitoring device continuously collects various data, including cargo physical parameters (such as humidity and pressure, if relevant), location information, temperature data at the first target detection point, and position offset data at the second target detection point. The frequency of data collection depends on the cargo characteristics and transportation environment. For high-value, perishable cargo, the collection frequency may be higher, such as every few minutes or even every second; for general cargo, the collection frequency may be lower, such as every half hour. Through multi-channel, high-frequency data collection, a comprehensive understanding of the real-time status of the cargo is achieved. The collected data may contain noise, outliers, or issues of varying magnitude, thus requiring preprocessing. Regarding data normalization, the value ranges are determined for different data types. When filtering noise, a reasonable threshold is set to identify noise. Taking temperature data as an example, if the temperature fluctuation within a short period exceeds the normal fluctuation range (for example, if the temperature fluctuation does not exceed 1°C per 10 minutes under normal circumstances, a threshold of 3°C / 10 minutes is set), the data point may be considered noise. For data points suspected of being noise, the trend of adjacent data points is referenced for evaluation. If the data point does not conform to the trend, it is removed or corrected. Outlier correction identifies outliers based on the distribution of cargo data under normal conditions. For example, position offset data is identified as an outlier if it exceeds the expected offset range for cargo under normal ship navigation (pre-set based on factors such as ship type and navigation environment; for example, the maximum allowable offset distance for cargo relative to the ship in calm seas is 3 meters). Statistical methods are used to correct outliers, such as using historical data from similar ships under similar shipping conditions to identify the most similar normal data point to replace the current outlier.

[0101] The preprocessed data is then comprehensively analyzed and calculated using different weights to determine the target correction value. These weights are determined based on the nature of the cargo, transportation requirements, and the impact of each data point on cargo safety. For example, for high-value, perishable cargo, temperature data might be given a higher weight, say 0.6; position offset data a weight of 0.2; other physical state parameters a weight of 0.1; and location information a weight of 0.1. For standard cargo, on the other hand, the weights for each data point might be relatively balanced. Through weighted calculations, these various data types are integrated into a comprehensive risk indicator, the target correction value.

[0102] Different types of cargo have different risk tolerances. For high-value, perishable goods, such as fresh, high-end seafood and rare medicines, even small changes in risk can lead to significant losses. For example, when transporting fresh salmon, even the slightest temperature fluctuation can affect its quality. Therefore, a lower threshold might be set for this type of cargo. Perhaps an emergency response is triggered when the target correction value reaches 0.3, ensuring that timely measures can be taken to protect the cargo as soon as a risk arises. For general cargo, such as daily necessities, which have a relatively high risk tolerance, a higher threshold might be set, such as a target correction value of 0.6. Transportation requirements include factors such as shipping time and route. For urgent transport missions, where time constraints are extremely high and a cargo risk could disrupt the smooth operation of the entire supply chain, a lower threshold might be set. For example, when transporting emergency medical supplies, to ensure timely and safe delivery, an emergency response might be initiated when the target correction value reaches 0.4. Lower thresholds might also be required if the transport route passes through complex waters or areas with unpredictable weather. For example, if the transport route passes through areas frequently infested by pirates or prone to hurricanes, such as seas frequently infested by pirates or hurricanes, the threshold might be lowered.

[0103] As external risks increase, the uncertainty facing cargo increases. A threshold of 0.35 might be set to react to even the slightest increase in inherent cargo risk. By reviewing past risk profiles of similar cargo shipments, appropriate thresholds can be identified. For example, if, in past shipments of a certain type of cargo, damage or delays were more likely to occur when the target correction value reached 0.5, the threshold could be set to 0.45 for subsequent shipments of that type of cargo, enabling proactive risk prevention and control. Simultaneously, historical data is continuously updated, and as transportation environments and technical conditions change, the threshold is dynamically adjusted to meet actual transportation needs.

[0104] The fleet's central control platform continuously obtains the latest target correction values ​​and compares them in real time with pre-set thresholds. The platform's efficient data processing and comparison mechanisms enable rapid determination of the magnitude relationship between the two. When the target correction value exceeds or equals the pre-set threshold, the platform immediately triggers the subsequent emergency response process. This process involves sending emergency response instructions to auxiliary vessels and main propulsion vessels. These instructions may include adjusting the fleet's speed, correcting the route, or initiating emergency response mechanisms to ensure cargo safety and smooth transportation.

[0105] The extent of the speed adjustment will be determined based on the cargo's current risk profile and the vessel's navigational environment. If the cause of the problem is a potentially hazardous temperature abnormality, and the cargo is sensitive to temperature fluctuations, while the vessel is currently sailing in a relatively safe area (e.g., away from other vessels and obstacles), the speed may be appropriately reduced to minimize the impact of vessel sway on the cargo and prevent further exacerbation of the temperature abnormality. For example, a fleet originally sailing at 15 knots might reduce the speed to 10 knots. Conversely, if the cause of the problem is a positional error, and the surrounding navigation conditions permit, the speed may be appropriately increased to more quickly reach the appropriate location for cargo adjustments or repairs.

