A method and system for processing beacon monitoring data of ship water pollutant discharge

Abstract

The application provides a navigation mark monitoring data processing method and system for ship water pollutant discharge, applied to the technical field of sewage discharge. The method obtains the readings of multiple types of water quality sensors on the navigation mark in real time, calculates the change rates of the readings within a preset monitoring time period, and then intelligently judges according to the combination mode of the change rates of different sensor readings. The method can intelligently distinguish between sensor faults and actual water pollution, significantly improving the accuracy of monitoring data and the reliability of pollution event identification, thereby avoiding false attribution of pollution responsibility and providing a more accurate and reliable monitoring means for water environment protection.

Description

Technical Field

[0001] This application relates to the field of wastewater discharge technology, and in particular to a method and system for processing navigation mark monitoring data of ship water pollutant discharge. Background Technology

[0002] With increasing emphasis on aquatic environmental protection, effective monitoring of ship-generated water pollutants is crucial. Multifunctional monitoring buoys, as important monitoring equipment, typically integrate water quality sensors such as turbidity sensors, chemical oxygen demand (COD) sensors, and oil sensors to collect water data in real time. However, these sensors, operating underwater for extended periods, are inevitably affected by marine organism fouling, a process known as biofouling.

[0003] Over time, tiny marine microorganisms and larger marine organisms can form biofilms or directly adhere to sensor surfaces, severely interfering with the accuracy of sensor measurements. For example, when a chemical oxygen demand (COD) sensor is covered by a biofilm, the diffusion path of the target analyte is blocked, potentially causing sensor readings to be far below the actual pollution level, thus causing real pollution events to be missed by the system. For optical sensors such as turbidity sensors, biofouling can directly scatter or absorb light, resulting in abnormally high turbidity readings even in clean water, triggering numerous false alarms, or, in severe cases, completely masking the actual pollution situation.

[0004] This biofouling directly and severely interferes with the performance of water quality sensors, making the "abnormal" water quality signals received by the monitoring center unclear: is it that pollutants are actually present in the water, or is the sensor itself generating erroneous readings due to biofouling? This ambiguity poses a serious challenge to the system's core function—accurately attributing pollution events to specific vessels. Although some advanced navigation aids attempt to integrate antifouling mechanisms, these solutions often have limitations, only mitigating rather than completely preventing biofouling.

[0005] The key issue is that existing data processing methods primarily focus on identifying pollution events based on changes in water quality parameters. They generally lack a real-time, reliable mechanism to assess the degree and type of biofouling on individual sensors and to distinguish its impact from actual environmental changes or pollution events. Without this ability to identify and compensate for errors caused by biofouling in real time, the system cannot reliably correct the raw sensor data, ultimately leading to significant or complete misattribution of pollution responsibility, or even failing to accurately identify the true polluter.

[0006] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0007] In view of the shortcomings of the prior art, this application provides a method and system for processing navigation mark monitoring data of ship water pollutant discharge, which aims to solve the technical problems in the existing ship water pollutant discharge monitoring, such as inaccurate monitoring data, false alarms or missed pollution events caused by factors such as biofouling of navigation mark sensors, and the difficulty in accurately distinguishing sensor malfunctions from actual pollution events.

[0008] In a first aspect, a method for processing navigation mark monitoring data on ship water pollutant emissions, the method comprising the following steps:

[0009] S1: Real-time acquisition of the first reading from the turbidity sensor, the second reading from the chemical oxygen demand sensor, and the third reading from the oil sensor on the navigation mark;

[0010] S2: Calculate the first rate of change of the first reading, the second rate of change of the second reading, and the third rate of change of the third reading within a preset monitoring time period;

[0011] S3: If the first rate of change exceeds a preset turbidity change threshold, and the second rate of change and the third rate of change do not exceed the corresponding preset change threshold, then the first reading is marked to remind the user to repair the turbidity sensor.

[0012] S4: If the first rate of change does not exceed the preset turbidity change threshold, and the second rate of change and the third rate of change both exceed the corresponding preset change threshold, and the values ​​of the second rate of change and the third rate of change are the same, then an oil pollution event alert is issued.

[0013] S5: If the first rate of change exceeds the preset turbidity change threshold, and at the same time, the second rate of change or the third rate of change exceeds the corresponding preset change threshold, an oil pollution event alert is issued.

[0014] This application proposes a data processing method for monitoring navigation marks of ship water pollutant emissions. By intelligently analyzing the changing trends of multi-sensor data and based on the combination patterns of different sensor reading change rates, it effectively distinguishes between sensor malfunctions (such as abnormal readings caused by biofouling) and actual water pollution events, thereby significantly reducing false alarms and missed alarms and improving the accuracy and reliability of ship water pollutant emission monitoring.

[0015] Furthermore, step S1 includes:

[0016] S11: At the preset sampling time, a data acquisition command is simultaneously sent to the turbidity sensor, the chemical oxygen demand sensor, and the oil sensor;

[0017] S12: Record the system timestamp when the data acquisition command is sent;

[0018] S13: After receiving the first reading of the turbidity sensor, the second reading of the chemical oxygen demand sensor, and the third reading of the oil sensor, the system timestamp is uniformly marked to all the received first reading, second reading, and third reading.

[0019] The present application proposes a data processing method for monitoring navigation marks of ship water pollutant emissions, which ensures strict time synchronization of data collected by different sensors, providing a solid foundation for subsequent accurate calculation of change rate and multi-sensor data correlation analysis, and effectively avoiding misjudgment caused by data time misalignment.

[0020] Furthermore, step S2 includes:

[0021] S21: Obtain the water flow velocity and water temperature of the area where the navigation mark is located;

[0022] S22: Adjust the preset monitoring time period according to the water flow velocity and the water temperature;

[0023] S23: Calculate the first rate of change of the first reading, the second rate of change of the second reading, and the third rate of change of the third reading within the preset monitoring time period after adjustment.

[0024] This application proposes a data processing method for monitoring navigation marks of ship water pollutant emissions. It introduces environmental parameters such as water flow velocity and water temperature to dynamically adjust the monitoring time period, so that the calculation of the rate of change can better adapt to the dynamic changes of the actual water environment, thereby improving the accuracy and robustness of pollution event and sensor anomaly judgment.

