Nutrient salt analysis method and system suitable for high-turbidity sea area

Abstract

The invention discloses a nutritive salt analysis method and system suitable for a high-turbidity sea area, and the method comprises the steps: introducing a pretreated water sample to be detected into a filter membrane filtration module, collecting the initial operation state parameter of a filter membrane and the background parameter of the system, and setting a static threshold value; collecting real-time operation state parameters and system background parameters of the filter membrane, and updating the dynamic threshold and each abnormal duration according to the parameters; judging the state of the filter membrane, and if the filter membrane is in an early warning or blocking state, correspondingly carrying out membrane replacement operation and then repeating the operation; if the state is the fault state, the operation is repeated after the fault is removed; and if the water sample is in a normal state, performing nutritive salt analysis on the filtered water sample and uploading a result. According to the invention, continuous sampling and efficient filtration of a high-turbidity water sample are realized, the filter membrane is automatically replaced according to conditions, the blockage frequency of the filter membrane is effectively reduced, and the reliability of a nutritive salt analysis result is improved.

Description

Technical Field

[0001] The present invention relates to the field of automatic water quality monitoring, and in particular to a nutrient salt analysis method and system suitable for high turbidity sea areas. Background Art

[0002] The composition of nutrients in marine waters (such as nitrate, nitrite, phosphate, and ammonium) is an important indicator for measuring water quality, predicting marine eutrophication trends, and assessing ecosystem health. As environmental monitoring requirements become increasingly sophisticated, in-situ, continuous, and automated nutrient monitoring has become a key research and application area in water quality monitoring.

[0003] Currently, mainstream online nutrient analysis methods are mostly based on colorimetry combined with sequential injection analysis (SIA). These methods utilize automated injectors, valve-controlled modules, and optical colorimetric cells to automatically quantify trace nutrients. These methods are relatively well-established in clean or low-turbidity waters, but present significant challenges in high-turbidity environments (such as estuaries, urban areas with black and odorous waters, flood-prone rivers, and coastal sediment flushing zones).

[0004] In high-turbidity sea areas, the content of suspended matter, sediment, algae, and colloidal particles in water samples increases significantly, which will place higher demands on the front-end water sample pretreatment system. Currently, mainstream online nutrient analysis methods often use filter membranes to filter water samples for particulate matter. However, existing equipment mostly uses fixed filter membrane structures, and their service life is heavily dependent on the on-site water quality conditions. After the filter membrane is clogged, it usually requires manual inspection and replacement. This is particularly prominent in the following application scenarios: remote / unmanned monitoring points (such as plateau reservoirs and offshore buoy stations); areas with large turbidity fluctuations (such as southern river basins or coastal deltas during typical typhoon and rainstorm seasons); high-frequency automatic sampling points (such as water quality black and odor warning platforms, river and lake intelligent patrol ships), etc.

[0005] Once the filter membrane is clogged and not dealt with in time, the water sample will not be able to enter the reaction pathway normally, which will affect the accuracy of nutrient measurement and even cause the instrument to shut down, seriously reducing the system's ability to operate continuously.

[0006] However, existing devices often lack accurate judgment of the working life of the filter membrane under high turbidity of water samples and multi-parameter linkage control strategies, resulting in untimely or frequent membrane replacement, affecting measurement continuity and accuracy. In addition, existing nutrient analysis equipment generally has the following deficiencies: (1) Outdated membrane replacement mechanism: The membrane replacement method is mostly manual, lacking a mechanical membrane replacement system or automatic control logic; (2) Fixed replacement timing and lack of intelligent judgment ability: Most systems only set static thresholds (such as pressure difference upper limit) to trigger alarms, and cannot dynamically adjust according to actual water quality fluctuations; (3) Weak equipment remote management capabilities: Most devices lack a membrane service life estimation module and remote communication mechanism, resulting in delayed membrane replacement and low operation and maintenance efficiency; (4) Poor continuous operation capability: There is a lack of a membrane channel hot backup mechanism, which cannot seamlessly switch to the backup channel after the current membrane is blocked.

[0007] In summary, existing technologies have obvious shortcomings in terms of filter membrane blockage response, membrane replacement intelligence, filter membrane life monitoring and remote maintenance capabilities, and are unable to meet the needs of in-situ continuous nutrient monitoring in high turbidity and dynamic complex sea environments. Summary of the Invention

[0008] In response to technical problems in the existing technology such as the inability to replace the filter membrane in time after it is blocked, frequent system interruptions, heavy manual maintenance burden, single status judgment logic, and weak remote collaboration capabilities, the present invention proposes a nutrient salt analysis method and system suitable for high turbidity sea areas. This method and system have an automatic filter membrane replacement mechanism, multi-channel parallel control capabilities, and an intelligent judgment strategy based on multiple parameters, which can achieve long-term stable, automated, unmanned and remote collaborative monitoring of nutrients.

