A cargo hold water accumulation monitoring method, system and storage medium based on the Internet of Things

Abstract

The present application relates to the technical field of cargo hold water accumulation monitoring, and discloses a cargo hold water accumulation monitoring method, system and storage medium based on the Internet of Things, with the following steps: set distance sensors facing the bottom of the hold in multiple areas in the cargo hold to obtain distance parameters, and use a first illumination light source to illuminate the bottom of the hold at the same time, and synchronously capture the image of water accumulation through a camera; calculate the water accumulation area of ​​each area based on the water accumulation image, and obtain the regional water accumulation amount and the total water accumulation amount in combination with the distance parameter; obtain the value group of distance parameter of each area in a set time period, calculate the discrete coefficient to obtain the water surface fluctuation value, and correct the total water accumulation amount; issue an early warning when the corrected water accumulation amount exceeds the set threshold. This method monitors water accumulation in the cargo hold in multiple dimensions, accurately calculates the amount of water accumulation, and issues timely early warnings to ensure cargo hold safety.

Description

Technical Field

[0001] The present application relates to the technical field of cargo hold water accumulation monitoring, and in particular to a cargo hold water accumulation monitoring method, system and storage medium based on the Internet of Things. Background Art

[0002] Bilge water refers to the water accumulated on the bottom of the bilge during the transportation of the ship due to leakage of the outer plate, inadequate watertightness of the hatch cover, leakage of pipes and equipment, leakage of shaft stuffing boxes, moisture condensation caused by temperature difference, and water discharge during equipment maintenance.

[0003] The hazards of bilge water are as follows: 1. Corrosion of the hull: Oil substances in the bilge water can corrode the hull steel plate, causing rust on the hull structure and affecting the navigation performance and safety of the ship.

[0004] 2. Increase air humidity: Long-term accumulation of water in the bilge will cause electromechanical equipment to become damp, affecting the normal operation of the equipment.

[0005] 3. Impact on safety: The free liquid surface created by excessive bilge water threatens the stability of the ship. Moving machinery such as flywheels touching water will deteriorate the operation of the equipment and the engine room environment.

[0006] The bilge water system is a piping system specially used to discharge bilge water. The bilge water system usually consists of a sewage well, a water level alarm, a bilge water pump, a bilge water pipe, related valves, a suction filter and an oil-water separator.

[0007] Water level alarms are usually monitored by water level sensors. However, in actual applications, such as when a ship is in a river, the bottom of the cargo hold is often uneven and the accumulated water is shaking. The water level value actually measured by the water level sensor is different from the actual value. Summary of the invention

[0008] In order to improve the monitoring accuracy of cargo hold water accumulation parameters, the present application provides a cargo hold water accumulation monitoring method, system and storage medium based on the Internet of Things.

[0009] In the first aspect, the present application provides a cargo hold water accumulation monitoring method based on the Internet of Things, which adopts the following technical solutions: A cargo hold water accumulation monitoring method based on the Internet of Things, the method comprising the following steps: Obtaining corresponding distance parameters based on distance sensors disposed in multiple areas within the cargo hold and facing the bottom of the cargo hold; Based on the first illumination light source irradiating the bottom of the cargo hold, the image of the bottom of the cargo hold captured by the camera is a water accumulation image; wherein the water accumulation image captured by the camera and the distance parameter collected by the distance sensor are synchronous data; Calculate the water accumulation area corresponding to each region according to the water accumulation image, and calculate the regional water accumulation amount of the current region according to the distance parameter and the water accumulation area in the current region; The sum of the regional waterlogging in all areas is calculated as the total waterlogging; Obtaining a value group of the distance parameter in each area within a set time period; Calculate the coefficient of dispersion of the distance parameter in each area; The average value of the coefficient of dispersion of all regions is calculated as the water surface fluctuation value; The total accumulated water volume is corrected according to the water surface fluctuation value to obtain a corrected accumulated water volume; corrected accumulated water volume=total accumulated water volume×water surface fluctuation value / (water surface fluctuation value+1); If the corrected water accumulation amount exceeds the set water accumulation threshold, an early warning prompt is issued.

[0010] By adopting the above technical solution, distance parameters are obtained by setting distance sensors facing the bottom of the cargo hold in multiple areas in the cargo hold, and the camera is used to capture the image of water accumulation at the bottom of the cargo hold, and the data of the two are synchronized, which provides a rich and accurate data basis for subsequent precise calculations. The water accumulation area is calculated using the water accumulation image, and the regional water accumulation volume is calculated in combination with the distance parameter, and then the total water accumulation volume is obtained. This multi-dimensional data fusion calculation method greatly improves the accuracy of water accumulation parameter monitoring. The discrete coefficient of the distance parameter of each area is calculated, and the water surface fluctuation value is obtained. The total water accumulation volume is corrected to obtain the corrected water accumulation volume, which fully considers the impact of water surface fluctuations on water accumulation measurement, and further optimizes the accuracy of the monitoring results. Moreover, when the corrected water accumulation volume exceeds the set water accumulation threshold, an early warning prompt can be issued in time, allowing staff to take measures quickly to avoid damage to the cargo due to excessive water accumulation, ensuring the safety of cargo transportation and reducing the risk of economic losses.

[0011] Optionally, the method for calculating the average value of the discrete coefficients of all regions as the water surface fluctuation value further includes the following steps: Based on the attitude sensor, acquiring attitude data of the ship; Calculating the matching degree between the ship's attitude change law and the water surface fluctuation value according to the attitude data; If the matching degree is greater than a preset matching threshold, the current corrected water volume is maintained; If the matching degree is less than or equal to a preset matching threshold, the water surface fluctuation value is corrected using the posture data; and the corrected water accumulation amount is updated using the corrected data.

