Navigation mark monitoring data processing method and system for ship water pollutant discharge

Abstract

The invention provides a navigation mark monitoring data processing method and a navigation mark monitoring data processing system for ship water pollutant discharge, which are applied to the technical field of sewage discharge, and are used for obtaining readings of various types of water quality sensors on navigation marks in real time and calculating the change rate of the readings in a preset monitoring time period. And intelligent judgment is carried out according to a combination mode of reading change rates of different sensors. The method can intelligently distinguish the fault of the sensor from the actual water pollution, and significantly improves the accuracy of the monitoring data and the reliability of pollution event identification, thereby avoiding the wrong attribution of the pollution responsibility, and providing a more accurate and more reliable monitoring means for the water environment protection.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of sewage discharge, and particularly relates to a navigation beacon monitoring data processing method and system for ship water pollutant discharge. BACKGROUND

[0002] Today, with increasing attention to water environment protection, effective monitoring of ship water pollutant discharge is particularly crucial. As important monitoring equipment, multifunctional monitoring navigation beacons usually integrate water quality sensors such as turbidity sensors, chemical oxygen demand sensors and oil sensors to collect water data in real time. However, these sensors working underwater for a long time will inevitably be affected by marine biological attachment, i.e. biofouling.

[0003] With the passage of time, tiny marine microorganisms and larger marine organisms will form a biofilm on the surface of the sensor or directly attach to it, seriously interfering with the measurement accuracy of the sensor. For example, when a chemical oxygen demand sensor is covered with a biofilm, the diffusion path of the target analyte is blocked, which can cause the sensor reading to be much lower than the actual pollution level, thereby causing a real pollution event to be missed by the system. For optical sensors such as turbidity sensors, biofouling will directly scatter or absorb light, resulting in abnormally high turbidity readings even in clean water, triggering a large number of false alarms, or completely masking the actual pollution situation when the fouling is severe.

[0004] This biofouling directly and severely interferes with the operational performance of the water quality sensor, making the "abnormal" water quality signals received by the monitoring center ambiguous: is there indeed a pollutant in the water body, or is the sensor itself producing an erroneous reading due to biological attachment? This ambiguity poses a serious challenge to the core function of the system, which is to accurately attribute pollution events to specific ships. Although some advanced navigation beacons attempt to integrate anti-fouling mechanisms, these solutions often have limitations and can only slow down but not completely prevent the occurrence of biofouling.

[0005] The key is that existing data processing methods mainly focus on judging pollution events according to changes in water quality parameters, and they generally lack a real-time and reliable mechanism to assess the degree and type of biofouling on individual sensors and 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 original sensor data, ultimately leading to significant deviations or complete errors in attributing pollution responsibility, or even failing to accurately identify the real polluter.

[0006] In view of the above problems, the existing technology needs to be improved. SUMMARY

[0007] In view of the deficiencies of the prior art described above, the present application provides a navigation beacon monitoring data processing method and system for ship water pollutant discharge, aiming to solve the technical problems that in the existing ship water pollutant discharge monitoring, due to factors such as biological fouling of navigation beacon sensors, the monitoring data is inaccurate, false or missed pollution events, and it is difficult to accurately distinguish between sensor faults and actual pollution events.

[0008] In a first aspect, a navigation beacon monitoring data processing method for ship water pollutant discharge is provided, the method comprising the steps of: S1: acquiring a first reading of a turbidity sensor, a second reading of a chemical oxygen demand sensor and a third reading of an oil sensor on a navigation beacon in real time; S2: calculating a first change rate of the first reading, a second change rate of the second reading and a third change rate of the third reading within a preset monitoring time period; S3: if the first change rate exceeds a preset turbidity change threshold, and the second change rate and the third change rate do not exceed the corresponding preset change thresholds, marking the first reading to remind the user to maintain the turbidity sensor; S4: if the first change rate does not exceed the preset turbidity change threshold, the second change rate and the third change rate exceed the corresponding preset change thresholds at the same time, and the values of the second change rate and the third change rate are consistent, an oil pollution event reminder is issued; S5: if the first change rate exceeds the preset turbidity change threshold, and the second change rate or the third change rate exceeds the corresponding preset change threshold, an oil pollution event reminder is issued.

[0009] The navigation beacon monitoring data processing method for ship water pollutant discharge provided by the present application effectively distinguishes between sensor faults (such as abnormal readings caused by biological fouling) and actual water pollution events by intelligently analyzing the change trend of multi-sensor data and according to the combination mode of different sensor reading change rates, thereby significantly reducing false positives and false negatives, and improving the accuracy and reliability of ship water pollutant discharge monitoring.

[0010] Further, step S1 comprises: S11: at a preset sampling time, synchronously sending a data collection instruction to the turbidity sensor, the chemical oxygen demand sensor and the oil sensor; S12: recording the system timestamp when the data collection instruction 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, uniformly marking the system timestamp to all received first readings, second readings and third readings.

[0011] The navigation beacon monitoring data processing method for ship water pollutant discharge provided in the application ensures strict time synchronization of data collected by different sensors, provides a solid foundation for subsequent accurate calculation of change rate and multi-sensor data correlation analysis, and effectively avoids misjudgment caused by data time misplacement.

[0012] Further, step S2 comprises: S21: acquiring a water flow speed and a water temperature of a water area where the navigation beacon is located; S22: adjusting the preset monitoring time period according to the water flow speed and the water temperature; S23: calculating a first change rate of the first reading, a second change rate of the second reading and a third change rate of the third reading in the adjusted preset monitoring time period.

