A method and system for nutrient salt analysis suitable for high turbidity sea areas

Abstract

The application discloses a kind of nutrient salt analysis method and system suitable for high turbidity sea area, comprising: the pretreated water sample to be measured is filtered into filter membrane filtration module, the initial operating state parameter and system background parameter of filter membrane are collected, and static threshold is set;The real-time operating state parameter and system background parameter of filter membrane are collected, and the dynamic threshold, the duration of each anomaly is updated accordingly;Filter membrane state is judged, if early warning or blockage state, corresponding membrane replacement operation is carried out, and the above operation is repeated;If fault state, repeat the above operation after removing fault;If normal state, the filtered water sample is analyzed and the result is uploaded.The application realizes continuous sampling and efficient filtration of high turbidity water sample, filter membrane is automatically replaced according to the situation, effectively reduces the frequency of filter membrane blockage, and improves the reliability of nutrient salt analysis result.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of automatic water quality monitoring, and in particular to a method and system for analyzing nutrients suitable for high-turbidity sea areas. BACKGROUND

[0002] The nutrient salt composition (such as nitrate, nitrite, phosphate, ammonium salt, etc.) in the sea area is an important indicator for measuring water environmental quality, predicting the eutrophication trend of the sea area, and evaluating the health status of the ecological system. With the increasing refinement of environmental monitoring needs, in-situ, continuous, and automatic monitoring of nutrients has become an important research and application direction in the field of water quality monitoring.

[0003] Most of the current mainstream online nutrient analysis methods are based on colorimetry combined with sequential injection analysis technology (hereinafter referred to as SIA), which automatically quantitatively analyzes trace nutrients through an automatic injector, a valve control module, and an optical colorimetric cell. This method is relatively mature in clean or low-turbidity sea areas, but it faces great challenges in high-turbidity water environments (such as estuary, urban black and odorous sea area, river or coastal silt scouring area during flood period).

[0004] In high-turbidity sea areas, the content of suspended solids, silt, algae, and colloidal particles in the water sample increases significantly, which puts higher requirements on the front-end water sample pretreatment system. In the current mainstream online nutrient analysis method, a filter membrane is often used to filter the water sample, but the existing equipment mostly uses a fixed filter membrane structure, whose service life depends heavily on the on-site water quality, and after the filter membrane is clogged, manual inspection and replacement are usually required. This is particularly prominent in the following application scenarios: remote / unattended monitoring sites (such as highland reservoirs, offshore buoy stations); areas with large fluctuations in turbidity (such as southern river basins or coastal deltas during typical typhoon and rainstorm periods); high-frequency automatic sampling points (such as water quality black and odorous early warning platforms, intelligent river and lake patrol ships), etc.

[0005] If the filter membrane clogging is not handled in time, it will cause the water sample to fail to enter the reaction channel normally, thereby affecting the accuracy of the nutrient measurement value, and even causing the instrument to stop, which seriously reduces the continuous operation capability of the system.

[0006] However, existing devices often lack accurate judgment of filter membrane working life under high turbidity of water samples and multi-parameter linkage control strategy, resulting in untimely or frequent filter membrane replacement, affecting measurement continuity and accuracy. In addition, the existing nutrient salt analysis equipment generally has the following shortcomings: (1) outdated filter membrane replacement mechanism: the filter membrane replacement is mostly manual operation, lacking a mechanical filter membrane replacement system or automatic control logic; (2) fixed replacement timing, lacking intelligent judgment capability: most systems only set a static threshold (such as upper pressure difference limit) to trigger an alarm, and cannot dynamically adjust according to actual water quality fluctuations; (3) weak remote management capability of the equipment: most equipment lacks filter membrane service life estimation module and remote communication mechanism, resulting in delayed filter membrane replacement and low operation and maintenance efficiency; (4) poor continuous operation capability: lacking filter membrane channel hot backup mechanism, unable to seamlessly switch to standby channel after the current filter membrane is blocked.

[0007] In summary, the existing technology has obvious shortcomings in filter membrane blockage response, filter membrane intelligentization degree, filter membrane life monitoring and remote maintenance capability, and is difficult to meet the in-situ nutrient salt continuous monitoring demand in high turbidity and dynamic complex sea environment. SUMMARY

[0008] In view of the technical problems in the prior art that the filter membrane cannot be replaced in time after being blocked, the system is frequently interrupted, the manual maintenance burden is heavy, the state judgment logic is single, and the remote collaboration capability is weak, the present application provides a nutrient salt analysis method and system suitable for high turbidity sea areas, which has an automatic filter membrane replacement mechanism, multi-channel parallel control capability and intelligent judgment strategy based on multiple parameters, and can realize long-term stable, automated, unattended and remote collaborative monitoring of nutrient salts.

[0009] The specific technical solutions are as follows:

[0010] A nutrient salt analysis method suitable for high turbidity sea areas, comprising the following steps:

[0011] S1: collecting a water sample to be measured and removing large particle impurities by pretreatment;

[0012] S2: passing the pretreated water sample into a filter membrane filtration module, collecting initial operation state parameters and system background parameters of the filter membrane, and setting a static threshold; the filter membrane filtration module comprises two stepper motors, two synchronous belt transmission mechanisms, a membrane supply roller, a belt-shaped filter membrane and a membrane collection roller; the two stepper motors drive the membrane supply roller and the membrane collection roller to rotate in the same direction through the two synchronous belt transmission mechanisms, one end of the belt-shaped filter membrane is wound on the membrane supply roller, and the other end passes through the water sample channel vertically and is wound on the membrane collection roller;

[0013] The operation state parameters include differential pressure, turbidity, and flow rate; the system background parameters include temperature and operation time; and the static threshold values include a maximum operation time threshold value of filter membrane aging, a differential pressure fluctuation abnormality threshold value, a differential pressure abnormality tolerance time length, a turbidity abnormality tolerance time length, a flow rate abnormality tolerance time length, and a differential pressure fluctuation abnormality tolerance time length.

[0014] S3: Collect real-time operation state parameters and system background parameters of the filter membrane, and update dynamic threshold values and each abnormality duration time on the basis of the same; the dynamic threshold values include a filter membrane differential pressure dynamic clogging threshold value, a turbidity abnormality dynamic threshold value, and a flow rate drop alarm threshold value; and the each abnormality duration time includes a differential pressure duration time exceeding a limit, a turbidity duration abnormality time length, and a flow rate duration abnormality time length.