[0012] By adopting the above technical solution, during the voyage, the ship will produce attitude changes due to factors such as wind and waves, which can easily interfere with the monitoring of water accumulation in the cargo hold. By obtaining the ship attitude data through the attitude sensor and calculating the degree of matching with the water surface fluctuation value, the false water accumulation fluctuation signal caused by the ship's own shaking can be effectively identified. When the matching degree is less than the preset threshold, the water surface fluctuation value is corrected using the attitude data and the corrected water accumulation amount is updated to ensure that the water accumulation monitoring data truly reflects the actual situation of water accumulation in the cargo hold, avoiding the misjudgment of water accumulation data due to the interference of the ship's attitude, and greatly improving the reliability of the water accumulation monitoring data. When the matching degree is greater than the preset matching threshold, the current corrected water accumulation amount is maintained, and this setting maintains the stability of the data. Preventing frequent adjustment of water accumulation data due to normal slight shaking of the ship avoids excessive fluctuations in the data, providing stable and referenceable water accumulation information for staff to facilitate accurate decision-making. Overall, the solution effectively improves the accuracy and reliability of cargo hold water accumulation monitoring, and provides strong support for ensuring the safe transportation of goods.

[0013] Optionally, based on a temperature sensor and a humidity sensor provided in the cargo hold, the method further includes: Obtain the power parameters of the heating equipment in the cargo hold; Acquire temperature data in the cargo hold based on the temperature sensor, and acquire humidity data in the cargo hold based on the humidity sensor; Calculating the change trend of the temperature data as a temperature change trend; Calculating the change trend of the humidity data as a humidity change trend; According to the temperature change trend and the humidity change trend, the matching degree between the power parameter and the corrected water accumulation amount is calculated as a matching value. If the matching value is lower than a set threshold, an early warning prompt is issued.

[0014] By adopting the above technical solution, combined with the power parameters of the heating equipment in the cargo hold, and the change curves of the temperature and humidity monitoring in the cargo hold, the accuracy of the data can be assisted in analysis. Under normal circumstances, the power of the heating equipment, temperature and humidity, and water accumulation are in a relatively stable equilibrium state. By obtaining the power parameters of the heating equipment and the data fed back by the temperature and humidity sensors in real time, and calculating their changing trends, the matching degree is calculated with the corrected water accumulation. Once the matching value is lower than the set threshold, it means that an abnormal condition has occurred in the cargo hold, and the system immediately issues an early warning prompt, allowing the staff to quickly detect and take measures.

[0015] Optionally, the method of updating the corrected water accumulation volume using the corrected data further comprises: Within a set time period T, calculating the growth rate of the corrected water accumulation; Growth rate = (V2-V1) / T, where V1 is the corrected water volume at the start of time period T, and V2 is the corrected water volume at the end of time period T; If the growth rate exceeds the corresponding speed threshold, an early warning prompt is issued.

[0016] By adopting the above-mentioned technical solution, the abnormally rapid growth of water accumulation can be detected in advance, allowing staff to have more time to deal with the risk of water accumulation, such as making drainage preparations before a large amount of water rushes in, to avoid damage to the cargo hold structure or damage to the cargo due to excessive water accumulation.

[0017] Optionally, a calibration distance sensor is installed on the top of the cargo hold through a steering gear, and the method further includes the following steps: Calculating a rotation angle of the steering gear according to the acquired attitude data, wherein the rotation angle is opposite to the tilt angle of the ship in the attitude data, so that the orientation of the distance sensor always remains consistent with the direction of gravity; When there is no movable object at the bottom of the cargo hold, the measurement parameter of the calibration distance sensor is the simulated vertical distance; According to the simulated vertical distance, a distance curve model based on different corrected water accumulation amounts is constructed as a simulation curve; The measurement parameter of the distance sensor obtained in real time is the real-time distance; In a set time period, a real-time distance curve model is constructed as a real-time curve according to the real-time distance and the corrected water accumulation amount; Within a preset horizontal axis range, calculating the real-time integral value of the real-time curve and calculating the simulated integral value of the simulated curve; Calculate the absolute value of the difference between the simulated integral value and the real-time integral value as a verification difference; If the verification difference is greater than a preset verification threshold, an early warning is issued.

[0018] By adopting the above technical solution, by calculating the steering gear rotation angle according to the ship's attitude data, the calibration distance sensor is always kept consistent with the gravity direction, which can effectively avoid the measurement error caused by the ship's tilt. In a complex navigation environment, the ship will inevitably shake and tilt. It is difficult for traditional fixed sensors to ensure the accuracy of measurement, especially when items fall to the bottom of the cargo hold, or when items move abnormally at the bottom of the cargo hold, the data detected by the fixed distance sensor will have certain errors. This dynamic calibration mechanism ensures that the calibration distance sensor can provide stable and reliable measurement data under any circumstances, laying a solid foundation for subsequent analysis and judgment. The simulated vertical distance is used to construct a simulation curve, and the real-time distance is combined to construct a real-time curve, and the difference between the integral values ​​of the two is calculated to determine whether there is an abnormality. This model comparison-based method can keenly capture subtle changes in the water accumulation in the cargo hold. When the calibration difference is greater than the preset threshold, an early warning is issued to detect potential water accumulation risks in advance, such as abnormal leakage at the bottom of the hold, so that the staff can take timely measures to avoid the deterioration of the water accumulation problem. Regular calibration and data comparison of the calibration distance sensor can help to timely detect possible faults or deviations in the sensor itself. Through the early warning mechanism, staff are reminded to inspect and maintain sensors to ensure the data reliability of the entire monitoring system.

[0019] Optionally, the method for calculating the water accumulation area corresponding to each region according to the water accumulation image comprises the following sub-steps: Preprocessing the water accumulation image into a grayscale image, and denoising the grayscale image to obtain a smooth image; Performing edge detection on the smoothed image using an edge detection algorithm to obtain an edge contour; Using an image extraction algorithm, based on the edge contour obtained by edge detection, a bright spot contour is extracted; and based on a screening algorithm, the bright spot contour is screened to obtain an actual bright spot contour of accumulated water; The contour area of ​​the bright spot of accumulated water is calculated as the accumulated water area.