[0013] The navigation beacon monitoring data processing method for ship water pollutant discharge provided in the application introduces environmental parameters such as water flow speed and water temperature to dynamically adjust the monitoring time period, so that the calculation of change rate 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.

[0014] Further, step S22 comprises: S221: when the water flow speed exceeds a preset upper limit of flow speed threshold or when the water temperature exceeds a preset upper limit of water temperature threshold, the preset monitoring time period is shortened; S222: when the water flow speed exceeds a preset lower limit of flow speed threshold or when the water temperature exceeds a preset lower limit of water temperature threshold, the preset monitoring time period is lengthened.

[0015] The navigation beacon monitoring data processing method for ship water pollutant discharge provided in the application clearly defines the specific strategy of adjusting the monitoring time period according to the water flow speed and the water temperature, that is, shortening the monitoring period to respond quickly when the environment changes dramatically, and lengthening the monitoring period to smooth the data when the environment is relatively stable, further optimizing the adaptability and accuracy of change rate calculation.

[0016] Further, the method further comprises: S6: if the first change rate does not exceed a preset turbidity change threshold, and the second change rate or the third change rate exceeds a corresponding preset change threshold, the average readings of the first reading, the second reading and the third reading in a preset long-term monitoring period are continuously acquired to obtain a first average reading, a second average reading and a third average reading; S7: judging whether the first average reading continuously increases in the continuous multiple long-term monitoring periods and the cumulative increase amount exceeds a preset long-term drift threshold value; S8: judging whether the second average reading and the third average reading are kept in respective preset normal fluctuation ranges in the continuous multiple long-term monitoring periods; S9: if the first average reading continuously increases in the continuous multiple long-term monitoring periods and the cumulative increase amount exceeds a preset long-term drift threshold value, while the second average reading and the third average reading are kept in respective preset normal fluctuation ranges in the continuous multiple long-term monitoring periods, marking the first reading to remind the user to maintain the turbidity sensor; otherwise, sending a normal ship water pollutant emission information.

[0017] Further, the step S3 comprises: S31: if the first change rate exceeds a preset turbidity change threshold value, and the second change rate and the third change rate do not exceed the corresponding preset change threshold values, generating 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; S32: sending the alarm message to a preset receiver list.

[0018] Further, the step S31 comprises: S311: presetting multiple alarm message templates, the templates containing the corresponding relationship between the problem type of the turbidity sensor and the corresponding recommended maintenance operation; S312: selecting a matched alarm message template from the multiple alarm message templates according to the identified problem type of the turbidity sensor; S313: filling the identification information of the turbidity sensor in the selected alarm message template; S314: generating an alarm message containing the identification information of the turbidity sensor, the problem type of the turbidity sensor and the recommended maintenance operation according to the filled alarm message template.

[0019] Further, the step S4 comprises: S41: if the first change rate does not exceed the preset turbidity change threshold value, and the second change rate and the third change rate exceed the corresponding preset change threshold values at the same time, obtaining the ratio of the second change rate and the third change rate; S42: when the ratio is in the preset ratio range, determining that the values of the second change rate and the third change rate are consistent, and issuing an oil pollution event reminder.

[0020] Further, the step S42 comprises: S421: acquiring a variation direction of the water salinity in a preset monitoring time period; S422: if the variation direction of the water salinity is upward, then expanding the preset proportion range; if the variation direction of the water salinity is downward, then narrowing the preset proportion range; S423: when the proportion is in the adjusted preset proportion range, then determining that the values of the second variation rate and the third variation rate are consistent, and issuing an oil pollution event reminder.

[0021] In a second aspect, a navigation beacon monitoring data processing system for water pollution discharge of a ship is provided, which is characterized in that it is used to implement any of the above methods, and comprises: an acquisition module: acquiring a first reading of a turbidity sensor, a second reading of a chemical oxygen demand sensor, and a third reading of an oil sensor on a navigation beacon in real time; a calculation module: calculating a first variation rate of the first reading, a second variation rate of the second reading, and a third variation rate of the third reading in a preset monitoring time period; a first comparison module: if the first variation rate exceeds a preset turbidity variation threshold, and the second variation rate and the third variation rate both do not exceed corresponding preset variation thresholds, then marking the first reading to remind a user to maintain the turbidity sensor; a second comparison module: if the first variation rate does not exceed the preset turbidity variation threshold, the second variation rate and the third variation rate both exceed corresponding preset variation thresholds, and the values of the second variation rate and the third variation rate are consistent, then issuing an oil pollution event reminder; a third comparison module: if the first variation rate exceeds a preset turbidity variation threshold, and the second variation rate or the third variation rate exceeds a corresponding preset variation threshold, then issuing an oil pollution event reminder.