[0015] S4: If all real-time operation state parameters are within a set range, and there is no sensor failure or communication failure, it is determined that the filter membrane is in a normal state, nutrient salt analysis is performed on the filtered water sample, the result is uploaded, and the current water sample detection is ended.

[0016] If a difference between any real-time operation state parameter and a corresponding dynamic threshold value is less than a set threshold value, and the clogging comprehensive replacement judgment condition is not met, it is determined that the filter membrane is in a pre-alarm state, a micro-step membrane replacement length is set to replace the membrane, and S2 is executed; if the clogging comprehensive replacement judgment condition is met, it is determined that the filter membrane is in a clogging state, a standard segment membrane replacement length is set to replace the membrane, and S2 is executed; the clogging comprehensive replacement judgment condition includes that any one of the differential pressure, the turbidity, the flow rate, and the differential pressure fluctuation exceeds a corresponding threshold value and a duration time exceeds a corresponding static threshold value, or an operation time length exceeds the maximum operation time threshold value of filter membrane aging; if a sensor or communication is abnormal, it is determined that the filter membrane is in a failure state, abnormal alarm information is uploaded, and S2 is executed after the failure is eliminated.

[0017] Further, in S3, the filter membrane differential pressure dynamic clogging threshold value is a linear sum of a deviation correction term of initial differential pressure and turbidity and a differential pressure standard deviation; the deviation correction term of turbidity is a difference between real-time turbidity and initial turbidity; and the differential pressure standard deviation is obtained by using a sliding window statistical method.

[0018] The turbidity abnormality dynamic threshold value is a linear sum of a deviation correction term of initial turbidity and temperature and a turbidity standard deviation; the deviation correction term of temperature is a difference between real-time temperature and initial temperature; and the turbidity standard deviation is obtained by using a sliding window statistical method.

[0019] The flow rate drop alarm threshold value is a difference between an initial flow rate adjusted by a decay coefficient and a result obtained by scaling a flow rate standard deviation by a weight factor; and the flow rate standard deviation is obtained by using a sliding window statistical method.

[0020] Further, in the S3, the time point at which the real-time pressure difference is first detected to exceed the dynamic blocking threshold of the filter membrane pressure difference is recorded as the starting time point of the duration of the continuously exceeding limit of the pressure difference; if the subsequent real-time pressure difference continuously exceeds the limit, the duration of the continuously exceeding limit of the pressure difference is continuously updated; if the real-time pressure difference returns to the safe range at a subsequent time point, the duration of the continuously exceeding limit of the pressure difference is reset to zero, and the corresponding starting time point is cleared.

[0021] The time point at which the real-time turbidity is first detected to exceed the dynamic abnormality threshold of the turbidity is recorded as the starting time point of the duration of the continuously abnormal turbidity; if the subsequent real-time turbidity continuously exceeds the limit, the duration of the continuously abnormal turbidity is continuously updated; if the real-time turbidity returns to the safe range at a subsequent time point, the duration of the continuously abnormal turbidity is reset to zero, and the corresponding starting time point is cleared.

[0022] The time point at which the real-time flow rate is first detected to be lower than the flow rate drop alarm threshold is recorded as the starting time point of the duration of the continuously abnormal flow rate; if the subsequent real-time flow rate continuously falls below the threshold, the duration of the continuously abnormal flow rate is continuously updated; if the real-time flow rate returns to the safe range at a subsequent time point, the duration of the continuously abnormal flow rate is reset to zero, and the corresponding starting time point is cleared.

[0023] The time point at which the real-time pressure difference standard deviation is first detected to exceed the pressure difference fluctuation threshold is recorded as the starting time point of the duration of the continuously fluctuating abnormal pressure difference; if the subsequent real-time pressure difference standard deviation continuously exceeds the limit, the duration of the continuously fluctuating abnormal pressure difference is continuously updated; if the real-time pressure difference standard deviation returns to the safe range at a subsequent time point, the duration of the continuously fluctuating abnormal pressure difference is reset to zero, and the corresponding starting time point is cleared.

[0024] Further, in the S4, in the pre-warning state, the micro-step membrane replacement length is the sum of the micro-step minimum membrane replacement length and the increment length, the increment length is the difference between the micro-step maximum membrane replacement length and the micro-step minimum membrane replacement length, and the product of the maximum value among the pressure difference proximity degree, the turbidity proximity degree, and the flow rate drop degree.

[0025] Further, the filter membrane filtration module includes a plurality of filter membrane units, each of which includes two stepper motors, two synchronous belt transmission mechanisms, a membrane supply roller, a belt-shaped filter membrane, and a membrane collection roller; the continuous filtration operation is realized by switching the plurality of filter membrane units.

[0026] Further, in the S4, in the blocking state, it is determined whether the remaining amount of the belt-shaped filter membrane on the membrane supply roller in the current filter membrane unit is less than a threshold value; if not, the standard segment membrane replacement length is set, which is the difference between the micro-step maximum membrane replacement length and the micro-step minimum membrane replacement length; if yes, the other filter membrane unit is switched, and the current filter membrane unit is marked as a to-be-replaced state.

[0027] Further, in the pre-warning state and the blockage state, if the remaining amount of the strip-shaped filter membrane on the membrane supply roller is not less than the threshold value, the step motor is driven according to the set membrane replacement length to realize membrane replacement, the remaining amount of the unused strip-shaped filter membrane is updated, the current cumulative step number is recorded, and the state timing time is cleared;

[0028] If the remaining amount of the strip-shaped filter membrane is less than the threshold value, the filter membrane switching unit operation is performed, the remaining amount of the filter membrane after switching is recorded, and the cumulative step number of the step motor is cleared.

[0029] The remaining amount of the strip-shaped filter membrane is calculated according to the completed proportion of the cumulative step number, and the completed proportion is the product of the ratio of the cumulative step number of the step motor to the total step number of the full roll strip-shaped filter membrane and the correction factor. The correction factor is obtained by performing sliding smoothing processing on the ratio of the actual step number to the theoretical total step number in multiple membrane replacement histories.

[0030] A nutrient salt analysis system suitable for high-turbidity sea areas is used to realize the nutrient salt analysis method suitable for high-turbidity sea areas, and comprises a water sample collection module, a filter membrane filtration module, a nutrient salt analysis module, a control module, a communication module, and an integrated water quality sensor module.

[0031] The water sample collection module is used to collect a water sample to be measured, and after filtering out large-particle impurities through an internal pre-filtering component, the water sample is input into the filter membrane filtration module.