[0106] Correcting a route requires comprehensive consideration of multiple factors. First, the risk type of cargo. For example, a fleet transporting hazardous chemicals, if there's a risk of leakage, needs to avoid densely populated areas and sensitive waters (such as water sources and nature reserves). Second, the vessel's current location, destination, surrounding geography, and weather conditions must be considered. For example, if a vessel is currently near a shallow area and the corrected route requires passing through it, a careful assessment of feasibility is required. Furthermore, if severe weather, such as a hurricane, is encountered, consideration must be given to whether the new route can avoid the effects of the weather. By comprehensively weighing these factors, a new, safe route is determined to guide the fleet.

[0107] Different emergency response mechanisms are in place for different cargo types and risk situations. For dangerous goods, if an abnormally high cargo temperature is detected, the cooling spray system may be activated to cool the cargo. When activating this mechanism, it is necessary to check the equipment status of the cooling spray system, including whether the nozzles are blocked, whether the water source is sufficient, whether the pressure is normal, etc. If the cargo shifts in position, a procedure to strengthen the cargo securing device may be initiated, such as tightening ropes, adding supports, etc., while ensuring that no further damage is caused to the cargo during the operation. For ordinary cargo, if a large amount of cargo shifts, a procedure to re-arrange the cargo may be initiated, and the crew may be arranged to rearrange and secure the cargo according to certain operating specifications.

[0108] After receiving emergency response instructions, auxiliary vessels and main propulsion vessels begin executing the corresponding operations. During this process, various sensors and monitoring equipment on the vessels collect execution information in real time. For example, when adjusting sailing speed, the vessel's speed sensor obtains the current actual speed; when correcting a course, the navigation system monitors whether the vessel is following the new route and the extent of deviation; when initiating emergency response mechanisms, status indicators, pressure sensors, temperature sensors, and other on-device feedback are used to indicate the equipment's operating status and response effectiveness. This information is fed back to the fleet's central control platform in real time via a collaborative communication network. The platform analyzes and organizes this feedback data to determine the execution results of the emergency response instructions.

[0109] In a preferred embodiment of the present invention, step 5, based on the execution results of the emergency response instructions and the target correction value, is shared in real time with external ports or logistics platforms via the fleet communication link to form monitoring data and dynamic scheduling basis for the entire transportation chain, which may include:

[0110] Step 500: Obtain the execution result of the demand response instruction, including the charge and discharge state adjustment record of the energy storage device, the load switching response time, and the backup power supply access effect;

[0111] Step 501: Associating the execution result with the target correction value to form a data set including a timestamp, correction value risk level, and execution effect;

[0112] Step 502: The data set is transmitted in real time to an external port or logistics platform via an energy management link, and dynamically matched with the real-time load data and energy scheduling plan of the power grid platform to obtain a matching result, which specifically includes:

[0113] After receiving the data set, the external port or logistics platform dynamically matches the data set with the current port operation plan, real-time waterway status data, and logistics scheduling requirements. This means adjusting the port cargo acceptance priority based on the corrected risk level. The dynamic allocation of waterway resources is optimized based on the execution effect of emergency response instructions. Based on the risk change trend of the target corrected value, the potential risk areas of the transportation task are predicted to obtain matching results.

[0114] Step 503 : Generate a dynamic control basis for the energy supply and demand link based on the matching result, wherein the basis includes the current grid supply and demand balance state, energy storage equipment operation efficiency evaluation, and user demand priority adaptability analysis.

[0115] In an embodiment of the present invention, after the energy storage device receives an instruction to adjust the charge and discharge rate, its internal monitoring system will record the changes in the charge and discharge state in real time. By recording the changes in the charge and discharge current, voltage, and power at different time points, a charge and discharge state adjustment record is formed. For example, the start time and end time of each charge and discharge rate adjustment, the current and voltage values ​​before and after the adjustment, and the increase or decrease in the power of the energy storage device during the adjustment process are recorded. When the load switching instruction is issued, a time mark point is set on the load switching device. The time interval starts from the moment the instruction is issued and ends when the load successfully switches to the new power supply mode or backup power supply. This time interval is the load switching response time. By accurately recording the time of this process, the response speed and reliability of the load switching system can be evaluated.

[0116] When the backup power source is connected, monitor parameters such as the output voltage, frequency, and power to ensure they remain stable within specified ranges. Check the normal operation of powered equipment (such as critical cargo-related equipment and ship navigation equipment) after the backup power source is connected, and check for any abnormal noise, vibration, or equipment errors. Also, record the impact of the backup power source on the entire ship's power system, such as any voltage fluctuations in other equipment, to comprehensively evaluate the effectiveness of the backup power source connection.