[0025] Furthermore, step S22 includes:

[0026] S221: When the water flow velocity exceeds the preset flow velocity threshold limit, or when the water temperature exceeds the preset water temperature threshold limit, the preset monitoring time period is shortened;

[0027] S222: When the water flow velocity exceeds the preset lower limit of the flow velocity threshold, or when the water temperature exceeds the preset lower limit of the water temperature threshold, the preset monitoring time period is extended.

[0028] This application proposes a method for processing navigation mark monitoring data on ship water pollutant emissions, which clarifies a specific strategy for adjusting the monitoring time period based on water flow velocity and water temperature. Specifically, the monitoring cycle is shortened to enable rapid response when environmental changes are drastic, and the monitoring cycle is extended to smooth the data when the environment is relatively stable, further optimizing the adaptability and accuracy of the rate of change calculation.

[0029] Furthermore, the method also includes:

[0030] S6: If the first rate of change does not exceed the preset turbidity change threshold, and the second rate of change or the third rate of change exceeds the corresponding preset change threshold, then the average readings of the first reading, the second reading and the third reading within the preset long-term monitoring period are continuously acquired to obtain the first average reading, the second average reading and the third average reading.

[0031] S7: Determine whether the first average reading continues to rise over multiple consecutive long-term monitoring periods, and whether its cumulative increase exceeds a preset long-term drift threshold.

[0032] S8: Determine whether the second average reading and the third average reading remain within their respective preset normal fluctuation ranges during the multiple consecutive long-term monitoring periods;

[0033] S9: If the first average reading continues to rise over multiple consecutive long-term monitoring periods, and its cumulative increase exceeds a preset long-term drift threshold, and the second average reading and the third average reading remain within their respective preset normal fluctuation ranges over the multiple consecutive long-term monitoring periods, then the first reading is marked to remind the user to maintain the turbidity sensor; otherwise, normal information on ship water pollutant discharge is sent.

[0034] Furthermore, step S3 includes:

[0035] S31: If the first rate of change exceeds a preset turbidity change threshold, and the second rate of change and the third rate of change do not exceed the corresponding preset change threshold, an alarm message containing the identification information of the turbidity sensor, the problem type of the turbidity sensor, and suggested maintenance operations is generated to mark the first reading.

[0036] S32: Send the alarm message to a preset list of recipients.

[0037] Furthermore, step S31 includes:

[0038] S311: Preset multiple alarm message templates, the templates containing the correspondence between the problem types of the turbidity sensor and the corresponding suggested maintenance operations;

[0039] S312: Based on the identified problem type of the turbidity sensor, select a matching alarm message template from the multiple alarm message templates;

[0040] S313: Fill in the identification information of the turbidity sensor in the selected alarm message template;

[0041] S314: Based on the filled alarm message template, generate an alarm message containing the identification information of the turbidity sensor, the problem type of the turbidity sensor, and suggested maintenance operations.

[0042] Furthermore, step S4 includes:

[0043] S41: If the first rate of change does not exceed the preset turbidity change threshold, and the second rate of change and the third rate of change both exceed the corresponding preset change threshold, then obtain the ratio of the second rate of change and the third rate of change.

[0044] S42: When the ratio is within the preset ratio range, it is determined that the values ​​of the second rate of change and the third rate of change are consistent, and an oil pollution incident alert is issued.

[0045] Furthermore, step S42 includes:

[0046] S421: Obtain the direction of change in water salinity within a preset monitoring time period;

[0047] S422: If the salinity of the water body changes in the direction of increasing, then the preset ratio range is expanded; if the salinity of the water body changes in the direction of decreasing, then the preset ratio range is narrowed.

[0048] S423: When the ratio is within the adjusted preset ratio range, it is determined that the values ​​of the second change rate and the third change rate are consistent, and an oil pollution event alert is issued.

[0049] In a second aspect, a navigation mark monitoring data processing system for ship water pollutant emissions, characterized in that it is used to implement the method described in any one of the above claims, the system comprising:

[0050] Acquisition module: Real-time acquisition of the first reading from the turbidity sensor, the second reading from the chemical oxygen demand sensor, and the third reading from the oil sensor on the navigation mark;

[0051] Calculation module: Calculates the first rate of change of the first reading, the second rate of change of the second reading, and the third rate of change of the third reading within a preset monitoring time period;

[0052] First comparison module: If the first rate of change exceeds a preset turbidity change threshold, and the second rate of change and the third rate of change do not exceed the corresponding preset change threshold, then the first reading is marked to remind the user to repair the turbidity sensor;

[0053] Second comparison module: If the first rate of change does not exceed the preset turbidity change threshold, the second rate of change and the third rate of change both exceed the corresponding preset change threshold, and the values ​​of the second rate of change and the third rate of change are the same, then an oil pollution event alert is issued.

[0054] The third comparison module: If the first rate of change exceeds the preset turbidity change threshold, and at the same time, the second rate of change or the third rate of change exceeds the corresponding preset change threshold, an oil pollution event alert will be issued.

[0055] Beneficial Effects: This application proposes a method and system for processing navigation mark monitoring data on ship water pollutant emissions. It acquires real-time readings from multiple water quality sensors on the navigation mark and calculates the rate of change of these readings within a preset monitoring period. Then, it makes intelligent judgments based on the combination patterns of different sensor reading change rates. Specifically, when the turbidity sensor reading change rate abnormally increases, while the chemical oxygen demand (COD) and oil sensor reading change rates do not exceed their corresponding preset change thresholds, the system can identify this as a possible malfunction of the turbidity sensor itself (e.g., abnormal optical scattering or absorption caused by biofouling) and promptly issue a maintenance reminder, avoiding misjudging sensor malfunction as water pollution. Conversely, when the turbidity sensor reading change rate is normal, but the COD and oil sensor reading change rates simultaneously increase abnormally and are consistent, it accurately identifies an oil pollution event. Furthermore, when the turbidity sensor reading change rate increases abnormally, and the COD or oil sensor reading change rates also increase abnormally, it can also be identified as an oil pollution event. Through the above technical solution, this application effectively solves the problem in the prior art where sensor data distortion caused by factors such as biofouling leads to numerous false alarms or missed reports of real pollution events. This method can intelligently distinguish between sensor malfunctions and actual water pollution, significantly improving the accuracy of monitoring data and the reliability of pollution event identification. This avoids misattribution of pollution responsibility and provides a more accurate and reliable monitoring method for aquatic environmental protection. Attached Figure Description

[0056] Figure 1 This is a flowchart illustrating a method for processing navigation mark monitoring data of ship water pollutant emissions proposed in this application.