[0020] By adopting the above technical solution, the interference information in the image is effectively removed by preprocessing the water accumulation image into a grayscale image and denoising it, making the subsequently processed image clearer and more accurate; the bright spot contour is obtained by using the edge detection algorithm and the image extraction algorithm, and then the actual water accumulation bright spot contour is determined by the screening algorithm. This series of steps can accurately identify the water accumulation area and avoid interference from other irrelevant bright spots. Finally, the area of ​​the water accumulation bright spot contour is calculated to obtain the water accumulation area, which greatly improves the accuracy of the water accumulation area calculation.

[0021] Optionally, the method for correcting the posture data further includes: According to a specific ripple threshold, identifying ripple features from the water accumulation image; According to the ripple characteristics, the fluctuation amplitude and displacement frequency corresponding to the ripple characteristics are calculated; Calculating the matching degree between the fluctuation amplitude and the displacement frequency and the posture data according to the fluctuation amplitude and the displacement frequency; If the matching degree is less than a preset parameter value, the gesture data is corrected using the fluctuation amplitude and displacement frequency.

[0022] By adopting the above technical solution, the ripple features are identified from the water accumulation image and its fluctuation amplitude and displacement frequency are calculated, and then the matching degree is calculated with the ship's attitude data. When the matching degree is less than the preset parameter value, the attitude data is corrected using the fluctuation amplitude and displacement frequency, integrating the water accumulation ripple information and the ship's attitude information. Not only can the ship's attitude data be verified and corrected based on the fluctuation of the water accumulation to improve the accuracy of the attitude data, but it can also further improve the comprehensive analysis of the relationship between the water accumulation in the cargo hold and the ship's operating status, providing strong support for more accurate cargo hold water monitoring and safe ship operation.

[0023] In the second aspect, the present application provides a cargo hold water accumulation monitoring system based on the Internet of Things, which adopts the following technical solutions: A cargo hold water accumulation monitoring system based on the Internet of Things comprises a processor, wherein the processor executes the steps of any one of the above-mentioned cargo hold water accumulation monitoring methods based on the Internet of Things.

[0024] In a third aspect, the present application provides a storage medium, which adopts the following technical solution: A storage medium having a program stored therein, wherein the program, when executed by a processor, implements the steps of any one of the above-mentioned methods for monitoring water accumulation in a cargo hold based on the Internet of Things.

[0025] In summary, the present application includes at least one of the following beneficial technical effects: Distance parameters are obtained by setting distance sensors facing the bottom of the cargo hold in multiple areas in the cargo hold, and using cameras to capture images of water accumulation at the bottom of the cargo hold. The data of the two are synchronized to provide a rich and accurate data basis for precise calculation. The water accumulation area is calculated using the water accumulation image, and the regional water accumulation volume is calculated in combination with the distance parameters, and then the total water accumulation volume is obtained. This multi-dimensional data fusion calculation method greatly improves the accuracy of water accumulation parameter monitoring.

[0026] The discrete coefficient of the distance parameter of each area is calculated to obtain the water surface fluctuation value, which is used to correct the total water accumulation to obtain the corrected water accumulation volume. This fully considers the impact of water surface fluctuations on water accumulation measurement and further optimizes the accuracy of the monitoring results.

[0027] When the corrected water accumulation exceeds the set water accumulation threshold, an early warning will be issued in time, allowing staff to take quick measures to avoid damage to goods due to excessive water accumulation, ensuring the safety of cargo transportation and reducing the risk of economic losses.

[0028] By obtaining the ship's attitude data through the attitude sensor and calculating the degree of matching between the data and the water surface fluctuation value, the false water accumulation fluctuation signal caused by the ship's own shaking can be effectively identified. When the matching degree is less than the preset threshold, the attitude data is used to correct the water surface fluctuation value and update the corrected water accumulation amount to ensure that the water accumulation monitoring data truly reflects the actual situation of water accumulation in the cargo hold, avoiding the misjudgment of water accumulation data caused by the interference of the ship's attitude, and greatly improving the reliability of the water accumulation monitoring data; when the matching degree is greater than the preset threshold, the current corrected water accumulation amount is maintained to maintain data stability, prevent excessive data fluctuations, and facilitate staff to make accurate decisions.

[0029] Combined with the power parameters of the heating equipment in the cargo hold, and the change curves of the temperature and humidity monitoring in the cargo hold, the accuracy of the data is analyzed. By obtaining the power parameters of the heating equipment in real time, as well as the data fed back by the temperature and humidity sensors, and calculating their change trends, the matching degree is calculated with the corrected water accumulation. Once the matching value is lower than the set threshold, the system immediately issues an early warning prompt, allowing the staff to quickly detect and take measures.

[0030] By calculating the steering gear rotation angle based on the ship's attitude data, the calibration distance sensor is always kept consistent with the gravity direction, effectively avoiding measurement errors caused by the ship's tilt. The simulated vertical distance is used to construct a simulation curve, and the real-time distance is combined to construct a real-time curve. The difference between the integral values ​​of the two is calculated to determine whether there is an abnormality, discover potential water accumulation risks in advance, and help to promptly discover possible faults or deviations in the sensor itself, ensuring the data reliability of the entire monitoring system. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a step diagram of a cargo hold water accumulation monitoring method based on the Internet of Things.

[0032] Figure 2 This is a step-by-step diagram of a method for calculating the average value of the dispersion coefficient of all areas as the water surface fluctuation value.

[0033] Figure 3 It is a method step diagram based on the case where a temperature sensor and a humidity sensor are installed in the cargo hold. DETAILED DESCRIPTION

[0034] Embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings.