[0022] Beneficial effects: The ship water pollutant emission beacon monitoring data processing method and system proposed in the application can obtain the readings of multiple types of water quality sensors on the beacon in real time, calculate the change rates of the readings in a preset monitoring time period, and then intelligently judge according to the combination mode of the change rates of different sensor readings. Specifically, when the change rate of the turbidity sensor reading abnormally increases, and the change rates of the chemical oxygen demand and oil sensor readings also abnormally increase at the same time, the system can identify that this may be a fault of the turbidity sensor itself (such as optical scattering or absorption anomaly caused by biological fouling), and timely send a maintenance reminder, avoiding misjudgment of sensor failure as water pollution. On the contrary, when the change rate of the turbidity sensor reading is normal, and the change rates of the chemical oxygen demand and oil sensor readings abnormally increase at the same time and have consistent values, it is accurately judged as an oil pollution event. In addition, when the change rate of the turbidity sensor reading abnormally increases, and the change rates of the chemical oxygen demand or oil sensor readings abnormally increase at the same time, it can also be identified as an oil pollution event. Through the above technical solutions, the application effectively solves the problem that the sensor data is distorted due to biological fouling and other factors in the prior art, and then a large number of false alarms or missed real pollution events are caused. The method can intelligently distinguish between sensor failure and actual water pollution, significantly improves the accuracy of the monitoring data and the reliability of the pollution event identification, thereby avoiding incorrect attribution of pollution responsibility, and provides a more accurate and reliable monitoring means for water environment protection. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 A flowchart of a ship water pollutant emission beacon monitoring data processing method proposed in the application.

[0024] Figure 2 A structural diagram of a ship water pollutant emission beacon monitoring data processing system proposed in the application.

[0025] Figure 3 An architecture diagram of a ship water pollutant emission beacon monitoring data processing system proposed in the application.

[0026] Label explanation: 201, acquisition module; 202, calculation module; 203, first comparison module; 204, second comparison module; 205, third comparison module. DETAILED DESCRIPTION

[0027] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. The components of the embodiments of the present application described and shown in the drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0028] It should be noted that similar reference numerals and letters refer to similar items throughout the drawings, and therefore, once an item is defined in one drawing, it need not be further defined and explained in subsequent drawings. Meanwhile, in the description of the present application, the terms "first", "second", and the like are only used to distinguish descriptions, and cannot be understood as indicating or implying relative importance.

[0029] Please refer to Figure 1 A method for processing navigation beacon monitoring data of ship water pollutant discharge, the method comprising steps of: S1: acquiring a first reading of a turbidity sensor, a second reading of a chemical oxygen demand sensor and a third reading of an oil sensor on a navigation beacon in real time; S2: calculating a first change rate of the first reading, a second change rate of the second reading and a third change rate of the third reading in a preset monitoring time period; S3: if the first change rate exceeds a preset turbidity change threshold, and the second change rate and the third change rate both do not exceed the corresponding preset change threshold, the first reading is marked to remind the user to maintain the turbidity sensor; S4: if the first change rate does not exceed the preset turbidity change threshold, the second change rate and the third change rate both exceed the corresponding preset change threshold, and the values of the second change rate and the third change rate are consistent, an oil pollution event reminder is issued; S5: if the first change rate exceeds the preset turbidity change threshold, and the second change rate or the third change rate exceeds the corresponding preset change threshold, an oil pollution event reminder is issued.

[0030] The present application can effectively distinguish between sensor failure (such as abnormality caused by biological fouling) and actual water pollution event by acquiring multiple sensor readings in real time and calculating the change rates thereof, and making multi-dimensional judgment in combination with preset thresholds.

[0031] Specifically, the turbidity sensor is used to measure the turbidity of the water body, and its reading is referred to as the first reading. The chemical oxygen demand sensor is used to measure the degree of organic pollution in the water body, and its reading is referred to as the second reading. The oil sensor is used to detect the content of oil substances in the water body, and its reading is referred to as the third reading. These sensors are designed for long-term underwater work and can collect water quality data in real time.

[0032] The change rate refers to the change amplitude of the sensor reading relative to its initial value or the value at the previous moment within a certain time period. For example, the first change rate refers to the change amplitude of the first reading within the preset monitoring time period. The preset change threshold of each type refers to the numerical limit preset before the system runs according to experience, experiment or standard, which is used to judge whether the sensor reading or its change rate is abnormal. For example, the preset turbidity change threshold, the preset chemical oxygen demand change threshold and the preset oil change threshold are used to distinguish normal fluctuations, sensor failure or pollution events. The monitoring time period refers to the time window for calculating the change rate of the sensor reading, and its length can be set according to the actual monitoring needs. The preset chemical oxygen demand change threshold and the preset oil change threshold are the preset change thresholds corresponding to the second change rate and the third change rate, respectively.

[0033] In a specific embodiment, the turbidity sensor, the chemical oxygen demand sensor and the oil sensor configured on the beacon continuously monitor the surrounding water body. These sensors can be set to automatically collect data at a preset monitoring time period (such as every minute, every five minutes or every hour), and transmit the collected data to the data processing unit inside the beacon through wired or wireless means. After receiving these readings, the data processing unit stores them in the local memory and prepares for subsequent data analysis.

[0034] After obtaining the real-time sensor readings, the change trend of these readings needs to be quantified. Specifically, the data processing unit extracts multiple sets of first readings, second readings and third readings collected within the preset monitoring time period from the memory. For example, if the preset monitoring time period is one hour, all relevant readings in the past one hour will be extracted. The first change rate can be calculated as the difference between the maximum and minimum values of the first reading within the time period. Similarly, the second change rate and the third change rate can also be calculated in the same way. The calculation of these change rates aims to reflect the fluctuations of water quality parameters in a short period of time, providing a basis for subsequent judgment.