[0057] Figure 2 This is a structural diagram of a navigation mark monitoring data processing system for ship water pollutant emissions proposed in this application.

[0058] Figure 3 This is an architecture diagram of a navigation mark monitoring data processing system for ship water pollutant emissions proposed in this application.

[0059] Labeling Explanation: 201, Acquisition Module; 202, Calculation Module; 203, First Comparison Module; 204, Second Comparison Module; 205, Third Comparison Module. Detailed Implementation

[0060] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and marked in the accompanying drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0061] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0062] Please refer to Figure 1 A method for processing navigation mark monitoring data on ship water pollutant emissions, the method comprising the following steps:

[0063] S1: Real-time acquisition of the first reading from the turbidity sensor, the second reading from the chemical oxygen demand sensor, and the third reading from the oil sensor on the navigation mark;

[0064] S2: Calculate the first rate of change of the first reading, the second rate of change of the second reading, and the third rate of change of the third reading within the preset monitoring time period;

[0065] S3: If the first rate of change exceeds the preset turbidity change threshold, and the second and third rates of change do not exceed the corresponding preset change thresholds, then the first reading is marked to remind the user to repair the turbidity sensor.

[0066] S4: If the first rate of change does not exceed the preset turbidity change threshold, and the second and third rates of change both exceed the corresponding preset change thresholds, and the values ​​of the second and third rates of change are the same, then an oil pollution event alert will be issued.

[0067] S5: If the first rate of change exceeds the preset turbidity change threshold, and at the same time, the second or third rate of change exceeds the corresponding preset change threshold, an oil pollution event alert will be issued.

[0068] This application acquires multi-sensor readings in real time and calculates their rate of change, and combines them with preset thresholds for multi-dimensional judgment, which can effectively distinguish between sensor malfunctions (such as anomalies caused by biofouling) and actual water pollution events.

[0069] Specifically, turbidity sensors measure the degree of turbidity in water, and their readings are referred to as the first reading. Chemical oxygen demand (COD) sensors measure the degree of organic pollution in water, and their readings are referred to as the second reading. Oil sensors detect the content of oily substances in water, and their readings are referred to as the third reading. These sensors are designed to operate underwater for extended periods and are capable of acquiring water quality data in real time.

[0070] The rate of change refers to the magnitude of change in a sensor reading relative to its initial value or the value at a previous moment within a specific time period. For example, the first rate of change refers to the magnitude of change in the first reading within a preset monitoring time period. Preset threshold values ​​are numerical limits set before system operation based on experience, experiments, or standards, used to determine whether sensor readings or their rates of change are abnormal. For example, preset turbidity change thresholds, preset chemical oxygen demand (COD) change thresholds, and preset oil change thresholds are used to distinguish between normal fluctuations, sensor malfunctions, or pollution events. The monitoring time period refers to the time window used to calculate the rate of change in sensor readings; its length can be set according to actual monitoring needs. The preset COD change threshold and preset oil change threshold are the preset change thresholds corresponding to the second and third rates of change, respectively.

[0071] In one specific embodiment, turbidity sensors, chemical oxygen demand (COD) sensors, and oil sensors configured on the navigation buoy continuously monitor the surrounding water. These sensors can be programmed to automatically collect data at preset monitoring intervals (e.g., every minute, every five minutes, or every hour) and transmit the collected data to a data processing unit inside the navigation buoy via wired or wireless means. Upon receiving these readings, the data processing unit stores them in its local memory and prepares them for subsequent data analysis.

[0072] After acquiring real-time sensor readings, the trends in these readings need to be quantified. Specifically, the data processing unit retrieves multiple sets of first, second, and third readings collected within a preset monitoring period from the memory. For example, if the preset monitoring period is one hour, all relevant readings from the past hour will be retrieved. The first rate of change can be calculated as the difference between the maximum and minimum values ​​of the first reading within that period. Similarly, the second and third rates of change can be calculated in the same way. These rates of change are calculated to reflect the fluctuations in water quality parameters over a short period, providing a basis for subsequent judgments.

[0073] The data processing unit compares the calculated first rate of change with a preset turbidity change threshold. Simultaneously, the second and third rates of change are compared with preset chemical oxygen demand (COD) and oil change thresholds, respectively. When all three conditions are met—that is, the first rate of change exceeds the preset turbidity change threshold, and the second and third rates of change do not exceed their respective preset thresholds (i.e., the preset COD and preset oil change thresholds)—the system determines that the turbidity sensor may have a problem. For example, this might mean that the turbidity sensor has suffered severe biofouling, causing its reading to rise abnormally, while the COD and oil sensors do not show abnormal fluctuations. In this case, the system marks the first reading and displays it on the user interface or alerts the user through other means (such as sending a text message or email), suggesting that the turbidity sensor be inspected and repaired.

[0074] The data processing unit first determines whether the first rate of change is within a preset turbidity change threshold, meaning there is no significant abnormal increase in turbidity. Simultaneously, it checks whether the second and third rates of change both exceed their respective preset thresholds, indicating that both chemical oxygen demand (COD) and oil content may have increased significantly at the same time. Further, the system compares the values ​​of the second and third rates of change to determine if they exhibit consistency. For example, an allowable error range can be set; if the values ​​of the two rates of change fall within this error range, they are considered consistent. When all these conditions are met, the system determines that an oil pollution event may have occurred in the water body and immediately issues an oil pollution event alert. This multi-sensor collaborative judgment method helps eliminate false alarms caused by single sensor malfunctions and improves the accuracy of pollution event identification.

[0075] If the data processing unit determines that the first rate of change exceeds a preset turbidity change threshold, this may indicate a significant increase in water turbidity. Simultaneously, to more accurately determine whether the water body is polluted, the data processing unit further checks whether at least one of the second or third rates of change exceeds its corresponding preset threshold. For example, oil pollution is typically accompanied by increased water turbidity and may also lead to increases in chemical oxygen demand (COD) or the oil content itself. When these conditions are met, the system issues an oil pollution event alert. This judgment logic considers the potential combined impact of oil pollution on multiple water quality parameters, thereby improving the ability to identify complex pollution events.