[0035] In the description of this specification, the description with reference to the terms "certain embodiments", "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples" or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiments or examples are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0036] The present application embodiment discloses a cargo hold water accumulation monitoring method based on the Internet of Things, referring to Figure 1 , the method comprises the following steps: Obtaining corresponding distance parameters based on distance sensors disposed in multiple areas within the cargo hold and facing the bottom of the cargo hold; Based on the first light source illuminating the bottom of the cargo hold, the image of the bottom of the cargo hold captured by the camera is a water accumulation image; wherein, the water accumulation image captured by the camera and the distance parameter collected by the distance sensor are synchronized data. For example, in the four corners of the cargo hold, the middle position and the place near the door, the purpose is to fully cover the cargo hold space and ensure that accurate information at each position can be obtained. When the water accumulation in the cargo hold changes, these distance sensors start to work, measuring the distance between them and the bottom of the cargo hold in real time, so as to obtain the corresponding distance parameters. Assuming that the cargo hold is a large space with a length of 20 meters, a width of 10 meters and a height of 5 meters, distance sensors are installed in its four corners. When the depth of water accumulation changes at different positions, these sensors can capture the change in distance in time and provide basic data for subsequent analysis.

[0037] The corresponding water accumulation area of ​​each area is calculated based on the water accumulation image, and the regional water accumulation amount of the current area is calculated based on the distance parameters and water accumulation area in the current area. During the detection, the light source can evenly illuminate the bottom of the cargo hold without blind spots. At the same time, the installed camera can be one or more, so that it can clearly capture the image of the bottom of the cargo hold, that is, the water accumulation image. The two processes of the camera capturing the water accumulation image and the distance sensor collecting the distance parameters are carried out synchronously, which ensures the consistency of the acquired data in the time dimension and provides a reliable data basis for subsequent accurate calculations. For example, during a cargo transportation process, when water began to accumulate in the cargo hold, the distance sensor and the camera started working at the same time, synchronously recording the distance parameters and water accumulation images of each area in the early stage of water accumulation, which provided a guarantee for accurate judgment of the water accumulation situation.

[0038] Based on the acquired water accumulation image, a series of image processing algorithms are used to calculate the corresponding water accumulation area of ​​each area. Specifically, the water accumulation image is first grayed out to convert the color image into a black and white image to simplify the subsequent processing process. Then the grayscale image is denoised to remove the interference information in the image and make the image clearer. The edge detection algorithm is then used to identify the edge contour of the water accumulation area. Then, the image extraction algorithm is used to extract the bright spot contour based on the edge contour obtained by edge detection, and other irrelevant bright spots are removed through the screening algorithm. Finally, the actual bright spot contour of the water accumulation is determined, and the area of ​​the contour is calculated, which is the water accumulation area. After the water accumulation area of ​​each area is obtained, the water accumulation volume of the current area can be calculated in combination with the distance parameter collected by the distance sensor in the current area. For example, through the above image processing method, the water accumulation area of ​​a certain area is calculated to be 5 square meters, and the water accumulation depth (converted from the distance parameter) measured by the distance sensor in the area is 0.2 meters. Then, according to the volume formula, the water accumulation volume of the area is 5×0.2=1 cubic meter. The actual parameters shall be subject to actual measurements, and the examples are only used as reference for the implementation principles of the technical solutions; the same applies to other cases in the article.

[0039] The sum of the regional water accumulation in all areas is calculated as the total water accumulation; the total water accumulation can intuitively reflect the overall situation of water accumulation in the cargo hold.

[0040] Get the value group of the distance parameter in each area within the set time period; In order to further analyze the dynamic changes of water accumulation, it is necessary to get the value group of the distance parameter in each area within the set time period. For example, if the time period is set to 1 hour and the distance parameter is recorded every 10 minutes, then within this 1 hour, each area will get 6 distance parameters to form a value group.

[0041] Calculate the dispersion coefficient of the distance parameter in each area; the dispersion coefficient can reflect the degree of dispersion of the distance parameter, that is, the fluctuation of the water depth in the area. The larger the dispersion coefficient, the more unstable the change of the water depth in the area.

[0042] The average value of the dispersion coefficient of all areas is calculated as the water surface fluctuation value; the water surface fluctuation value comprehensively reflects the fluctuation of the water surface in the entire cargo hold.

[0043] The total amount of water accumulated is corrected according to the water surface fluctuation value to obtain the corrected amount of water accumulated; corrected amount of water accumulated = total amount of water accumulated × water surface fluctuation value / (water surface fluctuation value + 1).

[0044] If the corrected water accumulation exceeds the set water accumulation threshold, an early warning will be issued. If the corrected water accumulation exceeds the set water accumulation threshold, it means that the water accumulation in the cargo hold has reached a dangerous level, and the system will immediately issue an early warning. Early warnings can be implemented in a variety of ways, such as displaying a striking red warning message on the cockpit monitoring screen and issuing a sharp alarm to remind staff to take timely measures, such as starting drainage equipment, checking the tightness of the cargo hold, etc., to avoid damage to the cargo due to excessive water accumulation and ensure the safety of cargo transportation.

[0045] This cargo hold water accumulation monitoring method based on the Internet of Things can comprehensively and accurately obtain water accumulation information by setting up distance sensors and cameras in multiple areas of the cargo hold and collecting data synchronously. The area and total water accumulation volume are calculated using water accumulation images and distance parameters, and the total water accumulation volume is corrected using the water surface fluctuation value obtained by combining the distance parameter discrete coefficient, thereby improving measurement accuracy. In addition, a timely warning is issued when the corrected water accumulation volume exceeds the threshold, thereby ensuring the safety of cargo transportation.

[0046] Reference Figure 2 During the voyage of a ship, the ship's posture will constantly change due to factors such as wind and waves, which will interfere with the monitoring results of water accumulation in the cargo hold. In order to more accurately monitor the water accumulation in the cargo hold, a series of optimization steps are taken when calculating the average value of the discrete coefficients of all areas as the water surface fluctuation value. The method of calculating the average value of the discrete coefficients of all areas as the water surface fluctuation value also includes the following steps: The attitude data of the ship is obtained based on the attitude sensor; the ship is equipped with attitude sensors, which are distributed in key parts of the ship, such as the bow, stern and the middle of the hull, and they are always in working condition. Taking a certain transoceanic transport as an example, during the voyage, the attitude sensor will continuously capture the attitude data of the ship. These data cover the movement information of the ship in all directions, including the roll angle, pitch angle and changes in heading. For example, when a ship encounters wind and waves on the sea, the attitude sensor will accurately record the process of the ship's roll angle gradually increasing from 0° to 10°, and then slowly swinging back, as well as the numerical changes of the pitch angle at different times.