[0117] Step 501: Add an accurate time stamp to each set of execution result data to record the specific time when the data was generated. The accuracy of the timestamp can be determined according to actual needs, and can be accurate to the second or even millisecond level.

[0118] Based on the previously calculated target correction value, the corresponding risk level is determined according to pre-defined risk classification criteria. For example, a target correction value between 0 and 0.3 indicates a low risk level, 0.3-0.6 indicates a medium risk level, and 0.6-1 indicates a high risk level. By clarifying risk levels, the data becomes more readable and analytically valuable. Execution result data, such as the energy storage device's charge and discharge status adjustment records, load switching response time, and backup power supply access effectiveness, is collated and summarized, along with timestamps and correction value risk levels, to form a data set containing multi-dimensional information. During the integration process, data integrity and accuracy are ensured to avoid data omissions or errors.

[0119] In step 502, when transmitting the data set via the energy management link, encryption technology is employed to ensure data security. For example, the Advanced Encryption Standard (AES) algorithm is used to encrypt the data, converting the original data into ciphertext before transmission. This ensures that even if the data is stolen during transmission, it is difficult for the thief to decipher the data content. Furthermore, data stability is ensured by using a data verification mechanism, such as a cyclic redundancy check (CRC) algorithm. The sending end generates a checksum based on the data set and transmits it along with the data. The receiving end calculates the checksum of the received data and compares it with the checksum sent by the sending end. If there is a mismatch, it indicates a possible data transmission error, requiring retransmission.

[0120] After receiving the dataset, external ports or logistics platforms will review the current port operations plan, including information such as the utilization of various berths, the scheduling of loading and unloading equipment, and manpower allocation plans. The assessment of the corrected value risk level is based not only on the target corrected value but also on the specific characteristics of the cargo (such as whether it is dangerous goods or perishable goods). For high-risk cargo, priority is given to high-quality berths with comprehensive facilities and convenient operations to ensure rapid unloading. In the allocation of loading and unloading equipment, priority is given to efficient equipment suitable for cargo loading and unloading. For example, large gantry cranes are used for large container cargo. Furthermore, experienced and skilled manpower is rationally deployed to ensure the safety and efficiency of the cargo unloading process.

[0121] To optimize the dynamic allocation of waterway resources based on the effectiveness of emergency response directives, in-depth analysis of various data collected during the emergency response process is conducted. For example, through onboard sensor data, navigation records, and crew feedback, the specific reasons for potential risks in a particular waterway can be determined, such as narrow channels, complex currents, or the presence of underwater obstacles. Based on these factors, corresponding waterway resource adjustment strategies can be formulated. If a risk in a particular waterway causes cargo displacement during navigation, in addition to reducing the number of ships passing through that channel, categorized management of passing ships is required. For ships transporting high-risk cargo, strict restrictions will be imposed on their passage. For ships transporting general cargo, reasonable passage times and intervals will be arranged while ensuring safety. At the same time, freed-up waterway resources will be allocated to safer and more suitable channels, such as those with sufficient water depth and smooth currents. During the allocation process, factors such as the channel's carrying capacity, the type and number of ships, and other factors will be comprehensively considered to ensure the rational use of waterway resources.

[0122] When predicting potential risk areas for a transport mission based on the risk trend of the target correction value, in addition to analyzing whether the target correction value is gradually increasing, decreasing, or remaining stable, the rate of change must also be considered. A rapid increase in the target correction value indicates a rapid increase in risk and suggests that the potential risk area may be closer to the current location, requiring closer attention and proactive prevention. Considering the transport route, a detailed analysis of each leg of the route should be conducted, including information such as the geographic environment (e.g., proximity to shoals or reefs), meteorological conditions (e.g., frequent inclement weather), and accident records of passing vessels. Furthermore, the port's surroundings should be considered, such as the presence of sensitive areas such as chemical parks and densely populated areas near the port, as cargo risk incidents in these areas could have a greater impact. By integrating these factors and conducting an in-depth analysis of risk trends, it is possible to pinpoint potential risk areas and formulate appropriate preventative measures, such as pre-arranged patrol vessels and the installation of warning signs.

[0123] When dynamically matching datasets with real-time load data and energy dispatch plans from the power grid platform, the relationship between the energy storage device's charge and discharge status adjustment records and the real-time grid load is analyzed in depth. Changes in the real-time grid load during energy storage device charging are observed to determine whether these may lead to voltage fluctuations, frequency changes, or other issues. If the energy storage device's charging power is high and the grid load is at peak times, this may cause a drop in grid voltage, impacting the normal operation of other electrical devices. In this case, the impact of the energy storage device on the grid needs to be assessed. By analyzing factors such as grid capacity and backup power reserves, the system can determine whether the energy storage device's operation is within the grid's tolerances. Furthermore, based on the energy dispatch plan, a detailed understanding of the grid's power supply targets and requirements for different time periods is required. The current energy storage device's charge and discharge status and operating efficiency can be compared to determine compliance with the plan's requirements. If the energy storage device's charging time does not align with the off-peak electricity price periods specified in the energy dispatch plan, the charging and discharging strategy may need to be adjusted to reduce electricity costs and better align with grid dispatch.