[0035] The data processing unit compares the calculated first rate of change with a pre-set turbidity change threshold. Meanwhile, the second rate of change and the third rate of change are compared with pre-set chemical oxygen demand change threshold and pre-set oil change threshold, respectively. When all three conditions are met, i.e. the first rate of change exceeds the pre-set turbidity change threshold, and neither the second rate of change nor the third rate of change exceeds the respective pre-set change threshold (i.e. the pre-set chemical oxygen demand change threshold and the pre-set oil change threshold), the system determines that there might be a problem with the turbidity sensor. For example, this might mean that the turbidity sensor is severely biofouled, causing its reading to abnormally increase, while the chemical oxygen demand sensor and the oil sensor do not show abnormal fluctuations. In this case, the system flags the first reading and displays on the user interface or alerts the user through other means (e.g. sending a text message or an email) to suggest checking and repairing the turbidity sensor.

[0036] If the data processing unit first determines that the first rate of change is within the pre-set turbidity change threshold, i.e. there is no significant abnormal increase in turbidity. Meanwhile, it checks whether both the second rate of change and the third rate of change exceed their respective pre-set thresholds, which indicates that there might be a significant increase in both chemical oxygen demand and oil content. Further, the system compares the values of the second rate of change and the third rate of change to determine whether they show consistency. For example, an allowed error range can be set, and if the values of the two rates of change are within this error range, they are considered to be consistent. When all these conditions are met, the system determines that there might be an oil pollution event in the water body and immediately issues an oil pollution event alert. This multi-sensor collaborative determination approach helps to exclude false positives caused by single sensor failure and improves the accuracy of pollution event identification.

[0037] If the data processing unit determines that the first rate of change exceeds the pre-set turbidity change threshold, it might mean that the turbidity of the water body has significantly increased. Meanwhile, to more accurately determine whether the water body is polluted, the data processing unit further checks whether at least one of the second rate of change or the third rate of change exceeds the corresponding pre-set change threshold. For example, oil pollution usually accompanies an increase in water turbidity and can cause an increase in chemical oxygen demand or oil content itself. When these conditions are met, the system issues an oil pollution event alert. This determination logic takes into account the case where oil pollution can have a comprehensive impact on multiple water quality parameters, thereby improving the ability to identify complex pollution events.

[0038] The conventional existing data processing method mainly focuses on judging pollution events according to the absolute value or simple change of water quality parameters, and generally lacks real-time evaluation capability of the sensor state itself. For example, when the turbidity sensor abnormally rises due to biological fouling, the existing method may directly determine it as water pollution, thereby causing a large number of false alarms. Similarly, when the chemical oxygen demand sensor is covered by a biological membrane, causing the reading to be low, a real pollution event may be missed. This lack of recognition and compensation for sensor errors makes the "abnormal" signals received by the monitoring center ambiguous and difficult to accurately attribute. Compared with the basic scheme, the application significantly improves the accuracy of judgment by introducing the cooperative analysis of the change rates of multiple sensors. Specifically, the method not only focuses on the change of the reading of a single sensor, but more importantly, it comprehensively considers the mutual relationship between the change rates of turbidity, chemical oxygen demand and oil, which are three key water quality parameters. For example, when the first change rate (turbidity) abnormally rises, and the second change rate (chemical oxygen demand) and the third change rate (oil) also abnormally rise at the same time, the method can intelligently identify that this may be a sign of turbidity sensor failure (such as severe biological fouling), rather than a real oil pollution, and timely issue a maintenance reminder. This is in sharp contrast to the existing technology, which may directly determine the abnormal rise of turbidity as pollution, effectively avoiding false alarms.

[0039] Further, step S1 comprises: S11: synchronously sending data collection instructions to the turbidity sensor, the chemical oxygen demand sensor and the oil sensor at a preset sampling time; S12: recording the system timestamp when the data collection instruction 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, uniformly marking the system timestamp to all received first readings, second readings and third readings.

[0040] At a preset sampling time, data collection instructions are synchronously sent to the turbidity sensor, the chemical oxygen demand sensor and the oil sensor. The preset sampling time can be set according to actual monitoring needs, for example, data collection is performed every 1 minute, 5 minutes or 10 minutes. Synchronous sending of data collection instructions aims to ensure that data of different sensors are collected at the same time point, thereby ensuring the timeliness and consistency of the data.

[0041] The system timestamp when the data collection instruction is sent is recorded. The system timestamp is the exact time record of data collection, which is of great significance for subsequent data analysis and event tracing.

[0042] By step S13, each set of sensor readings can be associated with an accurate time point, facilitating subsequent data processing and analysis, such as calculating the rate of change based on accurate time series data.

[0043] Further, step S2 comprises: S21: obtaining the water flow speed and water temperature of the water area where the beacon is located; S22: adjusting the preset monitoring time period according to the water flow speed and water temperature; S23: calculating 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 adjusted preset monitoring time period.

[0044] Wherein, the water flow speed can be measured by a flow rate sensor installed on the beacon; the water temperature can be measured by a temperature sensor. The acquisition of these environmental parameters aims to provide a basis for subsequent monitoring time period adjustment.

[0045] The scheme of the present application introduces water flow speed and water temperature as the basis for adjusting the monitoring time period, solving the problem that fixed monitoring time period may lead to inaccurate data analysis in complex and variable water environment. Specifically, when the water flow speed is fast or the water temperature is high, the diffusion and dilution process of pollutants in the water body will accelerate. At this time, if a longer fixed monitoring time period is still used, the peak value of pollutant concentration may be averaged, thereby reducing the detection sensitivity of pollution events. By shortening the monitoring time period, the rapid change of pollutant concentration in a short time can be captured more timely, avoiding important information being diluted. On the contrary, when the water flow speed is slow or the water temperature is low, the change of pollutants may be slow. At this time, if the monitoring time period is too short, false change rates may be generated due to environmental noise or sensor fluctuations, leading to false positives. By extending the monitoring time period, the data can be smoothed for a longer time, effectively filtering out short-term fluctuations, so that the calculated change rate can better reflect the real pollution trend. It is this dynamic adjustment mechanism that makes the calculation of the rate of change better match the actual dynamics of the water environment, thereby improving the accuracy and reliability of data analysis.