[0047] The matching degree between the ship's attitude change pattern and the water surface fluctuation value is calculated based on the attitude data. The ship's attitude change is compared with the water surface fluctuation value. For example, when the wind is calm, the ship's attitude changes relatively smoothly, the roll angle may be within ±2°, and the pitch angle is also small. At this time, the water surface fluctuation value is relatively stable, and the calculated matching degree is high; when encountering a strong typhoon, the ship's attitude changes drastically, the roll angle may reach 30° in an instant, and the pitch angle also fluctuates greatly. At this time, the matching degree with the water surface fluctuation value will be low.

[0048] If the matching degree is greater than the preset matching threshold, the current corrected water volume is maintained. If the matching degree is greater than the preset matching threshold, this indicates that the change in the ship's attitude has little effect on the water surface fluctuation value, and the current water accumulation monitoring data is more reliable. In this case, the current corrected water volume is maintained without additional adjustment.

[0049] If the matching degree is less than or equal to the preset matching threshold, it means that the change in the ship's attitude has caused a significant interference to the water surface fluctuation value, and the water accumulation monitoring data may be biased. In this case, the attitude data is used to correct the water surface fluctuation value; the corrected data is used to update the corrected water accumulation amount. For example, based on the roll and pitch angles of the ship recorded by the attitude sensor, the actual fluctuation of the water surface is re-evaluated, and the original water surface fluctuation value is adjusted. After the adjustment is completed, the corrected data is used to update the corrected water accumulation amount. Ensure that the water accumulation monitoring data truly reflects the actual situation of water accumulation in the cargo hold, and avoid misjudgment of water accumulation data due to interference from the ship's attitude.

[0050] The ship's attitude data is obtained through the attitude sensor, and the degree of matching between it and the water surface fluctuation value is calculated. When the matching degree is greater than the preset threshold, the amount of water accumulation is corrected to ensure data stability and avoid frequent data changes due to normal slight shaking of the ship, providing a reliable reference for staff. When the matching degree is less than or equal to the preset threshold, the attitude data is used to correct the water surface fluctuation value and update the corrected water accumulation amount, effectively eliminating the interference of ship attitude changes on water accumulation monitoring and avoiding misjudgment of water accumulation data, which greatly improves the reliability of water accumulation monitoring data and lays a solid data foundation for ensuring the safe transportation of goods.

[0051] Reference Figure 3 Based on the temperature sensor and humidity sensor provided in the cargo hold, the method further includes: Obtain the power parameters of the heating equipment in the cargo hold; there are various types of heating equipment in the cargo hold, such as heating devices for cargo insulation, or some equipment that generates heat during operation, such as large refrigeration units that dissipate heat during operation. Take a cargo hold that transports medicines that need to be kept at a constant temperature as an example. The power of the heating device is 5 kilowatts. This power parameter is one of the important bases for subsequent comprehensive analysis.

[0052] The temperature data in the cargo hold is obtained based on the temperature sensor, and the humidity data in the cargo hold is obtained based on the humidity sensor. The temperature data in the cargo hold is obtained by relying on temperature sensors installed in different positions in the cargo hold. These temperature sensors are distributed on the top, bottom and surroundings of the cargo hold to ensure that the temperature changes in the cargo hold can be sensed in all directions. Assume that during a certain period of time, the temperature sensor records temperature data every 10 minutes, starting from the initial 25°C. As time goes by, the temperature gradually rises and is recorded to 25.5°C, 26°C, etc. At the same time, the humidity sensor is also working synchronously to obtain humidity data in the cargo hold. For example, in the same period of time, the humidity gradually changes from the initial 50% relative humidity to 52%, 54%, etc.

[0053] The change trend of temperature data is calculated as the temperature change trend; the change trend of humidity data is calculated as the humidity change trend. Through mathematical calculation and data analysis, it is found whether the temperature is rising, falling or relatively stable. For example, after calculation, it is found that in the past 2 hours, the temperature has been rising at a rate of 1°C per hour. Similarly, the humidity data is processed similarly, and the change trend of humidity data is calculated as the humidity change trend. If it is found that the humidity increases at a rate of 5% per hour in these 2 hours.

[0054] According to the temperature change trend and humidity change trend, the matching degree between the power parameter and the corrected water accumulation is calculated as the matching value. If the matching value is lower than the set threshold, an early warning prompt is issued. Under normal circumstances, the power of the heating equipment, the temperature and humidity, and the water accumulation are in a relatively stable equilibrium state. If the matching value is lower than the set threshold, it means that an abnormal condition has occurred in the cargo hold. For example, in a cargo transport, the set matching threshold is 80%, and the calculated matching value is only 60%. At this time, the system immediately issues an early warning prompt. After receiving the early warning, the staff quickly checked the cargo hold and found that there was a slight leak at the bottom of the cargo hold, which caused the abnormal water accumulation and affected the temperature and humidity balance in the cargo hold. Timely measures were taken to repair it, avoiding damage to the cargo due to water accumulation and abnormal temperature and humidity.

[0055] By obtaining the power parameters of the cargo hold heating equipment and combining the data collected by the temperature and humidity sensors to calculate the temperature and humidity change trend, and then comprehensively calculating the matching degree between the power parameters and the corrected water accumulation, an early warning will be issued once the matching value is lower than the set threshold. This can promptly detect potential problems in the cargo hold, such as abnormal water accumulation, temperature and humidity imbalance, etc., helping staff to quickly take countermeasures, effectively avoiding damage to cargo due to abnormal cargo hold environment, and ensuring the safety of cargo transportation in all aspects.