[0124] In step 503, when determining whether the current grid supply and demand are balanced, combining real-time load data from the grid platform and the charge and discharge status of the energy storage device, not only is the grid load at peak times considered when the energy storage device is charging, but factors such as the overall grid power generation capacity, the availability of backup power supplies, and the operating status of other large power-consuming equipment must also be considered. If the grid load is at peak times when the energy storage device is charging, the grid power generation capacity is nearing its limit, and backup power reserves are insufficient, charging the energy storage device is likely to increase the grid burden and even cause a grid failure. In this case, a comprehensive assessment is required to determine whether the energy storage device charging plan needs to be adjusted. This assessment considers factors such as the remaining energy storage device charge, the urgency of the cargo's power demand, and the subsequent trend of grid load changes. If the energy storage device's remaining charge is sufficient to maintain critical equipment operation for a period of time and the grid load is expected to decrease, charging the energy storage device can be temporarily suspended until the grid load decreases before resuming. If the cargo's power demand is extremely urgent and adjusting the charging plan could affect cargo safety, coordination with other power resources, such as activating the ship's backup power generation equipment, is necessary to ensure stable grid operation.

[0125] When evaluating the operating efficiency of energy storage devices based on their charge and discharge status adjustment records, backup power supply access effectiveness, and grid interaction data, in addition to calculating the device's charge and discharge conversion efficiency over a specific period, it's also necessary to analyze energy losses during the charging and discharging process. For example, observe whether heat generation, leakage, or other phenomena during charging increase energy losses. During discharge, determine whether the output power effectively meets the device's needs and whether there are any energy transmission losses. Furthermore, analyze how the energy storage device's efficiency varies under different operating conditions. For example, during a ship's voyage, the charging and discharging efficiency of an energy storage device may vary under different speeds and cargo loads. By comparing efficiency data under different operating conditions, it's possible to determine whether the energy storage device is operating optimally. If a significant decrease in efficiency is observed under certain operating conditions, this could be due to device aging, component damage, or improperly set operating parameters. Further inspection of the device is necessary to determine whether maintenance or adjustments are necessary, such as replacing aging components or optimizing operating parameters. When analyzing the compatibility of user needs with current energy supply priorities based on the cargo's modified risk level and the urgency of the transport mission, a detailed assessment of the characteristics and urgency of energy needs for high-risk, urgent transport missions is required. For example, for ships transporting high-value, perishable pharmaceuticals, it is crucial not only to ensure the stability of their energy supply but also to ensure the quality of the power supply to prevent voltage fluctuations from impacting pharmaceutical storage equipment. When ensuring energy supply, prioritize high-quality, stable power resources, such as using highly reliable power lines within the grid or activating the ship's more efficient backup power sources. Simultaneously, consider the energy needs of other transport missions and allocate energy resources appropriately. For ships with lower risk levels and less urgent transport missions, appropriate adjustments can be made to their energy supply schedules to prioritize the energy needs of high-priority transport missions.

[0126] By recording the charge and discharge status adjustments of energy storage devices, load switching response time, and backup power access effectiveness, the effectiveness of emergency response instructions can be accurately assessed. Execution results are associated with target correction values ​​to form a dataset containing timestamps, risk levels, and execution results, achieving data integration and standardization. Adjusting port cargo acceptance priorities based on the corrected risk level and optimizing waterway resource allocation based on execution results can improve port operational efficiency and ensure the rapid and safe transportation of cargo. Predicting potential risk areas based on risk trends of target correction values ​​facilitates proactive preventative measures, reduces transportation risks, and ensures the smooth completion of transportation missions. Analyzing the current power grid supply and demand balance can prevent excessive impacts on the grid caused by ship energy use. Evaluating the operational efficiency of energy storage equipment can identify operational issues, improve its utilization efficiency, and reduce energy consumption. Analyzing the adaptability of user demand priorities can rationally allocate energy resources based on cargo transportation needs, improve overall energy efficiency, and ensure the energy supply of key cargoes.

[0127] In a preferred embodiment of the present invention, in step 6 above, the fleet central control platform analyzes the execution effect of the emergency response instructions and the risk change trend of the target correction value based on the shared monitoring data and dynamic scheduling basis, and generates a cargo loading strategy optimization plan, which may include:

[0128] Step 600: Perform a multi-dimensional analysis of the shared dynamic control basis, including the execution efficiency of the demand response instruction, the risk trend of the target correction value, and the matching degree between the charging and discharging efficiency of the energy storage device and the grid load fluctuation, to obtain the analysis results;

[0129] Step 601: Generate an energy storage strategy optimization plan based on the analysis results, including adjusting the charge and discharge plan of the energy storage device, optimizing the load power supply mode switching logic, and dynamically configuring the access threshold of the backup power supply.