[0009] The specific technical solutions are as follows:

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

[0011] S1: Collect the water sample to be tested and pre-treat it to remove large particles of impurities;

[0012] S2: Pass the pretreated water sample into the membrane filtration module, collect the initial operating state parameters of the filter membrane and the system background parameters, and set a static threshold; the membrane filtration module includes: two stepper motors, two synchronous belt transmission mechanisms, a membrane supply drum, a strip filter membrane, and a membrane collection drum; the two stepper motors drive the membrane supply drum and the membrane collection drum to rotate synchronously in the same direction through the two synchronous belt transmission mechanisms, and one end of the strip filter membrane is wound on the membrane supply drum, and the other end passes vertically through the water sample channel and is wound on the membrane collection drum;

[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] Furthermore, in S3, the time point at which the real-time differential pressure is first detected to exceed the dynamic clogging threshold of the differential pressure of the filtration membrane is recorded as the starting time point of the duration of the differential pressure exceeding the limit; if the real-time differential pressure continues to exceed the limit, the duration of the differential pressure exceeding the limit is continuously updated; if the real-time differential pressure returns to a safe range at a certain time later, the duration of the differential pressure exceeding the limit is reset to zero, and the corresponding starting time point is cleared;

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

[0022] The time point when the real-time traffic is first detected to be lower than the traffic drop alarm threshold is recorded as the starting time point of the traffic abnormality duration; if the real-time traffic continues to be lower than the threshold, the traffic abnormality duration is continuously updated; if the real-time traffic returns to a safe range at a certain moment in the future, the traffic abnormality duration is reset to zero and the corresponding starting time point is cleared;

[0023] The time point when 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 pressure difference fluctuation abnormality duration; if the real-time pressure difference standard deviation continues to exceed the limit, the pressure difference fluctuation abnormality duration will be continuously updated; if at a certain moment in the future, the real-time pressure difference standard deviation returns to the safe range, the pressure difference fluctuation abnormality duration will be reset to zero and the corresponding starting time point will be cleared.

[0024] Furthermore, in S4, in the early warning state, the micro-stepping membrane replacement length is the sum of the micro-stepping minimum membrane replacement length and the incremental length, the incremental length is the difference between the micro-stepping maximum membrane replacement length and the micro-stepping minimum membrane replacement length, and the product of the maximum value of the pressure difference proximity, the turbidity proximity, and the flow rate drop degree.

[0025] Furthermore, the membrane filtration module includes several membrane units, each of which includes: two stepper motors, two synchronous belt transmission mechanisms, a membrane supply roller, a belt filter membrane, and a membrane collection roller; several membrane units are switched to achieve continuous filtration operation.

[0026] Furthermore, in the S4, in the blocked state, it is determined whether the remaining amount of the strip filter membrane on the membrane supply roller in the current filter membrane unit is less than the threshold value. If not, the standard segment membrane change length is set, and its length is the difference between the maximum membrane change length of the micro-stepping and the minimum membrane change length of the micro-stepping; if so, switch to other filter membrane units, and mark the current filter membrane unit as to be replaced.

[0027] Furthermore, in the warning state and the blocked state, if the remaining amount of the strip filter membrane on the film supply drum is not less than the threshold value, the stepper motor is driven to change the membrane according to the set membrane change length, the remaining amount of the unused strip filter membrane is updated, the current cumulative number of steps is recorded, and the timer of each state is cleared at the same time;

[0028] If the remaining amount of the strip filter membrane is less than the threshold, the filter membrane unit is switched, and the remaining amount of the filter membrane in the filter membrane unit after switching is recorded, and the accumulated number of steps of the stepper motor is cleared;

[0029] The remaining amount of the strip filter membrane is calculated based on the completed ratio of the cumulative number of steps, and the completed ratio is the product of the ratio of the cumulative number of steps of the stepping motor to the total number of steps of the full roll of the strip filter membrane and the correction factor; the correction factor is obtained by sliding and smoothing the ratio of the actual number of steps to the theoretical total number of steps in multiple membrane replacement histories.

[0030] A nutrient analysis system suitable for high turbidity sea areas, used to implement the nutrient analysis method suitable for high turbidity sea areas, comprising: a water sample collection module, a membrane filtration module, a nutrient 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 water samples to be tested, and after filtering out large particles of impurities through the internal pre-filtration component, input them into the membrane filtration module;

[0032] The membrane filtration module includes: two stepper motors, two synchronous belt transmission mechanisms, a film supply drum, a strip filter membrane, and a film collection drum; the two stepper motors drive the film supply drum and the film collection drum to rotate synchronously in the same direction through the two synchronous belt transmission mechanisms, one end of the strip filter membrane is wound on the film supply drum, and the other end is wound on the film collection drum after passing vertically through the water sample channel; the two stepper motors work synchronously under the unified coordination of the control module to ensure that the strip filter membrane maintains constant tension at both ends of the film supply and collection;

[0033] The nutrient salt analysis module is used to detect the nutrient salt composition of the water sample after filtering by the membrane filtration module based on sequential injection analysis and colorimetry;

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

[0035] The control module is used to identify the status of the filter membrane based on the data collected by the integrated water quality sensor module, and perform nutrient salt analysis when the filter membrane is in a normal state, and replace the filter membrane when it is in a warning state or a blocked state; in the event of a fault, the control system shuts down and sends an abnormal alarm message to the communication module;

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

[0037] Furthermore, the communication module uploads the data to the remote monitoring platform via the NB-IoT protocol.