[0076] Traditional data processing methods primarily focus on identifying pollution events based on the absolute values ​​or simple changes in water quality parameters, generally lacking the ability to assess the real-time condition of the sensors themselves. For example, when a turbidity sensor reading abnormally increases due to biofouling, existing methods may directly classify it as water pollution, triggering numerous false alarms. Similarly, when a chemical oxygen demand (COD) sensor is covered by a biofilm, causing a low reading, genuine pollution events may be missed. This lack of ability to identify and compensate for sensor errors makes the "abnormal" signals received by the monitoring center ambiguous and difficult to accurately attribute.

[0077] Compared to basic solutions, this application significantly improves the accuracy of judgment by introducing collaborative analysis of the change rates of multi-sensor data. Specifically, this method not only focuses on the changes in readings of individual sensors, but more importantly, it comprehensively considers the interrelationships between the change rates of three key water quality parameters: turbidity, chemical oxygen demand (COD), and oil. For example, when the first change rate (turbidity) rises abnormally, while the second change rate (COD) and the third change rate (oil) do not exceed their corresponding preset change thresholds, this method can intelligently identify that this may be a sign of malfunction of the turbidity sensor itself (such as severe biofouling) rather than actual oil pollution, and promptly issue a maintenance reminder. This contrasts sharply with the existing technology that may directly determine an abnormal increase in turbidity as pollution, effectively avoiding false alarms.

[0078] Furthermore, step S1 includes:

[0079] S11: At the preset sampling time, simultaneously send data acquisition commands to the turbidity sensor, chemical oxygen demand sensor and oil sensor;

[0080] S12: Records the system timestamp when the data acquisition command is sent;

[0081] S13: After receiving the first reading from the turbidity sensor, the second reading from the chemical oxygen demand sensor, and the third reading from the oil sensor, the system timestamp is uniformly marked to all the received first, second, and third readings.

[0082] At preset sampling times, data acquisition commands are simultaneously sent to the turbidity sensor, chemical oxygen demand sensor, and oil sensor. The preset sampling times can be set according to actual monitoring needs, such as data acquisition every 1 minute, 5 minutes, or 10 minutes. Simultaneous sending of data acquisition commands ensures that data from different sensors are collected at the same time, thereby guaranteeing data timeliness and consistency.

[0083] Record the system timestamp when the data acquisition command is sent. This system timestamp is a precise record of the time the data acquisition occurred, and it is of great significance for subsequent data analysis and event tracing.

[0084] Step S13 ensures that each set of sensor readings is associated with a precise point in time, facilitating subsequent data processing and analysis, such as calculating the rate of change based on accurate time series data.

[0085] Furthermore, step S2 includes:

[0086] S21: Obtain the water flow velocity and water temperature of the area where the navigation mark is located;

[0087] S22: Adjust the preset monitoring time period according to the water flow velocity and water temperature;

[0088] S23: Calculate the first rate of change of the first reading, the second rate of change of the second reading, and the third rate of change of the third reading within the preset monitoring time period after adjustment.

[0089] Among these parameters, water flow velocity can be measured using a flow velocity sensor installed on the navigation beacon; water temperature can be measured using a temperature sensor. The acquisition of these environmental parameters aims to provide a basis for adjusting subsequent monitoring time periods.

[0090] This application's solution addresses the issue of inaccurate data analysis that can occur in complex and variable aquatic environments by incorporating water flow velocity and water temperature as criteria for adjusting the monitoring time period. Specifically, when water flow velocity is high or water temperature is high, the diffusion and dilution of pollutants in the water accelerates. Using a long fixed monitoring time period in such cases may average out pollutant concentration peaks, reducing the sensitivity of pollution event detection. Shortening the monitoring time period allows for more timely capture of rapid changes in pollutant concentrations within a short timeframe, preventing the dilution of crucial information. Conversely, when water flow velocity is slow or water temperature is low, pollutant changes may be slower. In such cases, a short monitoring time period may produce false change rates due to environmental noise or sensor fluctuations, leading to false alarms. Extending the monitoring time period allows for longer-term data smoothing, effectively filtering out short-term fluctuations, making the calculated change rate more reflective of the true pollution trend. This dynamic adjustment mechanism allows the calculation of the change rate to better match the actual dynamics of the aquatic environment, thereby improving the accuracy and reliability of data analysis.

[0091] The method for adjusting the preset monitoring time period based on water flow velocity and water temperature is as follows: Step S22 includes:

[0092] S221: When the water flow velocity exceeds the preset flow velocity threshold limit, or when the water temperature exceeds the preset water temperature threshold limit, shorten the preset monitoring time period.

[0093] S222: When the water flow velocity exceeds the preset lower limit of the flow velocity threshold, or when the water temperature exceeds the preset lower limit of the water temperature threshold, extend the preset monitoring time period.

[0094] The preset monitoring time period is typically set under experimental conditions, targeting a standard water temperature of 19℃ (determined from the global average sea surface temperature) and a standard water flow velocity of 0.3 m / s (determined from the average surface seawater flow velocity in typical nearshore waters such as ports and waterways). This time period is used to balance the sensitivity and stability of various sensors and ensure the accuracy of the monitoring data. Therefore, the preset monitoring time period, through experimental setting, can be 10 minutes.

[0095] Among them, the preset upper limit of flow velocity threshold and the preset upper limit of water temperature threshold are used to define the conditions under which the water environment is in a highly dynamic or highly active state. Based on the general distribution of water flow velocity and water temperature in nearshore sea areas, the preset upper limit of flow velocity threshold and the preset upper limit of water temperature threshold can be set to 0.5 m / s and 28°C, respectively.

[0096] Similarly, the preset lower limit of flow velocity threshold and the preset lower limit of water temperature threshold are used to identify relatively stable conditions in the water environment, and can be set to 0.1 m / s and 10°C, respectively.

[0097] Within the preset flow rate threshold and the preset water temperature threshold, the preset flow rate threshold remains unchanged, with water data sampled every 10 minutes. However, if the real-time water flow velocity during monitoring is 0.6 m / s (exceeding the upper limit of 0.5 m / s), the preset monitoring period will be shortened, for example, to 5 minutes. This allows for more frequent data collection to capture rapid changes in pollutant levels under high flow conditions. Conversely, if the acquired water flow velocity is 0.08 m / s (below the lower limit of 0.1 m / s), the preset monitoring period will be extended, for example, to 15 minutes. This provides a longer sampling window in less dynamic environments to obtain more stable readings, thereby reducing noise and ensuring the reliability of change rate calculations. Similarly, if the water temperature rises to 30°C (exceeding the upper limit of 28°C), the monitoring period will be shortened, for example, to 5 minutes, to accommodate accelerated degradation processes. If the temperature drops to 8°C (below the lower limit of 10°C), the monitoring period may be extended to 15 minutes. This dynamic adjustment ensures that the monitoring system is always optimized according to the current environmental conditions, thereby achieving more accurate and reliable monitoring of ship water pollutant emissions.