[0130] In an embodiment of the present invention, when determining the time point at which the demand response instruction begins execution, the fleet central control platform will accurately record the moment the instruction is issued. For instructions to adjust the charge and discharge rates of energy storage devices, the platform not only records the time the instruction is issued, but also the time the instruction is transmitted to the energy storage device control system to ensure the accuracy of time recording. In a complex fleet communication network, there may be delays in instruction transmission. Accurately recording these time nodes can more accurately analyze the actual situation of instruction execution. It is also critical to clearly define the time point when the instruction execution is completed or the expected effect is achieved. For energy storage device charge and discharge rate adjustment, the platform needs to continuously monitor the operating parameters of the energy storage device, including current, voltage, power, etc. Only when these parameters fluctuate stably around the specified values ​​for a period of time (such as 5-10 minutes) can it be determined that the rate adjustment is complete and stable operation. This is because in actual operation, the parameters of the energy storage device may fluctuate briefly. If judgment is based solely on instantaneous values, it may lead to misjudgment of the instruction execution status.

[0131] When calculating the execution time of a command, the platform considers time synchronization to ensure the accuracy and consistency of start and end times. In a fleet shipping environment, clocks on different shipboard devices may deviate, necessitating regular time calibration. The platform also records the timing of key events during command execution, such as when the energy storage device begins adjusting its rate and when the rate approaches the specified value. When evaluating the alignment of execution results with expected targets, the deviation rate between the actual and expected values ​​must be calculated, taking into account various factors. While the energy storage device charging rate is expected to increase to a certain value, actual operation can be affected by various factors, such as battery aging and ambient temperature fluctuations. Therefore, a reasonable error range is established when calculating the deviation rate. If the actual value is within this error range, it can be considered to be generally in line with expectations. If it exceeds this error range, further analysis will be conducted. In addition to calculating the deviation rate, the platform also observes the trend of the actual value. For example, if the actual charging rate falls short of the expected value but is consistently increasing and approaching the expected value, the evaluation of the execution performance will differ compared to if the actual charging rate is consistently below expectations and shows no upward trend.

[0132] When evaluating execution efficiency based on both command execution time and deviation rate, different weights are assigned based on the command type and specific circumstances. For time-sensitive commands, such as adjusting the discharge rate of energy storage devices to power critical equipment in an emergency, execution time is weighted more heavily. For commands requiring high precision, such as adjusting the charging rate to ensure stable operation of cargo refrigeration equipment, deviation rate is weighted more heavily. By assigning appropriate weights, the execution efficiency of demand response commands can be more comprehensively and accurately assessed. When reviewing target correction data, record data at appropriate intervals. For fleets transporting high-value, perishable cargo, record target corrections every 15-30 minutes to promptly capture changes in risk. For general cargo transport, hourly or daily recording is sufficient. Data recording also includes relevant environmental parameters and equipment operating status, such as the temperature and humidity of the vessel's navigation area, and the ship's engine speed.

[0133] When observing changes in the target correction value over time, the rate of change is analyzed for any sustained upward or downward trends. For example, a slow increase in the target correction value over several consecutive time intervals will indicate different risk levels and response strategies than a rapid increase. A slow increase may provide more time to implement risk control measures, while a rapid increase requires immediate emergency action. For target correction values ​​that fluctuate within a certain range, reference standards are set when analyzing the amplitude and frequency of fluctuations. If the amplitude exceeds a normal range (e.g., ±5%) and the frequency of fluctuations is high (e.g., more than three fluctuations per hour), the risk is considered unstable. Furthermore, the cause of the risk trend is determined based on various factors during transportation. For example, if the weather in the vessel's navigation area suddenly changes, such as a storm, causing increased ship motion and cargo displacement, which in turn causes fluctuations in the target correction value, appropriate measures to address the weather factors may be necessary, such as adjusting the vessel's navigation attitude or strengthening cargo securing.

[0134] Analysis of the matching degree between energy storage equipment charging and discharging efficiency and grid load fluctuation:

[0135] When acquiring energy storage device charge and discharge efficiency data, detailed records are kept at each stage. During the charging phase, data such as the charge input, power consumption, and charging time are recorded at the start, mid-charge, and near-full charge stages. The same applies to the discharge phase, where data such as the discharge output, power consumption, and discharge time are recorded at the start, mid-charge, and near-full discharge stages. Furthermore, parameters such as the energy storage device's temperature and internal resistance are monitored, as these parameters can affect charge and discharge efficiency. For example, excessively high or low temperatures can reduce charge and discharge efficiency, and recording these parameters helps analyze the causes of efficiency fluctuations. When acquiring grid load fluctuation data, real-time data from the grid platform is connected to record information such as the timing, magnitude, and duration of power peaks and troughs. In addition to recording overall grid load fluctuations, local grid load data related to the fleet's location is also monitored. Because the fleet's electricity consumption may have a more direct impact on the local grid, understanding local grid load fluctuations can better analyze the compatibility between the energy storage device and the grid.