[0038] Furthermore, 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 to form a modular mounting structure; the control module schedules the operating status of each module, performs data collection, and issues control commands via the industrial bus.

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

[0040] (1) The present invention improves the long-term stable operation capability of the system in a high turbidity environment: through the membrane filtration module and the dynamic switching strategy under different states, continuous sampling and efficient filtration of high turbidity water samples are achieved, effectively reducing the frequency of membrane clogging and improving the instrument service life and data continuity.

[0041] (2) The present invention realizes real-time perception and intelligent judgment of the filter membrane status: combining multi-threshold judgment of parameters such as pressure difference, turbidity, flow, temperature, and operating time, integrating dynamic characteristics such as coefficient of variation, and constructing a filter membrane intelligent identification and control mechanism based on a state machine to achieve accurate classification and control response of normal, warning, blockage, and fault states.

[0042] (3) The present invention improves the intelligent and automated maintenance level: the system has the ability to automatically replace the filter membrane according to the results of the filter membrane status judgment, reducing manual intervention, and can perform automatic protection under abnormal conditions, i.e., fault conditions, to ensure equipment stability and operational safety.

[0043] (4) The present invention optimizes the accuracy and data quality of water sample analysis: by early identification and response to factors such as turbidity deterioration, pressure difference fluctuation, and flow rate drop, it avoids water sample contamination or dilution caused by deterioration of filter membrane performance and improves the reliability of nutrient salt analysis results.

[0044] (5) Support for expanded application capabilities in complex environments: The architecture of the present invention is applicable to a variety of complex hydrological environments such as the wild, lakes, reservoirs, and estuaries. It is particularly suitable for long-term deployment and continuous monitoring of nutrients in sea areas with drastic turbidity changes or high pollutant concentrations. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a schematic diagram of the hardware structure of a nutrient analysis system suitable for high turbidity sea areas proposed in an embodiment of the present invention.

[0046] Figure 2 This is a structural diagram of the membrane filtration module of the nutrient salt analysis system suitable for high turbidity sea areas proposed in an embodiment of the present invention.

[0047] Figure 3 This is a flow chart of a nutrient analysis method applicable to high turbidity sea areas proposed in an embodiment of the present invention.

[0048] Figure 4 This is a state transition diagram of the five-state state machine under the filter membrane intelligent identification and control mechanism proposed in an embodiment of the present invention.

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

[0050] The present invention will be described in detail below based on the accompanying drawings and preferred embodiments. The purpose and effects of the present invention will become more apparent. The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for explaining the present invention and are not intended to limit the present invention.

[0051] like Figure 1 As shown, a nutrient analysis system suitable for high turbidity sea areas includes: a water sample collection module, a membrane filtration module, a nutrient 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 tested and filter out large particles of impurities through the pre-filtration component.

[0053] The membrane filtration module is used to further filter the water sample output by the water sample collection module. Figure 2As shown, the membrane filtration module includes: a reel-type continuous strip filter membrane 4, a membrane storage bin, a membrane collection bin, and a drive mechanism; the membrane storage bin and the membrane collection bin are distributed on both sides of the water sample channel 5, and the strip filter membrane 4 moves from the membrane storage bin to the membrane collection bin through the water sample channel 5, and the filter membrane surface is perpendicular to the direction of water flow; the filter membrane referred to below specifically refers to the strip filter membrane 4 section located in the water sample channel 5 and acting as a filter membrane. The drive mechanism includes: a first stepper motor 1, a first synchronous belt transmission mechanism 2, a film supply roller 3, a film 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 film supply roller 3 are arranged in the membrane storage bin. The first stepper motor 1 drives the film supply roller 3 to rotate through the first synchronous belt transmission mechanism 2, thereby achieving high-precision control of the filter membrane supply end, ensuring that the length of each filter membrane step is accurate, the tension is appropriate, and the position is controllable. The second stepper motor 8, the second synchronous belt transmission mechanism 7 and the film collection roller 6 are arranged in the film collection bin. The second stepper motor 8 drives the film collection roller 6 to rotate through the second synchronous belt transmission mechanism 7 to cooperate with the film supply roller 3 to complete the synchronous winding of the used filter membrane.