[0032] The filter membrane filtration module comprises two step motors, two synchronous belt transmission mechanisms, a membrane supply roller, a strip-shaped filter membrane, and a membrane collection roller. The two step motors drive the membrane supply roller and the membrane collection roller to rotate in the same direction synchronously through the two synchronous belt transmission mechanisms. One end of the strip-shaped filter membrane is wound around the membrane supply roller, and the other end of the strip-shaped filter membrane is wound around the membrane collection roller after vertically penetrating through a water sample channel. The two step motors work synchronously under the unified coordination of the control module to ensure that the strip-shaped filter membrane maintains a constant tension at both the membrane supply end and the membrane collection end.

[0033] The nutrient salt analysis module is used to detect the nutrient salt components of the water sample filtered by the filter membrane filtration module based on sequential injection analysis and colorimetry.

[0034] The integrated water quality sensor module is used to monitor the filter membrane operation data in the filter membrane filtration module in real time, and is connected with the control module to upload the monitoring data to the control module.

[0035] The control module is used to identify the filter membrane state according to the data collected by the integrated water quality sensor module, and perform nutrient salt analysis when the filter membrane is in a normal state, perform filter membrane replacement operation when the filter membrane is in a pre-warning state or a blockage state, and control the system to shut down and send an abnormal alarm information to the communication module when the filter membrane is in a fault state.

[0036] The communication module is connected with the control module and the remote monitoring platform respectively, receives the filter membrane replacement reminder, abnormal alarm information and control instruction sent by the control module, and uploads them to the remote monitoring platform.

[0037] Further, the communication module uploads data to the remote monitoring platform through an NB-IoT protocol.

[0038] Further, the nutrient salt analysis module, the integrated water quality sensor module, the communication module and the control module are connected to the same system platform through an industrial bus mode, and constitute a modular mounting structure; the control module schedules the running state of each module, performs data collection and issues control commands through the industrial bus.

[0039] The beneficial effects of the present application are:

[0040] (1) The present application improves the long-term stable operation ability of the system in a high turbidity environment: through the dynamic switching strategy of the filter membrane filtration module in different states, continuous sampling and efficient filtration of high turbidity water samples are realized, the filter membrane blockage frequency is effectively reduced, and the instrument service life and data continuity are improved.

[0041] (2) The present application realizes real-time perception and intelligent judgment of the filter membrane state: combining multi-threshold judgment of parameters such as differential pressure, turbidity, flow, temperature and running time, and integrating dynamic characteristics such as coefficient of variation, a filter membrane intelligent recognition and control mechanism based on state machine is constructed, and accurate division and control response of normal, early warning, blockage and fault states are realized.

[0042] (3) The present application improves the intelligent and automatic level of maintenance: the system has the ability to automatically replace the filter membrane according to the filter membrane state determination result, reduces manual intervention, and can execute automatic protection under abnormal conditions, i.e. fault state, to ensure the stability and safety of the equipment.

[0043] (4) The present application optimizes the accuracy and data quality of water sample analysis: through early identification and response to factors such as turbidity deterioration, differential pressure fluctuation and flow decrease, water sample pollution or dilution caused by filter membrane performance degradation is avoided, and the reliability of nutrient salt analysis results is improved.

[0044] (5) Support for extended application capability in complex environment: the architecture of the present application is suitable for various complex hydrological environments such as field, lake, reservoir and estuary, and is especially suitable for long-term deployment and nutrient salt continuous monitoring in sea areas with severe turbidity changes or high pollutant concentration. BRIEF DESCRIPTION OF DRAWINGS

[0045] Figure 1 The hardware structure diagram of the nutrient salt analysis system suitable for high turbidity sea area proposed by the embodiment of the present application.

[0046] Figure 2 A filter membrane filtration module structure suitable for the nutrient salt analysis system for high-turbidity sea areas according to the embodiment of the present application.

[0047] Figure 3 A flow chart of the nutrient salt analysis method suitable for high-turbidity sea areas according to the embodiment of the present application.

[0048] Figure 4 A five-state state machine state transition diagram under the filter membrane intelligent identification and control mechanism according to the embodiment of the present application.

[0049] In the figure, the first step motor 1, the first synchronous belt transmission mechanism 2, the membrane supply roller 3, the belt-shaped filter membrane 4, the water sample channel 5, the membrane collection roller 6, the second synchronous belt transmission mechanism 7, and the second step motor 8. DETAILED DESCRIPTION

[0050] The purpose and effect of the present application will become more apparent from the following detailed description of the present application according to the accompanying drawings and preferred embodiments, and the present application will be further described in detail below in combination with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0051] As shown in Figure 1 A nutrient salt analysis system suitable for high-turbidity sea areas, comprising: a water sample collection module, a filter membrane filtration module, a nutrient salt analysis module, a control module, a communication module, and an integrated water quality sensor module.

[0052] The water sample collection module is used to collect water samples from the sea area to be measured, and to filter out large-particle impurities through a pre-filtering assembly.

[0053] The filter membrane filtration module is used to further filter the water sample output by the water sample collection module. As Figure 2As shown, the filter membrane filtration module comprises: a reel-type continuous belt filter membrane 4, a storage bin, a collection bin, a driving mechanism; the storage bin and the collection bin are distributed on both sides of the water sample channel 5, the belt filter membrane 4 moves through the water sample channel 5 from the storage bin to the collection bin, and the filter membrane surface is perpendicular to the water flow direction; the filter membrane hereinafter refers to the belt filter membrane 4 segment located in the water sample channel 5 and playing a filter membrane role. The driving mechanism comprises: a first stepper motor 1, a first synchronous belt transmission mechanism 2, a membrane supply roller 3, a membrane collection roller 6, a second synchronous belt transmission mechanism 7 and a second stepper motor 8. Among them, the first stepper motor 1, the first synchronous belt transmission mechanism 2 and the membrane supply roller 3 are arranged in the storage bin, the first stepper motor 1 drives the membrane supply roller 3 to rotate through the first synchronous belt transmission mechanism 2, realizes high-precision control of the filter membrane supply end, and ensures that the filter membrane step length is accurate, the tension is appropriate, and the position is controllable each time. The second stepper motor 8, the second synchronous belt transmission mechanism 7 and the membrane collection roller 6 are arranged in the collection bin, the second stepper motor 8 drives the membrane collection roller 6 to rotate through the second synchronous belt transmission mechanism 7, and is used for cooperating with the membrane supply roller 3 to complete the synchronous winding of the used filter membrane.