[0056] In the cargo hold water accumulation monitoring system, when the corrected data is used to update the water accumulation amount, there is also a key early warning mechanism that can effectively prevent the risk of water accumulation and ensure the safe transportation of goods. The method of using the corrected data to update the water accumulation amount also includes: Calculate the growth rate of the corrected water accumulation within the set time period T. The setting of the time period T is determined based on actual transportation conditions and experience. For example, in short-distance inland river transportation, the time period T can be set to 1 hour; in long-distance shipping, considering that the change of water accumulation is relatively slow, T can be set to 3 hours. Suppose that in a long-distance shipping, T is set to 3 hours.

[0057] Growth rate = (V2-V1) / T, where V1 is the corrected water volume at the start of time period T, and V2 is the corrected water volume at the end of time period T. For example, in the above example of shipping with a 3-hour cycle, the corrected water volume V1 at the start (0 hours) is 5 cubic meters, and 3 hours later (at the end of time period T), the corrected water volume V2 increases to 8 cubic meters. Then according to the formula, the growth rate = (8-5) ÷ 3 = 1 cubic meter / hour.

[0058] If the growth rate exceeds the corresponding speed threshold, an early warning will be issued. The speed threshold is determined based on multiple factors such as the cargo hold's carrying capacity and the cargo's water resistance. For example, for cargo holds loaded with electronic products, since electronic products are extremely sensitive to water, the set speed threshold may be lower, assuming it is 0.5 cubic meters per hour; while for cargo holds loaded with some goods that are not afraid of short-term moisture, the speed threshold can be relatively high. In this example, the calculated growth rate is 1 cubic meter per hour, which exceeds the set speed threshold of 0.5 cubic meters per hour. At this time, the system will immediately trigger an early warning. After receiving the early warning, the staff can respond quickly, such as checking whether there are new leaks in the cargo hold, starting the backup drainage equipment, and controlling the risk of water accumulation in the bud, avoiding damage to the cargo hold structure or damage to the cargo due to the influx of large amounts of water, and ensuring the safe arrival of the goods at their destination.

[0059] In the cargo hold water accumulation monitoring system, in order to ensure the accuracy and reliability of the water accumulation measurement data, a calibration distance sensor is installed on the top of the cargo hold through the servo. The use of this equipment involves a series of precise and rigorous steps.

[0060] First, during the voyage, the ship will undergo various attitude changes due to factors such as wind and waves. In order for the calibration distance sensor to always measure accurately, it is necessary to calculate the rotation angle of the servo based on the acquired attitude data. The ship attitude sensor will capture information such as the ship's tilt angle in real time. For example, during a voyage, the ship tilted 15° to the left. At this time, a specific algorithm is used to calculate the rotation angle of the servo to be 15° to the right, so that the direction of the distance sensor can always remain consistent with the direction of gravity. No matter how the ship shakes, the calibration distance sensor can measure at the correct angle to avoid measurement errors caused by the tilt of the ship.

[0061] When there is nothing moving at the bottom of the cargo hold, that is, the cargo hold is relatively stable and there is no debris interfering with the measurement, the measurement parameters of the calibration distance sensor are obtained and defined as the simulated vertical distance. Assuming that a ship docks at a port and the cargo in the cargo hold is sorted, there is nothing moving at the bottom of the cargo hold, and the simulated vertical distance measured by the calibration distance sensor is 5 meters.

[0062] Based on this simulated vertical distance, combined with different corrected water volumes, a distance curve model, i.e., a simulation curve, is constructed. For example, when the corrected water volumes are 1 cubic meter, 3 cubic meters, and 5 cubic meters, corresponding to different water level heights, a simulation curve is constructed through multiple measurements and data analysis. This curve can intuitively reflect the distance change trend that the calibration distance sensor should theoretically measure under different water volumes.

[0063] During the voyage, the calibration distance sensor will continue to work and obtain its measurement parameters in real time, which are defined as the real-time distance. Assume that at a certain moment during the voyage, the real-time distance measured by the calibration distance sensor is 4.8 meters.

[0064] Next, within a set time period, such as 1 hour, a real-time distance curve model, i.e., a real-time curve, is constructed based on the real-time distance obtained in real time and the corresponding corrected water accumulation. This curve shows the actual changes in the calibration distance sensor measurement data over time and changes in water accumulation during actual navigation.

[0065] Within the preset horizontal axis range, the horizontal axis here can be a parameter with reference significance such as time or water volume, and the real-time integral value of the real-time curve and the simulated integral value of the simulated curve are calculated respectively. The integral value can comprehensively reflect the changing characteristics of the curve within a certain range. For example, with time as the horizontal axis, within the preset range of 0-1 hour, the real-time integral value of the real-time curve obtained by integral operation is a specific value, assuming it is 50; at the same time, the simulated integral value of the simulated curve is calculated to be 55.

[0066] Then, the absolute value of the difference between the simulated integral value and the real-time integral value is calculated and defined as the calibration difference. In the above example, the calibration difference is |55-50|=5.

[0067] Finally, the calibration difference is compared with the preset calibration threshold. This calibration threshold is set based on a large amount of experimental data and actual experience, and is used to determine whether the current water accumulation monitoring situation is normal. For example, if the preset calibration threshold is 3, when the calibration difference 5 is greater than the preset calibration threshold 3, the system will immediately issue an early warning. This means that there may be problems such as abnormal leakage from the bottom of the hold or sensor failure. After receiving the early warning, the staff can promptly inspect the cargo hold, troubleshoot the cause of the abnormal water accumulation, calibrate or repair the sensor, and ensure the normal operation of the water accumulation monitoring system to avoid damage to the cargo due to water accumulation.

[0068] When monitoring water accumulation in the cargo hold, accurately calculating the corresponding water accumulation area in each area is a crucial step, and its implementation method includes the following series of rigorous and detailed sub-steps.