[0054] The first stepper motor 1 plays a leading role in conveying the filter membrane, controlling the supply length and movement rhythm of the filter membrane; the second stepper motor 8 assists in completing the synchronous winding of the filter membrane. The two work synchronously under the unified coordination of the control module to ensure that the filter membrane maintains constant tension at both ends of the supply and winding, avoiding relaxation or over-tensioning, and ensuring the stability and smoothness of the filter membrane during transmission.

[0055] The starting end of the strip filter membrane 4 is pre-wound on the membrane supply drum 3. After vertically passing through the water sample channel 5 to complete the filtration of the water sample, its end is taken up by the membrane take-up drum 6, forming a continuous membrane transmission path from the membrane supply drum 3 to the membrane take-up drum 6. The two drums work together to achieve step-by-step movement of the filter membrane, effectively preventing problems such as slipping, wrinkling, or accumulation of the filter membrane during the replacement process, ensuring the continuity and reliability of the filtration process.

[0056] Through the above-mentioned transmission structure, the filter membrane can be replaced continuously, step by step and accurately, ensuring the displacement consistency, tension controllability and operation stability of the filter membrane during the replacement process, and ensuring the consistency and reliability of the filtration performance.

[0057] Furthermore, the membrane filtration module is provided with a main membrane unit and at least one spare membrane unit. If the current membrane unit fails or triggers a membrane replacement operation, it automatically switches to another membrane unit to continue operation, that is, switching between different membrane channels to achieve continuous and uninterrupted filtration.

[0058] The nutrient salt analysis module is used to detect the nutrient salt composition of the water sample after filtering through the 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] Furthermore, the communication module supports data exchange with a remote monitoring platform (a remote cloud platform in this embodiment). Data transmitted by the communication module to the remote monitoring platform includes filter membrane status, remaining amount of filter strip 4, and alarm information. Commands transmitted by the remote monitoring platform to the communication module include remote start and stop commands. In this embodiment, data is uploaded to the remote monitoring platform via the NB-IoT protocol.

[0065] Furthermore, 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. This bus-based structure significantly improves system scalability and ease of maintenance, and is particularly suitable for modular online water quality monitoring needs in high-humidity and high-turbidity environments.

[0066] Furthermore, the control module serves as the main control core, scheduling the operating status of each module, executing data collection, issuing control instructions, and coordinating the overall operation of the system through the industrial bus.

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

[0068] S1: Water sample collection and pretreatment. Water samples from high turbidity areas are introduced through the water sample collection module and passed through the pre-filtration component to remove large particles and impurities, extending the life of the subsequent membrane filtration.

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

[0070] The initial operating state parameters of the filter membrane include: initial pressure difference ΔP0 (pressure difference before and after the filter membrane), initial turbidity NTU0 (after the filter membrane), initial flow rate Q0 (after the filter membrane); initial system background parameters include: initial temperature T0 (after the filter membrane), initial sampling timestamp t0 (the moment when the equipment starts and completes the collection of initial operating parameters of the filter membrane), etc.

[0071] Static thresholds include: abnormal pressure difference tolerance time t ΔP_hold , turbidity abnormal tolerance time t NTU_hold , Traffic abnormality tolerance time t Q_hold , Maximum operating time threshold of filter membrane aging t max , Abnormal tolerance time of pressure difference fluctuation , pressure difference fluctuation abnormal threshold .

[0072] S3: Real-time monitoring of filter membrane status and update of dynamic thresholds and abnormal duration.

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

[0074] Each dynamic threshold value changes adaptively with the real-time operating status parameters of the filter membrane, which is conducive to accurate judgment of the filter membrane status. The adaptive changes are as follows:

[0075] (1) Dynamic clogging threshold of membrane pressure difference ΔP th The expression is as follows:

[0076]

[0077] Among them, α1 and k1 are adjustable parameters, σ P is the standard deviation of the pressure difference.

[0078] In order to enhance the system's tolerance to data fluctuations and adapt to complex water quality changes, the pressure difference standard deviation σ P The sliding window statistical method is used for real-time calculation. The specific operations are as follows:

[0079] The system collects the current monitoring value filter pressure difference ΔP at a fixed sampling period and adds it to a sliding data queue with a length of N. When the queue length exceeds N, the earliest data is automatically removed to maintain a fixed window length. At any time, the N historical data {X1, X2, ..., X N}Calculate the standard deviation σ, the calculation formula is as follows:

[0080]

[0081] in, is the average value of the current window data, X i Represents the corresponding measured value filter membrane pressure difference ΔP.

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

[0083]

[0084] In the formula, α2 and k2 are adjustable parameters, σ N is the turbidity standard deviation, which is also calculated in real time using the 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] Where, The starting time of the current abnormal turbidity event; if the NTU exceeds the limit again after returning to the safe range, the starting time will be recorded again.