[0054] The first stepper motor 1 undertakes the leading role of filter membrane conveying, controls the filter membrane supply length and movement rhythm; the second stepper motor 8 assists in completing the synchronous winding of the filter membrane, and the two work synchronously under the unified coordination of the control module, so as to ensure that the filter membrane maintains constant tension at both ends of the membrane supply and the membrane collection, avoid slack or excessive tension, and guarantee the stability and smoothness of the filter membrane in the transmission process.

[0055] The starting end of the belt filter membrane 4 is pre-wound on the membrane supply roller 3, and after completing the filtration of the water sample vertically through the water sample channel 5, the end thereof is wound by the membrane collection roller 6, forming a continuous filter membrane transmission path from the membrane supply roller 3 to the membrane collection roller 6. The two rollers work cooperatively to realize the step-by-step movement of the filter membrane, effectively prevent the filter membrane from slipping, wrinkling or accumulating during the replacement process, and ensure the continuity and reliability of the filtration process.

[0056] Through the above transmission structure, the continuous, step-by-step and accurate replacement of the filter membrane can be realized, the displacement consistency, tension controllability and running stability of the filter membrane in the replacement process are ensured, and the consistency and reliability of the filtration performance are guaranteed.

[0057] Further, the filter membrane filtration module is provided with a main filter membrane unit and at least one standby filter membrane unit, if the current filter membrane unit fails or triggers a filter membrane replacement operation, it is automatically switched to another filter membrane unit to continue running, that is, the switching of different filter membrane channels, realizing continuous and uninterrupted filtration work.

[0058] The nutrient salt analysis module is used for detecting the nutrient salt components of the water sample filtered by the filter membrane filtration module based on sequential injection analysis and colorimetry.

[0059] An integrated water quality sensor module for real-time monitoring of filter membrane operation data in a filter membrane filtration module and uploading to a control module, the module comprising: a pressure sensor, a turbidity sensor, a flow meter, a temperature sensor, etc. The integrated water quality sensor module is in communication connection with the control module and uploads the monitored filter membrane operation data to the control module as a basis for filter membrane replacement (including continuous step-by-step replacement of filter membranes in a single filter membrane filtration module, and replacement between different filter membrane units) and dynamic adjustment of detection parameters.

[0060] A control module comprising a processor, a memory and an interface circuit, wherein the processor is used to run filter membrane state recognition, nutrient salt analysis sampling and detection process control (only in the "normal state" of the system), and to control filter membrane replacement operation according to the filter membrane state. The memory is used to store data and programs to provide fast access instructions and data support for the processor; the interface circuit serves as a bridge between the control module and external devices (integrated water quality sensor module, communication module, etc.), realizing signal conversion, level matching and protocol analysis. The data interaction function of the control module is dominated by the processor, the physical communication is completed through the interface circuit, and the memory assists in caching and managing operation data, so as to realize real-time data interaction between the integrated water quality sensor module and the communication module.

[0061] Further, the control module calculates the differential pressure threshold ΔP th of the filter membrane, the turbidity threshold NTU th , the flow threshold Q th after the filter membrane, the running time t run , the differential pressure ΔP continuous over-limit duration t ΔP , the turbidity NTU continuous abnormal duration t NTU , the flow Q continuous abnormal duration t Q based on the real-time parameters provided by the integrated water quality sensor module, and realizes filter membrane state recognition and intelligent control of filter membrane replacement accordingly.

[0062] Further, the control module estimates the remaining amount of the belt-shaped filter membrane 4 in the storage membrane bin by accumulating the number of steps of the stepping motor 1, and adaptively corrects the filter membrane use coefficient F in combination with the actual replacement behavior, to improve the estimation accuracy of the remaining amount of the belt-shaped filter membrane 4.

[0063] A communication module for receiving prompt information, abnormal alarm information and control instructions including reminding technicians to manually replace a whole roll of belt-shaped filter membrane 4 when the remaining amount of the belt-shaped filter membrane 4 is below a set threshold.

[0064] Further, the communication module supports data interaction with a remote monitoring platform (a remote cloud platform in this embodiment), and the data transmitted by the communication module to the remote monitoring platform includes: filter membrane state, remaining amount of the strip-shaped filter membrane 4, alarm information. The instructions transmitted by the remote monitoring platform to the communication module include remote start-stop instructions. In this embodiment, the data is uploaded to the remote monitoring platform through the NB-IoT protocol.

[0065] Further, the nutrient salt analysis module, the integrated water quality sensor module, the communication module and the control module are connected to the same system platform through an industrial bus mode to form a modular mounting structure. The bus type structure significantly improves the system expandability and maintenance convenience, and is particularly suitable for modular water quality online monitoring requirements in high humidity and high turbidity environments.

[0066] Further, the control module serves as the main control core, schedules the running states of the modules, performs data collection, issues control instructions, and coordinates the overall operation of the system.

[0067] As shown in Figure 3 , a nutrient salt analysis method suitable for high-turbidity sea areas specifically includes the following steps:

[0068] S1: Water sample collection and pretreatment. The water sample to be measured in the high-turbidity sea area is introduced by the water sample collection module, and large particle impurities are removed through the pre-filter assembly to prolong the service life of the subsequent filter membrane filtration.

[0069] S2: The pretreated water sample is introduced into the filter membrane filtration module. After the system is started, the initial running state parameters and initial system background parameters of the current filter membrane are collected by the integrated water quality sensor module, and a static threshold is set.

[0070] The initial running state parameters of the filter membrane include: initial differential pressure ΔP0 (pressure difference before and after the filter membrane), initial turbidity NTU0 (after the filter membrane), initial flow rate Q0 (after the filter membrane); the initial system background parameters include: initial temperature T0 (after the filter membrane), initial sampling time stamp t0 (the time when the device is started and the initial running parameter collection of the filter membrane is completed), etc.

[0071] The static threshold includes: differential pressure abnormal tolerance time t ΔP_hold , turbidity abnormal tolerance time t NTU_hold , flow abnormal tolerance time t Q_hold , maximum running time threshold t max of filter membrane aging, differential pressure fluctuation abnormal tolerance time t , differential pressure fluctuation abnormal threshold t .

[0072] S3: Real-time monitoring of filter membrane state and updating of dynamic threshold and abnormal duration.