[0136] When comparing the charge and discharge efficiency curves of energy storage devices with the grid load fluctuation curves, we not only observe whether the changes between the two are consistent or complementary, but also analyze the lead or lag relationship between their changes. For example, before the grid load reaches its peak, can the energy storage device increase its discharge capacity in advance to meet the upcoming peak power consumption? When the grid load reaches a low point, can the charging efficiency of the energy storage device be improved in time to fully utilize the remaining power? At the same time, consider the differences in grid load characteristics in different seasons and time periods. During the peak power consumption in summer, the grid load fluctuates greatly and the peak lasts for a long time, so the charging and discharging strategy of the energy storage device needs to adapt to it. In winter, the grid load characteristics may be different, and the matching method of the energy storage device's charge and discharge efficiency with the grid load also needs to be adjusted accordingly.

[0137] In step 601, when adjusting the energy storage device charge and discharge schedule, if the demand response instruction execution efficiency is low, rescheduling the charge and discharge times and rates requires comprehensive consideration of multiple factors. In addition to considering peak and off-peak times for the grid load, electricity price differences across different time periods are also analyzed. Charging during low-price periods not only reduces electricity costs but also alleviates the burden on the grid during peak periods. The lifespan and performance degradation of the energy storage device are also considered. Excessive charging and discharging, or charging and discharging at inappropriate temperatures, accelerate device aging. Therefore, when planning the charge and discharge times and rates, a reasonable charge and discharge plan is developed based on the technical parameters and usage of the energy storage device. For example, for energy storage devices that have been in use for a long time, the charge rate can be appropriately reduced to minimize battery loss. If the target correction value indicates an increased risk, the discharge capacity of the energy storage device is increased, prioritizing the power supply of equipment critical to cargo safety and ship operation. For ships transporting dangerous goods, such as chemical tankers, priority is given to ensuring power for cargo monitoring equipment, ventilation equipment, and emergency response equipment. Furthermore, the discharge capacity is adjusted appropriately based on the extent and duration of the increased risk. If the risk only increases temporarily, the discharge capacity will be appropriately increased to maintain critical equipment operation. If the risk persists and could threaten cargo safety, in addition to increasing the discharge capacity, consideration will be given to activating the backup power source or adjusting the vessel's sailing plan to seek additional power at a nearby port. When optimizing the load power supply mode switching logic, if the load switching response time is found to be excessively long, the triggering conditions and execution process for the switchover will be reevaluated. In addition to considering the backup power supply voltage, other parameters such as the frequency and phase of the backup power supply will also be considered. In actual operation, mismatches in voltage, frequency, and phase can cause voltage fluctuations during the switchover, impacting normal equipment operation. Therefore, the new switching logic may require that the backup power supply voltage, frequency, and phase be stable within a certain range before switching. At the same time, the execution process will be optimized to reduce unnecessary intermediate steps and increase switching speed. For example, the pre-connection testing process for the backup power supply can be simplified and more efficient testing technology can be adopted to shorten the switching response time while ensuring safety.

[0138] When dynamically configuring the backup power supply access threshold, if the energy storage device's charging and discharging efficiency doesn't match grid load fluctuations, the backup power supply should be connected early when the grid load is abnormal. Lowering the access threshold requires caution, and a safety buffer range is established. Furthermore, after lowering the access threshold, coordinated monitoring of the backup power supply and energy storage device is strengthened. Once the backup power supply is connected, the operating status of the energy storage device is monitored to prevent power conflicts between the two. If the match is good and the energy storage device can effectively guarantee power supply, the backup power supply access threshold is raised, and regular functional testing of the backup power supply is performed to ensure normal access when needed.

[0139] Through multi-dimensional analysis of demand response command execution efficiency, target correction risk trends, and the compatibility between energy storage equipment and the power grid, the fleet's central control platform can comprehensively understand the operational status of the energy management system throughout the entire transportation process. Analysis of target correction risk trends can proactively identify potential risks during cargo transportation, enabling the fleet to take timely preventative measures to ensure cargo safety and the smooth progress of transportation missions. Analyzing the compatibility between energy storage equipment charging and discharging efficiency and grid load fluctuations helps achieve coordinated optimization between ship energy management and the power grid, improving energy utilization efficiency and reducing energy costs.

[0140] Adjusting the charging and discharging plan of energy storage equipment can enable the energy storage equipment to operate in a more reasonable state, improve its charging and discharging efficiency, reduce equipment losses, extend equipment service life, and ensure the stability of power supply.