[0097] When the turbidity NTU exceeds the abnormal turbidity dynamic threshold NTU for the first time th When the time is recorded as the starting time point If NTU continues to exceed the limit, t will be updated continuously. NTU If NTU returns to the safe range (i.e. NTU ≤ NTU th ), then t NTU Reset to 0 and clear , until NTU is detected to be greater than NTU again th , repeat the above timing process.

[0098] (3) Duration of abnormal traffic flow t Q :

[0099]

[0100] Where, The starting time of the current traffic abnormality event. If Q returns to a safe range and falls below the threshold again, the starting time is recorded again.

[0101] When the flow rate Q is detected to be lower than the flow rate drop alarm threshold Q for the first time th When the time is recorded as the starting time point If the subsequent Q continues to be lower than Q th , then continue to update t Q If Q returns to a safe range (i.e. Q ≥ Q th ), then t Q Reset to 0 and clear , until Q is detected to be lower than Q again th , repeat the above timing process.

[0102] (4) Abnormal duration of pressure difference fluctuation :

[0103]

[0104] Where, is the starting time point of the current abnormal pressure difference fluctuation event; σ ΔP is the pressure difference standard deviation, which is used to represent the pressure difference fluctuation and is obtained by real-time calculation using the sliding window statistical method. is the pressure difference fluctuation abnormal threshold; if the subsequent σΔP If the limit is exceeded again after returning to the safe range, the starting time point will be recorded again.

[0105] When the pressure difference standard deviation σ ΔP The pressure difference fluctuation threshold is exceeded for the first time When the time is recorded as the starting time point If the subsequent σ ΔP If the limit is exceeded continuously, it will be updated continuously. :If σ ΔP Restore to a safe range (i.e. ), then Reset to 0 and clear , until σ is detected again ΔP Exceed , repeat the above timing process.

[0106] The above dynamic monitoring methods, such as the standard deviation and abnormal duration calculated by the sliding window statistical method, combined with the independent judgment criteria of multiple operating parameters, can effectively identify the pollution or aging trend of the filter membrane, enhance the system's adaptability to complex water sample environments, thereby optimizing the judgment and execution strategy of membrane replacement timing, and improving the stability and intelligence level of system operation.

[0107] S4: System status judgment and corresponding filter membrane replacement control strategy confirmation. This operation can effectively identify the trend of filter membrane contamination or aging, thereby optimizing the judgment and control strategy for membrane replacement timing, and improving the stability and intelligence level of system operation. Based on the static threshold and the real-time operating status parameters and dynamic thresholds of the filter membrane obtained in S3, the system status is judged. The system has four states, including: Normal, Warning, Clogged, and Error. The judgment method for each system status and the corresponding filter membrane replacement control strategy are as follows:

[0108] (4.1) Normal status: All real-time operating status parameters of the filter membrane are within the safe range and there are no sensor failures or communication failures.

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

[0110] If the system is judged to be in this state, a nutrient analysis will be performed and the analysis results will be uploaded to the remote monitoring platform through the communication module to complete the water sample analysis: To ensure the accuracy and credibility 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 colorimetry principles. With very small amounts of sample and reagent, it can quickly, accurately, and in situ measure trace concentrations of nutrient components dissolved in water. This control strategy ensures that nutrient analysis is only based on high-quality filtered water samples, avoiding filter membrane contamination or missampling and misanalysis under abnormal system conditions, thereby ensuring the scientific nature of the data and the stability of equipment operation.

[0111] (4.2) Warning status: If any real-time operating status parameter is close to its corresponding dynamic threshold range (i.e. any of the following degree indicators ∆P warn 、NTU warn , Q warn greater than 0), but the comprehensive blockage replacement judgment conditions 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 trend of pollution accumulation; use this filter membrane replacement control strategy to jump to S5. The formula for calculating the micro-stepping membrane replacement length is as follows:

[0113]

[0114]

[0115]

[0116]

[0117] Where, L step is the micro-stepping film length, L min L is the minimum film change length of micro-stepping, max The maximum film change length of micro-stepping; is the pressure difference warning margin coefficient, is the turbidity warning margin coefficient, ΔP is the flow warning margin coefficient. warn The pressure difference is close to the degree, the range of the degree parameter is Between; NTU warn is the turbidity proximity degree, the value range of this degree parameter is Between; Q warn is the degree of flow reduction, the value range of this parameter is The larger the values ​​of these three degree parameters are, the more serious the warning is.

[0118] (4.3) Blockage status: The filter membrane meets the comprehensive replacement judgment conditions for blockage. If the system is in this state, it will jump to the replacement execution state. The comprehensive replacement judgment conditions for blockage are specifically any of the following "filter membrane blockage judgment criteria":

[0119] ① Pressure difference criterion: ΔP>ΔP th and , that is, the pressure difference exceeds the threshold and the duration exceeds the normal tolerance time of the pressure difference.