[0098] The specific time for shortening or extending the preset monitoring period can be set according to the actual situation. Alternatively, under experimental conditions, it can be measured in advance that various sensors can stably monitor water data and maintain sensitive monitoring time under different water flow velocities and water temperatures. The experimental data results can be recorded in a table, so that in practical applications, the preset time for shortening or extending the monitoring period can be obtained directly by looking up the table.

[0099] Traditional methods for monitoring ship water pollutant emissions, such as those described above, primarily focus on quickly identifying sudden water pollutant discharge events or momentary sensor malfunctions by acquiring sensor readings in real time and calculating their short-term rate of change. However, in practical applications, various sensors, especially turbidity sensors, may experience slow and continuous performance drift. This drift may not produce a sufficiently large rate of change in a short period to trigger a preset threshold alarm, but long-term accumulated drift can lead to inaccurate readings, affecting the reliability of monitoring data and potentially causing misjudgments of water quality conditions. To address this issue, further methods include:

[0100] S6: If the first rate of change does not exceed the preset turbidity change threshold, and the second or third rate of change exceeds the corresponding preset change threshold, then the average readings of the first reading, the second reading, and the third reading within the preset long-term monitoring period are continuously acquired to obtain the first average reading, the second average reading, and the third average reading.

[0101] S7: Determine whether the first average reading has been continuously increasing over multiple consecutive long-term monitoring periods, and whether its cumulative increase exceeds the preset long-term drift threshold.

[0102] S8: Determine whether the second average reading and the third average reading remain within their respective preset normal fluctuation ranges in multiple consecutive long-term monitoring periods;

[0103] S9: If the first average reading continues to rise over multiple consecutive long-term monitoring periods, and its cumulative increase exceeds the preset long-term drift threshold, and the second and third average readings remain within their respective preset normal fluctuation ranges over multiple consecutive long-term monitoring periods, then the first reading is marked to remind the user to maintain the turbidity sensor; otherwise, normal information on ship water pollutant discharge is sent.

[0104] Specifically, if the first rate of change of the turbidity sensor does not exceed the preset turbidity change threshold, but the second rate of change of the chemical oxygen demand sensor or the third rate of change of the oil sensor does not exceed the corresponding preset change threshold, this indicates that the turbidity sensor itself may be contaminated, or the water quality may be slightly polluted. To clearly distinguish which problem it is, the system will activate a long-term monitoring mechanism. It continuously acquires the average readings of the first, second, and third readings over a preset long-term monitoring period. This aims to eliminate the influence of instantaneous fluctuations by averaging the data over a period of time, thereby more accurately reflecting the long-term trend of the sensor readings. The long-term monitoring period can be set according to the actual application scenario and sensor characteristics, for example, it can be several hours, one day, or several days. Thus, the first average reading, second average reading, and third average reading representing the long-term trend can be obtained.

[0105] The system determines whether the first average reading (i.e., the long-term average reading of the turbidity sensor) shows a continuously increasing trend over multiple consecutive long-term monitoring periods. Here, "continuous increase" means that the first average reading exhibits a monotonically increasing or nearly monotonically increasing pattern over multiple consecutive monitoring periods. Simultaneously, it also determines whether the cumulative increase exceeds a preset long-term drift threshold. This long-term drift threshold is specifically designed to identify slow sensor drift; its value is typically lower than the threshold used to identify sudden pollution events, ensuring that minute, cumulative drifts can be captured. The long-term drift threshold can be set based on the annual drift index obtained from the manufacturer's sensor manual. Specifically, the daily drift amount is calculated based on the annual drift index and then multiplied by the total number of monitoring days to obtain the long-term drift threshold.

[0106] To rule out the possibility of actual water pollution, the system simultaneously determines whether the second average reading (long-term average reading of the chemical oxygen demand (COD) sensor) and the third average reading (long-term average reading of the oil sensor) remain within their respective preset normal fluctuation ranges over multiple consecutive long-term monitoring periods. This means that if the turbidity reading shows a long-term increase, but the COD and oil readings remain normal, the possibility that the increased turbidity is caused by COD or oil pollution can be preliminarily ruled out. The preset normal fluctuation range is determined based on historical data or calibration data of the sensors under normal water quality conditions.

[0107] If all the above conditions are met—that is, the first average reading of the turbidity sensor continuously increases over multiple long-term monitoring periods and the cumulative increase exceeds a preset long-term drift threshold, while the average readings of the chemical oxygen demand (COD) sensor and the oil sensor remain within normal fluctuation ranges—then the system determines that the turbidity sensor may have a long-term drift problem. In this case, the first reading will be marked, and a reminder will be sent to the user, suggesting that the turbidity sensor be repaired or calibrated. Conversely, if these conditions are not fully met, the system determines that the ship's water pollutant discharge status is normal and sends corresponding normal status information.

[0108] Furthermore, step S3 includes:

[0109] S31: If the first rate of change exceeds the preset turbidity change threshold, and the second and third rates of change do not exceed the corresponding preset change thresholds, generate an alarm message containing the identification information of the turbidity sensor, the problem type of the turbidity sensor, and the recommended maintenance operation to mark the first reading.

[0110] S32: Send the alarm message to the preset recipient list.

[0111] Specifically, when the system determines that a turbidity sensor may be faulty, it no longer simply records the reading, but generates a structured alarm message. This alarm message is designed to include several key pieces of information, such as the turbidity sensor's identification information, which could be a unique device ID, installation location coordinates, or model information, to facilitate quick location and identification of the specific faulty device. Furthermore, the alarm message clearly indicates the type of problem with the turbidity sensor, such as sensor drift, calibration error, physical damage, or power supply abnormality, which helps maintenance personnel make an initial assessment of the nature of the fault. Going further, the alarm message also includes recommended maintenance actions for this type of problem, such as recalibrating, cleaning the sensor probe, checking the power connection, or directly replacing the sensor, providing maintenance personnel with direct action guidelines.