[0141] Optimizing load power mode switching logic ensures stable operation of ship equipment when switching between different power supply modes, preventing equipment failures caused by improper switching and safeguarding cargo safety and the ship's normal navigation. Dynamic configuration of backup power access thresholds allows for flexible control of backup power access based on actual conditions, ensuring timely access to backup power in emergencies while avoiding unnecessary access, thereby improving backup power efficiency and reliability.

[0142] like Figure 2 As shown, an embodiment of the present invention further provides a combined fleet intelligent cargo tracking system, comprising:

[0143] A data acquisition module is used to deploy cargo status monitoring devices on the barges and auxiliary function ships of the combined fleet to collect physical status parameters and location information of the cargo in real time, wherein the monitoring device includes a first target detection point and a second target detection point;

[0144] The data processing and analysis module is used by the fleet central control platform to integrate and analyze the physical state parameters, location information, temperature data of the first target detection point, and position offset data of the second target detection point of the cargo to generate a target correction value;

[0145] The instruction trigger module is used to generate cargo tracking reports based on the target correction value, trigger collaborative operation instructions according to preset rules, and monitor the execution status of collaborative operation instructions in real time;

[0146] An emergency response module is used to send emergency response instructions from the fleet central control platform to the auxiliary function ships and main propulsion ships through the collaborative communication network when the target correction value is greater than or equal to the preset threshold. The emergency response instructions may include adjusting the fleet's navigation speed, correcting the route, or initiating an emergency processing mechanism, and obtaining the execution results of the emergency response instructions;

[0147] The data sharing module is used to share the execution results and target correction values ​​of emergency response instructions with external ports or logistics platforms in real time through fleet communication links, forming monitoring data and dynamic scheduling basis for the entire transportation chain;

[0148] The solution optimization module is used by the fleet central control platform to analyze the execution effect of emergency response instructions and the risk change trend of target correction values ​​based on shared monitoring data and dynamic scheduling basis, and generate cargo loading strategy optimization solutions.

[0149] It should be noted that this system is a system corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effects.

[0150] An embodiment of the present invention further provides a computing device comprising: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the above-described method. All implementations in the above-described method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0151] The embodiment of the present invention further provides a computer-readable storage medium storing instructions, which, when executed on a computer, causes the computer to execute the above-described method. All implementations in the above-described method embodiment are applicable to this embodiment and can achieve the same technical effects.

[0152] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A combined fleet intelligent cargo tracking method, characterized in that: The method comprises: Step 1: Deploy cargo status monitoring devices on barges and auxiliary vessels of the combined fleet to collect physical status parameters and location information of the cargo in real time, wherein the monitoring devices include a first target detection point and a second target detection point; Step 2: The fleet central control platform integrates and analyzes the physical state parameters, position information, temperature data of the first target detection point, and position offset data of the second target detection point of the cargo to generate a target correction value, including: collecting the physical state parameters, position information, temperature data of the first target detection point, and position offset data of the second target detection point of the cargo through a cargo state monitoring device, and performing preprocessing, including data normalization, noise filtering, and outlier correction, to obtain processed data; calculating the cargo temperature anomaly coefficient based on the temperature data of the first target detection point in the preprocessed data, the temperature anomaly coefficient being the deviation ratio between the current temperature and the preset safe temperature range; calculating the cargo position offset coefficient based on the position offset data of the second target detection point in the preprocessed data, the position offset coefficient being the ratio of the real-time deviation distance between the current position of the cargo and the preset path to the maximum allowable deviation; classifying and determining the temperature anomaly coefficient and the position offset coefficient based on the cargo type and transportation priority data to obtain a classification determination result; generating a target correction value based on the classification determination result, the target correction value being a comprehensive risk indicator of the temperature anomaly coefficient and the position offset coefficient; Step 3: Generate a cargo tracking report based on the target correction value, trigger collaborative operation instructions according to preset rules, and monitor the execution status of the collaborative operation instructions in real time, including: classifying the cargo status risk level according to the numerical range of the target correction value, and generating a cargo tracking report based on the risk level, the cargo tracking report including the cargo temperature anomaly detection results and historical temperature change trends, cargo position offset and real-time deviation analysis from the preset position, target correction value risk level assessment and recommended operation priority; match the corresponding cargo status risk level according to the preset rules, and trigger collaborative operation instructions for the energy storage device, the collaborative operation instructions including adjusting the charge and discharge rate of the energy storage device, switching the load power supply mode, or activating the backup power supply access mechanism; issue the collaborative operation instructions to the corresponding barges, main propulsion vessels, and auxiliary function vessels through the fleet central control platform, and monitor the execution status of the collaborative operation instructions in real time; Step 4: When the target correction value is greater than or equal to a preset threshold, the fleet central control platform sends an emergency response instruction to the auxiliary function ship and the main propulsion ship via the collaborative communication network. The emergency response instruction may include adjusting the fleet's navigation speed, correcting the route, or initiating an emergency response mechanism, and obtaining the execution result of the emergency response instruction; Step 5: Based on the execution results of the emergency response instructions and the target correction values, they are shared in real time with external ports or logistics platforms via the fleet communication link, forming monitoring data for the entire transportation chain and a basis for dynamic scheduling; In step 6, the fleet central control platform analyzes the execution effect of the emergency response instructions and the risk change trend of the target correction value based on the shared monitoring data and dynamic scheduling basis, and generates an optimization plan for the cargo loading strategy.