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

[0121] ③Flow criterion: Q<Q th and , that is, the traffic flow is lower than the threshold and the duration exceeds the traffic abnormality tolerance time.

[0122] ④ Operation duration criterion: the actual operation duration after the current filter membrane is activated , then the current filter membrane is judged to have entered the aging state, otherwise it is judged to have not entered the aging state. The expression of the actual operating time after the current filter membrane is activated is as follows:

[0123]

[0124] ⑤ Criteria for abnormal pressure difference fluctuation: and , that is, the pressure difference standard deviation exceeds the threshold and the duration exceeds the pressure difference fluctuation abnormal tolerance duration.

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

[0126] ① If the strip filter membrane 4 in the current membrane storage bin has not been used up (the remaining amount is greater than or equal to 5%, and the membrane can still be replaced), set the complete standard section membrane replacement length. The complete standard section membrane replacement length is defined as: L clogged =L max -L min , that is, replace it with a complete uncontaminated filter membrane to quickly restore the filtration performance.

[0127] ② If the strip filter membrane 4 in the current membrane storage bin is exhausted (the remaining amount is less than 5%), set the instruction to switch to the spare filter membrane unit, and mark the current filter membrane unit as "to be replaced" state, waiting for subsequent manual replacement of the entire roll of strip filter membrane 4.

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

[0129] (4.4) Fault status: A sensor abnormality or communication abnormality is detected, and the fault status conditions are as follows:

[0130]

[0131] Where, Sensor_Error is the sensor fault flag, and Comm_Error is the communication fault flag.

[0132] In this state, the machine stops immediately and jumps to S6.

[0133] S5: Filter membrane replacement strategy execution: triggered by the warning state or the blockage state, the system performs the filter membrane replacement action in this state and continues to execute S6.

[0134] In this state, the system sets the membrane replacement length (such as L) according to the membrane replacement control strategy confirmed in the corresponding state. step or L clogged ) or switch the spare filter unit to perform the specific membrane replacement action.

[0135] For the membrane replacement length (L step or L clogged ), drives the stepper motor 1 and the synchronous belt transmission mechanism 2 to achieve precise transmission and complete the filter membrane movement; after the movement is completed, the filter membrane remaining amount R is updated, and the current cumulative number of steps n is recorded, and the duration t of the continuous excess pressure difference is cleared. ΔP , turbidity abnormal duration t NTU , Duration of abnormal traffic flow t Q , Abnormal duration of pressure difference fluctuation .

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

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

[0138]

[0139] Where n total is the total number of steps corresponding to the full roll of strip filter membrane 4; F is the filter membrane usage correction factor, which is adaptively updated according to the historical membrane replacement behavior and the actual remaining situation, and is used to dynamically correct the remaining amount estimation error.

[0140] When the remaining amount R of the main filter membrane unit is lower than 5%, the main filter membrane unit depletion warning signal is triggered; the system automatically switches to the spare filter membrane unit and continues to operate; at the same time, the remaining amount of the strip filter membrane 4 of the spare filter membrane unit is monitored in advance. If the remaining amount R of the strip filter membrane 4 of the spare filter membrane unit is lower than 25%, the filter membrane replacement prompt is triggered in advance; if the spare filter membrane unit is also exhausted, the control module stops the equipment operation and sends an emergency maintenance notification to the remote monitoring platform through the communication module.

[0141] The update of the filter membrane 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, there have been M membrane changes recently), n actual,i is the actual number of steps of stepper motor 1 when the filter membrane is exhausted for the most recent i-th time; n predicted This is the theoretical total number of steps when the current filter membrane is exhausted.

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

[0145] S6: Remote communication and intelligent maintenance. It includes the following sub-steps:

[0146] (6.1) Remote Communication: The NB-IoT communication module uploads the current filter membrane status, filter membrane remaining quantity R, cumulative number of steps of stepper motor 1 n, abnormal alarm information, etc. to the remote monitoring platform, and receives remote control commands (including active membrane replacement commands, system start and stop control commands, etc.) issued by the remote monitoring platform.

[0147] (6.2) Intelligent Maintenance: If the uploaded information contains abnormal alarm information, the staff will be reminded to take corresponding actions based on the abnormal alarm information, process the alarm information, troubleshoot the fault, and restore the system to normal status. If it does not contain abnormal alarm information, proceed directly to step (6.3).

[0148] (6.3) Jump to S2, which resamples the initial operating parameters of the current filter membrane, updates the initial sampling timestamp t0, and uses the membrane again to filter the water sample and determine the system status. Repeat this cycle until the water sample meets the requirements and proceeds to nutrient analysis.