[0073] During the system operation, real-time running state parameters and real-time system background parameters of the filter membrane are continuously collected, the real-time running state parameters include: filter membrane differential pressure ΔP, water sample turbidity NTU, water sample flow rate Q; the real-time system background parameters include: water sample temperature T, running time t (the running time can also be used as an aging index). The dynamic thresholds include: filter membrane differential pressure dynamic blocking threshold ΔP th , turbidity abnormality dynamic threshold NTU th , flow rate drop alarm threshold Q th .

[0074] Each dynamic threshold changes adaptively with the real-time running state parameters of the filter membrane, which is beneficial to accurately judge the state of the filter membrane, and the adaptive change is as follows:

[0075] (1) The expression of the filter membrane differential pressure dynamic blocking threshold ΔP th is as follows:

[0076]

[0077] Wherein, α1 and k1 are adjustable parameters, and σ P is the differential pressure standard deviation.

[0078] In order to enhance the fault tolerance of the system to data fluctuations and adapt to complex water quality change environment, the differential pressure standard deviation σ P is obtained by real-time calculation using a sliding window statistical method, and the specific operation is as follows:

[0079] The system collects the current monitoring value of the filter membrane differential pressure ΔP at a fixed sampling period, and adds it to the sliding data queue with a length of N, and automatically removes the earliest data when the queue length exceeds N, so as to maintain a fixed window length. At any time, the standard deviation σ can be calculated according to the N historical data {X1, X2,..., X N} in the current sliding window, and the calculation formula is as follows:

[0080]

[0081] Wherein, is the average value of the current window data, and X i represents the corresponding measured value of the filter membrane differential pressure ΔP.

[0082] (2) The expression of the turbidity abnormality dynamic threshold NTU th is as follows:

[0083]

[0084] In the formula, α2 and k2 are adjustable parameters, and σ N is the turbidity standard deviation, which is also obtained by real-time calculation using a sliding window statistical method.

[0085] (3) Flow rate drop alarm threshold Q th The expression is as follows:

[0086]

[0087] In the formula, β and k3 are adjustable parameters, σ Q is the flow rate standard deviation, which is also calculated in real time using a sliding window method.

[0088] The adjustable parameters in the above expression are flexibly configured according to the specific characteristics of the water sample to be measured in the high-turbidity sea area, the signal-to-noise ratio tolerance of the sensor, and the system response sensitivity. During the initial operation of the system, the technical personnel manually configure the adjustable parameters according to the pre-experiment results, equipment characteristics, and environmental conditions; during the long-term operation of the system, based on the accumulated historical monitoring data, the adjustable parameter values are dynamically trained and optimized through machine learning methods to improve the judgment accuracy and adaptability of the system in complex water environments.

[0089] According to the above dynamic threshold, the abnormal duration is updated, which is used for the judgment of the subsequent system state. The abnormal duration includes: the differential pressure continuous over-limit duration t ΔP , the turbidity continuous abnormal duration t NTU , the flow rate continuous abnormal duration t Q , and the differential pressure fluctuation abnormal duration . The specific calculation method is as follows:

[0090] (1) The differential pressure continuous over-limit duration t ΔP :

[0091]

[0092] In the formula, t cur is the current time, is the starting time point of the current differential pressure continuous over-limit event; if ΔP exceeds the safe range again after recovery, the starting time point is recorded again.

[0093] When the filter membrane differential pressure ΔP is detected to exceed the filter membrane differential pressure dynamic clogging threshold ΔP th for the first time, the time point is recorded as the starting time point . If the subsequent ΔP continues to exceed the limit, t ΔP is continuously updated; if ΔP recovers to the safe range (i.e., ΔP≤ΔP th ), t ΔP is reset to 0, and is cleared, until ΔP exceeds ΔP th is detected again, and the above timing process is repeated.

[0094] (2) The turbidity continuous abnormal duration t NTU:

[0095]

[0096] wherein, is the starting time point of the current turbidity duration abnormal event; if NTU exceeds the limit again after recovering to the safe range, the starting time point is recorded again.

[0097] When the turbidity NTU is detected to exceed the turbidity abnormal dynamic threshold NTU th for the first time, the time point is recorded as the starting time point . If the subsequent NTU continuously exceeds the limit, t NTU is continuously updated; if the NTU recovers to the safe range (i.e. NTU≤NTU th ), t NTU is reset to 0, and t is cleared, until the NTU is detected to exceed NTU th again, and the above timing process is repeated.

[0098] (3) Flow duration abnormality time t Q :

[0099]

[0100] wherein, is the starting time point of the current flow duration abnormal event; if Q recovers to the safe range and then is detected to be lower than the threshold again, the starting time point is recorded again.

[0101] When the flow Q is detected to be lower than the flow drop alarm threshold Q th for the first time, the time point is recorded as the starting time point . If the subsequent Q continuously is lower than Q th , t Q is continuously updated; if the Q recovers to the safe range (i.e. Q≥Q th ), t Q is reset to 0, and t is cleared, until the Q is detected to be lower than Q th again, and the above timing process is repeated.

[0102] (4) Pressure difference fluctuation abnormality time t :

[0103]

[0104] wherein, is the starting time point of the current pressure difference fluctuation duration abnormal event; σ ΔP is the pressure difference standard deviation, used to represent the pressure difference fluctuation, and is obtained by real-time calculation using a sliding window statistical method, is the pressure difference fluctuation abnormal threshold; if the subsequent σΔP If the pressure differential exceeds the safe range again, the starting time point is recorded again.

[0105] When the pressure differential standard deviation σ ΔP When the pressure differential exceeds the fluctuation threshold σ for the first time, the time point is recorded as the starting time point. If the subsequent σ ΔP continuously exceeds the safe range, the time is continuously updated. If σ ΔP returns to the safe range (i.e. ), σ is reset to 0, and σ is cleared until σ ΔP exceeds σ again, and the above timing process is repeated.

[0106] The standard deviation and abnormal duration calculated by introducing the sliding window statistical method, as well as the dynamic monitoring method, combined with the independent criterion of multiple operating parameters, can effectively identify the pollution or aging trend of the filter membrane, enhance the adaptability of the system to complex water sample environment, and thus optimize the judgment and execution strategy of the membrane replacement time, and improve the stability and intelligent level of the system operation.

[0107] S4: System state judgment and corresponding filter membrane replacement control strategy confirmation. Through this operation, the pollution or aging trend of the filter membrane can be effectively identified, thereby optimizing the judgment and control strategy of the membrane replacement time and improving the stability and intelligent level of the system operation. According to the static threshold and the real-time operating state parameters and dynamic threshold of the filter membrane obtained in S3, the state of the system is judged, and the system has four states, including: normal (Normal), warning (Warning), clogging (Clogged), and fault (Error). The judgment method of each system state and the corresponding filter membrane replacement control strategy are as follows:

[0108] (4.1) Normal state: all real-time operating state parameters of the filter membrane are within the safe range, and there is no sensor failure or communication failure.