[0112] The generated alert messages will be sent to a pre-defined recipient list. This recipient list may include contact information for technicians responsible for equipment maintenance, system administrators, or specific emergency response teams, such as mobile phone numbers, email addresses, or user IDs for internal communication systems. Alert messages can be sent via various communication methods, such as SMS, email, mobile application push notifications, or integrated into the alert interface of a central monitoring platform, to ensure that alerts are delivered to relevant personnel in a timely and accurate manner.

[0113] Furthermore, step S31 includes:

[0114] S311: Preset multiple alarm message templates, each containing the type of problem with the turbidity sensor and the corresponding recommended maintenance operations;

[0115] S312: Select a matching alarm message template from multiple alarm message templates based on the identified problem type of the turbidity sensor;

[0116] S313: Fill in the identification information of the turbidity sensor in the selected alarm message template;

[0117] S314: Based on the filled alarm message template, generate an alarm message containing the identification information of the turbidity sensor, the problem type of the turbidity sensor, and recommended maintenance operations.

[0118] Specifically, the purpose of pre-setting multiple alarm message templates is to standardize and automate the alarm message generation process. These templates can be pre-stored in the system's database. Each template is designed to correspond to one or more specific problem types of turbidity sensors and includes corresponding suggested maintenance actions. For example, when the problem type "turbidity sensor readings continue to rise abnormally" is identified, a template can be pre-set, which includes suggested maintenance actions such as "Please check if the probe of turbidity sensor A is covered with dirt and clean it."

[0119] After identifying the specific problem type of turbidity sensor A, the system will automatically match and select the template that best matches the current problem situation based on the preset correspondence. For example, if the system determines that turbidity sensor A has a "data drift" problem, it will select the preset "data drift" alarm template.

[0120] This template-based generation method avoids the tediousness and potential errors of manually writing alarm messages every time a problem occurs, ensuring the accuracy, completeness, and consistency of alarm content. Specifically, the preset templates provide a clear correspondence between problem types and recommended maintenance operations, enabling the system to quickly locate the correct handling solution based on diagnostic results; while filling in sensor identification information ensures the alarm's specificity, allowing the recipient to immediately know which specific device requires attention and action. Thus, the entire alarm generation process is optimized, improving response efficiency and maintenance accuracy.

[0121] Furthermore, step S4 includes:

[0122] S41: If the first rate of change does not exceed the preset turbidity change threshold, and the second rate of change and the third rate of change both exceed the corresponding preset change threshold, then obtain the ratio of the second rate of change and the third rate of change.

[0123] S42: When the ratio is within the preset ratio range, it is determined that the values ​​of the second change rate and the third change rate are consistent, and an oil pollution incident alert is issued.

[0124] The ratio of the second and third rates of change refers to the ratio between the rate of change of the second reading of the chemical oxygen demand (COD) sensor and the rate of change of the third reading of the oil sensor when specific conditions are met (i.e., the first rate of change does not exceed a preset turbidity change threshold, and both the second and third rates of change exceed their corresponding preset thresholds). Calculating this ratio helps quantify the correlation between the two. The preset ratio range can be understood as a pre-defined set of numerical intervals used to define the relative relationship under which the second and third rates of change are considered "numerically consistent." For example, this range can be set based on historical data, experimental results, or expert experience to reflect the typical proportional relationship between COD and oil indicators in real oil pollution events. When the calculated ratio falls within this preset range, it is considered that the trends and magnitudes of the two changes are highly correlated, thus supporting the judgment of oil pollution events. In practical applications, the preset ratio range can be set between 0.9 and 1.1.

[0125] Through the above technical solution, this application can avoid false alarms caused by abnormal single indicators or non-oil pollution events, and improve the accuracy of oil pollution event identification.

[0126] Furthermore, step S42 includes:

[0127] S421: Obtain the direction of change in water salinity within a preset monitoring time period;

[0128] S422: If the salinity of the water body changes in the direction of increasing, then expand the preset ratio range; if the salinity of the water body changes in the direction of decreasing, then shrink the preset ratio range.

[0129] S423: When the ratio is within the adjusted preset ratio range, the values ​​of the second change rate and the third change rate are determined to be consistent, and an oil pollution incident alert is issued.

[0130] Obtaining the direction of water salinity change within a preset monitoring period can be understood as monitoring water salinity in real time using salinity sensors configured on navigation marks and analyzing its trend over a specific time period. For example, this can be determined by calculating the difference between the average salinity in the current monitoring period and the average salinity in the previous monitoring period.

[0131] If the salinity of the water body changes in an upward direction, the preset proportional range is expanded; if the salinity changes in a downward direction, the preset proportional range is narrowed. In practical applications, changes in water salinity may affect the solubility and dispersion of chemical oxygen demand (COD) and oils in the water, as well as the sensor's sensitivity to these substances. For example, in waters with high salinity, the conductivity of the water medium may change, thus affecting the measurement accuracy of the COD and oil sensors, leading to a larger fluctuation range in the proportional relationship between their readings. Therefore, expanding the preset proportional range can accommodate such normal fluctuations caused by salinity changes and avoid false alarms. Conversely, in waters with low or stable salinity, the sensor readings may be more stable; in this case, narrowing the preset proportional range can improve the accuracy of the judgment and reduce false alarms.

[0132] Specifically, the amount of change in expanding or shrinking the preset ratio range can be set according to the actual situation. Alternatively, the ratio between the rate of change of various sensor readings under different water salinity can be measured in advance under experimental conditions, and the experimental data results can be recorded in a table. In practical applications, the adjusted preset ratio range can be obtained directly by looking up the table.

[0133] For example, during a certain monitoring period, the system detects an upward trend in water salinity. According to the above scheme, the preset ratio range is dynamically adjusted to [0.85, 1.15]. In this case, if the calculated ratio of the second rate of change to the third rate of change is 0.88, under the original fixed ratio range [0.9, 1.1], this ratio might be considered inconsistent, thus failing to issue an oil pollution event alert. However, under the adjusted ratio range [0.85, 1.15], 0.88 falls within this range, therefore the system can accurately determine that the values ​​of the second rate of change and the third rate of change are consistent and issue an oil pollution event alert. This indicates that even when salinity changes cause increased fluctuations in sensor readings, the scheme of this application can still accurately identify potential oil pollution.