[0149] By incorporating real-time operating parameter monitoring and intelligent membrane status determination mechanisms, the present invention initiates the nutrient analysis process only when the membrane is operating normally. This strategy, while ensuring the effective filtration performance of the membrane, mitigates detection errors caused by high turbidity environments and avoids data anomalies due to membrane contamination or clogging, thereby significantly improving the accuracy and reliability of nutrient analysis. Nutrient analysis operations will be executed cyclically based on this status determination mechanism until the system determines that the current water sample meets the analysis requirements, achieving stable monitoring and intelligent membrane replacement control in high-turbidity water environments.

[0150] Those skilled in the art will understand that the foregoing descriptions are merely preferred embodiments of the invention and are not intended to limit the invention. Although the invention has been described in detail with reference to the foregoing examples, those skilled in the art will still be able to modify the technical solutions described in the foregoing examples or substitute equivalents for some of the technical features therein. Any modifications, equivalent substitutions, etc. made within the spirit and principles of the invention shall be included within the scope of protection of the invention.

Claims

1. A nutrient analysis method suitable for high turbidity sea areas, characterized in that: The following steps are involved: S1: Collect the water sample to be tested and pre-treat it to remove large particles of impurities; S2: Pass the pretreated water sample into the membrane filtration module, collect the initial operating state parameters of the membrane and the system background parameters, and set the static threshold; The membrane filtration module includes: two stepper motors, two synchronous belt transmission mechanisms, a film supply drum, a strip filter membrane, and a film collection drum; the two stepper motors drive the film supply drum and the film collection drum to rotate synchronously in the same direction through the two synchronous belt transmission mechanisms, one end of the strip filter membrane is wound on the film supply drum, and the other end passes vertically through the water sample channel and is wound on the film collection drum; The operating status parameters include: pressure difference, turbidity, flow rate; the system background parameters include temperature and operating time; the static thresholds include: maximum operating time threshold for filter membrane aging, pressure difference fluctuation abnormality threshold, pressure difference abnormality tolerance time, turbidity abnormality tolerance time, flow abnormality tolerance time, and pressure difference fluctuation abnormality tolerance time; S3: Collecting the real-time operating status parameters of the filter membrane and the system background parameters, and updating the dynamic thresholds and the duration of each abnormality accordingly; the dynamic thresholds include: the dynamic blockage threshold of the filter pressure difference, the dynamic threshold of the abnormal turbidity, and the flow rate drop alarm threshold; the duration of each abnormality includes: the duration of the pressure difference exceeding the limit, the duration of the abnormal turbidity, and the duration of the abnormal flow rate; S4: If all real-time operating status parameters are within the set range and there is no sensor fault or communication fault, the filter membrane is determined to be in normal state, and the nutrient salt analysis of the filtered water sample is performed and the results are uploaded, and the current water sample test is ended; If the difference between any real-time operating status parameter and its corresponding dynamic threshold is less than the set threshold, and the comprehensive replacement judgment condition for blockage is not met, it is judged to be a warning state, the micro-step membrane replacement length is set to replace the membrane and jump to execute S2; if the comprehensive replacement judgment condition for blockage is met, it is judged to be a blockage state, the standard segment membrane replacement length is set to replace the membrane and jump to execute S2; the comprehensive replacement judgment condition for blockage includes: any one of the pressure difference, turbidity, flow rate, and pressure difference fluctuation exceeds the corresponding threshold and the duration exceeds the corresponding static threshold, or the operating time exceeds the maximum operating time threshold of the filter membrane aging; if the sensor or communication is abnormal, it is judged to be a fault state, the abnormal alarm information is uploaded, and after the fault is eliminated, jump to execute S2.

2. The nutrient analysis method for high turbidity sea areas according to claim 1, characterized in that: In S3, the dynamic clogging threshold of the filter pressure difference is the linear sum of the initial pressure difference and the turbidity deviation correction term and the pressure difference standard deviation; the turbidity deviation correction term is the difference between the real-time turbidity and the initial turbidity; the pressure difference standard deviation is obtained using a sliding window statistical method; The abnormal turbidity dynamic threshold is the linear sum of the initial turbidity and temperature deviation correction term and the turbidity standard deviation; the temperature deviation correction term is the difference between the real-time temperature and the initial temperature; the turbidity standard deviation is obtained using a sliding window statistical method; The flow drop alarm threshold is the difference between the initial flow after adjustment by the attenuation coefficient and the flow standard deviation after scaling by the weight factor; the flow 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 said S3, the time point when the real-time pressure difference is first detected to exceed the dynamic blockage threshold of the filtration membrane pressure difference is recorded as the starting time point of the duration of the pressure difference exceeding the limit; if the real-time pressure difference continues to exceed the limit, the duration of the pressure difference exceeding the limit is continuously updated; if at a certain moment in the future, the real-time pressure difference returns to a safe range, the duration of the pressure difference exceeding the limit is reset to zero, and the corresponding starting time point is cleared; The time point when the real-time turbidity is first detected to exceed the turbidity abnormal dynamic threshold is recorded as the starting time point of the turbidity abnormal duration; if the real-time turbidity continues to exceed the limit, the turbidity abnormal duration is continuously updated; if the real-time turbidity returns to the safe range at a certain time later, the turbidity abnormal duration is reset to zero and the corresponding starting time point is cleared; The time point when 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 abnormal flow rate; If the subsequent real-time traffic continues to be lower than the threshold, the duration of the traffic abnormality will be continuously updated; if the real-time traffic returns to a safe range at a certain moment, the duration of the traffic abnormality will be reset to zero and the corresponding starting time point will be cleared; The time point when 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 abnormal pressure difference fluctuation duration; If the subsequent real-time pressure difference standard deviation continues to exceed the limit, the pressure difference fluctuation abnormal duration will be continuously updated; if at a certain moment in the future, the real-time pressure difference standard deviation returns to the safe range, the pressure difference fluctuation abnormal duration will be reset to zero and the corresponding starting time point will be cleared.