[0069] First, after obtaining the water accumulation image at the bottom of the cargo hold, it is necessary to pre-process it into a grayscale image. The color water accumulation image contains a lot of rich color information, but for the calculation of the water accumulation area, too much color details may interfere with subsequent processing. Take an image of water accumulation in a corner of the cargo hold as an example. The image not only contains water accumulation, but also the color packaging of the surrounding goods and the logo on the bulkhead. Through grayscale processing, this color image is converted into an image with only black and white grayscale differences, removing color interference and making the image information more focused on the brightness difference between the water accumulation area and the non-water accumulation area. After that, the grayscale image is denoised. Since the image may be affected by various factors during the acquisition and transmission process, some noise points are generated, which will affect the accuracy of subsequent analysis. Use professional denoising algorithms, such as Gaussian filtering algorithms, to process the grayscale image. After processing, a smooth image is obtained, making the boundary of the water accumulation area clearer and more coherent, providing a good data basis for subsequent operations.

[0070] Next, edge detection is performed on the smooth image using an edge detection algorithm. The principle of the edge detection algorithm is to determine the edge position of the object in the image by calculating the gradient change of the pixel points in the image. Taking the commonly used Canny edge detection algorithm as an example, it will analyze each pixel point in the smooth image. When the gradient value of the pixel point exceeds a certain threshold, it is considered that the point is on the edge of the object, thereby obtaining the edge contour. In the water accumulation image, these edge contours preliminarily outline the boundary between the water accumulation area and the surrounding environment.

[0071] Then, the image extraction algorithm is used to extract the bright spot contour based on the edge contour obtained by edge detection. Because in the waterlogged area, the waterlogged part usually presents bright spot characteristics due to light reflection and other reasons. The image extraction algorithm will extract the bright spot contour that may belong to the waterlogged area from the edge contour based on these characteristics. However, these bright spot contours may contain some interfering bright spots that are not in the waterlogged area, such as reflective spots on the surface of the cargo. Therefore, the bright spot contours are screened based on the screening algorithm. The screening algorithm will make a comprehensive judgment based on multiple factors such as the area size, shape characteristics, and position distribution of the bright spot. For example, a minimum area threshold is set, and the bright spot contours with an area smaller than the threshold are directly excluded; the bright spot contours whose shapes do not conform to the common shapes of the waterlogged area (such as approximate circles, irregular polygons, etc.) are also eliminated; at the same time, according to the position of the bright spot in the cargo hold image, it is judged whether it is in an area where waterlogging may occur, such as the bottom of the cargo hold instead of the bulkhead. After such strict screening, the actual bright spot contour of waterlogged areas can be obtained.

[0072] Finally, calculate the contour area of ​​the bright spot of water accumulation, which is the desired area of ​​water accumulation. Through professional area calculation algorithms, such as pixel counting methods, the number of pixels contained in the bright spot of water accumulation is counted, and then converted into the actual area size based on image resolution and other parameters. For example, if the image resolution contains 100 pixels per centimeter, and it is counted that there are 10,000 pixels in the bright spot of water accumulation, then the converted area of ​​water accumulation is 1 square centimeter.

[0073] By preprocessing the waterlogged image into a grayscale image and denoising it, the interference information in the image is effectively removed, making the image processed later clearer and more accurate; the bright spot contour is obtained by using the edge detection algorithm and the image extraction algorithm, and then the actual bright spot contour of the waterlogged image is determined by the screening algorithm. This series of steps can accurately identify the waterlogged area and avoid interference from other irrelevant bright spots. Finally, the bright spot contour area of ​​the waterlogged image is calculated to obtain the waterlogged area, which greatly improves the accuracy of the calculation of the waterlogged area. This is of great significance for accurately assessing the waterlogging situation in the cargo hold, taking corresponding measures in a timely manner, and ensuring the safe transportation of goods.

[0074] When correcting the attitude data of the ship under navigation status, a unique method combined with water accumulation image analysis is adopted to control the actual operation of the ship more comprehensively and accurately.

[0075] First, based on a specific pre-set ripple threshold, ripple features are identified from the image of water accumulation at the bottom of the cargo hold. This process requires the use of advanced image processing technology to conduct in-depth analysis of the pixel information in the water accumulation image. For example, during a ship's voyage, an image of water accumulation is obtained. Through the image processing algorithm, the brightness changes and texture features of the pixels in the image are compared with the pre-set ripple threshold. If the pixel changes in a certain area of ​​the image conform to a specific ripple pattern, and features such as brightness differences exceed the ripple threshold, it is determined that the area has ripple features. These ripple features may appear as regular concentric circles, or irregular waves formed by the shaking of the ship and the impact of water flow.

[0076] Next, for the identified ripple features, the corresponding fluctuation amplitude and displacement frequency are further calculated. The fluctuation amplitude is calculated by analyzing the pixel position difference between the crest and the trough in the ripple image, and then combining the actual size ratio of the image to convert the actual physical distance difference to determine the fluctuation amplitude. For example, after measurement and calculation, it is found from the image that the actual distance difference between the crest and the trough of a certain section of ripples is 5 cm, which is the fluctuation amplitude of the ripple feature. The displacement frequency is calculated by tracking and analyzing the changes in the ripple position in multiple consecutive frames of water accumulation images. Assuming that a frame of water accumulation image is taken every 1 second for 10 consecutive seconds, by comparing the position of the same ripple in different frames, it is found that the ripple has moved 10 times in 10 seconds, then its displacement frequency can be calculated to be 1 time per second.

[0077] After that, the calculated fluctuation amplitude and displacement frequency are calculated to match the ship's attitude data. The ship's attitude data contains information such as roll angle, pitch angle, and heading change. By establishing a specific mathematical model, the fluctuation amplitude and displacement frequency of the ripples are correlated and analyzed with the various parameters in the ship's attitude data. For example, if the ship's roll angle changes more drastically, and the ripple fluctuation amplitude in the water image is also large, and the displacement frequency is high, then the matching degree between them is relatively high; on the contrary, if the ship's attitude is relatively stable, but the fluctuation amplitude and displacement frequency of the ripples change abnormally, then the matching degree is low.

[0078] An embodiment of the present application also discloses a cargo hold water accumulation monitoring system based on the Internet of Things, including a processor, in which the steps of any one of the above-mentioned cargo hold water accumulation monitoring methods based on the Internet of Things are executed.