[0046] Wherein, the way of adjusting the preset monitoring time period according to the water flow speed and water temperature is that step S22 comprises: S221: when the water flow speed exceeds the preset upper limit of flow speed threshold, or when the water temperature exceeds the preset upper limit of water temperature threshold, shorten the preset monitoring time period; S222: when the water flow speed exceeds the preset lower limit of flow speed threshold, or when the water temperature exceeds the preset lower limit of water temperature threshold, extend the preset monitoring time period.

[0047] The preset monitoring time period is usually set under experimental conditions for a standard water body temperature of 19°C (determined by the average global sea surface temperature) and a standard water flow speed of 0.3 m / s (determined by the average surface water flow speed in general nearshore areas such as ports and channels). The preset monitoring time period is set to balance the sensitivity and stability of various sensors to ensure the accuracy of the monitoring data. Therefore, the preset monitoring time period can be set to 10 minutes through experiments.

[0048] The preset upper flow speed threshold and the preset upper water temperature threshold are used to define conditions under which the water environment is in a high dynamic or high active state. The preset upper flow speed threshold and the preset upper water temperature threshold can be set to 0.5 m / s and 28°C, respectively, based on the flow speed distribution and water temperature distribution in general nearshore areas.

[0049] Similarly, the preset lower flow speed threshold and the preset lower water temperature threshold are used to identify conditions under which the water environment is relatively stable. The preset lower flow speed threshold and the preset lower water temperature threshold can be set to 0.1 m / s and 10°C, respectively.

[0050] Between the preset upper and lower flow speed thresholds and the preset upper and lower water temperature thresholds, the preset flow speed threshold is not changed, and water body data is sampled once every 10 minutes. However, if the real-time acquired water flow speed is 0.6 m / s (exceeding the upper limit of 0.5 m / s), the preset monitoring time period will be shortened, for example, to 5 minutes. This makes data collection more frequent in order to capture rapid changes in pollutant levels under high flow conditions. Conversely, if the acquired water flow speed is 0.08 m / s (lower than the lower limit of 0.1 m / s), the preset monitoring time period will be extended, for example, to 15 minutes. This provides a longer sampling window in less dynamic environments to obtain more stable readings, reducing noise and ensuring the reliability of the change rate calculation. Similarly, if the water temperature rises to 30°C (exceeding the upper limit of 28°C), the monitoring time period will be shortened, for example, to 5 minutes to adapt to the accelerated degradation process. If the temperature drops to 8°C (lower than the lower limit of 10°C), the monitoring time period may be extended to 15 minutes. This dynamic adjustment ensures that the monitoring system is always optimally configured according to the current environmental conditions, resulting in more accurate and reliable monitoring of ship water pollutant emissions.

[0051] The specific time for shortening or extending the preset monitoring time period is set according to actual conditions and can also be measured under experimental conditions to determine the monitoring time at which various sensors can stably monitor water body data while maintaining sensitivity. The experimental data results are recorded in a table, so that in actual application, the shortened or extended time of the preset monitoring time period can be directly obtained by looking up the table.

[0052] The conventional existing ship water pollutant emission monitoring method, such as the above method, mainly focuses on quickly identifying a sudden water pollutant emission event or a sensor instantaneous failure by acquiring a sensor reading in real time and calculating a short-term change rate thereof. However, in actual application, various sensors, especially turbidity sensors, can experience a slow and continuous performance drift, which can not produce a large enough change rate in a short time to trigger a preset threshold alarm, but the long-term accumulated drift can cause inaccurate readings, thereby affecting the reliability of the monitoring data, and even can cause misjudgment of the water quality condition. In order to solve this problem, further, the method further comprises: S6: if the first change rate does not exceed the preset turbidity change threshold, and the second change rate or the third change rate exceeds the corresponding preset change threshold, then continuously acquiring average readings of the first reading, the second reading and the third reading in a preset long-term monitoring period to obtain first average reading, second average reading and third average reading; S7: judging whether the first average reading continuously increases in the continuous multiple long-term monitoring periods and the cumulative increase amount thereof exceeds a preset long-term drift threshold; S8: judging whether the second average reading and the third average reading are kept in respective preset normal fluctuation ranges in the continuous multiple long-term monitoring periods; S9: if the first average reading continuously increases in the continuous multiple long-term monitoring periods and the cumulative increase amount thereof exceeds the preset long-term drift threshold, and the second average reading and the third average reading are kept in respective preset normal fluctuation ranges in the continuous multiple long-term monitoring periods, then marking the first reading to remind the user to maintain the turbidity sensor; otherwise, sending a normal ship water pollutant emission information.