4. The nutrient salt analysis method applicable to high turbidity sea areas according to claim 1, characterized in that: In S4, in the early warning state, the micro-stepping membrane replacement length is the sum of the micro-stepping minimum membrane replacement length and the incremental length, and the incremental length is the difference between the micro-stepping maximum membrane replacement length and the micro-stepping minimum membrane replacement length, multiplied by the maximum value of the pressure difference approach degree, the turbidity approach degree, and the flow rate drop degree.

5. The nutrient salt analysis method applicable to high turbidity sea areas according to claim 1, characterized in that: The membrane filtration module includes several 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; several membrane units are switched to achieve continuous filtration operation.

6. The nutrient salt analysis method applicable to high turbidity sea areas according to claim 5, characterized in that: In the above S4, in the blocked state, it is determined whether the remaining amount of the strip filter membrane on the membrane supply roller in the current filter membrane unit is less than the threshold value. If not, the standard segment membrane replacement length is set, and its length is the difference between the maximum membrane replacement length of micro-stepping and the minimum membrane replacement length of micro-stepping; if so, switch to other filter membrane units, and mark the current filter membrane unit as to be replaced.

7. The nutrient salt analysis method applicable to high turbidity sea areas according to claim 6, characterized in that: In the warning state and the blocked state, if the remaining amount of the strip filter membrane on the film supply drum is not less than the threshold value, the stepper motor is driven to change the membrane according to the set membrane change length, the remaining amount of the unused strip filter membrane is updated, the current cumulative number of steps is recorded, and the timer of each state is cleared at the same time; If the remaining amount of the strip filter membrane is less than the threshold, the filter membrane unit is switched, and the remaining amount of the filter membrane in the filter membrane unit after switching is recorded, and the accumulated number of steps of the stepper motor is cleared; The remaining amount of the strip filter membrane is calculated based on the completed ratio of the cumulative number of steps, where the completed ratio is the product of the ratio of the cumulative number of steps of the stepper motor to the total number of steps of the full roll of the strip filter membrane and the correction factor; The correction factor is obtained by performing sliding smoothing processing on the ratio of the actual number of steps to the theoretical total number of steps in multiple membrane replacement histories.

8. A nutrient salt analysis system suitable for high turbidity sea areas, used to implement the nutrient salt analysis method suitable for high turbidity sea areas according to any one of claims 1 to 7, characterized in that: include: Water sample collection module, membrane filtration module, nutrient salt analysis module, control module, communication module, integrated water quality sensor module; The water sample collection module is used to collect water samples to be tested, and after filtering out large particles of impurities through the internal pre-filtration component, input them into the membrane filtration module; The membrane filtration module includes: two stepper motors, two synchronous belt transmission mechanisms, a film supply drum, a strip filter membrane, and a film collection drum; the two stepper motors respectively drive the film supply drum and the film collection drum to rotate synchronously in the same direction through the two synchronous belt transmission mechanisms, one end of the strip filter membrane is wound on the film supply drum, and the other end is wound on the film collection drum after passing vertically through the water sample channel; the two stepper motors work synchronously under the unified coordination of the control module to ensure that the strip filter membrane maintains constant tension at both ends of the film supply and collection; The nutrient salt analysis module is used to detect the nutrient salt composition of the water sample after filtering by the membrane filtration module based on sequential injection analysis and colorimetry; The integrated water quality sensor module is used to monitor the membrane operation data in the 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 status of the filter membrane based on the data collected by the integrated water quality sensor module, and perform nutrient salt analysis when the filter membrane is in a normal state, and replace the filter membrane when it is in a warning state or a blocked state; in the event of a fault, the control system shuts down 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 the filter membrane replacement reminder, abnormal alarm information, and control instructions sent by the control module and uploads them to the remote monitoring platform.

9. The nutrient analysis system suitable 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 suitable 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 to form a modular mounting structure; the control module schedules the operating status of each module, performs data collection, and issues control commands via the industrial bus.

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