[0109] In this state, the corresponding filter membrane replacement control strategy is: maintaining the current operation through the control module, i.e. the filter membrane is stationary, and no membrane replacement operation is performed.

[0110] If the system is determined to be in this state, nutrient analysis is performed, and the results are uploaded to the remote monitoring platform via the communication module, completing the water sample analysis. To ensure the accuracy and reliability of the nutrient analysis data, the system only allows sampling and analysis operations when the filter membrane is in a "normal state." Nutrient analysis is based on sequential injection analysis technology and standard colorimetric principles, enabling rapid, accurate, and in-situ measurement of trace concentrations of dissolved nutrients in water with minimal sample and reagent usage. This control strategy ensures that nutrient analysis is performed only on high-quality filtered water samples, avoiding missampling and misanalysis due to filter membrane fouling or system malfunctions, thus guaranteeing the scientific validity of the data and the stability of equipment operation.

[0111] (4.2) Warning status: If any real-time operating status parameter approaches its corresponding dynamic threshold range (i.e., any of the following degree indicators ∆P) warn NTU warn Q warn (greater than 0), but the criteria for comprehensive replacement of blockages are not met.

[0112] In this state, the corresponding filter membrane replacement control strategy is: set the micro-stepping membrane replacement length and execute it to slow down the fouling accumulation trend; using this filter membrane replacement control strategy, jump to execution S5. The formula for calculating the micro-stepping membrane replacement length is as follows:

[0113]

[0114]

[0115]

[0116]

[0117] In the formula, L step For the micro-step membrane replacement length, L min For the minimum membrane replacement length in microstepping, L max This represents the maximum membrane replacement length for micro-stepping. This is the differential pressure warning margin coefficient. This is the turbidity warning margin coefficient. ΔP is the traffic flow warning margin coefficient. warn The degree of proximity of the pressure difference is measured in the range of values ​​for this parameter. Between; NTU warn The degree of turbidity proximity is measured in the range of [value range missing]. Between; Q warn The degree of traffic decrease is measured, and this parameter ranges from [value range missing]. Between; the larger the value of these three degree parameters, the more severe the warning.

[0118] (4.3) Clogging state: the filter membrane satisfies the clogging comprehensive replacement judgment condition, if the system is in this state, jump to the replacement execution state. The clogging comprehensive replacement judgment condition is specifically that any one of the following "filter membrane clogging criteria" is satisfied:

[0119] ① Differential pressure criterion: ΔP > ΔP th and , that is, the differential pressure exceeds the threshold value and the duration exceeds the differential pressure abnormal tolerance duration.

[0120] ② Turbidity criterion: NTU > NTU th and , that is, the turbidity of the water sample exceeds the threshold value and the duration exceeds the turbidity abnormal tolerance duration.

[0121] ③ Flow criterion: Q < Q th and , that is, the flow is below the threshold value and the duration exceeds the flow abnormal tolerance duration.

[0122] ④ Running time criterion: if the actual running time of the current filter membrane after being enabled , it is judged that the current filter membrane enters the aging state, otherwise it is judged that it does not enter the aging state. The expression of the actual running time of the current filter membrane after being enabled is as follows:

[0123]

[0124] ⑤ Differential pressure fluctuation abnormal criterion: and , that is, the differential pressure standard deviation exceeds the threshold value and the duration exceeds the differential pressure fluctuation abnormal tolerance duration.

[0125] In this state, the corresponding filter membrane replacement control strategy is: according to the filter membrane resource status, one of the following two strategies is adopted:

[0126] ① If the current storage membrane bin still has not been used up (the remaining amount is greater than or equal to 5%, and the filter membrane can still be replaced), set the complete standard segment replacement length, which is defined as: L clogged = L max - L min , that is, a complete unpolluted filter membrane is used for replacement to quickly restore the filtering performance.

[0127] ② If the current storage membrane bin has been used up (the remaining amount is less than 5%), set the instruction to switch the standby filter membrane unit, and mark the current filter membrane unit as "to be replaced" state, and wait for the subsequent manual replacement of the entire roll of filter membrane.

[0128] The filter membrane replacement control strategy is adopted to jump to S5.

[0129] (4.4) Fault state: sensor abnormality or communication abnormality is detected, and the fault state condition is met as follows:

[0130]

[0131] In the formula, Sensor_Error is a sensor fault flag, and Comm_Error is a communication fault flag.

[0132] In this state, the machine is immediately stopped and jumps to execute S6.

[0133] S5: Filter membrane replacement strategy execution: triggered by entering from the pre-warning state or the clogging state, the system performs filter membrane replacement actions in this state, and continues to execute S6.

[0134] In this state, the system performs specific membrane replacement actions according to the replacement length (such as L step or L clogged ) set by the filter membrane replacement control strategy confirmed in the corresponding state or the instruction to switch the standby filter membrane unit.

[0135] For the replacement length (L step or L clogged ) specified according to the current state, the precision transmission is realized by driving the stepper motor 1 and the synchronous belt transmission mechanism 2 to complete the filter membrane movement; after the movement is completed, the filter membrane remaining amount R is updated, and the current cumulative step number n is recorded, and the differential pressure duration overrun time t ΔP , the turbidity duration abnormality time t NTU , the flow duration abnormality time t Q , and the differential pressure fluctuation abnormality time are cleared.

[0136] For the instruction to switch the standby filter membrane unit, after the filter membrane unit replacement operation is completed, the current filter membrane remaining amount R is recorded, and the stepper motor cumulative step number n is cleared.

[0137] The filter membrane remaining amount R is calculated based on the cumulative step number n of the stepper motor 1, and the expression is as follows:

[0138]

[0139] In the formula, n total is the total number of steps corresponding to the full roll filter membrane 4; F is a filter membrane use correction factor, which is updated adaptively according to historical membrane replacement behavior and actual remaining situation, and is used for dynamic correction of remaining amount estimation error.