[0053] Specifically, when the first change rate of the turbidity sensor does not exceed the preset turbidity change threshold, but the second change rate of the chemical oxygen demand sensor or the third change rate of the oil sensor does not exceed the corresponding preset change threshold, it represents that the turbidity sensor itself can be contaminated, or the water quality is slightly polluted. In order to clearly distinguish which problem, the system will start the long-term monitoring mechanism. Continuously acquiring average readings of the first reading, the second reading and the third reading in a preset long-term monitoring period aims to eliminate the influence of instantaneous fluctuation by averaging the data in a period of time, thereby more accurately reflecting the long-term trend of the sensor reading. The long-term monitoring period can be set according to the actual application scene and the sensor characteristics, for example, it can be several hours, one day or several days. Thus, the first average reading, the second average reading and the third average reading representing the long-term trend can be obtained.

[0054] The system determines whether the first average reading (i.e. the long-term average reading of the turbidity sensor) presents a continuous increasing trend in the consecutive multiple long-term monitoring periods. Here, the "continuous increasing" means that the first average reading presents a monotonically increasing or approximately monotonically increasing pattern in the multiple consecutive monitoring periods. Meanwhile, it is also determined whether the cumulative increasing amount exceeds a preset long-term drift threshold. The long-term drift threshold is a threshold specially used to identify the slow drift of the sensor, and its value is usually smaller than the change threshold used to identify the sudden pollution event, so as to ensure that the slight and cumulative drift can be captured. The long-term drift threshold can be set based on the annual drift index obtained by consulting the sensor manual of the manufacturer, and the daily drift amount is calculated according to the annual drift index, and then multiplied by the total number of monitoring days to obtain the long-term drift threshold.

[0055] In order to exclude the possibility of actual water pollution, the system simultaneously determines whether the second average reading (the long-term average reading of the chemical oxygen demand sensor) and the third average reading (the long-term average reading of the oil sensor) are kept within the respective preset normal fluctuation range in the consecutive multiple long-term monitoring periods. This means that if the turbidity reading presents a long-term increase, but the chemical oxygen demand and oil readings remain normal, the possibility of the water body being polluted by chemical oxygen demand or oil leading to the increase of turbidity can be initially excluded. The preset normal fluctuation range is determined according to the historical data or calibration data of the sensor under normal water quality conditions.

[0056] If all the above conditions are met, that is, the first average reading of the turbidity sensor continuously increases in the consecutive multiple long-term monitoring periods and the cumulative increasing amount exceeds the preset long-term drift threshold, and the average readings of the chemical oxygen demand sensor and the oil sensor are kept within the normal fluctuation range, the system determines that the turbidity sensor may have a long-term drift problem. At this time, the first reading is marked, and a reminder is sent to the user, suggesting that the turbidity sensor be repaired or calibrated. Otherwise, if these conditions are not completely met, the system determines that the ship water pollutant discharge condition is normal, and sends the corresponding normal information.

[0057] Further, step S3 comprises: S31: if the first change rate exceeds the preset turbidity change threshold, and the second change rate and the third change rate 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 the recommended maintenance operation is generated to mark the first reading; S32: the alarm message is sent to a preset recipient list.

[0058] Specifically, when the system determines that there might be a fault in the turbidity sensor, instead of simply marking the reading, a structured alarm message will be generated. This alarm message is designed to contain multiple key information, such as the identification information of the turbidity sensor, which can be a unique device ID, installation location coordinates, or model information, to facilitate quick positioning and identification of the specific faulty device. In addition, the alarm message will also explicitly indicate the problem type of the turbidity sensor, such as sensor drift, calibration error, physical damage, or power anomaly, which helps maintenance personnel to preliminarily judge the nature of the fault. Further, the alarm message will also contain the recommended maintenance operation for this problem type, such as recommending recalibration, cleaning the sensor probe, checking the power connection, or directly replacing the sensor, etc., to provide direct action guidelines for maintenance personnel.

[0059] The generated alarm message will be sent to a pre-set recipient list. This recipient list can include the contact information of technical personnel responsible for device maintenance, system administrators, or specific emergency response teams, such as mobile phone numbers, email addresses, or internal communication system user IDs. The sending of the alarm message can be achieved through various communication methods, such as SMS, email, mobile application push notifications, or integrated into the alarm interface of the central monitoring platform, to ensure that the alarm can be timely and accurately delivered to the relevant personnel.

[0060] Further, step S31 includes: S311: Pre-set multiple alarm message templates, which contain the correspondence between the problem type of the turbidity sensor and the corresponding recommended maintenance operation; S312: According to the identified problem type of the turbidity sensor, select the 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: According to 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 the recommended maintenance operation.

[0061] Specifically, the purpose of pre-setting multiple alarm message templates is to standardize and automate the generation process of alarm messages. These templates can be pre-stored in the database of the system, each template is designed for one or more specific problem types of the turbidity sensor, and contains the corresponding recommended maintenance operation. For example, when the problem type of "continuous abnormal increase in turbidity sensor reading" is identified, a template can be pre-set, which contains the recommended maintenance operation of "please check whether the probe of turbidity sensor A is covered with dirt and clean it".

[0062] After identifying the specific problem type of turbidity sensor A, the system automatically matches and selects the template that best fits the current problem situation according to 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.

[0063] This template-based generation method avoids the tediousness and possible errors of manually writing alarm messages each time a problem occurs, ensuring the accuracy, completeness and consistency of the alarm content. Specifically, the preset template provides a clear correspondence between problem types and recommended maintenance operations, allowing the system to quickly locate the correct solution based on the diagnosis result; and filling in the sensor identification information ensures the directionality of the alarm, so that the recipient can immediately know which specific device needs attention and handling. As a result, the entire alarm generation process is optimized, improving response efficiency and maintenance accuracy.