[0134] Conversely, if the system detects a decrease in water salinity, the preset ratio range is dynamically adjusted to [0.95, 1.05]. In this case, if the calculated ratio of the second rate of change to the third rate of change is 0.92, this ratio might be considered consistent within the original fixed ratio range [0.9, 1.1]. However, within the adjusted ratio range [0.95, 1.05], 0.92 is not within this range, and the system determines that the values ​​of the second rate of change and the third rate of change are inconsistent. This helps improve the sensitivity of the judgment when the water environment is relatively stable, avoiding misjudging non-oil pollution events as oil pollution, thereby reducing false alarms.

[0135] Please refer to Figure 2 , Figure 3 A navigation mark monitoring data processing system for ship water pollutant emissions, characterized in that it is used to implement any of the above methods, and the system includes:

[0136] Acquisition Module 201: Acquires in real time the first reading of the turbidity sensor, the second reading of the chemical oxygen demand sensor, and the third reading of the oil sensor on the navigation mark;

[0137] Calculation module 202: Calculates the first rate of change of the first reading, the second rate of change of the second reading, and the third rate of change of the third reading within a preset monitoring time period;

[0138] First comparison module 203: If the first rate of change exceeds the preset turbidity change threshold, and the second rate of change and the third rate of change do not exceed the corresponding preset change threshold, then the first reading is marked to remind the user to repair the turbidity sensor.

[0139] Second comparison module 204: If the first rate of change does not exceed the preset turbidity change threshold, the second rate of change and the third rate of change both exceed the corresponding preset change threshold, and the values ​​of the second rate of change and the third rate of change are the same, then an oil pollution event alert is issued.

[0140] Third comparison module 205: If the first rate of change exceeds the preset turbidity change threshold, and at the same time, the second rate of change or the third rate of change exceeds the corresponding preset change threshold, an oil pollution event alert will be issued.

[0141] The acquisition module 201 is configured to acquire in real time the first reading from the turbidity sensor, the second reading from the chemical oxygen demand (COD) sensor, and the third reading from the oil sensor on the navigation beacon. Specifically, this module is responsible for communicating with various sensors on the navigation beacon and collecting their real-time output data to ensure the timeliness and accuracy of the monitoring data. For example, the acquisition module 201 may include a data interface, a communication unit, etc., for receiving data streams from the turbidity sensor, the COD sensor, and the oil sensor.

[0142] The calculation module 202 is configured to calculate the first rate of change of the first reading, the second rate of change of the second reading, and the third rate of change of the third reading within a preset monitoring time period. This module receives the raw readings collected by the acquisition module and calculates the rate of change of each indicator according to a preset algorithm and time window, providing a data basis for subsequent judgment. For example, the rate of change can be calculated using various statistical methods such as the difference method and the moving average method.

[0143] The first comparison module 203 is configured to mark the first reading when the first rate of change exceeds a preset turbidity change threshold, and when neither the second rate of change nor the third rate of change exceeds the corresponding preset change threshold, to remind the user to repair the turbidity sensor. This module is responsible for making a preliminary judgment on the rate of change output by the calculation module and identifying possible faults in the sensor.

[0144] The second comparison module 204 is configured to issue an oil contamination event alert when the first rate of change does not exceed the preset turbidity change threshold, the second rate of change and the third rate of change simultaneously exceed their corresponding preset change thresholds, and the values ​​of the second rate of change and the third rate of change are consistent. This module is used to identify oil contamination events under specific patterns, and its judgment logic is based on the collaborative analysis of the rate of change of readings from multiple sensors.

[0145] The third comparison module 205 is configured to issue an oil contamination event alert when the first rate of change exceeds a preset turbidity change threshold, and simultaneously, the second rate of change or the third rate of change exceeds a corresponding preset change threshold. This module, as another mechanism for identifying oil contamination events, supplements the judgment scope of the second comparison module, ensuring comprehensive coverage of different contamination scenarios.

[0146] The aforementioned system can further implement the specific steps in the above method, such as sending data acquisition commands, marking system timestamps, acquiring water flow velocity and water temperature and adjusting the monitoring time period, generating and sending alarm messages, and acquiring and judging the average readings over a long-term monitoring period, thereby achieving comprehensive and intelligent monitoring and early warning of ship water pollutant emissions.

[0147] The solution in this application visualizes each logical step in the data processing method for monitoring navigation aids of ship water pollutant emissions as an independent system module, thereby automating and intelligentizing the monitoring process. Specifically, the acquisition module 201 serves as the data entry point, continuously collecting raw data from navigation aid sensors to provide a real-time data stream for the entire system. Subsequently, the calculation module 202 preprocesses and extracts features from this raw data, calculating key rate-of-change indicators and transforming the raw, discrete data into information with analytical value. Based on this, the first comparison module 203, the second comparison module 204, and the third comparison module 205 intelligently judge these rate-of-change indicators according to preset logical rules. For example, the first comparison module 203 focuses on identifying anomalies in the sensors themselves. By comprehensively judging the rate of change of turbidity, chemical oxygen demand, and oil readings, it promptly detects and marks potential faults in the turbidity sensor, avoiding false alarms or missed alarms caused by sensor malfunction. Meanwhile, the second comparison module 204 and the third comparison module 205 work together to identify and issue early warnings for water pollution events under different modes, especially for the accurate identification of oil pollution events. Through the linkage analysis of multiple indicators, the accuracy and reliability of pollution event identification are improved. Thus, the entire system forms a closed-loop data processing and decision-making chain, which can efficiently and accurately complete the process from data acquisition to event early warning.