2. The combined fleet intelligent cargo tracking method according to claim 1, characterized in that: Based on the execution results of emergency response instructions and target correction values, they are shared in real time with external ports or logistics platforms through fleet communication links, forming monitoring data and dynamic scheduling basis for the entire transportation chain, including: Obtain the execution results of the demand response instruction, including the charge and discharge status adjustment records of the energy storage device, the load switching response time, and the backup power supply access effect; The execution results are linked to the target correction values ​​to form a data set containing timestamps, correction value risk levels, and execution effects. The data set is transmitted to an external port or logistics platform in real time through the energy management link, and dynamically matched with the real-time load data and energy scheduling plan of the power grid platform to obtain the matching results; Based on the matching results, a dynamic control basis for the energy supply and demand link is generated, which includes the current power grid supply and demand balance status, energy storage equipment operation efficiency evaluation and user demand priority adaptability analysis.

3. The combined fleet intelligent cargo tracking method according to claim 2, characterized in that: The data set is transmitted in real time to an external port or logistics platform through the energy management link, and dynamically matched with the real-time load data and energy scheduling plan of the power grid platform to obtain matching results, including: After receiving the dataset, the external port or logistics platform dynamically matches the dataset with the current port operation plan, real-time waterway status data, and logistics scheduling needs. That is, the port cargo reception priority is adjusted according to the corrected value risk level; the dynamic allocation of waterway resources is optimized based on the execution effect of the emergency response instructions; based on the risk change trend of the target corrected value, the potential risk areas of the transportation task are predicted to obtain matching results.

4. The combined fleet intelligent cargo tracking method according to claim 3, characterized in that: Based on shared monitoring data and dynamic scheduling, the fleet's central control platform analyzes the effectiveness of emergency response instructions and the risk trend of target revisions, generating optimized cargo loading strategies, including: Conduct a multi-dimensional analysis of the shared dynamic control basis, including the execution efficiency of demand response instructions, the risk trend of target correction values, and the matching degree between the charging and discharging efficiency of energy storage equipment and grid load fluctuations, to obtain analysis results; Based on the analysis results, an energy storage strategy optimization plan is generated, including adjusting the charging and discharging plans of energy storage equipment, optimizing the load power supply mode switching logic, and dynamically configuring the access threshold of the backup power supply.

5. The combined fleet intelligent cargo tracking method according to claim 4, characterized in that: The first target detection point is a cargo temperature detection unit, which is used to monitor the temperature change of the cargo in real time; the second target detection point is a cargo position offset detection unit, which is used to monitor the offset of the cargo from the preset position in real time.

6. A combined fleet intelligent cargo tracking system, the system implementing the method according to any one of claims 1 to 5, characterized in that: include: A data acquisition module is used to deploy cargo status monitoring devices on the barges and auxiliary function ships of the combined fleet to collect physical status parameters and location information of the cargo in real time, wherein the monitoring device includes a first target detection point and a second target detection point; The data processing and analysis module is used by the fleet central control platform to integrate and analyze the physical state parameters, location information, temperature data of the first target detection point, and position offset data of the second target detection point of the cargo to generate a target correction value; The instruction trigger module is used to generate cargo tracking reports based on the target correction value, trigger collaborative operation instructions according to preset rules, and monitor the execution status of collaborative operation instructions in real time; An emergency response module is used to send emergency response instructions from the fleet central control platform to the auxiliary function ships and main propulsion ships through the collaborative communication network when the target correction value is greater than or equal to the preset threshold. The emergency response instructions may include adjusting the fleet's navigation speed, correcting the route, or initiating an emergency processing mechanism, and obtaining the execution results of the emergency response instructions; The data sharing module is used to share the execution results and target correction values ​​of emergency response instructions with external ports or logistics platforms in real time through fleet communication links, forming monitoring data and dynamic scheduling basis for the entire transportation chain; The solution optimization module is used by the fleet central control platform to analyze the execution effect of emergency response instructions and the risk change trend of target correction values ​​based on shared monitoring data and dynamic scheduling basis, and generate cargo loading strategy optimization solutions.

7. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program, which implements the method according to any one of claims 1 to 5 when executed by a processor.

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