[0140] When the remaining amount R of the main filter membrane unit is lower than 5%, a main filter membrane unit depletion early warning signal is triggered; the system automatically switches to the standby filter membrane unit and continues to run; at the same time, the remaining amount of the strip-shaped filter membrane 4 of the standby filter membrane unit is monitored in advance, and if the remaining amount R of the strip-shaped filter membrane 4 of the standby filter membrane unit is lower than 25%, a filter membrane replacement prompt is triggered in advance; if the standby filter membrane unit is also depleted, the control module stops the equipment running, and sends an emergency maintenance notice to the remote monitoring platform through the communication module.

[0141] The update of the filter membrane use correction factor F is calculated in real time according to the following formula:

[0142]

[0143] Where M is the number of historical sampling times (for example, M times of membrane replacement have occurred in total recently), n actual,i is the actual number of steps of the stepper motor 1 when the i-th filter membrane is used up recently; n predicted is the theoretical total number of steps when the current filter membrane is used up.

[0144] To improve the estimation accuracy and robustness, the update of the correction factor F does not depend on a single n actual Instead, the sliding average method is used to fuse multiple historical membrane replacement behaviors, that is, the F is processed by sliding average through multiple membrane replacement historical behaviors, which improves the estimation stability.

[0145] S6: Remote communication and intelligent maintenance. Specifically, the following sub-steps are included:

[0146] (6.1) Remote communication: through the NB-IoT communication module, the current filter membrane state, the filter membrane remaining amount R, the cumulative number of steps n of the stepper motor 1, abnormal alarm information, etc. are uploaded to the remote monitoring platform, and the remote control instructions (including active membrane replacement instructions, system start-stop control instructions, etc.) issued by the remote monitoring platform are received.

[0147] (6.2) Intelligent maintenance: if the uploaded information contains abnormal alarm information, the worker is reminded to perform corresponding operations according to the abnormal alarm information, process the alarm information, eliminate the fault, and make the system return to the normal state. If there is no abnormal alarm information, step (6.3) is directly executed.

[0148] (6.3) Jump to execute S2, that is, re-sample the initial running state parameters of the current filter membrane, update the initial sampling time stamp t0, and use the filter membrane again to filter the water sample and judge the system state. Repeat this cycle of operation until the water sample meets the requirements and the nutrient salt analysis is performed.

[0149] The application introduces real-time operation parameter monitoring and filter state intelligent discrimination mechanism, and only starts the nutrient salt analysis process under the premise that the filter operation state is normal. The strategy can avoid detection errors caused by high turbidity environment, avoid data anomalies caused by filter pollution or blockage, and significantly improve the accuracy and reliability of nutrient salt analysis on the basis of guaranteeing the effective filtering performance of the filter. The nutrient salt analysis operation will be executed in a cycle based on the above state judgment mechanism until the system determines that the current water sample meets the analysis requirements, realizing stable monitoring and intelligent membrane replacement collaborative control in high turbidity water environment.

[0150] Those skilled in the art can understand that the above description is only preferred examples of the application and is not used to limit the application, although the application is described in detail with reference to the foregoing examples, those skilled in the art can still modify the technical solutions recorded in the foregoing examples or make equivalent replacement for part of the technical features. Any modification, equivalent replacement, etc. within the spirit and principles of the application shall be included in the protection scope of the application.

Claims

1. A method for nutrient analysis suitable for high-turbidity sea areas, characterized in that, Includes the following steps: S1: Collect water samples to be tested and pre-treat them to remove large particulate impurities; S2: Pass the pretreated water sample into the membrane filtration module, collect the initial operating status parameters of the membrane and the system background parameters, and set the static threshold; The filter membrane filtration module includes: two stepper motors, two synchronous belt drive mechanisms, a membrane supply roller, a strip filter membrane, and a membrane take-up roller; the two stepper motors drive the membrane supply roller and the membrane take-up roller to rotate synchronously in the same direction through the two synchronous belt drive mechanisms respectively; one end of the strip filter membrane is wound on the membrane supply roller, and the other end passes vertically through the water sample channel and is wound on the membrane take-up roller. The operating status parameters include: differential pressure, turbidity, and flow rate; the system background parameters include temperature and operating time; the static thresholds include: maximum operating time threshold for filter membrane aging, abnormal differential pressure fluctuation threshold, abnormal differential pressure tolerance time, abnormal turbidity tolerance time, abnormal flow rate tolerance time, and abnormal differential pressure fluctuation tolerance time. S3: Collect real-time operating status parameters of the filter membrane and system background parameters, and update dynamic thresholds and duration of each abnormality accordingly; the dynamic thresholds include: dynamic clogging threshold of filter membrane differential pressure, dynamic threshold of turbidity abnormality, and alarm threshold of flow rate decrease; the duration of each abnormality includes: duration of differential pressure exceeding the limit, duration of turbidity abnormality, and duration of flow rate abnormality. S4: If all real-time operating status parameters are within the set range and there is no sensor or communication failure, the filter membrane is determined to be in normal condition. Nutrient analysis is performed on the filtered water sample and the results are uploaded. The current water sample testing ends. If the difference between any real-time operating status parameter and its corresponding dynamic threshold is less than the set threshold, and the comprehensive clogging replacement judgment condition is not met, it is determined to be a warning state, the micro-step membrane replacement length is set to perform membrane replacement, and the process jumps to S2; if the comprehensive clogging replacement judgment condition is met, it is determined to be a clogging state, the standard section membrane replacement length is set to perform membrane replacement, and the process jumps to S2; the comprehensive clogging replacement judgment condition includes: any one of differential pressure, turbidity, flow rate, and differential pressure fluctuation exceeds the corresponding threshold and the duration exceeds the corresponding static threshold, or the running time exceeds the maximum running time threshold for filter membrane aging; if the sensor or communication is abnormal, it is determined to be a fault state, abnormal alarm information is uploaded, and after troubleshooting, the process jumps to S2.

2. The nutrient analysis method applicable to high-turbidity sea areas according to claim 1, characterized in that, In step S3, the dynamic clogging threshold of the filter membrane pressure difference is the linear sum of the deviation correction term of the initial pressure difference and turbidity, and the standard deviation of the pressure difference; the deviation correction term of turbidity is the difference between the real-time turbidity and the initial turbidity; the standard deviation of the pressure difference is obtained using a sliding window statistical method. The dynamic threshold for turbidity anomaly is the linear sum of the deviation correction term between the initial turbidity and temperature, and the standard deviation of turbidity; the deviation correction term for temperature is the difference between the real-time temperature and the initial temperature; the standard deviation of turbidity is obtained using a sliding window statistical method. The flow rate decrease alarm threshold is the difference between the initial flow rate after adjustment by the attenuation coefficient and the flow rate standard deviation after scaling by the weighting factor; the flow rate standard deviation is obtained using a sliding window statistical method.