[0148] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0149] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for monitoring navigational aids for ship water pollutant emissions, characterized in that, The method includes the following steps: S1: Real-time acquisition of the first reading from the turbidity sensor, the second reading from the chemical oxygen demand sensor, and the third reading from the oil sensor on the navigation mark; S2: Calculate the first rate of change of the first reading, the second rate of change of the second reading, and the third rate of change of the third reading within a preset monitoring time period; S3: If the first rate of change exceeds a preset turbidity change threshold, and the second rate of change and the third rate of change do not exceed the corresponding preset change threshold, then the first reading is marked to remind the user to repair the turbidity sensor. S4: If the first rate of change does not exceed the preset turbidity change threshold, and the second rate of change and the third rate of change both exceed the corresponding preset change threshold, then obtain the ratio of the second rate of change and the third rate of change. When the ratio is within a preset ratio range, determine that the values ​​of the second rate of change and the third rate of change are consistent, and issue an oil pollution event alert. S5: If the first rate of change exceeds the preset turbidity change threshold, and at the same time, the second rate of change or the third rate of change exceeds the corresponding preset change threshold, an oil pollution event alert will be issued. S6: If the first rate of change does not exceed the preset turbidity change threshold, and the second rate of change or the third rate of change exceeds the corresponding preset change threshold, then the average readings of the first reading, the second reading and the third reading within the preset long-term monitoring period are continuously acquired to obtain the first average reading, the second average reading and the third average reading. S7: Determine whether the first average reading continues to rise over multiple consecutive long-term monitoring periods, and whether its cumulative increase exceeds a preset long-term drift threshold. S8: Determine whether the second average reading and the third average reading remain within their respective preset normal fluctuation ranges during the multiple consecutive long-term monitoring periods; S9: If the first average reading continues to rise over multiple consecutive long-term monitoring periods, and its cumulative increase exceeds a preset long-term drift threshold, and the second average reading and the third average reading remain within their respective preset normal fluctuation ranges over the multiple consecutive long-term monitoring periods, then the first reading is marked to remind the user to maintain the turbidity sensor; otherwise, normal information on ship water pollutant discharge is sent.

2. The method for monitoring navigation marks of ship water pollutant emissions according to claim 1, characterized in that, Step S1 includes: S11: At the preset sampling time, a data acquisition command is simultaneously sent to the turbidity sensor, the chemical oxygen demand sensor, and the oil sensor; S12: Record the system timestamp when the data acquisition command is sent; S13: After receiving the first reading of the turbidity sensor, the second reading of the chemical oxygen demand sensor, and the third reading of the oil sensor, the system timestamp is uniformly marked to all the received first reading, second reading, and third reading.

3. The method for monitoring navigation marks of ship water pollutant emissions according to claim 2, characterized in that, Step S2 includes: S21: Obtain the water flow velocity and water temperature of the area where the navigation mark is located; S22: Adjust the preset monitoring time period according to the water flow velocity and the water temperature; S23: Calculate the first rate of change of the first reading, the second rate of change of the second reading, and the third rate of change of the third reading within the preset monitoring time period after adjustment.

4. The method for monitoring navigation marks of ship water pollutant emissions according to claim 3, characterized in that, Step S22 includes: S221: When the water flow velocity exceeds the preset flow velocity threshold upper limit, or when the water temperature exceeds the preset water temperature threshold... When the value reaches its upper limit, the preset monitoring time period is shortened; S222: When the water flow velocity exceeds the preset lower limit of the flow velocity threshold, or when the water temperature exceeds the preset lower limit of the water temperature threshold, the preset monitoring time period is extended.

5. The method for monitoring navigation marks of ship water pollutant emissions according to claim 1, characterized in that, Step S3 includes: S31: If the first rate of change exceeds a preset turbidity change threshold, and the second rate of change and the third rate of change do not exceed the corresponding preset change threshold, an alarm message containing the identification information of the turbidity sensor, the problem type of the turbidity sensor, and suggested maintenance operations is generated to mark the first reading. S32: Send the alarm message to a preset list of recipients.

6. The method for monitoring navigation marks of ship water pollutant discharge according to claim 5, characterized in that, Step S31 includes: S311: Preset multiple alarm message templates, the templates containing the correspondence between the problem types of the turbidity sensor and the corresponding suggested maintenance operations; S312: Based on the identified problem type of the turbidity sensor, select a matching alarm message template from the multiple alarm message templates; S313: Fill in the identification information of the turbidity sensor in the selected alarm message template; S314: Based on the filled alarm message template, generate an alarm message containing the identification information of the turbidity sensor, the problem type of the turbidity sensor, and suggested maintenance operations.

7. The method for monitoring navigation marks of ship water pollutant discharge according to claim 1, characterized in that, In step S4, when the ratio is within a preset ratio range, it is determined that the values ​​of the second rate of change and the third rate of change are consistent, and an oil contamination incident alert is issued, including: S421: Obtain the direction of change in water salinity within a preset monitoring time period; S422: If the salinity of the water body changes in the direction of increasing, then the preset ratio range is expanded; if the salinity of the water body changes in the direction of decreasing, then the preset ratio range is narrowed. S423: When the ratio is within the adjusted preset ratio range, it is determined that the values ​​of the second change rate and the third change rate are consistent, and an oil pollution event alert is issued.

8. A navigation mark monitoring system for ship water pollutant emissions, characterized in that, The system for implementing the method according to any one of claims 1-7 comprises: Acquisition module: Real-time acquisition of the first reading from the turbidity sensor, the second reading from the chemical oxygen demand sensor, and the third reading from the oil sensor on the navigation mark; Calculation module: Calculates the first rate of change of the first reading, the second rate of change of the second reading, and the third rate of change of the third reading within a preset monitoring time period; First comparison module: If the first rate of change exceeds a preset turbidity change threshold, and the second rate of change and the third rate of change do not exceed the corresponding preset change threshold, then the first reading is marked to remind the user to repair the turbidity sensor; Second comparison module: If the first rate of change does not exceed the preset turbidity change threshold, and the second rate of change and the third rate of change both exceed the corresponding preset change threshold, then obtain the ratio of the second rate of change and the third rate of change. When the ratio is within a preset ratio range, determine that the values ​​of the second rate of change and the third rate of change are consistent, and issue an oil pollution event alert. The third comparison module: If the first rate of change exceeds the preset turbidity change threshold, and at the same time, the second rate of change or the third rate of change exceeds the corresponding preset change threshold, an oil pollution event alert will be issued. If the first rate of change does not exceed the preset turbidity change threshold, and the second rate of change or the third rate of change exceeds the corresponding preset change threshold, then the average readings of the first reading, the second reading, and the third reading within the preset long-term monitoring period are continuously acquired to obtain the first average reading, the second average reading, and the third average reading. Determine whether the first average reading continues to rise over multiple consecutive long-term monitoring periods, and whether its cumulative increase exceeds a preset long-term drift threshold. Determine whether the second average reading and the third average reading remain within their respective preset normal fluctuation ranges during the multiple consecutive long-term monitoring periods; If the first average reading continues to rise over multiple consecutive long-term monitoring periods, and its cumulative increase exceeds a preset long-term drift threshold, while the second average reading and the third average reading remain within their respective preset normal fluctuation ranges over the multiple consecutive long-term monitoring periods, then the first reading is marked to remind the user to maintain the turbidity sensor; otherwise, normal information on ship water pollutant discharge is sent.

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