[0064] Further, step S4 includes: S41: If the first change rate does not exceed the preset turbidity change threshold, and the second change rate and the third change rate both exceed the corresponding preset change threshold, then obtain the ratio of the second change rate and the third change rate; 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 event reminder is issued.

[0065] Wherein, obtaining the ratio of the second change rate and the third change rate refers to calculating the ratio between the change rate of the second reading of the chemical oxygen demand sensor and the change rate of the third reading of the oil sensor when certain conditions are met (i.e. the first change rate does not exceed the preset turbidity change threshold, and the second change rate and the third change rate both exceed the corresponding preset change threshold). The calculation of the ratio helps to quantify the correlation between the two. The preset ratio range can be understood as a set of numerical intervals that are preset to define under what relative relationship the second change rate and the third change rate are considered "numerically consistent". For example, this range can be set according to historical data, experimental results or expert experience to reflect the typical ratio relationship of chemical oxygen demand and oil indicators in real oil pollution events. When the calculated ratio falls within this preset range, it is considered that the change trend and amplitude of the two are highly correlated, thus supporting the judgment of oil pollution events. In practical applications, the preset ratio range can be set to 0.9-1.1.

[0066] Through the above technical solutions, the present application can avoid false positives caused by single indicator anomalies or non-oil pollution events, improving the accuracy of oil pollution event identification.

[0067] Further, step S42 includes: S421: Obtain the change direction of the water body salinity in a preset monitoring time period; S422: If the change direction of the water body salinity is upward, expand the preset proportion range; if the change direction of the water body salinity is downward, reduce the preset proportion range; S423: When the proportion is in the adjusted preset proportion range, it is determined that the values of the second change rate and the third change rate are consistent, and an oil pollution event reminder is issued.

[0068] Obtaining the change direction of the water body salinity in a preset monitoring time period can be understood as monitoring the water body salinity in real time through the salinity sensor configured on the navigation mark, and analyzing the change trend thereof in a specific time period. For example, the difference between the average salinity in the current monitoring time period and the average salinity in the previous monitoring time period can be calculated to determine.

[0069] If the change direction of the water body salinity is upward, the preset proportion range is expanded; if the change direction of the water body salinity is downward, the preset proportion range is reduced. In actual application, the change of the water body salinity can affect the chemical oxygen demand and the solubility, dispersion state of oil substances in the water body, and the response sensitivity of the sensor to these substances. For example, in a water area with high salinity, the conductivity of the water medium can change, thereby affecting the measurement accuracy of the chemical oxygen demand sensor and the oil sensor, resulting in a larger fluctuation range of the proportion relationship between the readings thereof. Therefore, by expanding the preset proportion range, such normal fluctuations caused by the change of salinity can be accommodated, and false judgments can be avoided. Conversely, in a water area with low or stable salinity, the readings of the sensor can be more stable, and at this time, reducing the preset proportion range can improve the accuracy of the judgment and reduce false alarms.

[0070] Specifically, the change amount of the expansion or reduction of the preset proportion range is set according to the actual situation, and the ratio between the change rates of the monitoring readings of various sensors under different water body salinities can also be measured under experimental conditions in advance, and the experimental data results are recorded as a table, so that in actual application, the adjusted preset proportion range can be directly obtained by looking up the table.

[0071] For example, in a certain monitoring period, the system obtains that the change direction of the water body salinity is rising. According to the above scheme, the preset proportion range is dynamically adjusted to [0.85, 1.15]. At this time, if the calculated proportion of the second change rate and the third change rate is 0.88, under the original fixed proportion range [0.9, 1.1], the proportion may be determined to be inconsistent, so that the oil pollution event reminder cannot be issued. However, under the adjusted proportion range [0.85, 1.15], 0.88 is within the range, so the system can accurately determine that the values of the second change rate and the third change rate are consistent, and issue an oil pollution event reminder. This shows that even in the case of increased sensor reading fluctuations caused by salinity changes, the scheme of the present application can still accurately identify potential oil pollution.

[0072] Conversely, if the system obtains that the change direction of the water body salinity is falling. The preset proportion range is dynamically adjusted to [0.95, 1.05]. At this time, if the calculated proportion of the second change rate and the third change rate is 0.92, under the original fixed proportion range [0.9, 1.1], the proportion may be determined to be consistent. But under the adjusted proportion range [0.95, 1.05], 0.92 is not within the range, the system determines that the values of the second change rate and the third change rate are inconsistent. This helps to improve the sensitivity of the judgment when the water environment is relatively stable, avoids misjudging non-oil pollution events as oil pollution, and thus reduces false alarms.

[0073] Please refer to Figure 2 , Figure 3 A navigation beacon monitoring data processing system for ship water pollutant discharge, characterized in that it is used to implement any of the above methods, and the system comprises: An acquisition module 201: acquiring a first reading of a turbidity sensor, a second reading of a chemical oxygen demand sensor, and a third reading of an oil sensor on a navigation beacon in real time; A calculation module 202: calculating a first change rate of the first reading, a second change rate of the second reading, and a third change rate of the third reading in a preset monitoring period; A first comparison module 203: if the first change rate exceeds a preset turbidity change threshold, and the second change rate and the third change rate both do not exceed the corresponding preset change threshold, the first reading is marked to remind the user to maintain the turbidity sensor; A second comparison module 204: if the first change rate does not exceed the preset turbidity change threshold, the second change rate and the third change rate both exceed the corresponding preset change threshold, and the values of the second change rate and the third change rate are consistent, an oil pollution event reminder is issued; The third comparison module 205: if the first change rate exceeds the preset turbidity change threshold, and at the same time, the second change rate or the third change rate exceeds the corresponding preset change threshold, an oil pollution event reminder is issued.