[0079] An embodiment of the present application further discloses a storage medium, in which a program is stored. When the program is executed by a processor, the steps of any one of the above-mentioned methods for monitoring water accumulation in a cargo hold based on the Internet of Things are implemented.

[0080] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in the field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A cargo hold water accumulation monitoring method based on the Internet of Things, characterized in that: The method comprises the following steps: Obtaining corresponding distance parameters based on distance sensors disposed in multiple areas within the cargo hold and facing the bottom of the cargo hold; Based on the first illumination light source irradiating the bottom of the cargo hold, the image of the bottom of the cargo hold captured by the camera is a water accumulation image; wherein the water accumulation image captured by the camera and the distance parameter collected by the distance sensor are synchronous data; Calculate the water accumulation area corresponding to each region according to the water accumulation image, and calculate the regional water accumulation amount of the current region according to the distance parameter and the water accumulation area in the current region; The sum of the regional waterlogging in all areas is calculated as the total waterlogging; Obtaining a value group of the distance parameter in each area within a set time period; Calculate the coefficient of dispersion of the distance parameter in each area; The average value of the coefficient of dispersion of all regions is calculated as the water surface fluctuation value; The total accumulated water volume is corrected according to the water surface fluctuation value to obtain a corrected accumulated water volume; corrected accumulated water volume=total accumulated water volume×water surface fluctuation value / (water surface fluctuation value+1); If the corrected water accumulation amount exceeds the set water accumulation threshold, an early warning prompt is issued.

2. The method for monitoring water accumulation in a cargo hold based on the Internet of Things according to claim 1, characterized in that: The method for calculating the average value of the dispersion coefficient of all regions as the water surface fluctuation value also includes the following steps: Based on the attitude sensor, acquiring attitude data of the ship; Calculating the matching degree between the ship's attitude change law and the water surface fluctuation value according to the attitude data; If the matching degree is greater than a preset matching threshold, the current corrected water volume is maintained; If the matching degree is less than or equal to a preset matching threshold, the water surface fluctuation value is corrected using the posture data; The corrected water volume is updated using the corrected data.

3. The method for monitoring water accumulation in a cargo hold based on the Internet of Things according to claim 1, characterized in that: Based on the temperature sensor and humidity sensor provided in the cargo hold, the method further includes: Obtain the power parameters of the heating equipment in the cargo hold; Acquire temperature data in the cargo hold based on the temperature sensor, and acquire humidity data in the cargo hold based on the humidity sensor; Calculating the change trend of the temperature data as a temperature change trend; Calculating the change trend of the humidity data as a humidity change trend; According to the temperature change trend and the humidity change trend, the matching degree between the power parameter and the corrected water accumulation amount is calculated as a matching value. If the matching value is lower than a set threshold, an early warning prompt is issued.

4. The method for monitoring water accumulation in cargo holds based on the Internet of Things according to claim 3 is characterized in that: The method of updating the corrected water accumulation volume using the corrected data further comprises: Within a set time period T, calculating the growth rate of the corrected water accumulation; Growth rate = (V2-V1) / T, where V1 is the corrected water volume at the start of time period T, and V2 is the corrected water volume at the end of time period T; If the growth rate exceeds the corresponding speed threshold, an early warning prompt is issued.

5. The method for monitoring water accumulation in cargo holds based on the Internet of Things according to claim 2, characterized in that: A calibration distance sensor is also installed on the top of the cargo hold through a steering gear, and the method also includes the following steps: Calculating a rotation angle of the steering gear according to the acquired attitude data, wherein the rotation angle is opposite to the tilt angle of the ship in the attitude data, so that the orientation of the distance sensor always remains consistent with the direction of gravity; When there is no movable object at the bottom of the cargo hold, the measurement parameter of the calibration distance sensor is the simulated vertical distance; According to the simulated vertical distance, a distance curve model based on different corrected water accumulation amounts is constructed as a simulation curve; The measurement parameter of the distance sensor obtained in real time is the real-time distance; In a set time period, a real-time distance curve model is constructed as a real-time curve according to the real-time distance and the corrected water accumulation amount; Within a preset horizontal axis range, calculating the real-time integral value of the real-time curve and calculating the simulated integral value of the simulated curve; Calculate the absolute value of the difference between the simulated integral value and the real-time integral value as a verification difference; If the verification difference is greater than a preset verification threshold, an early warning is issued.

6. The method for monitoring water accumulation in cargo holds based on the Internet of Things according to claim 2, characterized in that: The method for calculating the water accumulation area corresponding to each region according to the water accumulation image comprises the following sub-steps: Preprocessing the water accumulation image into a grayscale image, and denoising the grayscale image to obtain a smooth image; Performing edge detection on the smoothed image using an edge detection algorithm to obtain an edge contour; Using an image extraction algorithm, based on the edge contour obtained by edge detection, a bright spot contour is extracted; and based on a screening algorithm, the bright spot contour is screened to obtain an actual bright spot contour of accumulated water; The contour area of ​​the bright spot of accumulated water is calculated as the accumulated water area.

7. The method for monitoring water accumulation in a cargo hold based on the Internet of Things according to claim 6, characterized in that: The method for correcting the posture data also includes: According to a specific ripple threshold, identifying ripple features from the water accumulation image; According to the ripple characteristics, the fluctuation amplitude and displacement frequency corresponding to the ripple characteristics are calculated; Calculating the matching degree between the fluctuation amplitude and the displacement frequency and the posture data according to the fluctuation amplitude and the displacement frequency; If the matching degree is less than a preset parameter value, the gesture data is corrected using the fluctuation amplitude and displacement frequency.

8. A cargo hold water accumulation monitoring system based on the Internet of Things, characterized in that: It includes a processor, in which the steps of the cargo hold water accumulation monitoring method based on the Internet of Things as described in any one of claims 1 to 7 are executed.

9. A storage medium, characterized in that: The medium stores a program, and when the program is executed by the processor, the steps of the cargo hold water accumulation monitoring method based on the Internet of Things described in any one of claims 1 to 7 are implemented.

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