3. The nutrient analysis method applicable to high-turbidity sea areas according to claim 1, characterized in that, In step S3, the time point at which the dynamic blockage threshold of the real-time differential pressure ultrafiltration membrane is first detected is recorded as the starting time point of the duration of continuous differential pressure exceeding the limit; if the real-time differential pressure continues to exceed the limit, the duration of continuous differential pressure exceeding the limit is continuously updated; if the real-time differential pressure recovers to the safe range at some point in the future, the duration of continuous differential pressure exceeding the limit is reset to zero and the corresponding starting time point is cleared. The time point at which the real-time turbidity is first detected to exceed the dynamic threshold for turbidity abnormality is recorded as the starting time point of the duration of continuous turbidity abnormality. If the real-time turbidity continues to exceed the limit, the duration of continuous turbidity abnormality will be continuously updated. If the real-time turbidity returns to a safe range at some point in the future, the duration of continuous turbidity abnormality will be reset to zero and the corresponding starting time point will be cleared. The time point at which the real-time traffic is first detected to be below the traffic decline alarm threshold is recorded as the starting time point of the duration of continuous traffic abnormality. If the real-time traffic continues to be below the threshold, the duration of the abnormal traffic will be continuously updated; if the real-time traffic recovers to a safe range at some point in the future, the duration of the abnormal traffic will be reset to zero and the corresponding start time point will be cleared. The time point at which the first detection of the real-time differential pressure standard deviation exceeding the differential pressure fluctuation threshold is recorded as the starting time point of the differential pressure fluctuation anomaly duration. If the real-time differential pressure standard deviation continues to exceed the limit, the duration of differential pressure fluctuation anomalies will be continuously updated; if at some point in the future the real-time differential pressure standard deviation returns to the safe range, the duration of differential pressure fluctuation anomalies will be reset to zero and the corresponding start time point will be cleared.

4. The nutrient analysis method applicable to high-turbidity sea areas according to claim 1, characterized in that, In S4, under the warning state, the micro-step membrane replacement length is the sum of the minimum micro-step membrane replacement length and the incremental length. The incremental length is the product of the difference between the maximum micro-step membrane replacement length and the minimum micro-step membrane replacement length, and the maximum value among the pressure difference proximity, turbidity proximity, and flow rate decrease.

5. The nutrient analysis method applicable to high-turbidity sea areas according to claim 1, characterized in that, The filter membrane filtration module includes several filter membrane units, each of which includes: two stepper motors, two synchronous belt drive mechanisms, a membrane supply roller, a strip filter membrane, and a membrane take-up roller; the switching of several filter membrane units enables continuous filtration operation.

6. The nutrient analysis method for high-turbidity sea areas according to claim 5, characterized in that, In step S4, under the blocked state, it is determined whether the remaining amount of strip filter membrane on the membrane supply roller in the current filter membrane unit is less than the threshold. If not, the standard section membrane replacement length is set, which is the difference between the maximum membrane replacement length of the micro-step and the minimum membrane replacement length of the micro-step. If yes, it is switched to another filter membrane unit and the current filter membrane unit is marked as waiting to be replaced.

7. The nutrient analysis method for high-turbidity sea areas according to claim 6, characterized in that, In the warning state and the blockage state, if the remaining amount of strip filter membrane on the membrane supply roller is not less than the threshold, the stepper motor is driven to replace the membrane according to the set membrane replacement length, the remaining amount of unused strip filter membrane is updated, the current cumulative number of steps is recorded, and the timer time of each state is cleared. If the remaining amount of the strip filter membrane is less than the threshold, the filter membrane unit switching operation is performed, and the remaining amount of the filter membrane in the filter membrane unit after switching is recorded, and the cumulative step count of the stepper motor is cleared to zero. The remaining amount of the strip filter membrane is calculated based on the completed percentage of the cumulative steps. The completed percentage is the product of the ratio of the cumulative steps of the stepper motor to the total number of steps of the entire roll of strip filter membrane and the correction factor. The correction factor is obtained by smoothing the ratio of the actual number of steps to the theoretical total number of steps in multiple membrane replacement histories.

8. A nutrient analysis system suitable for high-turbidity sea areas, used to implement the nutrient analysis method for high-turbidity sea areas as described in any one of claims 1-7, characterized in that, include: Water sample collection module, membrane filtration module, nutrient analysis module, control module, communication module, and integrated water quality sensor module; The water sample collection module is used to collect the water sample to be tested, and after filtering out large particulate impurities through the internal pre-filtration component, it is input into the filter membrane filtration module. The membrane filtration module includes: two stepper motors, two synchronous belt drive mechanisms, a membrane supply roller, a strip filter membrane, and a membrane take-up roller. The two stepper motors drive the membrane supply roller and the membrane take-up roller to rotate synchronously in the same direction through the two synchronous belt drive mechanisms. One end of the strip filter membrane is wound around the membrane supply roller, and the other end passes vertically through the water sample channel and is wound around the membrane take-up roller. The two stepper motors work synchronously under the unified coordination of the control module to ensure that the strip filter membrane maintains a constant tension at both the membrane supply and take-up ends. The nutrient analysis module is used to detect the nutrient composition of water samples filtered by the membrane filtration module based on sequential injection analysis and colorimetry. The integrated water quality sensor module is used to monitor the filter membrane operation data in the filter membrane filtration module in real time, and is connected to the control module to upload the monitoring data to the control module; The control module is used to identify the filter membrane status based on the data collected by the integrated water quality sensor module, and to perform nutrient analysis when the filter membrane is in a normal state, and to perform filter membrane replacement when it is in an early warning state or a blockage state; when it is in a fault state, the control system stops and sends an abnormal alarm message to the communication module. The communication module is connected to the control module and the remote monitoring platform respectively. The communication module receives filter replacement reminders, abnormal alarm information and control commands issued by the control module and uploads them to the remote monitoring platform.

9. The nutrient analysis system for high-turbidity sea areas according to claim 8, characterized in that, The communication module uploads data to the remote monitoring platform via the NB-IoT protocol.

10. The nutrient analysis system for high-turbidity sea areas according to claim 8, characterized in that, The nutrient analysis module, integrated water quality sensor module, communication module, and control module are connected to the same system platform via an industrial bus, forming a modular mounting structure. The control module schedules the operating status of each module, performs data acquisition, and issues control commands through the industrial bus.

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