[0074] The acquisition module 201 is configured to acquire 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 beacon in real time. Specifically, this module is responsible for communicating with various sensors on the beacon and collecting real-time data output by them, ensuring the timeliness and accuracy of the monitoring data. For example, the acquisition module 201 can include a data interface, a communication unit, etc., for receiving data streams from the turbidity sensor, the chemical oxygen demand sensor, and the oil sensor.

[0075] The calculation module 202 is configured to calculate the first change rate of the first reading, the second change rate of the second reading, and the third change rate of the third reading within a preset monitoring period. This module receives the original readings collected by the acquisition module and calculates the change rates of various indicators according to the preset algorithm and time window, providing a data basis for subsequent judgments. For example, the calculation of the change rate can use various statistical methods such as difference method and moving average method.

[0076] The first comparison module 203 is configured to mark the first reading when the first change rate exceeds the preset turbidity change threshold, and the second change rate and the third change rate do not exceed the corresponding preset change threshold, to remind the user to maintain the turbidity sensor. This module is responsible for the preliminary judgment of the change rates output by the calculation module, identifying possible faults of the sensor.

[0077] The second comparison module 204 is configured to issue an oil pollution event reminder when the first change rate does not exceed the preset turbidity change threshold, the second change rate and the third change rate simultaneously exceed the corresponding preset change threshold, and the numerical values of the second change rate and the third change rate are consistent. This module is used to identify oil pollution events in a specific mode, and its judgment logic is based on the collaborative analysis of the change rates of various sensor readings.

[0078] The third comparison module 205 is configured to issue an oil pollution event reminder when the first change rate exceeds the preset turbidity change threshold, and at the same time, the second change rate or the third change rate exceeds the corresponding preset change threshold. This module serves as another identification mechanism for oil pollution events, supplementing the judgment range of the second comparison module and ensuring comprehensive coverage of different pollution scenarios.

[0079] The system can further implement the specific steps in the method regarding the sending of data collection instructions, the marking of system timestamps, the acquisition of water flow speed and water temperature, the adjustment of monitoring time periods, the generation and sending of alarm messages, the acquisition and judgment of average readings in a long-term monitoring period, and the like, thereby achieving comprehensive and intelligent monitoring and early warning of ship water pollutant emissions.

[0080] The scheme of the present application realizes the automation and intelligentization of the monitoring process by embodying each logical step in the navigation beacon monitoring data processing method of ship water pollutant emissions as an independent system module. Specifically, the acquisition module 201 serves as a data inlet, continuously collecting raw data from the navigation beacon sensors to provide real-time data flow for the operation of the entire system. Subsequently, the calculation module 202 pre-processes and extracts features from these raw data, calculates key change rate indicators, and converts raw and discrete data into information with analytical value. On this basis, the first comparison module 203, the second comparison module 204, and the third comparison module 205 respectively make intelligent judgments on these change rate indicators according to preset logical rules. For example, the first comparison module 203 focuses on identifying abnormalities in the sensor itself, and through comprehensive judgment of the change rates of turbidity, chemical oxygen demand, and oil readings, it can timely discover and mark potential faults of the turbidity sensor, thereby avoiding false positives or false negatives caused by sensor failure. Meanwhile, the second comparison module 204 and the third comparison module 205 work in coordination to identify and warn of water pollution events in different modes, especially for accurate judgment of oil pollution events, through joint analysis of multiple indicators, the accuracy and reliability of pollution event identification are improved. Thus, the entire system forms a closed loop of data processing and decision-making chain, from data collection to event warning, which can be efficiently and accurately completed.

[0081] In this document, the term "example" is used to introduce an implementation, an example, or implementations or examples of the present application. This document may also refer to "one example." Such a reference means that a particular feature, structure, or characteristic described in connection with the example is included in at least one implementation of the application. The appearance of the phrase "in one example" in various places in the specification are not necessarily all referring to the same example, nor are they necessarily referring to any one example. Furthermore, the term "comprising" does not exclude the presence of elements or steps other than those listed in a claim.

[0082] The above only describes the embodiments of the present application and is not used to limit the protection scope of the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for processing navigation mark monitoring data on 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, 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. 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.

2. The method for processing navigation mark monitoring data of ship water pollutant discharge 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 processing navigation mark monitoring data on 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 processing navigation mark monitoring data on 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 limit, or when the water temperature exceeds the preset water temperature threshold 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 processing navigation mark monitoring data of ship water pollutant discharge according to claim 1, characterized in that, The method further includes: 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.

6. The method for processing navigation mark monitoring data of ship water pollutant discharge according to claim 5, 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.

7. The method for processing navigation mark monitoring data on ship water pollutant discharge according to claim 6, 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.

8. The method for processing navigation mark monitoring data of ship water pollutant discharge according to claim 1, characterized in that, Step S4 includes: 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. 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.

9. A method for processing navigation mark monitoring data of ship water pollutant discharge according to claim 8, characterized in that, Step S42 includes: 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.

10. A navigation mark monitoring data processing system for ship water pollutant emissions, characterized in that, The system for implementing the method according to any one of claims 1-9 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, 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. 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.

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